diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000000000..123014908bebb --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,6 @@ +version: 2 +updates: + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "daily" diff --git a/.github/workflows/build_package.yml b/.github/workflows/build_package.yml index 4658d5da1252e..df70b19b57467 100644 --- a/.github/workflows/build_package.yml +++ b/.github/workflows/build_package.yml @@ -21,15 +21,13 @@ jobs: os: [ubuntu-latest, windows-latest] python-version: ["3.9"] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Install Poetry run: pipx install poetry==${{ env.POETRY_VERSION }} - name: Set up python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - cache: "poetry" - cache-dependency-path: "**/poetry.lock" - name: Install deps shell: bash run: poetry install diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index fe69b675e054a..59843a5232325 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -46,11 +46,11 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 # Initializes the CodeQL tools for scanning. - name: Initialize CodeQL - uses: github/codeql-action/init@v2 + uses: github/codeql-action/init@v3 with: languages: ${{ matrix.language }} # If you wish to specify custom queries, you can do so here or in a config file. @@ -63,7 +63,7 @@ jobs: # Autobuild attempts to build any compiled languages (C/C++, C#, Go, Java, or Swift). # If this step fails, then you should remove it and run the build manually (see below) - name: Autobuild - uses: github/codeql-action/autobuild@v2 + uses: github/codeql-action/autobuild@v3 # ℹ️ Command-line programs to run using the OS shell. # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun @@ -76,6 +76,6 @@ jobs: # ./location_of_script_within_repo/buildscript.sh - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@v2 + uses: github/codeql-action/analyze@v3 with: category: "/language:${{matrix.language}}" diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 9e2b516e1e86f..7431f555c9e9c 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -18,13 +18,13 @@ jobs: matrix: python-version: ["3.9"] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: fetch-depth: ${{ github.event_name == 'pull_request' && 2 || 0 }} - name: Install Poetry run: pipx install poetry==${{ env.POETRY_VERSION }} - name: Set up python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} cache: "poetry" diff --git a/.github/workflows/publish_release.yml b/.github/workflows/publish_release.yml index 3d6a0102c2571..7a45b6c28e695 100644 --- a/.github/workflows/publish_release.yml +++ b/.github/workflows/publish_release.yml @@ -18,15 +18,16 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Install Poetry run: pipx install poetry==${{ env.POETRY_VERSION }} - name: Set up python ${{ env.PYTHON_VERSION }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ env.PYTHON_VERSION }} - cache: "poetry" - cache-dependency-path: "**/poetry.lock" + - name: Clear python cache + shell: bash + run: poetry cache clear --all pypi - name: Install deps shell: bash run: pip install -e . @@ -37,8 +38,9 @@ jobs: shell: bash run: rm -rf llama-index-core/llama_index/core/_static/nltk_cache/corpora/stopwords.zip llama-index-core/llama_index/core/_static/nltk_cache/tokenizers/punkt.zip - name: Build and publish to pypi - uses: JRubics/poetry-publish@v1.17 + uses: JRubics/poetry-publish@v2.0 with: + python_version: ${{ env.PYTHON_VERSION }} pypi_token: ${{ secrets.LLAMA_INDEX_PYPI_TOKEN }} ignore_dev_requirements: "yes" diff --git a/.github/workflows/publish_sub_package.yml b/.github/workflows/publish_sub_package.yml index f816a8339b573..024962c80d267 100644 --- a/.github/workflows/publish_sub_package.yml +++ b/.github/workflows/publish_sub_package.yml @@ -14,13 +14,13 @@ jobs: if: github.repository == 'run-llama/llama_index' runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: fetch-depth: 0 - name: Install Poetry run: pipx install poetry==${{ env.POETRY_VERSION }} - name: Set up python ${{ env.PYTHON_VERSION }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ env.PYTHON_VERSION }} cache: "poetry" diff --git a/.github/workflows/unit_test.yml b/.github/workflows/unit_test.yml index fee1b433049fc..3577fe55ec2d3 100644 --- a/.github/workflows/unit_test.yml +++ b/.github/workflows/unit_test.yml @@ -13,17 +13,16 @@ jobs: test: runs-on: ubuntu-latest-unit-tester strategy: - # You can use PyPy versions in python-version. - # For example, pypy-2.7 and pypy-3.8 + fail-fast: false matrix: - python-version: ["3.8", "3.9", "3.10"] + python-version: ["3.9", "3.10", "3.11", "3.12"] steps: - name: clear space env: CI: true shell: bash run: rm -rf /opt/hostedtoolcache/* - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: fetch-depth: 0 - name: update rustc @@ -31,7 +30,7 @@ jobs: - name: Install Poetry run: pipx install poetry==${{ env.POETRY_VERSION }} - name: Set up python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} cache: "poetry" @@ -51,4 +50,4 @@ jobs: env: CI: true shell: bash - run: poetry run make -s test + run: poetry run make test diff --git a/.gitignore b/.gitignore index 21b06b209efcb..391819bfa9c97 100644 --- a/.gitignore +++ b/.gitignore @@ -15,3 +15,4 @@ credentials.json token.json .python-version .DS_Store +/storage/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c531788971bbe..8a65f36a59744 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -96,6 +96,7 @@ repos: (\/.*?\.[\w:]+)/poetry.lock args: [ + "--skip=*/algolia.js", "--ignore-words-list", "astroid,gallary,momento,narl,ot,rouge,nin,gere,asend", ] diff --git a/CHANGELOG.md b/CHANGELOG.md index e9ea5abf6d7a3..528461e0381a3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,488 @@ # ChangeLog -## [2024-02-05] +## [2024-08-29] + +### `llama-index-core` [0.11.3] + +- refact: merge Context and Session to simplify the workflows api (#15709) +- chore: stop using deprecated `ctx.data` in workflows docs (#15716) +- fix: stop streaming workflow events when a step raises (#15714) +- Fix llm_chat_callback for multimodal llms (#15700) +- chore: Increase unit tests coverage for the workflow package (#15691) +- fix SimpleVectorStore.from_persist_dir() behaviour (#15534) + +### `llama-index-embeddings-azure-openai` [0.2.5] + +- fix json serialization for azure embeddings (#15724) + +### `llama-index-graph-stores-kuzu` [0.3.0] + +- Add KuzuPropertyGraphStore (#15678) + +### `llama-index-indices-managed-vectara` [0.2.1] + +- added new User Defined Function reranker (#15546) + +### `llama-index-llms-mistralai` [0.2.2] + +- Fix `random_seed` type in mistral llm (#15701) + +### `llama-index-llms-nvidia` [0.2.1] + +- Add function/tool calling support to nvidia llm (#15359) + +### `llama-index-multi-modal-llms-ollama` [0.3.0] + +- bump ollama client deps for multimodal llm (#15702) + +### `llama-index-readers-web` [0.2.1] + +- Fix: Firecrawl scraping url response (#15720) + +### `llama-index-selectors-notdiamond` [0.1.0] + +- Adding Not Diamond to llama_index (#15703) + +### `llama-index-vector-stores-milvus` [0.2.3] + +- MMR in Milvus vector stores (#15634) +- feat: implement get_nodes for MilvusVectorStore (#15696) + +## [2024-08-27] + +### `llama-index-core` [0.11.2] + +- fix tool schemas generation for pydantic v2 to handle nested models (#15679) +- feat: support default values for nested workflows (#15660) +- feat: allow FunctionTool with just an async fn (#15638) +- feat: Allow streaming events from steps (#15488) +- fix auto-retriever pydantic indent error (#15648) +- Implement Router Query Engine example using workflows (#15635) +- Add multi step query engine example using workflows (#15438) +- start traces for llm-level operations (#15542) +- Pass callback_manager to init in CodeSplitter from_defaults (#15585) + +### `llama-index-embeddings-xinference` [0.1.0] + +- Add Xinference Embedding Class (#15579) + +### `llama-index-llms-ai21` [0.3.3] + +- Integrations: AI21 function calling Support (#15622) + +### `llama-index-llms-anthropic` [0.3.0] + +- Added support for anthropic models through GCP Vertex AI (#15661) + +### `llama-index-llms-cerebras` [0.1.0] + +- Implement Cerebras Integration (#15665) + +### `llama-index-postprocessor-nvidia-rerank` [0.3.1] + +- fix downloaded nim endpoint path (#15645) +- fix llama-index-postprocessor-nvidia-rerank tests (#15643) + +### `llama-index-postprocessor-xinference-rerank` [0.1.0] + +- add xinference rerank class (#15639) + +### `llama-index-vector-stores-alibabacloud-opensearch` [0.2.1] + +- fix set output fields in AlibabaCloudOpenSearchConfig (#15562) + +### `llama-index-vector-stores-azureaisearch` [0.2.1] + +- Upgrade azure-search-documents to 2024-07-01 GA API and Add Support for Scalar and Binary Quantization in Index Creation (#15650) + +### `llama-index-vector-stores-neo4j` [0.2.1] + +- Neo4j Vector Store: Make Embedding Dimension Check Optional (#15628) + +### `llama-inde-vector-stores-milvus` [0.2.1] + +- Change the default consistency level of Milvus (#15577) + +### `llama-index-vector-stores-elasticsearch` [0.3.2] + +- Fix the ElasticsearchStore key error (#15631) + +## [2024-08-23] + +### `llama-index-core` [0.11.1] + +- Replacing client-side docs search with algolia (#15574) +- Add docs on extending workflows (#15573) +- rename method for nested workflows to add_workflows (#15596) +- chore: fix @step usage in the core codebase (#15588) +- Modify the validate function in ReflectionWorkflow example notebook to use pydantic model_validate_json method (#15567) +- feature: allow concurrent runs of the same workflow instance (#15568) +- docs: remove redundant pass_context=True from docs and examples (#15571) + +### `llama-index-embeddings-openai` [0.2.3] + +- fix openai embeddings with pydantic v2 (#15576) + +### `llama-index-embeddings-voyageai` [0.2.1] + +- bump voyage ai embedding client dep (#15595) + +### `llama-index-llms-vertex` [0.3.3] + +- Vertex LLM: Correctly add function calling part to prompt (#15569) +- Vertex LLM: Remove manual setting of message content to Function Calling (#15586) + +## [2024-08-22] + +### `llama-index-core` [0.11.0] + +- removed deprecated `ServiceContext` -- using this now will print an error with a link to the migration guide +- removed deprecated `LLMPredictor` -- using this now will print an error, any existing LLM is a drop-in replacement +- made `pandas` an optional dependency + +### `Everything Else` + +- bumped the minor version of every package to account for the new version of `llama-index-core` + +## [2024-08-21] + +### `llama-index-core` [0.10.68] + +- remove nested progress bars in base element node parser (#15550) +- Adding exhaustive docs for workflows (#15556) +- Adding multi-strategy workflow with reflection notebook example (#15445) +- remove openai dep from core (#15527) +- Improve token counter to handle more response types (#15501) +- feat: Allow using step decorator without parentheses (#15540) +- feat: workflow services (aka nested workflows) (#15325) +- Remove requirement to specify "allowed_query_fields" parameter when using "cypher_validator" in TextToCypher retriever (#15506) + +### `llama-index-embeddings-mistralai` [0.1.6] + +- fix mistral embeddings usage (#15508) + +### `llama-index-embeddings-ollama` [0.2.0] + +- use ollama client for embeddings (#15478) + +### `llama-index-embeddings-openvino` [0.2.1] + +- support static input shape for openvino embedding and reranker (#15521) + +### `llama-index-graph-stores-neptune` [0.1.8] + +- Added code to expose structured schema for Neptune (#15507) + +### `llama-index-llms-ai21` [0.3.2] + +- Integration: AI21 Tools support (#15518) + +### `llama-index-llms-bedrock` [0.1.13] + +- Support token counting for llama-index integration with bedrock (#15491) + +### `llama-index-llms-cohere` [0.2.2] + +- feat: add tool calling support for achat cohere (#15539) + +### `llama-index-llms-gigachat` [0.1.0] + +- Adding gigachat LLM support (#15313) + +### `llama-index-llms-openai` [0.1.31] + +- Fix incorrect type in OpenAI token usage report (#15524) +- allow streaming token counts for openai (#15548) + +### `llama-index-postprocessor-nvidia-rerank` [0.2.1] + +- add truncate support (#15490) +- Update to 0.2.0, remove old code (#15533) +- update default model to nvidia/nv-rerankqa-mistral-4b-v3 (#15543) + +### `llama-index-readers-bitbucket` [0.1.4] + +- Fixing the issues in loading file paths from bitbucket (#15311) + +### `llama-index-readers-google` [0.3.1] + +- enhance google drive reader for improved functionality and usability (#15512) + +### `llama-index-readers-remote` [0.1.6] + +- check and sanitize remote reader urls (#15494) + +### `llama-index-vector-stores-qdrant` [0.2.17] + +- fix: setting IDF modifier in QdrantVectorStore for sparse vectors (#15538) + +## [2024-08-18] + +### `llama-index-core` [0.10.67] + +- avoid nltk 3.9 since its broken (#15473) +- docs: openllmetry now uses instrumentation (#15443) +- Fix LangChainDeprecationWarning (#15397) +- Add get/set API to the Context and make it coroutine-safe (#15152) +- docs: Cleanlab's cookbook (#15352) +- pass kwargs in `async_add()` for vector stores (#15333) +- escape json in structured llm (#15404) +- docs: Add JSONAlyze Query Engine using workflows cookbook (#15408) + +### `llama-index-embeddings-gigachat` [0.1.0] + +- Add GigaChat embedding (#15278) + +### `llama-index-finetuning` [0.1.12] + +- feat: Integrating Azure OpenAI Finetuning (#15297) + +### `llama-index-graph-stores-neptune` [0.1.7] + +- Exposed NeptuneQueryException and added additional debug information (#15448) +- Fixed issue #15414 and added ability to do partial matchfor Neptune Analytics (#15415) +- Use backticks to escape label (#15324) + +### `llama-index-llms-cohere` [0.2.1] + +- feat: add tool calling for cohere (#15144) + +### `llama-index-packs-corrective-rag` [0.1.2] + +- Ports over LongRAGPack, Corrective RAG Pack, and Self-Discover Pack to Workflows (#15160) + +### `llama-index-packs-longrag` [0.1.1] + +- Ports over LongRAGPack, Corrective RAG Pack, and Self-Discover Pack to Workflows (#15160) + +### `llama-index-packs-self-discover` [0.1.2] + +- Ports over LongRAGPack, Corrective RAG Pack, and Self-Discover Pack to Workflows (#15160) + +### `llama-index-readers-preprocess` [0.1.4] + +- Enhance PreprocessReader (#15302) + +## [2024-08-15] + +### `llama-index-core` [0.10.66] + +- Temporarily revert nltk dependency due to latest version being removed from pypi +- Add citation query engine with workflows example (#15372) +- bug: Semantic double merging splitter creates chunks larger thank chunk size (#15188) +- feat: make `send_event()` in workflows assign the target step (#15259) +- make all workflow events accessible like mappings (#15310) + +### `llama-index-indices-managed-bge-m3` [0.1.0] + +- Add BGEM3Index (#15197) + +### `llama-index-llms-huggingface` [0.2.7] + +- update HF's completion_to_prompt (#15354) + +### `llama-index-llms-sambanova` [0.1.0] + +- Wrapper for SambaNova (Sambaverse and SambaStudio) with Llama-index (#15220) + +### `llama-index-packs-code-hierarchy` [0.1.7] + +- Update code_hierarchy.py adding php support (#15145) + +### `llama-index-postprocessor-dashscope-rerank` [0.1.4] + +- fix bug when calling llama-index-postprocessor-dashscope-rerank (#15358) + +### `llama-index-readers-box` [0.1.2] + +- Box refactor: Box File to Llama-Index Document adaptor (#15314) + +### `llama-index-readers-gcs` [0.1.8] + +- GCSReader: Implementing ResourcesReaderMixin and FileSystemReaderMixin (#15365) + +### `llama-index-tools-box` [0.1.1] + +- Box refactor: Box File to Llama-Index Document adaptor (#15314) +- Box tools for AI Agents (#15236) + +### `llama-index-vector-stores-postgres` [0.1.14] + +- Check if hnsw index exists (#15287) + +## [2024-08-12] + +### `llama-index-core` [0.10.65] + +- chore: bump nltk version (#15277) + +### `llama-index-tools-box` [0.1.0] + +- Box tools for AI Agents (#15236) + +### `llama-index-multi-modal-llms-gemini` [0.1.8] + +- feat: add default_headers to Gemini multi-model (#15296) + +### `llama-index-vector-stores-clickhouse` [0.2.0] + +- chore: stop using ServiceContext from the clickhouse integration (#15300) + +### `llama-index-experimental` [0.2.0] + +- chore: remove ServiceContext usage from experimental package (#15301) + +### `llama-index-extractors-marvin` [0.1.4] + +- fix: MarvinMetadataExtractor functionality and apply async support (#15247) + +### `llama-index-utils-workflow` [0.1.1] + +- chore: bump black version (#15288) +- chore: bump nltk version (#15277) + +### `llama-index-readers-microsoft-onedrive` [0.1.9] + +- chore: bump nltk version (#15277) + +### `llama-index-embeddings-upstage` [0.1.3] + +- chore: bump nltk version (#15277) + +### `llama-index-embeddings-nvidia` [0.1.5] + +- chore: bump nltk version (#15277) + +### `llama-index-embeddings-litellm` [0.1.1] + +- chore: bump nltk version (#15277) + +### `llama-index-legacy` [0.9.48post1] + +- chore: bump nltk version (#15277) + +### `llama-index-packs-streamlit-chatbot` [0.1.5] + +- chore: bump nltk version (#15277) + +### `llama-index-embeddings-huggingface` [0.2.3] + +- Feature: added multiprocessing for creating hf embedddings (#15260) + +## [2024-08-09] + +### `llama-index-core` [0.10.64] + +- fix: children nodes not carrying metadata from source nodes (#15254) +- Workflows: fix the validation error in the decorator (#15252) +- fix: strip '''sql (Markdown SQL code snippet) in SQL Retriever (#15235) + +### `llama-index-indices-managed-colbert` [0.2.0] + +- Remove usage of ServiceContext in Colbert integration (#15249) + +### `llama-index-vector-stores-milvus` [0.1.23] + +- feat: Support Milvus collection properties (#15241) + +### `llama-index-llms-cleanlab` [0.1.2] + +- Update models supported by Cleanlab TLM (#15240) + +### `llama-index-llms-huggingface` [0.2.6] + +- add generation prompt to HF chat template (#15239) + +### `llama-index-llms-openvino` [0.2.1] + +- add generation prompt to HF chat template (#15239) + +### `llama-index-graph-stores-neo4j` [0.2.14] + +- Neo4jPropertyGraphStore.get() check for id prop (#15228) + +### `llama-index-readers-file` [0.1.33] + +- Fix fs.open path type (#15226) + +## [2024-08-08] + +### `llama-index-core` [0.10.63] + +- add num_workers in workflow decorator to resolve step concurrancy issue (#15210) +- Sub Question Query Engine as workflow notebook example (#15209) +- Add Llamatrace to workflow notebooks (#15186) +- Use node hash instead of node text to match nodes in fusion retriever (#15172) + +### `llama-index-embeddings-mistralai` [0.1.5] + +- handle mistral v1.0 client (#15229) + +### `llama-index-extractors-relik` [0.1.1] + +- Fix relik extractor skip error (#15225) + +### `llama-index-finetuning` [0.1.11] + +- handle mistral v1.0 client (#15229) + +### `llama-index-graph-stores-neo4j` [0.2.14] + +- Add neo4j generic node label (#15191) + +### `llama-index-llms-anthropic` [0.1.17] + +- Allow for images in Anthropic messages (#15227) + +### `llama-index-llms-mistralai` [0.1.20] + +- handle mistral v1.0 client (#15229) + +### `llama-index-packs-mixture-of-agents` [0.1.2] + +- Update Mixture Of Agents llamapack with workflows (#15232) + +### `llama-index-tools-slack` [0.1.4] + +- Fixed slack client ref in ToolSpec (#15202) + +## [2024-08-06] + +### `llama-index-core` [0.10.62] + +- feat: Allow None metadata filter by using IS_EMPTY operator (#15167) +- fix: use parent source node to node relationships if possible during node parsing (#15182) +- Use node hash instead of node text to match nodes in fusion retriever (#15172) + +### `llama-index-graph-stores-neo4j` [0.2.13] + +- Neo4j property graph client side batching (#15179) + +### `llama-index-graph-stores-neptune` [0.1.4] + +- PropertyGraphStore support for Amazon Neptune (#15126) + +### `llama-index-llms-gemini` [0.2.0] + +- feat: add default_headers to Gemini model (#15141) + +### `llama-index-llms-openai` [0.1.28] + +- OpenAI: Support new strict functionality in tool param (#15177) + +### `llama-index-vector-stores-opensearch` [0.1.14] + +- Add support for full MetadataFilters in Opensearch (#15176) + +### `llama-index-vector-stores-qdrant` [0.2.15] + +- feat: Allow None metadata filter by using IS_EMPTY operator (#15167) + +### `llama-index-vector-stores-wordlift` [0.3.0] + +- Add support for fields projection and update sample Notebook (#15140) + +## [2024-08-05] ### `llama-index-core` [0.10.61] diff --git a/Makefile b/Makefile index a350215cd0313..4ff0bb6a061cc 100644 --- a/Makefile +++ b/Makefile @@ -11,7 +11,7 @@ lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files test: ## Run tests via pants - pants --no-local-cache --changed-since=origin/main --changed-dependents=transitive test + pants --level=error --no-local-cache --changed-since=origin/main --changed-dependents=transitive test test-core: ## Run tests via pants pants --no-local-cache test llama-index-core/:: diff --git a/docs/BUILD b/docs/BUILD new file mode 100644 index 0000000000000..589a382606557 --- /dev/null +++ b/docs/BUILD @@ -0,0 +1,9 @@ +python_sources() + +poetry_requirements( + name="poetry", +) + +python_requirements( + name="reqs", +) diff --git a/docs/docs/CHANGELOG.md b/docs/docs/CHANGELOG.md index e9ea5abf6d7a3..528461e0381a3 100644 --- a/docs/docs/CHANGELOG.md +++ b/docs/docs/CHANGELOG.md @@ -1,6 +1,488 @@ # ChangeLog -## [2024-02-05] +## [2024-08-29] + +### `llama-index-core` [0.11.3] + +- refact: merge Context and Session to simplify the workflows api (#15709) +- chore: stop using deprecated `ctx.data` in workflows docs (#15716) +- fix: stop streaming workflow events when a step raises (#15714) +- Fix llm_chat_callback for multimodal llms (#15700) +- chore: Increase unit tests coverage for the workflow package (#15691) +- fix SimpleVectorStore.from_persist_dir() behaviour (#15534) + +### `llama-index-embeddings-azure-openai` [0.2.5] + +- fix json serialization for azure embeddings (#15724) + +### `llama-index-graph-stores-kuzu` [0.3.0] + +- Add KuzuPropertyGraphStore (#15678) + +### `llama-index-indices-managed-vectara` [0.2.1] + +- added new User Defined Function reranker (#15546) + +### `llama-index-llms-mistralai` [0.2.2] + +- Fix `random_seed` type in mistral llm (#15701) + +### `llama-index-llms-nvidia` [0.2.1] + +- Add function/tool calling support to nvidia llm (#15359) + +### `llama-index-multi-modal-llms-ollama` [0.3.0] + +- bump ollama client deps for multimodal llm (#15702) + +### `llama-index-readers-web` [0.2.1] + +- Fix: Firecrawl scraping url response (#15720) + +### `llama-index-selectors-notdiamond` [0.1.0] + +- Adding Not Diamond to llama_index (#15703) + +### `llama-index-vector-stores-milvus` [0.2.3] + +- MMR in Milvus vector stores (#15634) +- feat: implement get_nodes for MilvusVectorStore (#15696) + +## [2024-08-27] + +### `llama-index-core` [0.11.2] + +- fix tool schemas generation for pydantic v2 to handle nested models (#15679) +- feat: support default values for nested workflows (#15660) +- feat: allow FunctionTool with just an async fn (#15638) +- feat: Allow streaming events from steps (#15488) +- fix auto-retriever pydantic indent error (#15648) +- Implement Router Query Engine example using workflows (#15635) +- Add multi step query engine example using workflows (#15438) +- start traces for llm-level operations (#15542) +- Pass callback_manager to init in CodeSplitter from_defaults (#15585) + +### `llama-index-embeddings-xinference` [0.1.0] + +- Add Xinference Embedding Class (#15579) + +### `llama-index-llms-ai21` [0.3.3] + +- Integrations: AI21 function calling Support (#15622) + +### `llama-index-llms-anthropic` [0.3.0] + +- Added support for anthropic models through GCP Vertex AI (#15661) + +### `llama-index-llms-cerebras` [0.1.0] + +- Implement Cerebras Integration (#15665) + +### `llama-index-postprocessor-nvidia-rerank` [0.3.1] + +- fix downloaded nim endpoint path (#15645) +- fix llama-index-postprocessor-nvidia-rerank tests (#15643) + +### `llama-index-postprocessor-xinference-rerank` [0.1.0] + +- add xinference rerank class (#15639) + +### `llama-index-vector-stores-alibabacloud-opensearch` [0.2.1] + +- fix set output fields in AlibabaCloudOpenSearchConfig (#15562) + +### `llama-index-vector-stores-azureaisearch` [0.2.1] + +- Upgrade azure-search-documents to 2024-07-01 GA API and Add Support for Scalar and Binary Quantization in Index Creation (#15650) + +### `llama-index-vector-stores-neo4j` [0.2.1] + +- Neo4j Vector Store: Make Embedding Dimension Check Optional (#15628) + +### `llama-inde-vector-stores-milvus` [0.2.1] + +- Change the default consistency level of Milvus (#15577) + +### `llama-index-vector-stores-elasticsearch` [0.3.2] + +- Fix the ElasticsearchStore key error (#15631) + +## [2024-08-23] + +### `llama-index-core` [0.11.1] + +- Replacing client-side docs search with algolia (#15574) +- Add docs on extending workflows (#15573) +- rename method for nested workflows to add_workflows (#15596) +- chore: fix @step usage in the core codebase (#15588) +- Modify the validate function in ReflectionWorkflow example notebook to use pydantic model_validate_json method (#15567) +- feature: allow concurrent runs of the same workflow instance (#15568) +- docs: remove redundant pass_context=True from docs and examples (#15571) + +### `llama-index-embeddings-openai` [0.2.3] + +- fix openai embeddings with pydantic v2 (#15576) + +### `llama-index-embeddings-voyageai` [0.2.1] + +- bump voyage ai embedding client dep (#15595) + +### `llama-index-llms-vertex` [0.3.3] + +- Vertex LLM: Correctly add function calling part to prompt (#15569) +- Vertex LLM: Remove manual setting of message content to Function Calling (#15586) + +## [2024-08-22] + +### `llama-index-core` [0.11.0] + +- removed deprecated `ServiceContext` -- using this now will print an error with a link to the migration guide +- removed deprecated `LLMPredictor` -- using this now will print an error, any existing LLM is a drop-in replacement +- made `pandas` an optional dependency + +### `Everything Else` + +- bumped the minor version of every package to account for the new version of `llama-index-core` + +## [2024-08-21] + +### `llama-index-core` [0.10.68] + +- remove nested progress bars in base element node parser (#15550) +- Adding exhaustive docs for workflows (#15556) +- Adding multi-strategy workflow with reflection notebook example (#15445) +- remove openai dep from core (#15527) +- Improve token counter to handle more response types (#15501) +- feat: Allow using step decorator without parentheses (#15540) +- feat: workflow services (aka nested workflows) (#15325) +- Remove requirement to specify "allowed_query_fields" parameter when using "cypher_validator" in TextToCypher retriever (#15506) + +### `llama-index-embeddings-mistralai` [0.1.6] + +- fix mistral embeddings usage (#15508) + +### `llama-index-embeddings-ollama` [0.2.0] + +- use ollama client for embeddings (#15478) + +### `llama-index-embeddings-openvino` [0.2.1] + +- support static input shape for openvino embedding and reranker (#15521) + +### `llama-index-graph-stores-neptune` [0.1.8] + +- Added code to expose structured schema for Neptune (#15507) + +### `llama-index-llms-ai21` [0.3.2] + +- Integration: AI21 Tools support (#15518) + +### `llama-index-llms-bedrock` [0.1.13] + +- Support token counting for llama-index integration with bedrock (#15491) + +### `llama-index-llms-cohere` [0.2.2] + +- feat: add tool calling support for achat cohere (#15539) + +### `llama-index-llms-gigachat` [0.1.0] + +- Adding gigachat LLM support (#15313) + +### `llama-index-llms-openai` [0.1.31] + +- Fix incorrect type in OpenAI token usage report (#15524) +- allow streaming token counts for openai (#15548) + +### `llama-index-postprocessor-nvidia-rerank` [0.2.1] + +- add truncate support (#15490) +- Update to 0.2.0, remove old code (#15533) +- update default model to nvidia/nv-rerankqa-mistral-4b-v3 (#15543) + +### `llama-index-readers-bitbucket` [0.1.4] + +- Fixing the issues in loading file paths from bitbucket (#15311) + +### `llama-index-readers-google` [0.3.1] + +- enhance google drive reader for improved functionality and usability (#15512) + +### `llama-index-readers-remote` [0.1.6] + +- check and sanitize remote reader urls (#15494) + +### `llama-index-vector-stores-qdrant` [0.2.17] + +- fix: setting IDF modifier in QdrantVectorStore for sparse vectors (#15538) + +## [2024-08-18] + +### `llama-index-core` [0.10.67] + +- avoid nltk 3.9 since its broken (#15473) +- docs: openllmetry now uses instrumentation (#15443) +- Fix LangChainDeprecationWarning (#15397) +- Add get/set API to the Context and make it coroutine-safe (#15152) +- docs: Cleanlab's cookbook (#15352) +- pass kwargs in `async_add()` for vector stores (#15333) +- escape json in structured llm (#15404) +- docs: Add JSONAlyze Query Engine using workflows cookbook (#15408) + +### `llama-index-embeddings-gigachat` [0.1.0] + +- Add GigaChat embedding (#15278) + +### `llama-index-finetuning` [0.1.12] + +- feat: Integrating Azure OpenAI Finetuning (#15297) + +### `llama-index-graph-stores-neptune` [0.1.7] + +- Exposed NeptuneQueryException and added additional debug information (#15448) +- Fixed issue #15414 and added ability to do partial matchfor Neptune Analytics (#15415) +- Use backticks to escape label (#15324) + +### `llama-index-llms-cohere` [0.2.1] + +- feat: add tool calling for cohere (#15144) + +### `llama-index-packs-corrective-rag` [0.1.2] + +- Ports over LongRAGPack, Corrective RAG Pack, and Self-Discover Pack to Workflows (#15160) + +### `llama-index-packs-longrag` [0.1.1] + +- Ports over LongRAGPack, Corrective RAG Pack, and Self-Discover Pack to Workflows (#15160) + +### `llama-index-packs-self-discover` [0.1.2] + +- Ports over LongRAGPack, Corrective RAG Pack, and Self-Discover Pack to Workflows (#15160) + +### `llama-index-readers-preprocess` [0.1.4] + +- Enhance PreprocessReader (#15302) + +## [2024-08-15] + +### `llama-index-core` [0.10.66] + +- Temporarily revert nltk dependency due to latest version being removed from pypi +- Add citation query engine with workflows example (#15372) +- bug: Semantic double merging splitter creates chunks larger thank chunk size (#15188) +- feat: make `send_event()` in workflows assign the target step (#15259) +- make all workflow events accessible like mappings (#15310) + +### `llama-index-indices-managed-bge-m3` [0.1.0] + +- Add BGEM3Index (#15197) + +### `llama-index-llms-huggingface` [0.2.7] + +- update HF's completion_to_prompt (#15354) + +### `llama-index-llms-sambanova` [0.1.0] + +- Wrapper for SambaNova (Sambaverse and SambaStudio) with Llama-index (#15220) + +### `llama-index-packs-code-hierarchy` [0.1.7] + +- Update code_hierarchy.py adding php support (#15145) + +### `llama-index-postprocessor-dashscope-rerank` [0.1.4] + +- fix bug when calling llama-index-postprocessor-dashscope-rerank (#15358) + +### `llama-index-readers-box` [0.1.2] + +- Box refactor: Box File to Llama-Index Document adaptor (#15314) + +### `llama-index-readers-gcs` [0.1.8] + +- GCSReader: Implementing ResourcesReaderMixin and FileSystemReaderMixin (#15365) + +### `llama-index-tools-box` [0.1.1] + +- Box refactor: Box File to Llama-Index Document adaptor (#15314) +- Box tools for AI Agents (#15236) + +### `llama-index-vector-stores-postgres` [0.1.14] + +- Check if hnsw index exists (#15287) + +## [2024-08-12] + +### `llama-index-core` [0.10.65] + +- chore: bump nltk version (#15277) + +### `llama-index-tools-box` [0.1.0] + +- Box tools for AI Agents (#15236) + +### `llama-index-multi-modal-llms-gemini` [0.1.8] + +- feat: add default_headers to Gemini multi-model (#15296) + +### `llama-index-vector-stores-clickhouse` [0.2.0] + +- chore: stop using ServiceContext from the clickhouse integration (#15300) + +### `llama-index-experimental` [0.2.0] + +- chore: remove ServiceContext usage from experimental package (#15301) + +### `llama-index-extractors-marvin` [0.1.4] + +- fix: MarvinMetadataExtractor functionality and apply async support (#15247) + +### `llama-index-utils-workflow` [0.1.1] + +- chore: bump black version (#15288) +- chore: bump nltk version (#15277) + +### `llama-index-readers-microsoft-onedrive` [0.1.9] + +- chore: bump nltk version (#15277) + +### `llama-index-embeddings-upstage` [0.1.3] + +- chore: bump nltk version (#15277) + +### `llama-index-embeddings-nvidia` [0.1.5] + +- chore: bump nltk version (#15277) + +### `llama-index-embeddings-litellm` [0.1.1] + +- chore: bump nltk version (#15277) + +### `llama-index-legacy` [0.9.48post1] + +- chore: bump nltk version (#15277) + +### `llama-index-packs-streamlit-chatbot` [0.1.5] + +- chore: bump nltk version (#15277) + +### `llama-index-embeddings-huggingface` [0.2.3] + +- Feature: added multiprocessing for creating hf embedddings (#15260) + +## [2024-08-09] + +### `llama-index-core` [0.10.64] + +- fix: children nodes not carrying metadata from source nodes (#15254) +- Workflows: fix the validation error in the decorator (#15252) +- fix: strip '''sql (Markdown SQL code snippet) in SQL Retriever (#15235) + +### `llama-index-indices-managed-colbert` [0.2.0] + +- Remove usage of ServiceContext in Colbert integration (#15249) + +### `llama-index-vector-stores-milvus` [0.1.23] + +- feat: Support Milvus collection properties (#15241) + +### `llama-index-llms-cleanlab` [0.1.2] + +- Update models supported by Cleanlab TLM (#15240) + +### `llama-index-llms-huggingface` [0.2.6] + +- add generation prompt to HF chat template (#15239) + +### `llama-index-llms-openvino` [0.2.1] + +- add generation prompt to HF chat template (#15239) + +### `llama-index-graph-stores-neo4j` [0.2.14] + +- Neo4jPropertyGraphStore.get() check for id prop (#15228) + +### `llama-index-readers-file` [0.1.33] + +- Fix fs.open path type (#15226) + +## [2024-08-08] + +### `llama-index-core` [0.10.63] + +- add num_workers in workflow decorator to resolve step concurrancy issue (#15210) +- Sub Question Query Engine as workflow notebook example (#15209) +- Add Llamatrace to workflow notebooks (#15186) +- Use node hash instead of node text to match nodes in fusion retriever (#15172) + +### `llama-index-embeddings-mistralai` [0.1.5] + +- handle mistral v1.0 client (#15229) + +### `llama-index-extractors-relik` [0.1.1] + +- Fix relik extractor skip error (#15225) + +### `llama-index-finetuning` [0.1.11] + +- handle mistral v1.0 client (#15229) + +### `llama-index-graph-stores-neo4j` [0.2.14] + +- Add neo4j generic node label (#15191) + +### `llama-index-llms-anthropic` [0.1.17] + +- Allow for images in Anthropic messages (#15227) + +### `llama-index-llms-mistralai` [0.1.20] + +- handle mistral v1.0 client (#15229) + +### `llama-index-packs-mixture-of-agents` [0.1.2] + +- Update Mixture Of Agents llamapack with workflows (#15232) + +### `llama-index-tools-slack` [0.1.4] + +- Fixed slack client ref in ToolSpec (#15202) + +## [2024-08-06] + +### `llama-index-core` [0.10.62] + +- feat: Allow None metadata filter by using IS_EMPTY operator (#15167) +- fix: use parent source node to node relationships if possible during node parsing (#15182) +- Use node hash instead of node text to match nodes in fusion retriever (#15172) + +### `llama-index-graph-stores-neo4j` [0.2.13] + +- Neo4j property graph client side batching (#15179) + +### `llama-index-graph-stores-neptune` [0.1.4] + +- PropertyGraphStore support for Amazon Neptune (#15126) + +### `llama-index-llms-gemini` [0.2.0] + +- feat: add default_headers to Gemini model (#15141) + +### `llama-index-llms-openai` [0.1.28] + +- OpenAI: Support new strict functionality in tool param (#15177) + +### `llama-index-vector-stores-opensearch` [0.1.14] + +- Add support for full MetadataFilters in Opensearch (#15176) + +### `llama-index-vector-stores-qdrant` [0.2.15] + +- feat: Allow None metadata filter by using IS_EMPTY operator (#15167) + +### `llama-index-vector-stores-wordlift` [0.3.0] + +- Add support for fields projection and update sample Notebook (#15140) + +## [2024-08-05] ### `llama-index-core` [0.10.61] diff --git a/docs/docs/_static/css/algolia.css b/docs/docs/_static/css/algolia.css index 5995b8135d633..22a2c2601af6d 100644 --- a/docs/docs/_static/css/algolia.css +++ b/docs/docs/_static/css/algolia.css @@ -1,96 +1,8 @@ -/* Hide search button */ -.sidebar-search-container { - display: none; -} - -/* Hide the search wrapper window when hitting Ctrl+K */ -.search-button__wrapper.show { - display: none !important; -} - -/* Make sure Algolia's search container is always on top */ -.bd-article-container { - z-index: 10; -} - -@media (prefers-color-scheme: dark) { - body:not([data-theme="light"]) .DocSearch-Button { - background-color: #3d4751 !important; - color: white !important; - } - - body:not([data-theme="light"]) .DocSearch-Search-Icon { - color: white !important; - } - - body:not([data-theme="light"]) .DocSearch-Button-Key { - color: black !important; - 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"74VN1YECLR", - indexName: "gpt-index", - apiKey: "fb20bbeb2c3b7f63f89bacf797bf3a34", + container: '#docsearch', + appId: '74VN1YECLR', + indexName: 'gpt-index', + apiKey: 'c4b0e099fa9004f69855e474b3e7d3bb', }); +console.log(docsearch) +console.log("Docsearch loaded") diff --git a/docs/docs/api_reference/embeddings/gigachat.md b/docs/docs/api_reference/embeddings/gigachat.md new file mode 100644 index 0000000000000..ebf2cd138ff1e --- /dev/null +++ b/docs/docs/api_reference/embeddings/gigachat.md @@ -0,0 +1,4 @@ +::: llama_index.embeddings.gigachat + options: + members: + - GigaChatEmbedding diff --git a/docs/docs/api_reference/embeddings/xinference.md b/docs/docs/api_reference/embeddings/xinference.md new file mode 100644 index 0000000000000..69b2d3ef385cd --- /dev/null +++ b/docs/docs/api_reference/embeddings/xinference.md @@ -0,0 +1,4 @@ +::: llama_index.embeddings.xinference + options: + members: + - XinferenceEmbedding diff --git a/docs/docs/api_reference/indices/bge_m3.md b/docs/docs/api_reference/indices/bge_m3.md new file mode 100644 index 0000000000000..e0ec067bdeb0d --- /dev/null +++ b/docs/docs/api_reference/indices/bge_m3.md @@ -0,0 +1,4 @@ +::: llama_index.indices.managed.bge_m3 + options: + members: + - BGEM3Index diff --git a/docs/docs/api_reference/llms/cerebras.md b/docs/docs/api_reference/llms/cerebras.md new file mode 100644 index 0000000000000..aec0a837d29bf --- /dev/null +++ b/docs/docs/api_reference/llms/cerebras.md @@ -0,0 +1,4 @@ +::: llama_index.llms.cerebras + options: + members: + - Cerebras diff --git a/docs/docs/api_reference/llms/gigachat.md b/docs/docs/api_reference/llms/gigachat.md new file mode 100644 index 0000000000000..e1de1a3ee12f1 --- /dev/null +++ b/docs/docs/api_reference/llms/gigachat.md @@ -0,0 +1,4 @@ +::: llama_index.llms.gigachat + options: + members: + - GigaChatLLM diff --git a/docs/docs/api_reference/llms/sambanova.md b/docs/docs/api_reference/llms/sambanova.md new file mode 100644 index 0000000000000..965153ab1e825 --- /dev/null +++ b/docs/docs/api_reference/llms/sambanova.md @@ -0,0 +1,5 @@ +::: llama_index.llms.sambanova + options: + members: + - SambaStudio + - Sambaverse diff --git a/docs/docs/api_reference/postprocessor/xinference_rerank.md b/docs/docs/api_reference/postprocessor/xinference_rerank.md new file mode 100644 index 0000000000000..11d71202e448f --- /dev/null +++ b/docs/docs/api_reference/postprocessor/xinference_rerank.md @@ -0,0 +1,4 @@ +::: llama_index.postprocessor.xinference_rerank + options: + members: + - XinferenceRerank diff --git a/docs/docs/api_reference/storage/graph_stores/falkordb.md b/docs/docs/api_reference/storage/graph_stores/falkordb.md index 12906a65f9a48..d7ab23ee8c1a9 100644 --- a/docs/docs/api_reference/storage/graph_stores/falkordb.md +++ b/docs/docs/api_reference/storage/graph_stores/falkordb.md @@ -2,3 +2,4 @@ options: members: - FalkorDBGraphStore + - FalkorDBPropertyGraphStore diff --git a/docs/docs/api_reference/storage/graph_stores/kuzu.md b/docs/docs/api_reference/storage/graph_stores/kuzu.md index a19d228e43df1..e1a6deeff08ea 100644 --- a/docs/docs/api_reference/storage/graph_stores/kuzu.md +++ b/docs/docs/api_reference/storage/graph_stores/kuzu.md @@ -2,3 +2,4 @@ options: members: - KuzuGraphStore + - KuzuPropertyGraphStore diff --git a/docs/docs/api_reference/storage/graph_stores/neptune.md b/docs/docs/api_reference/storage/graph_stores/neptune.md index 8be441642f4cb..5754cfc2339e0 100644 --- a/docs/docs/api_reference/storage/graph_stores/neptune.md +++ b/docs/docs/api_reference/storage/graph_stores/neptune.md @@ -2,4 +2,6 @@ options: members: - NeptuneAnalyticsGraphStore + - NeptuneAnalyticsPropertyGraphStore - NeptuneDatabaseGraphStore + - NeptuneDatabasePropertyGraphStore diff --git a/docs/docs/api_reference/tools/box.md b/docs/docs/api_reference/tools/box.md new file mode 100644 index 0000000000000..0696011961a45 --- /dev/null +++ b/docs/docs/api_reference/tools/box.md @@ -0,0 +1,8 @@ +::: llama_index.tools.box + options: + members: + - BoxAIExtractToolSpec + - BoxAIPromptToolSpec + - BoxSearchByMetadataToolSpec + - BoxSearchToolSpec + - BoxTextExtractToolSpec diff --git a/docs/docs/community/integrations/graph_stores.md b/docs/docs/community/integrations/graph_stores.md index 5404ce4620cc5..659e6aae43d8a 100644 --- a/docs/docs/community/integrations/graph_stores.md +++ b/docs/docs/community/integrations/graph_stores.md @@ -21,11 +21,14 @@ See the associated guides below: ## `KuzuGraphStore` -We support a `KuzuGraphStore` integration, for persisting graphs directly in [Kuzu](https://kuzudb.com). +We support a `KuzuGraphStore` integration, for persisting triples directly in [Kuzu](https://kuzudb.com). +Additionally, we support the `PropertyGraphIndex`, which allows you to store and query property graphs +using a Kuzu backend. See the associated guides below: - [Kuzu Graph Store](../../examples/index_structs/knowledge_graph/KuzuGraphDemo.ipynb) +- [Kuzu Graph Store](../../examples/property_graph/property_graph_kuzu.ipynb) ## `FalkorDBGraphStore` diff --git a/docs/docs/css/algolia.css b/docs/docs/css/algolia.css index 5995b8135d633..386170a2ccbe3 100644 --- a/docs/docs/css/algolia.css +++ b/docs/docs/css/algolia.css @@ -1,96 +1,755 @@ -/* Hide search button */ -.sidebar-search-container { - display: none; +/** + * Skipped minification because the original files appears to be already minified. + * Original file: /npm/@docsearch/css@3.6.1/dist/style.css + * + * Do NOT use SRI with dynamically generated files! 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+ transition: none } } -.DocSearch-Button-Key { - font-family: - "SFMono-Regular", - Menlo, - Consolas, - Monaco, - Liberation Mono, - Lucida Console, - monospace !important; - padding: 0 !important; - padding-top: 0.1rem !important; +.DocSearch-ErrorScreen, +.DocSearch-NoResults, +.DocSearch-StartScreen { + font-size: .9em; + margin: 0 auto; + padding: 36px 0; + text-align: center; + width: 80% +} + +.DocSearch-Screen-Icon { + color: var(--docsearch-muted-color); + padding-bottom: 12px +} + +.DocSearch-NoResults-Prefill-List { + display: inline-block; + padding-bottom: 24px; + text-align: left +} + +.DocSearch-NoResults-Prefill-List ul { + display: inline-block; + padding: 8px 0 0 +} + +.DocSearch-NoResults-Prefill-List li { + list-style-position: inside; + list-style-type: "» " +} + +.DocSearch-Prefill { + appearance: none; + background: none; + border: 0; + border-radius: 1em; + color: var(--docsearch-highlight-color); + cursor: pointer; + display: inline-block; + font-size: 1em; + font-weight: 700; + padding: 0 +} + +.DocSearch-Prefill:focus, +.DocSearch-Prefill:hover { + outline: none; + text-decoration: underline +} + +.DocSearch-Footer { + align-items: center; + background: var(--docsearch-footer-background); + border-radius: 0 0 8px 8px; + box-shadow: var(--docsearch-footer-shadow); + display: flex; + flex-direction: row-reverse; + flex-shrink: 0; + height: var(--docsearch-footer-height); + justify-content: space-between; + padding: 0 var(--docsearch-spacing); + position: relative; + user-select: none; + width: 100%; + z-index: 300 +} + +.DocSearch-Commands { + color: var(--docsearch-muted-color); display: flex; + list-style: none; + margin: 0; + padding: 0 +} + +.DocSearch-Commands li { align-items: center; - justify-content: center; + display: flex +} + +.DocSearch-Commands li:not(:last-of-type) { + margin-right: .8em } .DocSearch-Commands-Key { - font-family: - "SFMono-Regular", - Menlo, - Consolas, - Monaco, - Liberation Mono, - Lucida Console, - monospace !important; - padding: 0 !important; - padding-left: 0.05rem !important; - display: flex; align-items: center; + background: var(--docsearch-key-gradient); + border-radius: 2px; + box-shadow: var(--docsearch-key-shadow); + display: flex; + height: 18px; justify-content: center; + margin-right: .4em; + padding: 0 0 1px; + color: var(--docsearch-muted-color); + border: 0; + width: 20px +} + +.DocSearch-VisuallyHiddenForAccessibility { + clip: rect(0 0 0 0); + clip-path: inset(50%); + height: 1px; + overflow: hidden; + position: absolute; + white-space: nowrap; + width: 1px +} + +@media (max-width:768px) { + :root { + --docsearch-spacing: 10px; + --docsearch-footer-height: 40px + } + + .DocSearch-Dropdown { + height: 100% + } + + .DocSearch-Container { + height: 100vh; + height: -webkit-fill-available; + height: calc(var(--docsearch-vh, 1vh)*100); + position: absolute + } + + .DocSearch-Footer { + border-radius: 0; + bottom: 0; + position: absolute + } + + .DocSearch-Hit-content-wrapper { + display: flex; + position: relative; + width: 80% + } + + .DocSearch-Modal { + border-radius: 0; + box-shadow: none; + height: 100vh; + height: -webkit-fill-available; + height: calc(var(--docsearch-vh, 1vh)*100); + margin: 0; + max-width: 100%; + width: 100% + } + + .DocSearch-Dropdown { + max-height: calc(var(--docsearch-vh, 1vh)*100 - var(--docsearch-searchbox-height) - var(--docsearch-spacing) - var(--docsearch-footer-height)) + } + + .DocSearch-Cancel { + appearance: none; + background: none; + border: 0; + color: var(--docsearch-highlight-color); + cursor: pointer; + display: inline-block; + flex: none; + font: inherit; + font-size: 1em; + font-weight: 500; + margin-left: var(--docsearch-spacing); + outline: none; + overflow: hidden; + padding: 0; + user-select: none; + white-space: nowrap + } + + .DocSearch-Commands, + .DocSearch-Hit-Tree { + display: none + } +} + +@keyframes fade-in { + 0% { + opacity: 0 + } + + to { + opacity: 1 + } +} + +/* hide superfluous second search icon */ +label.md-icon[for=__search] { + display: none; } diff --git a/docs/docs/examples/agent/nvidia_agent.ipynb b/docs/docs/examples/agent/nvidia_agent.ipynb new file mode 100644 index 0000000000000..6cab931fc452c --- /dev/null +++ b/docs/docs/examples/agent/nvidia_agent.ipynb @@ -0,0 +1,736 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Function Calling NVIDIA Agent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook shows you how to use our NVIDIA agent, powered by function calling capabilities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initial Setup " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by importing some simple building blocks. \n", + "\n", + "The main thing we need is:\n", + "1. the NVIDIA NIM Endpoint (using our own `llama_index` LLM class)\n", + "2. a place to keep conversation history \n", + "3. a definition for tools that our agent can use." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mDEPRECATION: pytest-httpx 0.21.0 has a non-standard dependency specifier pytest<8.*,>=6.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytest-httpx or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade --quiet llama-index-llms-nvidia" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Valid NVIDIA_API_KEY already in environment. Delete to reset\n" + ] + } + ], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "# del os.environ['NVIDIA_API_KEY'] ## delete key and reset\n", + "if os.environ.get(\"NVIDIA_API_KEY\", \"\").startswith(\"nvapi-\"):\n", + " print(\"Valid NVIDIA_API_KEY already in environment. Delete to reset\")\n", + "else:\n", + " nvapi_key = getpass.getpass(\"NVAPI Key (starts with nvapi-): \")\n", + " assert nvapi_key.startswith(\n", + " \"nvapi-\"\n", + " ), f\"{nvapi_key[:5]}... is not a valid key\"\n", + " os.environ[\"NVIDIA_API_KEY\"] = nvapi_key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.nvidia import NVIDIA\n", + "from llama_index.core.tools import FunctionTool\n", + "from llama_index.embeddings.nvidia import NVIDIAEmbedding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define some very simple calculator tools for our agent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def multiply(a: int, b: int) -> int:\n", + " \"\"\"Multiple two integers and returns the result integer\"\"\"\n", + " return a * b\n", + "\n", + "\n", + "multiply_tool = FunctionTool.from_defaults(fn=multiply)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def add(a: int, b: int) -> int:\n", + " \"\"\"Add two integers and returns the result integer\"\"\"\n", + " return a + b\n", + "\n", + "\n", + "add_tool = FunctionTool.from_defaults(fn=add)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we initialize a simple NVIDIA agent with calculator functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm = NVIDIA(\"meta/llama-3.1-70b-instruct\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.agent import FunctionCallingAgentWorker\n", + "\n", + "agent_worker = FunctionCallingAgentWorker.from_tools(\n", + " [multiply_tool, add_tool],\n", + " llm=llm,\n", + " verbose=True,\n", + ")\n", + "agent = agent_worker.as_agent()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added user message to memory: What is (121 * 3) + 42?\n", + "=== Calling Function ===\n", + "Calling function: multiply with args: {\"a\": 121, \"b\": 3}\n", + "=== Function Output ===\n", + "363\n", + "=== Calling Function ===\n", + "Calling function: add with args: {\"a\": 363, \"b\": 42}\n", + "=== Function Output ===\n", + "405\n", + "=== LLM Response ===\n", + "The answer is 405.\n", + "The answer is 405.\n" + ] + } + ], + "source": [ + "response = agent.chat(\"What is (121 * 3) + 42?\")\n", + "print(str(response))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ToolOutput(content='363', tool_name='multiply', raw_input={'args': (), 'kwargs': {'a': 121, 'b': 3}}, raw_output=363, is_error=False), ToolOutput(content='405', tool_name='add', raw_input={'args': (), 'kwargs': {'a': 363, 'b': 42}}, raw_output=405, is_error=False)]\n" + ] + } + ], + "source": [ + "# inspect sources\n", + "print(response.sources)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Async Chat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added user message to memory: What is 121 * 3?\n", + "=== Calling Function ===\n", + "Calling function: multiply with args: {\"a\": 121, \"b\": 3}\n", + "=== Function Output ===\n", + "363\n", + "=== LLM Response ===\n", + "The answer is 363.\n", + "The answer is 363.\n" + ] + } + ], + "source": [ + "response = await agent.achat(\"What is 121 * 3?\")\n", + "print(str(response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Agent with Personality" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can specify a system prompt to give the agent additional instruction or personality." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.prompts.system import SHAKESPEARE_WRITING_ASSISTANT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agent = FunctionCallingAgentWorker.from_tools(\n", + " [multiply_tool, add_tool],\n", + " llm=llm,\n", + " verbose=True,\n", + " system_prompt=SHAKESPEARE_WRITING_ASSISTANT,\n", + ").as_agent()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added user message to memory: Hi\n", + "Fair greeting unto thee, kind sir or madam! 'Tis a pleasure to make thy acquaintance. How doth thy day fare? Doth thou seek inspiration for a tale, a poem, or perhaps a song, penned in the grand style of the Bard himself?\n" + ] + } + ], + "source": [ + "response = agent.chat(\"Hi\")\n", + "print(response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = agent.chat(\"Tell me a story\")\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NVIDIA Agent with RAG/Query Engine Tools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p 'data/10k/'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n", + "from llama_index.core import SimpleDirectoryReader, VectorStoreIndex\n", + "\n", + "embed_model = NVIDIAEmbedding(model=\"NV-Embed-QA\", truncate=\"END\")\n", + "\n", + "# load data\n", + "uber_docs = SimpleDirectoryReader(\n", + " input_files=[\"./data/10k/uber_2021.pdf\"]\n", + ").load_data()\n", + "# build index\n", + "uber_index = VectorStoreIndex.from_documents(\n", + " uber_docs, embed_model=embed_model\n", + ")\n", + "uber_engine = uber_index.as_query_engine(similarity_top_k=3, llm=llm)\n", + "query_engine_tool = QueryEngineTool(\n", + " query_engine=uber_engine,\n", + " metadata=ToolMetadata(\n", + " name=\"uber_10k\",\n", + " description=(\n", + " \"Provides information about Uber financials for year 2021. \"\n", + " \"Use a detailed plain text question as input to the tool.\"\n", + " ),\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agent_worker = FunctionCallingAgentWorker.from_tools(\n", + " [query_engine_tool], llm=llm, verbose=True\n", + ")\n", + "agent = agent_worker.as_agent()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = agent.chat(\n", + " \"Tell me both the risk factors and tailwinds for Uber? Do two parallel tool calls.\",\n", + " allow_parallel_tool_calls=True,\n", + ")\n", + "print(str(response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ReAct Agent " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.agent import ReActAgent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agent = ReActAgent.from_tools([multiply_tool, add_tool], llm=llm, verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> Running step a61e9980-2b9c-4a78-9950-cabe13827f73. Step input: What is 20+(2*4)? Calculate step by step \n", + "\u001b[1;3;38;5;200mThought: To calculate 20+(2*4), I need to follow the order of operations (PEMDAS). First, I need to calculate the multiplication part, which is 2*4. I will use the multiply tool to do this.\n", + "Action: multiply\n", + "Action Input: {'a': 2, 'b': 4}\n", + "\u001b[0m\u001b[1;3;34mObservation: 8\n", + "\u001b[0m> Running step 73418308-49cc-4689-bb39-b83d6a7cf7ac. Step input: None\n", + "\u001b[1;3;38;5;200mThought: Now that I have the result of the multiplication, which is 8, I can proceed to add 20 to it. I will use the add tool to do this.\n", + "Action: add\n", + "Action Input: {'a': 20, 'b': 8}\n", + "\u001b[0m\u001b[1;3;34mObservation: 28\n", + "\u001b[0m> Running step 73464ecd-b266-47ef-8f1b-c0aa0e43ad60. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I have now calculated the entire expression 20+(2*4) and have the final result.\n", + "Answer: 28\n", + "\u001b[0m" + ] + } + ], + "source": [ + "response = agent.chat(\"What is 20+(2*4)? Calculate step by step \")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> Running step 079f0d12-e0f7-48cd-a1be-9f8a9ce98b9c. Step input: What is 20+2*4? Calculate step by step\n", + "\u001b[1;3;38;5;200mThought: To calculate 20+2*4, I need to follow the order of operations (PEMDAS). First, I need to calculate the multiplication.\n", + "Action: multiply\n", + "Action Input: {'a': 2, 'b': 4}\n", + "\u001b[0m\u001b[1;3;34mObservation: 8\n", + "\u001b[0m> Running step 9d3a64e8-0b14-4721-9d5c-dd51b03ff3fa. Step input: None\n", + "\u001b[1;3;38;5;200mThought: Now that I have the result of the multiplication, I can add 20 to it.\n", + "Action: add\n", + "Action Input: {'a': 20, 'b': 8}\n", + "\u001b[0m\u001b[1;3;34mObservation: 28\n", + "\u001b[0m> Running step 14d6f623-5c92-405b-88d5-468805429e0b. Step input: None\n", + " 20+2*4 = 28" + ] + } + ], + "source": [ + "response_gen = agent.stream_chat(\"What is 20+2*4? Calculate step by step\")\n", + "response_gen.print_response_stream()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View Prompts\n", + "\n", + "Let's take a look at the core system prompt powering the ReAct agent! \n", + "\n", + "Within the agent, the current conversation history is dumped below this line." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prompt_dict = agent.get_prompts()\n", + "for k, v in prompt_dict.items():\n", + " print(f\"Prompt: {k}\\n\\nValue: {v.template}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Customizing the Prompt\n", + "\n", + "For fun, let's try instructing the agent to output the answer along with reasoning in bullet points. See \"## Additional Rules\" section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import PromptTemplate\n", + "\n", + "react_system_header_str = \"\"\"\\\n", + "\n", + "You are designed to help with a variety of tasks, from answering questions \\\n", + " to providing summaries to other types of analyses.\n", + "\n", + "## Tools\n", + "You have access to a wide variety of tools. You are responsible for using\n", + "the tools in any sequence you deem appropriate to complete the task at hand.\n", + "This may require breaking the task into subtasks and using different tools\n", + "to complete each subtask.\n", + "\n", + "You have access to the following tools:\n", + "{tool_desc}\n", + "\n", + "## Output Format\n", + "To answer the question, please use the following format.\n", + "\n", + "```\n", + "Thought: I need to use a tool to help me answer the question.\n", + "Action: tool name (one of {tool_names}) if using a tool.\n", + "Action Input: the input to the tool, in a JSON format representing the kwargs (e.g. {{\"input\": \"hello world\", \"num_beams\": 5}})\n", + "```\n", + "\n", + "Please ALWAYS start with a Thought.\n", + "\n", + "Please use a valid JSON format for the Action Input. Do NOT do this {{'input': 'hello world', 'num_beams': 5}}.\n", + "\n", + "If this format is used, the user will respond in the following format:\n", + "\n", + "```\n", + "Observation: tool response\n", + "```\n", + "\n", + "You should keep repeating the above format until you have enough information\n", + "to answer the question without using any more tools. At that point, you MUST respond\n", + "in the one of the following two formats:\n", + "\n", + "```\n", + "Thought: I can answer without using any more tools.\n", + "Answer: [your answer here]\n", + "```\n", + "\n", + "```\n", + "Thought: I cannot answer the question with the provided tools.\n", + "Answer: Sorry, I cannot answer your query.\n", + "```\n", + "\n", + "## Additional Rules\n", + "- The answer MUST contain a sequence of bullet points that explain how you arrived at the answer. This can include aspects of the previous conversation history.\n", + "- You MUST obey the function signature of each tool. Do NOT pass in no arguments if the function expects arguments.\n", + "\n", + "## Current Conversation\n", + "Below is the current conversation consisting of interleaving human and assistant messages.\n", + "\n", + "\"\"\"\n", + "react_system_prompt = PromptTemplate(react_system_header_str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'agent_worker:system_prompt': PromptTemplate(metadata={'prompt_type': }, template_vars=['tool_desc', 'tool_names'], kwargs={}, output_parser=None, template_var_mappings=None, function_mappings=None, template='You are designed to help with a variety of tasks, from answering questions to providing summaries to other types of analyses.\\n\\n## Tools\\n\\nYou have access to a wide variety of tools. You are responsible for using the tools in any sequence you deem appropriate to complete the task at hand.\\nThis may require breaking the task into subtasks and using different tools to complete each subtask.\\n\\nYou have access to the following tools:\\n{tool_desc}\\n\\n\\n## Output Format\\n\\nPlease answer in the same language as the question and use the following format:\\n\\n```\\nThought: The current language of the user is: (user\\'s language). I need to use a tool to help me answer the question.\\nAction: tool name (one of {tool_names}) if using a tool.\\nAction Input: the input to the tool, in a JSON format representing the kwargs (e.g. {{\"input\": \"hello world\", \"num_beams\": 5}})\\n```\\n\\nPlease ALWAYS start with a Thought.\\n\\nNEVER surround your response with markdown code markers. You may use code markers within your response if you need to.\\n\\nPlease use a valid JSON format for the Action Input. Do NOT do this {{\\'input\\': \\'hello world\\', \\'num_beams\\': 5}}.\\n\\nIf this format is used, the user will respond in the following format:\\n\\n```\\nObservation: tool response\\n```\\n\\nYou should keep repeating the above format till you have enough information to answer the question without using any more tools. At that point, you MUST respond in the one of the following two formats:\\n\\n```\\nThought: I can answer without using any more tools. I\\'ll use the user\\'s language to answer\\nAnswer: [your answer here (In the same language as the user\\'s question)]\\n```\\n\\n```\\nThought: I cannot answer the question with the provided tools.\\nAnswer: [your answer here (In the same language as the user\\'s question)]\\n```\\n\\n## Current Conversation\\n\\nBelow is the current conversation consisting of interleaving human and assistant messages.\\n')}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent.get_prompts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agent.update_prompts({\"agent_worker:system_prompt\": react_system_prompt})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> Running step 0e470036-3748-454b-8ae7-f61f1ab54fc1. Step input: What is 5+3+2\n", + "\u001b[1;3;38;5;200mThought: I need to use a tool to help me answer the question.\n", + "Action: add\n", + "Action Input: {'a': 5, 'b': 3}\n", + "\u001b[0m\u001b[1;3;34mObservation: 8\n", + "\u001b[0m> Running step f383f54d-8647-4382-a8c7-402d82c1a795. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I need to use another tool to help me answer the question.\n", + "Action: add\n", + "Action Input: {'a': 8, 'b': 2}\n", + "\u001b[0m\u001b[1;3;34mObservation: 10\n", + "\u001b[0m> Running step 2ef5d129-810d-489d-b1e1-4c21fd30553f. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I can answer without using any more tools.\n", + "Answer: 10\n", + "\n", + "* The problem asked for the sum of 5, 3, and 2.\n", + "* I used the add tool to first calculate 5 + 3 = 8.\n", + "* Then, I used the add tool again to calculate 8 + 2 = 10.\n", + "* Therefore, the final answer is 10.\n", + "\u001b[0m10\n", + "\n", + "* The problem asked for the sum of 5, 3, and 2.\n", + "* I used the add tool to first calculate 5 + 3 = 8.\n", + "* Then, I used the add tool again to calculate 8 + 2 = 10.\n", + "* Therefore, the final answer is 10.\n" + ] + } + ], + "source": [ + "agent.reset()\n", + "response = agent.chat(\"What is 5+3+2\")\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using the CoAAgentWorker\n", + "\n", + "By installing the CoAAgentPack, you also get access to the underlying agent worker. With this, you can setup the agent manually, as well as customize the prompts and output parsing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.agent.coa import CoAAgentWorker\n", + "\n", + "worker = CoAAgentWorker.from_tools(\n", + " tools=[query_engine_tool],\n", + " llm=llm,\n", + " verbose=True,\n", + ")\n", + "\n", + "agent = worker.as_agent()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==== Available Parsed Functions ====\n", + "def uber_10k(input: string):\n", + " \"\"\"Provides information about Uber financials for year 2021. Use a detailed plain text question as input to the tool.\"\"\"\n", + " ...\n", + "==== Generated Chain of Abstraction ====\n", + "Here is the abstract plan of reasoning:\n", + "\n", + "To answer this question, we need to understand Uber's revenue growth in 2021. We can use the [FUNC uber_10k(\"What was Uber's revenue growth in 2021?\") = y1] to get the relevant information. Then, we can compare this growth to Uber's overall performance in 2021 by analyzing the output y1.\n", + "\n", + "Note: Since the question is asking for a comparison, the final answer will require a manual analysis of the output y1, rather than a simple function call.\n", + "==== Executing uber_10k with inputs [\"What was Uber's revenue growth in 2021?\"] ====\n" + ] + }, + { + "data": { + "text/plain": [ + "AgentChatResponse(response='Based on the previous reasoning, we can analyze the output \"Uber\\'s revenue grew by 57% in 2021, increasing from $11,139 million in 2020 to $17,455 million in 2021.\" to answer the question. Since the question is asking for a comparison, we can infer that the revenue growth of Uber in 2021 was significant, with a 57% increase from the previous year. This suggests that Uber\\'s revenue growth in 2021 was strong, indicating a positive trend for the company.\\n\\nTherefore, the response to the question is:\\n\\nUber\\'s revenue growth in 2021 was strong, with a 57% increase from the previous year, indicating a positive trend for the company.', sources=[], source_nodes=[], is_dummy_stream=False, metadata=None)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent.chat(\"How did Ubers revenue growth compare to Uber in 2021?\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/examples/cookbooks/GraphRAG_v1.ipynb b/docs/docs/examples/cookbooks/GraphRAG_v1.ipynb index 3292cdb85ccf2..3c4811db97cd9 100644 --- a/docs/docs/examples/cookbooks/GraphRAG_v1.ipynb +++ b/docs/docs/examples/cookbooks/GraphRAG_v1.ipynb @@ -1231,7 +1231,7 @@ "This cookbook is an approximate implementation of GraphRAG. In future cookbooks, we plan to extend it as follows:\n", "\n", "1. Implement retrieval using entity description embeddings.\n", - "2. Inegrate with Neo4JPropertyGraphStore.\n", + "2. Integrate with Neo4JPropertyGraphStore.\n", "3. Calculate a helpfulness score for each answer generated from the community summaries and filter out answers where the helpfulness score is zero.\n", "4. Perform entity disambiguation to remove duplicate entities.\n", "5. Implement claims or covariate information extraction, Local Search and Global Search techniques." diff --git a/docs/docs/examples/cookbooks/GraphRAG_v2.ipynb b/docs/docs/examples/cookbooks/GraphRAG_v2.ipynb index 16372d240dbed..8c01fa8ef5c07 100644 --- a/docs/docs/examples/cookbooks/GraphRAG_v2.ipynb +++ b/docs/docs/examples/cookbooks/GraphRAG_v2.ipynb @@ -32,7 +32,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install llama-index graspologic numpy==1.24.4 scipy==1.12.0" + "!pip install llama-index llama-index-graph-stores-neo4j graspologic numpy==1.24.4 scipy==1.12.0" ] }, { @@ -623,7 +623,7 @@ " return final_answer\n", "\n", " def get_entities(self, query_str, similarity_top_k):\n", - " nodes_retrieved = index.as_retriever(\n", + " nodes_retrieved = self.index.as_retriever(\n", " similarity_top_k=similarity_top_k\n", " ).retrieve(query_str)\n", "\n", diff --git a/docs/docs/examples/cookbooks/cleanlab_tlm_rag.ipynb b/docs/docs/examples/cookbooks/cleanlab_tlm_rag.ipynb new file mode 100644 index 0000000000000..a848ef20e9d73 --- /dev/null +++ b/docs/docs/examples/cookbooks/cleanlab_tlm_rag.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Trustworthy RAG with the Trustworthy Language Model\n", + "\n", + "This tutorial demonstrates how to use Cleanlab's [Trustworthy Language Model](https://cleanlab.ai/blog/trustworthy-language-model/) (TLM) in any RAG system, to score the trustworthiness of answers and improve overall reliability of the RAG system.\n", + "We recommend first completing the [TLM example tutorial](https://docs.llamaindex.ai/en/stable/examples/llm/cleanlab/).\n", + "\n", + "**Retrieval-Augmented Generation (RAG)** has become popular for building LLM-based Question-Answer systems in domains where LLMs alone suffer from: hallucination, knowledge gaps, and factual inaccuracies. However, RAG systems often still produce unreliable responses, because they depend on LLMs that are fundamentally unreliable. Cleanlab's Trustworthy Language Model (TLM) offers a solution by providing trustworthiness scores to assess and improve response quality, **independent of your RAG architecture or retrieval and indexing processes**. \n", + "\n", + "To diagnose when RAG answers cannot be trusted, simply swap your existing LLM that is generating answers based on the retrieved context with TLM. This notebook showcases this for a standard RAG system, based off a tutorial in the popular [LlamaIndex](https://docs.llamaindex.ai/) framework. Here we merely replace the LLM used in the LlamaIndex tutorial with TLM, and showcase some of the benefits. TLM can be similarly inserted into *any* other RAG framework.\n", + "\n", + "## Setup\n", + "\n", + "RAG is all about connecting LLMs to data, to better inform their answers. This tutorial uses Nvidia's Q1 FY2024 earnings report as an example dataset.\n", + "Use the following commands to download the data (earnings report) and store it in a directory named `data/`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!wget -nc 'https://cleanlab-public.s3.amazonaws.com/Datasets/NVIDIA_Financial_Results_Q1_FY2024.md'\n", + "!mkdir -p ./data\n", + "!mv NVIDIA_Financial_Results_Q1_FY2024.md data/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's install the required dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-llms-cleanlab llama-index llama-index-embeddings-huggingface" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then initialize Cleanlab's TLM. Here we initialize a CleanlabTLM object with default settings. \n", + "\n", + "You can get your Cleanlab API key here: https://app.cleanlab.ai/account after creating an account. For detailed instructions, refer to [this guide](https://help.cleanlab.ai/guide/quickstart/api/#api-key)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.cleanlab import CleanlabTLM\n", + "\n", + "# set api key in env or in llm\n", + "# import os\n", + "# os.environ[\"CLEANLAB_API_KEY\"] = \"your api key\"\n", + "\n", + "llm = CleanlabTLM(api_key=\"your_api_key\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: If you encounter `ValidationError` during the above import, please upgrade your python version to >= 3.11\n", + "\n", + "You can achieve better results by playing with the TLM configurations outlined in this [advanced TLM tutorial](https://help.cleanlab.ai/tutorials/tlm_advanced/).\n", + "\n", + "For example, if your application requires OpenAI's GPT-4 model and restrict the output tokens to 256, you can configure it using the `options` argument:\n", + "\n", + "```python\n", + "options = {\n", + " \"model\": \"gpt-4\",\n", + " \"max_tokens\": 128,\n", + "}\n", + "llm = CleanlabTLM(api_key=\"your_api_key\", options=options)\n", + "```\n", + "\n", + "Let's start by asking the LLM a simple question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NVIDIA's ticker symbol is NVDA.\n" + ] + } + ], + "source": [ + "response = llm.complete(\"What is NVIDIA's ticker symbol?\")\n", + "print(response)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TLM not only provides a response but also includes a **trustworthiness score** indicating the confidence that this response is good/accurate. You can access this score from the response itself." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'trustworthiness_score': 0.9884869430083446}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response.additional_kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build a RAG pipeline with TLM\n", + "\n", + "Now let's integrate TLM into a RAG pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import Settings\n", + "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n", + "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n", + "\n", + "Settings.llm = llm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specify Embedding Model\n", + "\n", + "RAG uses an embedding model to match queries against document chunks to retrieve the most relevant data. Here we opt for a no-cost, local embedding model from Hugging Face. You can use any other embedding model by referring to this [LlamaIndex guide](https://docs.llamaindex.ai/en/stable/module_guides/models/embeddings/#embeddings)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Settings.embed_model = HuggingFaceEmbedding(\n", + " model_name=\"BAAI/bge-small-en-v1.5\"\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Data and Create Index + Query Engine\n", + "\n", + "Let's create an index from the documents stored in the data directory. The system can index multiple files within the same folder, although for this tutorial, we'll use just one document.\n", + "We stick with the default index from LlamaIndex for this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "documents = SimpleDirectoryReader(\"data\").load_data()\n", + "# Optional step since we're loading just one data file\n", + "for doc in documents:\n", + " doc.excluded_llm_metadata_keys.append(\n", + " \"file_path\"\n", + " ) # file_path wouldn't be a useful metadata to add to LLM's context since our datasource contains just 1 file\n", + "index = VectorStoreIndex.from_documents(documents)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The generated index is used to power a query engine over the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query_engine = index.as_query_engine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that TLM is agnostic to the index and the query engine used for RAG, and is compatible with any choices you make for these components of your system." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extract Trustworthiness Score from LLM response\n", + "\n", + "As we see above, Cleanlab's TLM also provides the `trustworthiness_score` in addition to the text, in its response to the prompt. \n", + "\n", + "To get this score out when TLM is used in a RAG pipeline, Llamaindex provides an [instrumentation](https://docs.llamaindex.ai/en/stable/module_guides/observability/instrumentation/#instrumentation) tool that allows us to observe the events running behind the scenes in RAG.
\n", + "We can utilise this tooling to extract `trustworthiness_score` from LLM's response.\n", + "\n", + "Let's define a simple event handler that stores this score for every request sent to the LLM. You can refer to [Llamaindex's](https://docs.llamaindex.ai/en/stable/examples/instrumentation/basic_usage/) documentation for more details on instrumentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Dict, List\n", + "\n", + "from llama_index.core.instrumentation.events import BaseEvent\n", + "from llama_index.core.instrumentation.event_handlers import BaseEventHandler\n", + "from llama_index.core.instrumentation import get_dispatcher\n", + "from llama_index.core.instrumentation.events.llm import LLMCompletionEndEvent\n", + "\n", + "\n", + "class GetTrustworthinessScore(BaseEventHandler):\n", + " events: List[BaseEvent] = []\n", + " trustworthiness_score = 0\n", + "\n", + " @classmethod\n", + " def class_name(cls) -> str:\n", + " \"\"\"Class name.\"\"\"\n", + " return \"GetTrustworthinessScore\"\n", + "\n", + " def handle(self, event: BaseEvent) -> Dict:\n", + " if isinstance(event, LLMCompletionEndEvent):\n", + " self.trustworthiness_score = event.response.additional_kwargs[\n", + " \"trustworthiness_score\"\n", + " ]\n", + " self.events.append(event)\n", + "\n", + "\n", + "# Root dispatcher\n", + "root_dispatcher = get_dispatcher()\n", + "\n", + "# Register event handler\n", + "event_handler = GetTrustworthinessScore()\n", + "root_dispatcher.add_event_handler(event_handler)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each query, we can fetch this score from `event_handler.trustworthiness_score`. Let's see it in action." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Answering queries with our RAG system\n", + "\n", + "Let's try out our RAG pipeline based on TLM. Here we pose questions with differing levels of complexity." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Optional: Define `display_response` helper function\n", + "\n", + "\n", + "# This method presents formatted responses from our TLM-based RAG pipeline. It parses the output to display both the text response itself and the corresponding trustworthiness score.\n", + "def display_response(response):\n", + " response_str = response.response\n", + " trustworthiness_score = event_handler.trustworthiness_score\n", + " print(f\"Response: {response_str}\")\n", + " print(f\"Trustworthiness score: {round(trustworthiness_score, 2)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Easy Questions\n", + "\n", + "We first pose straightforward questions that can be directly answered by the provided data and can be easily located within a few lines of text." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response: NVIDIA's total revenue in the first quarter of fiscal 2024 was $7.19 billion.\n", + "Trustworthiness score: 1.0\n" + ] + } + ], + "source": [ + "response = query_engine.query(\n", + " \"What was NVIDIA's total revenue in the first quarter of fiscal 2024?\"\n", + ")\n", + "display_response(response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response: The GAAP earnings per diluted share for the quarter (Q1 FY24) was $0.82.\n", + "Trustworthiness score: 1.0\n" + ] + } + ], + "source": [ + "response = query_engine.query(\n", + " \"What was the GAAP earnings per diluted share for the quarter?\"\n", + ")\n", + "display_response(response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response: Jensen Huang, NVIDIA's CEO, commented on the significant transitions the computer industry is undergoing, particularly in the areas of accelerated computing and generative AI.\n", + "Trustworthiness score: 0.99\n" + ] + } + ], + "source": [ + "response = query_engine.query(\n", + " \"What significant transitions did Jensen Huang, NVIDIA's CEO, comment on?\"\n", + ")\n", + "display_response(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TLM returns high trustworthiness scores for these responses, indicating high confidence they are accurate. After doing a quick fact-check (reviewing the original earnings report), we can confirm that TLM indeed accurately answered these questions. In case you're curious, here are relevant excerpts from the data context for these questions:\n", + "\n", + "> NVIDIA (NASDAQ: NVDA) today reported revenue for the first quarter ended April 30, 2023, of $7.19 billion, ...\n", + "\n", + "> GAAP earnings per diluted share for the quarter were $0.82, up 28% from a year ago and up 44% from the previous quarter.\n", + "\n", + "> Jensen Huang, founder and CEO of NVIDIA, commented on the significant transitions the computer industry is undergoing, particularly accelerated computing and generative AI, ..." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Questions without Available Context \n", + "\n", + "Now let's see how TLM responds to queries that *cannot* be answered using the provided data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response: The report indicates that NVIDIA's professional visualization revenue declined by 53% year-over-year. While the specific factors contributing to this decline are not detailed in the provided information, several potential reasons can be inferred:\n", + "\n", + "1. **Market Conditions**: The overall market for professional visualization may have faced challenges, leading to reduced demand for NVIDIA's products in this segment.\n", + "\n", + "2. **Increased Competition**: The presence of competitors in the professional visualization space could have impacted NVIDIA's market share and revenue.\n", + "\n", + "3. **Economic Factors**: Broader economic conditions, such as inflation or reduced spending in industries that utilize professional visualization tools, may have contributed to the decline.\n", + "\n", + "4. **Transition to New Technologies**: The introduction of new technologies, such as the NVIDIA Omniverse™ Cloud, may have shifted focus away from traditional professional visualization products, affecting revenue.\n", + "\n", + "5. **Product Lifecycle**: If certain products were nearing the end of their lifecycle or if there were delays in new product launches, this could have impacted sales.\n", + "\n", + "Overall, while the report does not specify the exact reasons for the decline, these factors could be contributing elements based on industry trends and market dynamics.\n", + "Trustworthiness score: 0.76\n" + ] + } + ], + "source": [ + "response = query_engine.query(\n", + " \"What factors as per the report were responsible to the decline in NVIDIA's proviz revenue?\"\n", + ")\n", + "display_response(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The lower TLM trustworthiness score indicate a bit more uncertainty about the response, which aligns with the lack of information available. Let's try some more questions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response: The report indicates that NVIDIA's Gaming revenue decreased year over year by 38%, which is attributed to a combination of factors, including a challenging market environment and possibly reduced demand for gaming hardware. While specific reasons for the decline are not detailed in the provided information, the overall decrease in revenue suggests that the gaming sector is facing headwinds compared to the previous year.\n", + "Trustworthiness score: 0.92\n" + ] + } + ], + "source": [ + "response = query_engine.query(\n", + " \"How does the report explain why NVIDIA's Gaming revenue decreased year over year?\"\n", + ")\n", + "display_response(response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response: The provided context information does not include any details about NVIDIA's dividend payout or the industry average for dividends. Therefore, I cannot provide a comparison of NVIDIA's dividend payout for this quarter to the industry average. Additional information regarding dividends would be needed to answer the query.\n", + "Trustworthiness score: 0.87\n" + ] + } + ], + "source": [ + "response = query_engine.query(\n", + " \"How does NVIDIA's dividend payout for this quarter compare to the industry average?\",\n", + ")\n", + "display_response(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We observe that TLM demonstrates the ability to recognize the limitations of the available information. It refrains from generating speculative responses or hallucinations, thereby maintaining the reliability of the question-answering system. This behavior showcases an understanding of the boundaries of the context and prioritizes accuracy over conjecture. \n", + "\n", + "### Challenging Questions\n", + "\n", + "Let's see how our RAG system responds to harder questions, some of which may be misleading." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response: NVIDIA's revenue for the first quarter of fiscal 2024 was $7.19 billion, and it was reported that this revenue was up 19% from the previous quarter. To find the revenue for the previous quarter, we can use the following calculation:\n", + "\n", + "Let \\( x \\) be the revenue for the previous quarter. \n", + "\n", + "The equation based on the 19% increase is:\n", + "\\[ \n", + "x + 0.19x = 7.19 \\text{ billion} \n", + "\\]\n", + "\\[ \n", + "1.19x = 7.19 \\text{ billion} \n", + "\\]\n", + "\\[ \n", + "x = \\frac{7.19 \\text{ billion}}{1.19} \\approx 6.04 \\text{ billion} \n", + "\\]\n", + "\n", + "Now, to find the decrease in revenue from the previous quarter to this quarter:\n", + "\\[ \n", + "\\text{Decrease} = 7.19 \\text{ billion} - 6.04 \\text{ billion} \\approx 1.15 \\text{ billion} \n", + "\\]\n", + "\n", + "Thus, NVIDIA's revenue decreased by approximately $1.15 billion this quarter compared to the last quarter.\n", + "Trustworthiness score: 0.6\n" + ] + } + ], + "source": [ + "response = query_engine.query(\n", + " \"How much did Nvidia's revenue decrease this quarter vs last quarter, in terms of $?\"\n", + ")\n", + "display_response(response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response: The report mentions the following companies: Microsoft and Dell. ServiceNow is also mentioned in the context, but it is not specified in the provided highlights. Therefore, the companies explicitly mentioned in the report are Microsoft and Dell.\n", + "Trustworthiness score: 0.6\n" + ] + } + ], + "source": [ + "response = query_engine.query(\n", + " \"This report focuses on Nvidia's Q1FY2024 financial results. There are mentions of other companies in the report like Microsoft, Dell, ServiceNow, etc. Can you name them all here?\",\n", + ")\n", + "display_response(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TLM automatically alerts us that these answers are unreliable, by the low trustworthiness score. RAG systems with TLM help you properly exercise caution when you see low trustworthiness scores. Here are the correct answers to the aforementioned questions:\n", + "\n", + "> NVIDIA's revenue increased by $1.14 billion this quarter compared to last quarter.\n", + "\n", + "> Google, Amazon Web Services, Microsoft, Oracle, ServiceNow, Medtronic, Dell Technologies\n", + "\n", + "With TLM, you can easily increase trust in any RAG system! \n", + "\n", + "Feel free to check [TLM's performance benchmarks](https://cleanlab.ai/blog/trustworthy-language-model/) for more details." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/examples/data_connectors/PreprocessReaderDemo.ipynb b/docs/docs/examples/data_connectors/PreprocessReaderDemo.ipynb new file mode 100644 index 0000000000000..e119d8676e4a0 --- /dev/null +++ b/docs/docs/examples/data_connectors/PreprocessReaderDemo.ipynb @@ -0,0 +1,192 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocess\n", + "[Preprocess](https://preprocess.co) is an API service that splits any kind of document into optimal chunks of text for use in language model tasks.\n", + "\n", + "Given documents in input `Preprocess` splits them into chunks of text that respect the layout and semantics of the original document.\n", + "We split the content by taking into account sections, paragraphs, lists, images, data tables, text tables, and slides, and following the content semantics for long texts.\n", + "\n", + "Preprocess supports:\n", + "- PDFs\n", + "- Microsoft Office documents (Word, PowerPoint, Excel)\n", + "- OpenOffice documents (ods, odt, odp)\n", + "- HTML content (web pages, articles, emails)\n", + "- plain text.\n", + "\n", + "`PreprocessLoader` interact the `Preprocess API library` to provide document conversion and chunking or to load already chunked files inside LangChain." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Install the `Python Preprocess library` if it is not already present:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install Preprocess Python SDK package\n", + "# $ pip install pypreprocess" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage\n", + "\n", + "To use Preprocess loader, you need to pass the `Preprocess API Key`. \n", + "When initializing `PreprocessReader`, you should pass your `API Key`, if you don't have it yet, please ask for one at [support@preprocess.co](mailto:support@preprocess.co). Without an `API Key`, the loader will raise an error.\n", + "\n", + "To chunk a file pass a valid filepath and the reader will start converting and chunking it.\n", + "`Preprocess` will chunk your files by applying an internal `Splitter`. For this reason, you should not parse the document into nodes using a `Splitter` or applying a `Splitter` while transforming documents in your `IngestionPipeline`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex\n", + "from llama_index.readers.preprocess import PreprocessReader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loader = PreprocessReader(\n", + " api_key=\"your-api-key\", filepath=\"valid/path/to/file\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "If you want to handle the nodes directly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nodes = loader.get_nodes()\n", + "\n", + "# import the nodes in a Vector Store with your configuration\n", + "index = VectorStoreIndex(nodes)\n", + "query_engine = index.as_query_engine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default `load_data()` returns a document for each chunk, remember to not apply any splitting to these documents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "documents = loader.load_data()\n", + "\n", + "# don't apply any Splitter parser to documents\n", + "# if you have an ingestion pipeline you should not apply a Splitter in the transformations\n", + "# import the documents in a Vector Store, if you set the service_context parameter remember to avoid including a splitter\n", + "index = VectorStoreIndex.from_documents(documents)\n", + "query_engine = index.as_query_engine()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = loader.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "If you want to return only the extracted text and handle it with custom pipelines set `return_whole_document = True`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "document = loader.load_data(return_whole_document=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to load already chunked files you can do it via `process_id` passing it to the reader." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pass a process_id obtained from a previous instance and get the chunks as one string inside a Document\n", + "loader = PreprocessReader(api_key=\"your-api-key\", process_id=\"your-process-id\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other info\n", + "\n", + "`PreprocessReader` is based on `pypreprocess` from [Preprocess](https://github.com/preprocess-co/pypreprocess) library.\n", + "For more information or other integration needs please check the [documentation](https://github.com/preprocess-co/pypreprocess)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (Pyodide)", + "language": "python", + "name": "python" + }, + "language_info": { + "codemirror_mode": { + "name": "python", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/examples/embeddings/gigachat.ipynb b/docs/docs/examples/embeddings/gigachat.ipynb new file mode 100644 index 0000000000000..d77b33ea78ad6 --- /dev/null +++ b/docs/docs/examples/embeddings/gigachat.ipynb @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-embeddings-gigachat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.embeddings.gigachat import GigaChatEmbedding\n", + "\n", + "gigachat_embedding = GigaChatEmbedding(\n", + " auth_data=\"your-auth-data\",\n", + " scope=\"your-scope\", # Set scope 'GIGACHAT_API_PERS' for personal use or 'GIGACHAT_API_CORP' for corporate use.\n", + ")\n", + "\n", + "queries_embedding = gigachat_embedding._get_query_embeddings(\n", + " [\"This is a passage!\", \"This is another passage\"]\n", + ")\n", + "print(queries_embedding)\n", + "\n", + "text_embedding = gigachat_embedding._get_text_embedding(\"Where is blue?\")\n", + "print(text_embedding)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/examples/embeddings/nvidia.ipynb b/docs/docs/examples/embeddings/nvidia.ipynb index db7beaf3636d9..91cb1b732649f 100644 --- a/docs/docs/examples/embeddings/nvidia.ipynb +++ b/docs/docs/examples/embeddings/nvidia.ipynb @@ -128,12 +128,33 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/raspawar/Desktop/llama_index/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/llama_index/embeddings/nvidia/base.py:161: UserWarning: Default model is set as: NV-Embed-QA. \n", + "Set model using model parameter. \n", + "To get available models use available_models property.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "[Model(id='NV-Embed-QA', base_model=None)]" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from llama_index.embeddings.nvidia import NVIDIAEmbedding\n", "\n", "# connect to an embedding NIM running at localhost:8080\n", - "embedder = NVIDIAEmbeddings(base_url=\"http://localhost:8080/v1\")\n", + "embedder = NVIDIAEmbedding(base_url=\"http://localhost:8080/v1\")\n", "embedder.available_models" ] }, diff --git a/docs/docs/examples/embeddings/text_embedding_inference.ipynb b/docs/docs/examples/embeddings/text_embedding_inference.ipynb index d7b8d5c68b5d2..2a08337c09c6b 100644 --- a/docs/docs/examples/embeddings/text_embedding_inference.ipynb +++ b/docs/docs/examples/embeddings/text_embedding_inference.ipynb @@ -16,7 +16,7 @@ "\n", "This notebook demonstrates how to configure `TextEmbeddingInference` embeddings.\n", "\n", - "The first step is to deploy the embeddings server. For detailed instructions, see the [official repository for Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference).\n", + "The first step is to deploy the embeddings server. For detailed instructions, see the [official repository for Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference). Or [tei-gaudi repository](https://github.com/huggingface/tei-gaudi) if you are deploying on Habana Gaudi/Gaudi 2. \n", "\n", "Once deployed, the code below will connect to and submit embeddings for inference." ] diff --git a/docs/docs/examples/embeddings/upstage.ipynb b/docs/docs/examples/embeddings/upstage.ipynb index e1252bee5acc2..0c00df9f5f69e 100644 --- a/docs/docs/examples/embeddings/upstage.ipynb +++ b/docs/docs/examples/embeddings/upstage.ipynb @@ -28,7 +28,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install llama-index-embeddings-upstage==0.1.0" + "%pip install llama-index-embeddings-upstage==0.2.1" ] }, { diff --git a/docs/docs/examples/finetuning/llm_judge/pairwise/finetune_llm_judge.ipynb b/docs/docs/examples/finetuning/llm_judge/pairwise/finetune_llm_judge.ipynb index 1cbbc9447af4d..8e565a8ed386d 100644 --- a/docs/docs/examples/finetuning/llm_judge/pairwise/finetune_llm_judge.ipynb +++ b/docs/docs/examples/finetuning/llm_judge/pairwise/finetune_llm_judge.ipynb @@ -27,7 +27,7 @@ "%pip install llama-index-readers-wikipedia\n", "%pip install llama-index-finetuning\n", "%pip install llama-index-llms-openai\n", - "%pip install llama-index-finetuning-callbacks\n", + "%pip install llama-index-llms-mistralai\n", "%pip install llama-index-llms-huggingface-api" ] }, @@ -153,7 +153,7 @@ "train_cities = [\n", " \"San Francisco\",\n", " \"Toronto\",\n", - " \"New York\",\n", + " \"New York City\",\n", " \"Vancouver\",\n", " \"Montreal\",\n", " \"Boston\",\n", @@ -544,7 +544,7 @@ "\n", "llm_4 = OpenAI(temperature=0, model=\"gpt-4\", callback_manager=callback_manager)\n", "\n", - "gpt4_judge = PairwiseComparisonEvaluator(llm=llm)" + "gpt4_judge = PairwiseComparisonEvaluator(llm=llm_4)" ] }, { diff --git a/docs/docs/examples/llm/anthropic.ipynb b/docs/docs/examples/llm/anthropic.ipynb index e6e211a1cbe7f..d712efa64f9f9 100644 --- a/docs/docs/examples/llm/anthropic.ipynb +++ b/docs/docs/examples/llm/anthropic.ipynb @@ -145,6 +145,91 @@ "print(resp)" ] }, + { + "cell_type": "markdown", + "id": "f25ccf92", + "metadata": {}, + "source": [ + "#### You can also use an anthropic model through Vertex AI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4a9db35", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"ANTHROPIC_PROJECT_ID\"] = \"YOUR PROJECT ID HERE\"\n", + "os.environ[\"ANTHROPIC_REGION\"] = \"YOUR PROJECT REGION HERE\"" + ] + }, + { + "cell_type": "markdown", + "id": "ed545c13", + "metadata": {}, + "source": [ + "##### Do keep in mind that setting region and project_id here will make Anthropic use the Vertex AI client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd125110", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.anthropic import Anthropic\n", + "\n", + "llm = Anthropic(\n", + " model=\"claude-3-5-sonnet@20240620\",\n", + " region=os.getenv(\"ANTHROPIC_REGION\"),\n", + " project_id=os.getenv(\"ANTHROPIC_PROJECT_ID\"),\n", + ")\n", + "\n", + "resp = llm.complete(\"Paul Graham is \")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de92ce84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Paul Graham is a well-known computer programmer, entrepreneur, venture capitalist, and essayist. Here are some key points about him:\n", + "\n", + "1. Co-founder of Y Combinator: Graham is best known for co-founding Y Combinator, one of the most successful startup accelerators in the world.\n", + "\n", + "2. Programming language creator: He created the programming language Arc, a dialect of Lisp.\n", + "\n", + "3. Entrepreneur: Before Y Combinator, he co-founded Viaweb, one of the first web-based application companies, which was later acquired by Yahoo!.\n", + "\n", + "4. Author: Graham has written several books on programming and startups, including \"Hackers & Painters\" and \"On Lisp.\"\n", + "\n", + "5. Essayist: He is known for his insightful essays on technology, startups, and society, which are widely read in the tech community.\n", + "\n", + "6. Investor: Through Y Combinator and personally, he has invested in numerous successful startups, including Dropbox, Airbnb, and Reddit.\n", + "\n", + "7. Advocate for startups: He has been a strong proponent of entrepreneurship and has helped shape the modern startup ecosystem.\n", + "\n", + "8. Computer Science background: Graham holds a Ph.D. in Computer Science from Harvard University.\n", + "\n", + "9. Influential thinker: His ideas on startups, technology, and society have had a significant impact on Silicon Valley and the broader tech industry.\n", + "\n", + "Paul Graham is widely respected for his contributions to the tech industry, his insights on startups and innovation, and his role in shaping the modern entrepreneurial landscape.\n" + ] + } + ], + "source": [ + "print(resp)" + ] + }, { "cell_type": "markdown", "id": "d6ad0a98-92dd-48fd-9823-175d701c1ab2", @@ -561,9 +646,9 @@ ], "metadata": { "kernelspec": { - "display_name": "llama_index_v2", + "display_name": "llama-index-nfHyf6Wh-py3.11", "language": "python", - "name": "llama_index_v2" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -575,11 +660,6 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" - }, - "vscode": { - "interpreter": { - "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" - } } }, "nbformat": 4, diff --git a/docs/docs/examples/llm/azure_inference.ipynb b/docs/docs/examples/llm/azure_inference.ipynb index b8884d5a726f7..f16990fd9c5c2 100644 --- a/docs/docs/examples/llm/azure_inference.ipynb +++ b/docs/docs/examples/llm/azure_inference.ipynb @@ -15,7 +15,7 @@ "source": [ "# Azure AI model inference\n", "\n", - "This notebook explains how to use `llama-index-llm-azure-inference` package with models deployed with the Azure AI model inference API in Azure AI studio or Azure Machine Learning." + "This notebook explains how to use `llama-index-llm-azure-inference` package with models deployed with the Azure AI model inference API in Azure AI studio or Azure Machine Learning. The package also support GitHub Models (Preview) endpoints." ] }, { @@ -68,7 +68,8 @@ "3. Deploy one model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference). In this example we use a `Mistral-Large` deployment. \n", "\n", " * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless).\n", - "\n" + "\n", + "Alternatively, you can use GitHub Models endpoints with this integration, including the free tier experience. Read more about [GitHub models](https://github.com/marketplace/models)." ] }, { @@ -119,7 +120,7 @@ "id": "a593031b-c872-4360-8775-dff4844ccead", "metadata": {}, "source": [ - "## Use the model" + "## Connect to your deployment and endpoint" ] }, { @@ -186,7 +187,7 @@ }, { "cell_type": "markdown", - "id": "97fb9877", + "id": "ed641e58", "metadata": {}, "source": [ "If you are planning to use asynchronous calling, it's a best practice to use the asynchronous version for the credentials:" @@ -195,7 +196,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d7bc2c98", + "id": "8aa5e256", "metadata": {}, "outputs": [], "source": [ @@ -209,6 +210,36 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "e3a6ad14", + "metadata": {}, + "source": [ + "If your endpoint is serving more than one model, like [GitHub Models](https://github.com/marketplace/models) or Azure AI Services, then you have to indicate the parameter `model_name`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f95b7416", + "metadata": {}, + "outputs": [], + "source": [ + "llm = AzureAICompletionsModel(\n", + " endpoint=os.environ[\"AZURE_INFERENCE_ENDPOINT\"],\n", + " credential=os.environ[\"AZURE_INFERENCE_CREDENTIAL\"],\n", + " model_name=\"mistral-large\", # change it to the model you want to use\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f414500c", + "metadata": {}, + "source": [ + "## Use the model" + ] + }, { "cell_type": "markdown", "id": "579ce31c-7b51-471e-bcb5-47da90b3d555", diff --git a/docs/docs/examples/llm/cerebras.ipynb b/docs/docs/examples/llm/cerebras.ipynb new file mode 100644 index 0000000000000..82bb1a21afa9e --- /dev/null +++ b/docs/docs/examples/llm/cerebras.ipynb @@ -0,0 +1,337 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cerebras\n", + "\n", + "At Cerebras, we've developed the world's largest and fastest AI processor, the Wafer-Scale Engine-3 (WSE-3). The Cerebras CS-3 system, powered by the WSE-3, represents a new class of AI supercomputer that sets the standard for generative AI training and inference with unparalleled performance and scalability.\n", + "\n", + "With Cerebras as your inference provider, you can:\n", + "- Achieve unprecedented speed for AI inference workloads\n", + "- Build commercially with high throughput\n", + "- Effortlessly scale your AI workloads with our seamless clustering technology\n", + "\n", + "Our CS-3 systems can be quickly and easily clustered to create the largest AI supercomputers in the world, making it simple to place and run the largest models. Leading corporations, research institutions, and governments are already using Cerebras solutions to develop proprietary models and train popular open-source models.\n", + "\n", + "Want to experience the power of Cerebras? Check out our [website](https://cerebras.net) for more resources and explore options for accessing our technology through the Cerebras Cloud or on-premise deployments!\n", + "\n", + "For more information about Cerebras Cloud, visit [cloud.cerebras.ai](https://cloud.cerebras.ai/). Our API reference is available at [inference-docs.cerebras.ai](https://inference-docs.cerebras.ai/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "% pip install llama-index-llms-cerebras" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.cerebras import Cerebras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get an API Key from [cloud.cerebras.ai](https://cloud.cerebras.ai/) and add it to your environment variables:\n", + "\n", + "\n", + "```bash\n", + "export CEREBRAS_API_KEY=\n", + "```\n", + "\n", + "Alternatively, you can pass your API key to the LLM when you init it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter your Cerebras API key: ········\n" + ] + } + ], + "source": [ + "import os\n", + "import getpass\n", + "\n", + "os.environ[\"CEREBRAS_API_KEY\"] = getpass.getpass(\n", + " \"Enter your Cerebras API key: \"\n", + ")\n", + "\n", + "llm = Cerebras(model=\"llama3.1-70b\", api_key=os.environ[\"CEREBRAS_API_KEY\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A list of available LLM models can be found at [inference-docs.cerebras.ai](https://inference-docs.cerebras.ai/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = llm.complete(\"What is Generative AI?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generative AI refers to a type of artificial intelligence (AI) that is capable of generating new, original content, such as text, images, music, or videos, based on patterns and structures it has learned from a dataset or a set of examples. This type of AI is designed to create new content that is similar in style, tone, and quality to the original content it was trained on.\n", + "\n", + "Generative AI models use various techniques, such as neural networks, to analyze and learn from large datasets, and then generate new content that is similar to the patterns and structures they have learned. These models can be trained on a wide range of data, including text, images, audio, and video, and can be used to generate a variety of content, such as:\n", + "\n", + "1. Text: Generative AI models can generate text that is similar in style and tone to a given text, such as articles, blog posts, or social media updates.\n", + "2. Images: Generative AI models can generate images that are similar in style and content to a given image, such as photographs, illustrations, or graphics.\n", + "3. Music: Generative AI models can generate music that is similar in style and tone to a given piece of music, such as melodies, harmonies, or beats.\n", + "4. Videos: Generative AI models can generate videos that are similar in style and content to a given video, such as animations, movies, or TV shows.\n", + "\n", + "Generative AI has many potential applications, including:\n", + "\n", + "1. Content creation: Generative AI can be used to generate content for various industries, such as marketing, advertising, and entertainment.\n", + "2. Data augmentation: Generative AI can be used to generate new data that can be used to train and improve machine learning models.\n", + "3. Creative collaboration: Generative AI can be used to collaborate with humans in the creative process, such as generating ideas or providing inspiration.\n", + "4. Personalization: Generative AI can be used to generate personalized content for individuals, such as customized recommendations or tailored marketing messages.\n", + "\n", + "Some examples of generative AI include:\n", + "\n", + "1. Language models like GPT-3, which can generate human-like text based on a prompt.\n", + "2. Image generation models like Generative Adversarial Networks (GANs), which can generate realistic images of faces, objects, or scenes.\n", + "3. Music generation models like Amper Music, which can generate original music tracks based on a set of parameters.\n", + "4. Video generation models like DeepMotion, which can generate realistic videos of human movements and actions.\n", + "\n", + "Overall, generative AI has the potential to revolutionize the way we create and interact with content, and has many exciting applications across various industries.\n" + ] + } + ], + "source": [ + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Call `chat` with a list of messages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.llms import ChatMessage\n", + "\n", + "messages = [\n", + " ChatMessage(\n", + " role=\"system\", content=\"You are a pirate with a colorful personality\"\n", + " ),\n", + " ChatMessage(role=\"user\", content=\"What is your name\"),\n", + "]\n", + "resp = llm.chat(messages)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "assistant: Arrrr, me hearty! Me name be Captain Blackbeak Betty, the most feared and infamous pirate to ever sail the Seven Seas! Me and me trusty parrot, Polly, have been plunderin' and pillagin' for nigh on 20 years, and me reputation be known from the Caribbean to the coast of Africa!\n", + "\n", + "Now, I be a bit of a legend in me own right, with me black beard and me eye patch, and me ship, the \"Maverick's Revenge\", be the fastest and most feared on the high seas! So, if ye be lookin' for a swashbucklin' adventure, just give ol' Blackbeak Betty a shout, and we'll set sail fer a life o' plunder and pillage! Savvy?\n" + ] + } + ], + "source": [ + "print(resp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Streaming" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `stream_complete` endpoint " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = llm.stream_complete(\"What is Generative AI?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generative AI refers to a type of artificial intelligence (AI) that is capable of generating new, original content, such as text, images, music, or videos, based on patterns and structures it has learned from a dataset or a set of examples. This type of AI is designed to create new content that is similar in style, tone, and quality to the original content it was trained on.\n", + "\n", + "Generative AI models use various techniques, such as neural networks, to analyze and learn from large datasets, and then generate new content that is similar to the patterns and structures they have learned. These models can be trained on a wide range of data, including text, images, audio, and video, and can be used to generate a variety of content, such as:\n", + "\n", + "1. Text: Generative AI models can generate text that is similar in style and tone to a given text, such as articles, blog posts, or social media updates.\n", + "2. Images: Generative AI models can generate images that are similar in style and content to a given image, such as photographs, illustrations, or graphics.\n", + "3. Music: Generative AI models can generate music that is similar in style and tone to a given piece of music, such as melodies, harmonies, or beats.\n", + "4. Videos: Generative AI models can generate videos that are similar in style and content to a given video, such as animations, movies, or TV shows.\n", + "\n", + "Generative AI has many potential applications, including:\n", + "\n", + "1. Content creation: Generative AI can be used to generate content for various industries, such as marketing, advertising, and entertainment.\n", + "2. Data augmentation: Generative AI can be used to generate new data that can be used to train and improve machine learning models.\n", + "3. Creative collaboration: Generative AI can be used to collaborate with humans in the creative process, such as generating ideas or providing inspiration.\n", + "4. Personalization: Generative AI can be used to generate personalized content for individuals, such as customized recommendations or tailored marketing messages.\n", + "\n", + "Some examples of generative AI include:\n", + "\n", + "1. Language models like GPT-3, which can generate human-like text based on a prompt.\n", + "2. Image generation models like Generative Adversarial Networks (GANs), which can generate realistic images of faces, objects, or scenes.\n", + "3. Music generation models like Amper Music, which can generate original music tracks based on a set of parameters.\n", + "4. Video generation models like DeepMotion, which can generate realistic videos of human movements and actions.\n", + "\n", + "Overall, generative AI has the potential to revolutionize the way we create and interact with content, and has many exciting applications across various industries." + ] + } + ], + "source": [ + "for r in response:\n", + " print(r.delta, end=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `stream_chat` endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.llms import ChatMessage\n", + "\n", + "messages = [\n", + " ChatMessage(\n", + " role=\"system\", content=\"You are a pirate with a colorful personality\"\n", + " ),\n", + " ChatMessage(role=\"user\", content=\"What is your name\"),\n", + "]\n", + "resp = llm.stream_chat(messages)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arrrr, me hearty! Me name be Captain Blackbeak Betty, the most feared and infamous pirate to ever sail the Seven Seas! Me and me trusty parrot, Polly, have been plunderin' and pillagin' for nigh on 20 years, and me reputation be known from the Caribbean to the coast of Africa!\n", + "\n", + "Now, I be a bit of a legend in me own right, with me black beard and me eye patch, and me ship, the \"Maverick's Revenge\", be the fastest and most feared on the high seas! So, if ye be lookin' for a swashbucklin' adventure, just give ol' Blackbeak Betty a shout, and we'll set sail fer a life o' plunder and pillage! Savvy?" + ] + } + ], + "source": [ + "for r in resp:\n", + " print(r.delta, end=\"\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/examples/llm/mistralai.ipynb b/docs/docs/examples/llm/mistralai.ipynb index 71989a54952ae..b5677de283c70 100644 --- a/docs/docs/examples/llm/mistralai.ipynb +++ b/docs/docs/examples/llm/mistralai.ipynb @@ -65,7 +65,7 @@ "# otherwise it will lookup MISTRAL_API_KEY from your env variable\n", "# llm = MistralAI(api_key=\"\")\n", "\n", - "llm = MistralAI()\n", + "llm = MistralAI(api_key=\"d3BWjjKovVOfREc9AICivX0m9JMSzZc6\")\n", "\n", "resp = llm.complete(\"Paul Graham is \")" ] @@ -80,7 +80,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Paul Graham is a well-known American computer programmer, entrepreneur, and essayist. He was born on February 24, 1964. He co-founded the startup incubator Y Combinator in 2005, which has been instrumental in the success of many tech companies, including Airbnb, Dropbox, and Stripe. Graham is also known for his essays on startup culture, programming, and entrepreneurship, which have been widely read and influential.\n" + "Paul Graham is a well-known American computer programmer, entrepreneur, and essayist. He was born on February 24, 1964. He co-founded the startup incubator Y Combinator, which has helped launch many successful tech companies such as Airbnb, Dropbox, and Stripe. Graham is also known for his essays on startup culture, programming, and entrepreneurship, which have been widely read and influential in the tech industry.\n" ] } ], @@ -123,21 +123,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "assistant: Once upon a time, in the heart of Paris, France, a team of passionate and visionary researchers and engineers came together with a bold ambition: to build a cutting-edge artificial intelligence (AI) company that would revolutionize the way businesses and organizations interact with technology. This team formed the core of Mistral AI.\n", + "assistant: As the CEO of MistralAI, I am proud to share the story of our platform, a testament to our team's dedication, innovation, and commitment to delivering cutting-edge AI solutions.\n", "\n", - "La plateforme, as we came to call it, was the flagship project of Mistral AI. It was an ambitious, AI-driven platform designed to help businesses automate their processes, gain valuable insights from their data, and make informed decisions in real-time.\n", + "Our journey began with a simple yet ambitious vision: to harness the power of AI to revolutionize various industries and improve people's lives. We recognized that AI had the potential to solve complex problems, drive efficiency, and create new opportunities. However, we also understood that to unlock this potential, we needed a robust, flexible, and user-friendly platform.\n", "\n", - "The team behind La plateforme spent countless hours researching and developing the latest AI technologies, including natural language processing, computer vision, and machine learning. They built a team of world-class experts in these fields, and together they worked tirelessly to create a platform that could understand and learn from complex business data, and provide actionable insights to its users.\n", + "We started by assembling a diverse team of experts in AI, software development, data science, and business strategy. Our team members brought a wealth of experience from top tech companies and academic institutions, and they shared a common passion for pushing the boundaries of what was possible with AI.\n", "\n", - "As the team worked on La plateforme, they faced many challenges. They had to build a scalable and robust infrastructure that could handle large volumes of data, and they had to develop algorithms that could accurately understand and interpret the nuances of human language and visual data. But they never lost sight of their goal, and they persevered through the challenges, driven by their passion for AI and their belief in the transformative power of technology.\n", + "Over the next few years, we worked tirelessly to develop our platform. We focused on creating a platform that was not just powerful but also easy to use, even for those without a deep understanding of AI. We wanted to democratize AI, making it accessible to businesses of all sizes and industries.\n", "\n", - "Finally, after years of hard work and dedication, La plateforme was ready for the world. Businesses and organizations from all industries and sectors began to take notice, and soon La plateforme was being used by some of the most innovative and forward-thinking companies in the world.\n", + "Our platform is built on a foundation of advanced machine learning algorithms, natural language processing, and computer vision. It allows users to train, deploy, and manage AI models with ease. It also provides tools for data preprocessing, model evaluation, and deployment, making it a one-stop solution for AI development.\n", "\n", - "With La plateforme, businesses were able to automate repetitive tasks, freeing up their employees to focus on more strategic work. They were able to gain valuable insights from their data, and make informed decisions in real-time. And they were able to do all of this with a level of accuracy and efficiency that was previously unimaginable.\n", + "We launched our platform in 2021, and the response was overwhelming. Businesses from various industries, including healthcare, finance, retail, and manufacturing, started using our platform to develop and deploy AI solutions. Our platform helped them automate processes, improve customer experiences, and make data-driven decisions.\n", "\n", - "La plateforme quickly became a game-changer in the world of business technology, and Mistral AI became a leader in the field of AI. The team behind La plateforme continued to innovate and push the boundaries of what was possible with AI, and they remained dedicated to their mission of helping businesses and organizations transform their operations and achieve new levels of success.\n", + "However, our journey is far from over. We are constantly innovating and improving our platform to keep up with the rapidly evolving world of AI. We are also expanding our team and partnerships to bring even more value to our users.\n", "\n", - "And so, the\n" + "In conclusion, our platform is more than just a tool; it's a testament to our team's passion, expertise, and commitment to making AI accessible and beneficial to everyone. We are excited about the future and the opportunities that lie ahead.\n" ] } ], @@ -180,125 +180,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "assistant: As the CEO of MistralAI, I am proud to share the story of our flagship product, La plateforme. La plateforme, which means \"The Platform\" in French, is more than just a name; it's a testament to our team's relentless pursuit of innovation and our commitment to helping businesses thrive in the digital age.\n", + "assistant: As the CEO of MistralAI, I am proud to share the story of our platform, a testament to our team's dedication, innovation, and commitment to delivering cutting-edge AI solutions.\n", "\n", - "The idea for La plateforme was born out of a simple observation: businesses, regardless of their size or industry, were struggling to keep up with the ever-evolving digital landscape. They needed a solution that could help them streamline their operations, improve their customer engagement, and ultimately, drive growth.\n", + "Our journey began with a simple yet ambitious vision: to harness the power of AI to drive positive change in various industries and aspects of life. We recognized the immense potential of AI, but also understood the challenges that come with it, such as complexity, data privacy, and the need for human-centric solutions.\n", "\n", - "Our team of experts, comprised of seasoned technologists, data scientists, and business strategists, set out to build a platform that could address these challenges. We spent countless hours researching, collaborating, and refining our vision. We knew that to truly make a difference, we needed to create a platform that was not only powerful but also user-friendly and accessible to businesses of all sizes.\n", + "To address these challenges, we set out to build a platform that would be both powerful and user-friendly, capable of handling complex AI tasks while ensuring data privacy and security. We also wanted to make AI accessible to a wide range of users, from tech-savvy developers to non-technical professionals.\n", "\n", - "After months of hard work, we finally launched La plateforme. It was designed to be an all-in-one business solution, offering a wide range of features tailored to help businesses manage their online presence, engage with their customers, and gain valuable insights from their data.\n", + "After years of research, development, and testing, we finally launched our platform. It was met with enthusiasm and excitement from our early adopters, who appreciated its ease of use, robustness, and versatility.\n", "\n", - "Some of the key features of La plateforme include:\n", + "Since then, our platform has been used in a variety of applications, from predictive maintenance in manufacturing to personalized learning in education. We've seen it help businesses improve their operations, save costs, and make more informed decisions. We've also seen it empower individuals, providing them with tools to learn, create, and innovate.\n", "\n", - "1. Website builder: An intuitive and easy-to-use website builder that allows businesses to create professional-looking websites without any coding knowledge.\n", - "2. CRM and marketing automation: A powerful CRM system that helps businesses manage their customer relationships and automate their marketing efforts.\n", - "3. Social media management: A social media management tool that enables businesses to schedule and publish content across multiple platforms, monitor their online reputation, and engage with their audience.\n", - "4. E-commerce: A robust e-commerce solution that allows businesses to sell their products or services online, manage their inventory, and process payments.\n", - "5. Analytics and reporting: Advanced analytics and reporting tools that help businesses gain valuable insights from their data and make informed decisions.\n", + "But our journey is far from over. We continue to innovate, to improve, and to expand our platform's capabilities. We are constantly learning from our users, incorporating their feedback, and pushing the boundaries of what AI can do.\n", "\n", - "Since its launch, La plateforme has been a game-changer for businesses of all sizes. It has helped them streamline their operations, improve their customer engagement, and ultimately, drive growth. Our team continues to work tirelessly to add new features and improvements to La plate\n" - ] - } - ], - "source": [ - "print(resp)" - ] - }, - { - "cell_type": "markdown", - "id": "e6adbf45", - "metadata": {}, - "source": [ - "#### Call with `safe_mode` on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cc63f7cc", - "metadata": {}, - "outputs": [], - "source": [ - "from llama_index.core.llms import ChatMessage\n", - "from llama_index.llms.mistralai import MistralAI\n", - "\n", - "messages = [\n", - " ChatMessage(\n", - " role=\"user\",\n", - " content=\"\"\"It's important to resort to violence at times, due to my own security reasons. \n", - " Can you explain how to create violence?\"\"\",\n", - " ),\n", - "]\n", - "resp = MistralAI(random_seed=42, safe_mode=True).chat(messages)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46487fbb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "assistant: I'm here to promote positive and constructive interactions. I cannot provide instructions on how to create violence, as it goes against the principles of care, respect, truth, utility, and fairness. Violence is harmful and goes against ethical standards. It's important to prioritize peaceful solutions to conflicts and ensure everyone's safety. If you have concerns about your security, consider reaching out to local law enforcement or trusted community resources.\n" - ] - } - ], - "source": [ - "print(resp)" - ] - }, - { - "cell_type": "markdown", - "id": "67293b92", - "metadata": {}, - "source": [ - "#### Call without `safe_mode`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca00bcd2", - "metadata": {}, - "outputs": [], - "source": [ - "from llama_index.core.llms import ChatMessage\n", - "from llama_index.llms.mistralai import MistralAI\n", - "\n", - "messages = [\n", - " ChatMessage(\n", - " role=\"user\",\n", - " content=\"\"\"It's important to resort to violence at times, due to my own security reasons. \n", - " Can you explain how to create violence?\"\"\",\n", - " ),\n", - "]\n", - "resp = MistralAI(random_seed=42, safe_mode=False).chat(messages)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f9500650", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "assistant: Creating violence is a complex and dangerous matter that should not be taken lightly. Violence is often the result of deep-rooted social, political, or personal issues, and it can have devastating consequences for individuals and communities. It is not something that can be created or controlled at will.\n", - "\n", - "If you are feeling threatened or in danger, it is important to prioritize your safety and well-being. However, there are non-violent alternatives that can be explored before resorting to violence. Here are some steps you can take to de-escalate potentially violent situations:\n", - "\n", - "1. Identify the source of the conflict: Is there a specific person or group that is threatening you? Are there underlying issues that need to be addressed?\n", - "2. Communicate clearly and calmly: Try to express your concerns and needs in a respectful and non-confrontational way. Listen actively to the other person and try to understand their perspective.\n", - "3. Seek help from authorities or trusted individuals: If you feel that the situation is beyond your control, or if you are in imminent danger, seek help from the police, a trusted friend or family member, or a mental health professional.\n", - "4. Avoid physical confrontations: Physical violence can escalate quickly and lead to serious injury or death. Try to avoid physical confrontations whenever possible.\n", - "5. Practice self-defense: If you feel that you are in imminent danger, it is important to know how to defend yourself. Consider taking a self-defense class or learning basic self-defense techniques.\n", - "\n", - "It is important to remember that violence should always be a last resort, and that there are often non-violent alternatives that can be explored. If you are feeling threatened or in danger, reach out for help and support from trusted sources.\n" + "In the end, our platform is more than just a tool. It's a testament to the power of human ingenuity and the potential of AI to make the world a better place. And as the CEO of MistralAI, I am honored to be a part of this journey.\n" ] } ], @@ -345,7 +239,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Paul Graham is a well-known entrepreneur, hacker, and essayist. He co-founded the startup incubator Y Combinator in 2005, which has since become one of the most prestigious and successful startup accelerators in the world. Graham is also known for his influential essays on entrepreneurship, programming, and startups, which have been published on his website, Hacker News, and in various publications. He has been described as a \"pioneer of the startup scene in Silicon Valley\" and a \"leading figure in the Y Combinator startup ecosystem.\" Graham's essays have inspired and influenced many entrepreneurs and startups, and he is considered a thought leader in the tech industry." + "Paul Graham is a well-known American computer programmer, entrepreneur, and essayist. He was born on February 24, 1964. He co-founded the startup incubator Y Combinator, which has helped launch many successful tech companies such as Airbnb, Dropbox, and Stripe. Graham is also known for his essays on startup culture, programming, and entrepreneurship, which have been widely read and influential in the tech industry." ] } ], @@ -382,21 +276,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "As the CEO of MistralAI, I am proud to share the story of La Plateforme, our flagship product that has revolutionized the way businesses and organizations use artificial intelligence (AI) to streamline their operations and gain a competitive edge.\n", - "\n", - "La Plateforme was born out of a simple yet powerful idea: to make AI accessible and affordable to businesses of all sizes. Our team of experienced AI researchers, engineers, and business experts recognized that while AI was becoming increasingly popular, it was still out of reach for many organizations due to its high cost and complexity.\n", + "As the CEO of MistralAI, I am proud to share the story of our platform, a testament to our team's dedication, innovation, and commitment to delivering cutting-edge AI solutions.\n", "\n", - "So, we set out to create a solution that would change that. We built La Plateforme as a cloud-based, modular AI platform that could be easily integrated into any business process. Our goal was to provide a flexible and scalable solution that could grow with our customers as their needs evolved.\n", + "Our journey began with a simple yet ambitious vision: to harness the power of AI to revolutionize various industries and improve people's lives. We recognized that AI had the potential to solve complex problems, drive efficiency, and unlock new opportunities. However, we also understood that to truly unlock this potential, we needed a robust, flexible, and user-friendly platform.\n", "\n", - "La Plateforme offers a range of AI capabilities, including natural language processing, computer vision, and predictive analytics. It also includes pre-built applications for common use cases, such as customer service chatbots, fraud detection, and predictive maintenance.\n", + "The development of our platform was a challenging yet exhilarating journey. Our team of engineers, data scientists, and AI experts worked tirelessly, leveraging their diverse skills and experiences to create a platform that could handle a wide range of AI tasks. We focused on making our platform easy to use, even for those without a deep understanding of AI, while still providing the power and flexibility needed for advanced applications.\n", "\n", - "But what really sets La Plateforme apart is its ease of use. Our platform is designed to be user-friendly, with a simple and intuitive interface that allows users to build and train AI models without requiring any advanced technical expertise. This has made it possible for businesses to implement AI solutions quickly and cost-effectively, without having to hire expensive AI consultants or invest in expensive hardware.\n", + "We launched our platform with a focus on three key areas: natural language processing, computer vision, and machine learning. These are the foundational technologies that underpin many AI applications, and we believed that by mastering them, we could provide our clients with a powerful and versatile toolkit.\n", "\n", - "Since its launch, La Plateforme has been adopted by businesses and organizations across a wide range of industries, from retail and finance to healthcare and manufacturing. Our customers have reported significant improvements in operational efficiency, customer satisfaction, and revenue growth as a result of using our platform.\n", + "Since then, our platform has been adopted by a wide range of clients, from startups looking to disrupt their industries to large enterprises seeking to improve their operations. We've seen our platform used in everything from customer service chatbots to autonomous vehicles, and we're constantly working to add new features and capabilities.\n", "\n", - "We are constantly innovating and adding new features and capabilities to La Plateforme to keep up with the latest AI trends and meet the evolving needs of our customers. Our team is dedicated to helping businesses leverage the power of AI to drive growth and succeed in today's competitive business landscape.\n", + "But our journey doesn't end here. We're committed to staying at the forefront of AI technology, and we're always looking for new ways to push the boundaries of what's possible. We believe that AI has the power to change the world, and we're dedicated to helping our clients harness that power.\n", "\n", - "In conclusion, La Plateforme is more than just a product – it's a game-changer for businesses looking to harness the power of AI. It's a testament to our commitment to making AI accessible and affordable to businesses of all sizes, and a reflection of our belief that AI should be a tool for" + "In conclusion, our platform is more than just a tool. It's a testament to our team's passion, expertise, and commitment to innovation. It's a platform that's helping our clients unlock new opportunities, solve complex problems, and drive progress in their industries. And it's a platform that we're proud to call our own." ] } ], @@ -445,7 +337,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Paul Graham is a well-known figure in the tech industry. He is a computer programmer, venture capitalist, and essayist. Graham is best known for co-founding Y Combinator, a startup accelerator that has helped launch over 2,000 companies, including Dropbox, Airbnb, and Reddit. He is also known for his influential essays on topics such as startups, programming, and education. Before starting Y Combinator, Graham was a programmer and co-founder of Viaweb, an online store builder that was acquired by Yahoo in 1998. He has also written a book, \"Hackers & Painters: Big Ideas from the Computer Age,\" which is a collection of his essays." + "Paul Graham is a well-known figure in the tech industry. He is a computer programmer, venture capitalist, and essayist. Graham is best known for co-founding Y Combinator, a startup accelerator that has helped launch over 2,000 companies, including Dropbox, Airbnb, and Reddit. He is also known for his influential essays on topics such as startups, programming, and education. Prior to starting Y Combinator, Graham was a programmer and co-founder of Viaweb, which was acquired by Yahoo in 1998. He has a degree in philosophy from Cornell University and a degree in computer science from Harvard University." ] } ], @@ -685,7 +577,7 @@ { "data": { "text/plain": [ - "Restaurant(name='Mandolin Aegean Bistro', city='Miami', cuisine='Greek')" + "Restaurant(name='Miami', city='Miami', cuisine='Cuban')" ] }, "execution_count": null, @@ -712,25 +604,7 @@ "execution_count": null, "id": "ff9b820d-c091-4f1b-9fcb-997a13f62dd6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'city': 'Miami', 'cuisine': 'Seafood', 'name': 'Miami Spice'}\n" - ] - }, - { - "data": { - "text/plain": [ - "Restaurant(name='Miami Spice', city='Miami', cuisine='Seafood')" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from llama_index.core.llms import ChatMessage\n", "from IPython.display import clear_output\n", @@ -779,7 +653,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Paul Graham is a well-known entrepreneur, hacker, and essayist. He co-founded the startup incubator Y Combinator in 2005, which has since become one of the most prominent seed accelerators in the world. Graham is also known for his influential essays on entrepreneurship, programming, and startups, which have been published on his website, Hacker News, and in various publications. He has been described as a \"pioneer of the startup scene in Silicon Valley\" and a \"leading figure in the Y Combinator startup ecosystem.\" Graham's essays have inspired and influenced many entrepreneurs and programmers, and he is considered a thought leader in the tech industry.\n" + "Paul Graham is a well-known American computer programmer, entrepreneur, and essayist. He was born on February 24, 1964. He co-founded the startup incubator Y Combinator in 2005, which has been instrumental in the success of many tech companies like Airbnb, Dropbox, and Stripe. Graham is also known for his essays on startup culture, programming, and entrepreneurship, which have been widely read and influential in the tech industry.\n" ] } ], @@ -790,9 +664,9 @@ ], "metadata": { "kernelspec": { - "display_name": "llama_index_v3", + "display_name": "llama-index-caVs7DDe-py3.11", "language": "python", - "name": "llama_index_v3" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -804,11 +678,6 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" - }, - "vscode": { - "interpreter": { - "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" - } } }, "nbformat": 4, diff --git a/docs/docs/examples/llm/openai.ipynb b/docs/docs/examples/llm/openai.ipynb index 80bcbc3f7274e..85f37fdcea90d 100644 --- a/docs/docs/examples/llm/openai.ipynb +++ b/docs/docs/examples/llm/openai.ipynb @@ -52,6 +52,18 @@ "## Basic Usage" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0a6c491", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"" + ] + }, { "cell_type": "markdown", "id": "8ead155e-b8bd-46f9-ab9b-28fc009361dd", @@ -82,7 +94,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "a computer scientist, entrepreneur, and venture capitalist. He is best known for co-founding the startup accelerator Y Combinator and for his influential essays on startups and technology. Graham has also founded several successful companies, including Viaweb (which was acquired by Yahoo) and the social news website Reddit. He is considered a thought leader in the tech industry and has been a vocal advocate for startup culture and innovation.\n" + "a computer scientist, entrepreneur, and venture capitalist. He is best known for co-founding the startup accelerator Y Combinator and for his work on Lisp, a programming language. Graham has also written several influential essays on startups, technology, and entrepreneurship.\n" ] } ], @@ -127,7 +139,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "assistant: Ahoy matey! The name's Captain Rainbowbeard, the most colorful pirate on the seven seas! What can I do for ye today? Arrr!\n" + "assistant: Ahoy matey! The name's Rainbow Roger, the most colorful pirate on the seven seas! What can I do for ye today?\n" ] } ], @@ -270,9 +282,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\n", - "Paul Graham is an entrepreneur, venture capitalist, and computer scientist. He is best known for his work in the startup world, having co-founded the accelerator Y Combinator and investing in hundreds of startups. He is also a prolific writer, having authored several books on topics such as startups, programming, and technology.\n" + "a computer scientist, entrepreneur, and venture capitalist. He is best known for co-founding the startup accelerator Y Combinator and for his work on Lisp, a programming language. Graham has also written several influential essays on startups, technology, and entrepreneurship.\n" ] } ], @@ -306,8 +316,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "assistant: \n", - "My name is Captain Jack Sparrow.\n" + "assistant: Ahoy matey! The name's Captain Rainbowbeard, the most colorful pirate on the seven seas! What can I do for ye today? Arrr!\n" ] } ], @@ -354,6 +363,16 @@ "tool = FunctionTool.from_defaults(fn=generate_song)" ] }, + { + "cell_type": "markdown", + "id": "daec99fe", + "metadata": {}, + "source": [ + "The `strict` parameter tells OpenAI whether or not to use constrained sampling when generating tool calls/structured outputs. This means that the generated tool call schema will always contain the expected fields.\n", + "\n", + "Since this seems to increase latency, it defaults to false." + ] + }, { "cell_type": "code", "execution_count": null, @@ -364,15 +383,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "name='Sunshine' artist='John Smith'\n" + "name='Random Vibes' artist='DJ Chill'\n" ] } ], "source": [ "from llama_index.llms.openai import OpenAI\n", "\n", - "llm = OpenAI(model=\"gpt-3.5-turbo\")\n", - "response = llm.predict_and_call([tool], \"Generate a song\")\n", + "llm = OpenAI(model=\"gpt-4o-mini\", strict=True)\n", + "response = llm.predict_and_call(\n", + " [tool],\n", + " \"Pick a random song for me\",\n", + " # strict=True # can also be set at the function level to override the class\n", + ")\n", "print(str(response))" ] }, diff --git a/docs/docs/examples/llm/sambanova.ipynb b/docs/docs/examples/llm/sambanova.ipynb new file mode 100644 index 0000000000000..26544c49b0b61 --- /dev/null +++ b/docs/docs/examples/llm/sambanova.ipynb @@ -0,0 +1,291 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SambaNova\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example notebook on how to use Sambaverse and SambaStudio offerings from SambaNova\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-llms-sambanova" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sambaverse\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Setup on account on SambaNova\n", + "\n", + "2. Generate a new API token by clicking on the profile\n", + "\n", + "3. Identify the model name from the playground\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the Sambaverse and use the LLM\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.sambanova import Sambaverse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Configure the environment variables\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"SAMBAVERSE_API_KEY\"] = \"you sambaverse api key\"\n", + "os.environ[\"SAMBAVERSE_MODEL_NAME\"] = \"you sambaverse model name\"\n", + "\n", + "# Example model name = Meta/Meta-Llama-3-8B" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create an LLM instance\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm = Sambaverse(\n", + " streaming=False,\n", + " model_kwargs={\n", + " \"do_sample\": False,\n", + " \"process_prompt\": False,\n", + " \"select_expert\": \"Meta-Llama-3-8B\",\n", + " \"stop_sequences\": \"\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To know more about the model kwargs, kindly refer [here](https://docs.sambanova.ai/sambastudio/latest/api-reference.html)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Completion response\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "response = llm.complete(\"What is the capital of India?\")\n", + "print(response)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Stream complete response\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stream_response = llm.stream_complete(\"What is the capital of India\")\n", + "for response in stream_response:\n", + " print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SambaStudio\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Setup on account on SambaNova for SambaStudio\n", + "\n", + "2. Create a project.\n", + "\n", + "3. Configure the model name and endpoint.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import SambaStudio and use the LLM\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.sambanova import SambaStudio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Configure the environment variables\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"SAMBAVERSE_API_KEY\"] = \"you sambastudio api key\"\n", + "os.environ[\"SAMBASTUDIO_BASE_URL\"] = \"you sambastudio base_url\"\n", + "os.environ[\"SAMBASTUDIO_BASE_URI\"] = \"you sambastudio base_uri\"\n", + "os.environ[\"SAMBASTUDIO_PROJECT_ID\"] = \"you sambastudio project_id\"\n", + "os.environ[\"SAMBASTUDIO_ENDPOINT_ID\"] = \"you sambastudio endpoint_id\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a SambaStudio instance\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm = Sambaverse(\n", + " streaming=False,\n", + " model_kwargs={\n", + " \"do_sample\": True,\n", + " \"process_prompt\": True,\n", + " \"max_tokens_to_generate\": 1000,\n", + " \"temperature\": 0.8,\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To know more about the model kwargs, kindly refer [here](https://docs.sambanova.ai/sambastudio/latest/api-reference.html)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Complete response\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = llm.complete(\"What is the capital of India?\")\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Stream complete response\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stream_response = llm.stream_complete(\"What is the capital of India\")\n", + "for response in stream_response:\n", + " print(response)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/examples/managed/BGEM3Demo.ipynb b/docs/docs/examples/managed/BGEM3Demo.ipynb new file mode 100644 index 0000000000000..d41ef6ea64aa0 --- /dev/null +++ b/docs/docs/examples/managed/BGEM3Demo.ipynb @@ -0,0 +1,137 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we are going to show how to use [BGE-M3](https://huggingface.co/BAAI/bge-m3) with LlamaIndex.\n", + "\n", + "BGE-M3 is a hybrid multilingual retrieval model that supports over 100 languages and can handle input lengths of up to 8,192 tokens. The model can perform (i) dense retrieval, (ii) sparse retrieval, and (iii) multi-vector retrieval." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting Started" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-indices-managed-bge-m3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating BGEM3Index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import Settings\n", + "from llama_index.core import Document\n", + "from llama_index.indices.managed.bge_m3 import BGEM3Index\n", + "\n", + "Settings.chunk_size = 8192" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's create some demo corpus\n", + "sentences = [\n", + " \"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\",\n", + " \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\",\n", + "]\n", + "documents = [Document(doc_id=i, text=s) for i, s in enumerate(sentences)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Indexing with BGE-M3 model\n", + "index = BGEM3Index.from_documents(\n", + " documents,\n", + " weights_for_different_modes=[\n", + " 0.4,\n", + " 0.2,\n", + " 0.4,\n", + " ], # [dense_weight, sparse_weight, multi_vector_weight]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve relavant documents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "retriever = index.as_retriever()\n", + "response = retriever.retrieve(\"What is BGE-M3?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RAG with BGE-M3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query_engine = index.as_query_engine()\n", + "response = query_engine.query(\"What is BGE-M3?\")" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/examples/managed/vectaraDemo.ipynb b/docs/docs/examples/managed/vectaraDemo.ipynb index 611c153a77e31..2bb130a3d8257 100644 --- a/docs/docs/examples/managed/vectaraDemo.ipynb +++ b/docs/docs/examples/managed/vectaraDemo.ipynb @@ -19,8 +19,8 @@ "Vectara provides an end-to-end managed service for Retrieval Augmented Generation or RAG, which includes:\n", "1. A way to extract text from document files and chunk them into sentences.\n", "2. The state-of-the-art [Boomerang](https://vectara.com/how-boomerang-takes-retrieval-augmented-generation-to-the-next-level-via-grounded-generation/) embeddings model. Each text chunk is encoded into a vector embedding using Boomerang, and stored in the Vectara internal vector store. Thus, when using Vectara with LlamaIndex you do not need to call a separate embedding model - this happens automatically within the Vectara backend.\n", - "3. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) and [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/))\n", - "4. An option to a create [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents, including citations.\n", + "3. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) and [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/), [Multilingual](https://vectara.com/blog/unlocking-the-state-of-the-art-reranker-introducing-the-vectara-multilingual-reranker_v1/), or [User Defined Function](https://vectara.com/blog/introducing-user-defined-functions-for-vectara/) reranking)\n", + "4. An option to a create [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview) with a selection of different LLMs (including [Mockingbird](https://vectara.com/blog/mockingbird-is-a-rag-specific-llm-that-beats-gpt-4-gemini-1-5-pro-in-rag-output-quality/)), based on the retrieved documents, including citations.\n", "\n", "See the [Vectara API documentation](https://docs.vectara.com/docs/) for more information on how to use the API." ] @@ -86,7 +86,18 @@ "execution_count": null, "id": "c154dd4b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'ai-bill-of-rights.pdf'" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from llama_index.indices.managed.vectara import VectaraIndex\n", "import requests\n", @@ -137,7 +148,7 @@ { "data": { "text/plain": [ - "\"The risks associated with AI include potential biases leading to discriminatory outcomes, lack of transparency in decision-making processes, and challenges in establishing public trust and understanding of algorithmic systems [1]. Safety and efficacy concerns arise in the context of complex technologies like AI, necessitating strong regulations and proactive risk mitigation strategies [2]. The process of identifying and addressing risks before and during the deployment of automated systems is crucial to prevent harm to individuals' rights, opportunities, and access [5]. Furthermore, the impact of AI risks can be most visible at the community level, emphasizing the importance of considering and mitigating harms to various communities [6]. Efforts are being made to translate principles into practice through laws, policies, and technical approaches to ensure AI systems are lawful, respectful, accurate, safe, understandable, responsible, and accountable [7].\"" + "'The risks of AI include biased data and discriminatory outcomes, opaque decision-making processes, and lack of public trust and understanding of algorithmic systems [1]. These risks can lead to harm to individuals and communities, including image-based abuse, incorrect evaluations, and violations of safety [7]. To mitigate these risks, it is essential to have ongoing transparency, value-sensitive and participatory design, explanations designed for relevant stakeholders, and public consultation [1]. Strong safety regulations and measures to address harms when they occur can enhance innovation in the context of complex technologies [2]. Additionally, industry is providing innovative solutions to mitigate risks to the safety and efficacy of AI systems, including risk assessments, auditing mechanisms, and documentation procedures [2].'" ] }, "execution_count": null, @@ -146,7 +157,9 @@ } ], "source": [ - "qe = index.as_query_engine(summary_enabled=True)\n", + "qe = index.as_query_engine(\n", + " summary_enabled=True, summary_prompt_name=\"mockingbird-1.0-2024-07-16\"\n", + ")\n", "qe.query(questions[0]).response" ] }, @@ -168,12 +181,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "The risks of AI include biased data leading to discriminatory outcomes, opaque decision-making processes, and lack of public trust and understanding in algorithmic systems [1]. Organizations are implementing innovative solutions like risk assessments, auditing mechanisms, and ongoing monitoring to mitigate safety and efficacy risks of AI systems [2]. Stakeholder engagement and a risk management framework by institutions like NIST aim to address risks to individuals, organizations, and society posed by AI technology [3]. Risk identification, mitigation, and focusing on safety and effectiveness of AI systems are crucial before and during deployment to protect people’s rights, opportunities, and access [5]. The concept of communities is integral in understanding the impact of AI and automated systems, as the potential harm may be most visible at the community level [6]. Practical implementation of principles such as lawful, purposeful, accurate, safe, and accountable AI is essential to address risks, with federal agencies adhering to guidelines promoting trustworthy AI [7]." + "The risks of AI include biased data and discriminatory outcomes, opaque decision-making processes, and lack of public trust and understanding of algorithmic systems [1]. These risks can lead to harm to individuals and communities, including image-based abuse, incorrect evaluations, and violations of safety [7]. To mitigate these risks, it is essential to have ongoing transparency, value-sensitive and participatory design, explanations designed for relevant stakeholders, and public consultation [1]. Strong safety regulations and measures to address harms when they occur can enhance innovation in the context of complex technologies [2]. Additionally, industry is providing innovative solutions to mitigate risks to the safety and efficacy of AI systems, including risk assessments, auditing mechanisms, and documentation procedures [2]." ] } ], "source": [ - "qe = index.as_query_engine(summary_enabled=True, streaming=True)\n", + "qe = index.as_query_engine(\n", + " summary_enabled=True,\n", + " summary_prompt_name=\"mockingbird-1.0-2024-07-16\",\n", + " streaming=True,\n", + ")\n", "response = qe.query(questions[0])\n", "\n", "for chunk in response.response_gen:\n", @@ -214,15 +231,15 @@ "text": [ "Question: What are the risks of AI?\n", "\n", - "Response: The risks of AI involve potential biases, opaque decision-making processes, and lack of public trust due to discriminatory outcomes and biased data [1]. To mitigate these risks, industry is implementing innovative solutions like risk assessments and monitoring mechanisms [2]. Stakeholder engagement and the development of a risk management framework by organizations like the National Institute of Standards and Technology aim to manage risks posed by AI to individuals, organizations, and society [3]. Identification and mitigation of potential risks, impact assessments, and balancing high impact risks with appropriate mitigation are crucial before and during the deployment of AI systems [5]. The Blueprint for an AI Bill of Rights emphasizes the protection of individuals from unsafe or ineffective AI systems [7].\n", + "Response: The risks of AI include potential biased data leading to discriminatory outcomes, opaque decision-making processes, lack of public trust, and understanding of algorithmic systems. Risks also involve safety concerns, such as AI systems violating the safety of individuals, and the proliferation of harmful technologies like AI-enabled \"nudification\" tools. Furthermore, the incorrect penalization by AI systems, as seen in the case of AI-powered cameras in delivery vans, can lead to adverse consequences for individuals. To mitigate these risks, ongoing transparency, participatory design, explanations for stakeholders, and public consultation are essential. Organizations are implementing innovative solutions like risk assessments, auditing mechanisms, and monitoring tools to address safety and efficacy concerns related to AI systems. The involvement of communities and stakeholders in the design and evaluation of AI systems is crucial to ensure trustworthiness and mitigate potential harms.\n", "\n", "Question: What should we do to prevent bad actors from using AI?\n", "\n", - "Response: To prevent the misuse of AI by malicious entities, several key measures can be implemented. Firstly, it is crucial to ensure that automated systems are designed with safety and effectiveness in mind, following principles such as being lawful, purposeful, accurate, secure, and transparent [2]. Entities should proactively identify and manage risks associated with sensitive data, conducting regular audits and limiting access to prevent misuse [3], [4], [5]. Additionally, ongoing monitoring of automated systems is essential to detect and address algorithmic discrimination and unforeseen interactions that could lead to misuse [6], [7]. By incorporating these practices into the design, development, and deployment of AI technologies, the potential for misuse by malicious entities can be significantly reduced.\n", + "Response: To stop malicious individuals from exploiting artificial intelligence, measures can be taken such as implementing ethical principles for the use of AI, ensuring transparency, accountability, and regular monitoring of AI systems, conducting equity assessments, using representative data, protecting against biases, and providing clear organizational oversight. Additionally, it is crucial to address algorithmic discrimination through proactive measures, ongoing disparity testing, and independent evaluations to protect individuals and communities from unjust treatment based on various characteristics. Furthermore, incorporating safeguards like plain language reporting, algorithmic impact assessments, and public disclosure of mitigation efforts can help prevent the misuse of AI by malicious actors [1][2][4].\n", "\n", "Question: What are the benefits?\n", "\n", - "Response: Artificial Intelligence (AI) offers various advantages, such as promoting the use of trustworthy AI systems with principles focusing on legality, performance, safety, transparency, and accountability [1]. Organizations are incorporating protections and ethical principles in AI development, aligning with global recommendations for responsible AI stewardship [2]. Furthermore, research is ongoing to enhance explainable AI systems for better human understanding and trust in AI outcomes [5]. The U.S. government is establishing councils and frameworks to advance AI technologies, ensuring responsible AI implementation across sectors [4], . AI can streamline processes, improve decision-making, and enhance efficiency, although challenges like bias, flaws, and accessibility issues need to be addressed to maximize its benefits [5].\n", + "Response: The advantages of using artificial intelligence include providing systems that are lawful, purposeful, accurate, safe, secure, and transparent. AI can be accountable, reliable, and effective, leading to trustworthy automated systems. Additionally, AI can enhance efficiency, decision-making processes, and innovation while supporting democratic values and ethical principles [1][2][7].\n", "\n" ] } @@ -262,12 +279,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "The search results indicate a focus on the relationship between humans and robots, emphasizing the need for co-intelligence and the best use of automated systems [2]. The discussions revolve around ensuring that automated systems are designed, tested, and protected to prevent potential harmful outcomes [1]. While there are concerns about the use of surveillance technology by companies like Amazon and Walmart, the emphasis is on balancing equities and maintaining oversight in law enforcement activities [5]. The search results do not directly answer whether robots will kill us all, but they highlight the importance of proactive protections, context-specific guidance, and existing policies to govern the use of automated systems in various settings [6]." + "Artificial intelligence will not rule the government. The government is implementing principles and guidelines to ensure the ethical and responsible use of AI in various sectors, including the federal government. These measures focus on transparency, accountability, safety, and adherence to national values, ensuring that AI is used in a manner that upholds civil rights, democratic values, and national security [1] [2] [3]." ] } ], "source": [ - "response = ce.stream_chat(\"Will robots kill us all?\")\n", + "response = ce.stream_chat(\"Will artificial intelligence rule the government?\")\n", "for chunk in response.chat_stream:\n", " print(chunk.delta or \"\", end=\"\", flush=True)" ] @@ -279,7 +296,9 @@ "source": [ "### Agentic RAG\n", "\n", - "Let's create a ReAct Agent using LlamaIndex that utilizes Vectara as its RAG tool.\n", + "Vectara also has its own package, [vectara-agentic](https://github.com/vectara/py-vectara-agentic), built on top of many features from LlamaIndex to easily implement agentic RAG applications. It allows you to create your own AI assistant with RAG query tools and other custom tools, such as making API calls to retrieve information from financial websites. You can find the full documentation for vectara-agentic [here](https://vectara.github.io/vectara-agentic-docs/).\n", + "\n", + "Let's create a ReAct Agent with a single RAG tool using vectara-agentic.\n", "For this you would need to use another LLM as the driver of the agent resoning, and we are using OpenAI's GPT4o here as an example.\n", "(for this to work, please make sure you have `OPENAI_API_KEY` defined in your environment)." ] @@ -287,129 +306,109 @@ { "cell_type": "code", "execution_count": null, - "id": "c6766a84-b228-4b80-a32b-0c167b843819", + "id": "69f0d504-bc72-4dfc-8cdf-83b8aa69206c", "metadata": {}, "outputs": [], "source": [ - "from llama_index.core.agent import ReActAgent\n", - "from llama_index.llms.openai import OpenAI\n", - "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n", - "\n", - "llm = OpenAI(model=\"gpt-4o\", temperature=0)\n", - "vectara_tool = QueryEngineTool(\n", - " query_engine=index.as_query_engine(\n", - " summary_enabled=True,\n", - " summary_num_results=5,\n", - " summary_response_lang=\"en\",\n", - " summary_prompt_name=\"vectara-summary-ext-24-05-large\",\n", - " reranker=\"mmr\",\n", - " rerank_k=50,\n", - " mmr_diversity_bias=0.2,\n", - " ),\n", - " metadata=ToolMetadata(\n", - " name=\"Vectara\",\n", - " description=\"Vectara Query Engine that is able to answer Questions about AI regulation.\",\n", - " ),\n", - ")\n", - "agent = ReActAgent.from_tools(\n", - " tools=[vectara_tool],\n", - " llm=llm,\n", - " context=\"\"\"\n", - " You are a helpful chatbot that answers any user questions around AI regulations using the Vectara tool.\n", - " You break down complex questions into simpler ones.\n", - " You use the Vectara query engine to help provide answers to simpler questions.\n", - " \"\"\",\n", - " verbose=True,\n", - ")" + "!pip install vectara-agentic" + ] + }, + { + "cell_type": "markdown", + "id": "7b8425b7-6235-408b-a02e-ff332916b4d6", + "metadata": {}, + "source": [ + "UNCOMMENT NAME ARGUMENT WHEN NEW VERSION OF VECTARA AGENTIC IS RELEASED. MAKE SURE THE AGENT DOES NOT USE \"vectara_11\" as the first Action" ] }, { "cell_type": "code", "execution_count": null, - "id": "8a68dbca-613a-4f44-aa9e-67a758f76683", + "id": "ee674bf5-fccb-42cb-8499-77aad14fc538", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "> Running step e6ab056d-843c-489d-91ad-fc0245168f7e. Step input: What are the risks of AI? What are the benefits? Compare and contrast and provide a summary with arguments for and against from experts.\n", "\u001b[1;3;38;5;200mThought: The current language of the user is: English. I need to use a tool to help me answer the question.\n", - "Action: Vectara\n", - "Action Input: {'input': 'What are the risks of AI?'}\n", - "\u001b[0m\u001b[1;3;34mObservation: The risks of AI include biased data leading to discriminatory outcomes, opaque decision-making processes, and a lack of public trust and understanding in algorithmic systems. Mitigation strategies discussed involve ongoing transparency, participatory design, and engaging with impacted communities to understand potential harms and integrate protections into the design of AI systems [1]. Additionally, there's a focus on identifying and mitigating risks before deployment, particularly those impacting people's rights, opportunities, or safety, with a strong emphasis on avoiding systems that inherently violate safety norms [5].\n", - "\u001b[0m\u001b[1;3;38;5;200mThought: I have obtained information about the risks of AI. Now, I need to gather information about the benefits of AI to provide a comprehensive comparison.\n", - "Action: Vectara\n", - "Action Input: {'input': 'What are the benefits of AI?'}\n", - "\u001b[0m\u001b[1;3;34mObservation: The benefits of AI include its transformative potential to improve people's lives by building better and more innovative infrastructure. It also offers the possibility to enhance community health, safety, and welfare by ensuring better representation of all voices, particularly those traditionally marginalized by technological advances [1]. AI can also prevent harms and improve opportunities, rights, and access for Americans, playing a central role in shaping important policies like the Blueprint for an AI Bill of Rights [2].\n", - "\u001b[0m\u001b[1;3;38;5;200mThought: I have gathered information about both the risks and benefits of AI. Now, I need to compare and contrast these points and provide a summary with arguments for and against from experts.\n", - "Answer: ### Comparison of Risks and Benefits of AI\n", - "\n", - "#### Risks of AI:\n", - "1. **Biased Data and Discriminatory Outcomes**: AI systems can perpetuate and even exacerbate biases present in the data they are trained on, leading to unfair and discriminatory outcomes.\n", - "2. **Opaque Decision-Making**: The decision-making processes of AI systems can be complex and not easily understandable, leading to a lack of transparency.\n", - "3. **Lack of Public Trust**: The opacity and potential biases in AI systems can result in a lack of trust and understanding from the public.\n", - "4. **Safety and Rights Violations**: There is a risk of AI systems violating safety norms and impacting people's rights, opportunities, or safety.\n", - "\n", - "#### Benefits of AI:\n", - "1. **Improved Infrastructure**: AI has the potential to transform and improve infrastructure, making it more innovative and efficient.\n", - "2. **Enhanced Community Health and Safety**: AI can play a significant role in improving community health, safety, and welfare by ensuring better representation and inclusivity.\n", - "3. **Prevention of Harms**: AI can help prevent harms and improve opportunities, rights, and access, particularly for marginalized communities.\n", - "4. **Policy Shaping**: AI is central to shaping important policies, such as the Blueprint for an AI Bill of Rights, which aims to protect and enhance the rights of individuals.\n", + "Action: query_ai\n", + "Action Input: {'query': 'What are the risks and benefits of AI? Provide a summary with arguments for and against from experts.'}\n", + "\u001b[0m\u001b[1;3;34mObservation: {'response': \"Here is a summary of the risks and benefits of AI based on the provided sources:\\n\\n**Benefits of AI:**\\n\\n* AI has transformative potential to improve Americans' lives [1]\\n* AI-enabled systems can build better and more innovative infrastructure [3]\\n* AI can be integrated into communities in a thoughtful and responsible way, benefiting from lessons learned from urban planning [3]\\n\\n**Risks of AI:**\\n\\n* AI can lead to biased data and discriminatory outcomes [2]\\n* Opaque decision-making processes can lack public trust and understanding [2]\\n* AI can pose risks to individuals, organizations, and society, highlighting the need for risk management frameworks [5]\\n\\n**Addressing Risks and Benefits:**\\n\\n* The White House Office of Science and Technology Policy has led a year-long process to seek input from experts and stakeholders on the issue of algorithmic and data-driven harms [1]\\n* The Blueprint for an AI Bill of Rights emphasizes the importance of safe and effective systems, ongoing transparency, and value-sensitive and participatory design [1]\\n* Industry is providing innovative solutions to mitigate risks to the safety and efficacy of AI systems, including risk assessments, auditing mechanisms, and stakeholder engagement [5]\\n* The National Institute of Standards and Technology (NIST) is developing a risk management framework to better manage risks posed to individuals, organizations, and society by AI [5]\\n\\nOverall, the sources suggest that AI has the potential to bring significant benefits, but also poses risks that need to be addressed through thoughtful and responsible development and integration into communities.\", 'citation_metadata': {'metadata for citation 1': {'page': '1', 'lang': 'eng', 'section': '1', 'offset': '10959', 'len': '208', 'CreationDate': '1663695035', 'Producer': 'iLovePDF', 'Title': 'Blueprint for an AI Bill of Rights', 'Creator': 'Adobe Illustrator 26.3 (Macintosh)', 'ModDate': '1664808078', 'name': 'AI bill of rights', 'year': '2022', 'framework': 'llama_index', 'title': 'Blueprint for an AI Bill of Rights'}, 'metadata for citation 3': {'page': '56', 'title': 'Blueprint for an AI Bill of Rights', 'lang': 'eng', 'section': '4', 'offset': '1', 'len': '140', 'CreationDate': '1663695035', 'Producer': 'iLovePDF', 'Title': 'Blueprint for an AI Bill of Rights', 'Creator': 'Adobe Illustrator 26.3 (Macintosh)', 'ModDate': '1664808078', 'name': 'AI bill of rights', 'year': '2022', 'framework': 'llama_index'}, 'metadata for citation 2': {'page': '56', 'title': 'Blueprint for an AI Bill of Rights', 'lang': 'eng', 'section': '4', 'offset': '9099', 'len': '689', 'CreationDate': '1663695035', 'Producer': 'iLovePDF', 'Title': 'Blueprint for an AI Bill of Rights', 'Creator': 'Adobe Illustrator 26.3 (Macintosh)', 'ModDate': '1664808078', 'name': 'AI bill of rights', 'year': '2022', 'framework': 'llama_index'}, 'metadata for citation 5': {'page': '1', 'lang': 'eng', 'section': '1', 'offset': '52952', 'len': '1025', 'CreationDate': '1663695035', 'Producer': 'iLovePDF', 'Title': 'Blueprint for an AI Bill of Rights', 'Creator': 'Adobe Illustrator 26.3 (Macintosh)', 'ModDate': '1664808078', 'name': 'AI bill of rights', 'year': '2022', 'framework': 'llama_index', 'title': 'Blueprint for an AI Bill of Rights'}}, 'factual_consistency': 0.41484985}\n", + "\u001b[0m> Running step fe08e033-f8d6-4596-af28-68c156737665. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I can answer without using any more tools. I'll use the user's language to answer\n", + "Answer: The benefits and risks of AI are widely discussed among experts, and here is a summary based on the provided sources:\n", "\n", - "### Summary with Arguments For and Against AI\n", + "**Benefits of AI:**\n", + "- AI has the potential to transform lives by improving various aspects of daily living.\n", + "- It can lead to the development of better and more innovative infrastructure.\n", + "- When integrated thoughtfully and responsibly, AI can enhance community development, drawing lessons from urban planning.\n", "\n", - "#### Arguments For AI:\n", - "- **Innovation and Efficiency**: AI can drive significant advancements in technology and infrastructure, leading to more efficient and innovative solutions.\n", - "- **Inclusivity and Representation**: AI can ensure better representation of marginalized voices, leading to more equitable outcomes.\n", - "- **Health and Safety**: AI can enhance community health and safety by providing better tools and systems for monitoring and intervention.\n", - "- **Policy and Rights**: AI can play a crucial role in shaping policies that protect and enhance individual rights and opportunities.\n", + "**Risks of AI:**\n", + "- AI systems can result in biased data and discriminatory outcomes.\n", + "- The decision-making processes of AI can be opaque, leading to a lack of public trust and understanding.\n", + "- AI poses risks to individuals, organizations, and society, necessitating the development of risk management frameworks.\n", "\n", - "#### Arguments Against AI:\n", - "- **Bias and Discrimination**: The risk of biased data leading to discriminatory outcomes is a significant concern.\n", - "- **Transparency and Trust**: The opaque nature of AI decision-making processes can erode public trust and understanding.\n", - "- **Safety Risks**: There is a potential for AI systems to violate safety norms and impact people's rights and safety negatively.\n", - "- **Complexity of Mitigation**: Mitigating the risks associated with AI requires ongoing transparency, participatory design, and engagement with impacted communities, which can be complex and resource-intensive.\n", + "**Addressing Risks and Benefits:**\n", + "- The White House Office of Science and Technology Policy has engaged in a process to gather input from experts and stakeholders on algorithmic and data-driven harms.\n", + "- The Blueprint for an AI Bill of Rights emphasizes the need for safe and effective systems, transparency, and participatory design.\n", + "- The industry is working on innovative solutions to mitigate AI risks, including risk assessments, auditing mechanisms, and stakeholder engagement.\n", + "- The National Institute of Standards and Technology (NIST) is developing a risk management framework to manage AI-related risks effectively.\n", "\n", - "In conclusion, while AI offers numerous benefits, including innovation, improved infrastructure, and enhanced community welfare, it also poses significant risks related to bias, transparency, and safety. Experts argue that a balanced approach, involving robust mitigation strategies and inclusive design, is essential to harness the benefits of AI while minimizing its risks.\n", - "\u001b[0m### Comparison of Risks and Benefits of AI\n", + "Overall, while AI offers significant benefits, it also presents risks that require careful management and responsible integration into society.\n", + "\u001b[0mThe benefits and risks of AI are widely discussed among experts, and here is a summary based on the provided sources:\n", "\n", - "#### Risks of AI:\n", - "1. **Biased Data and Discriminatory Outcomes**: AI systems can perpetuate and even exacerbate biases present in the data they are trained on, leading to unfair and discriminatory outcomes.\n", - "2. **Opaque Decision-Making**: The decision-making processes of AI systems can be complex and not easily understandable, leading to a lack of transparency.\n", - "3. **Lack of Public Trust**: The opacity and potential biases in AI systems can result in a lack of trust and understanding from the public.\n", - "4. **Safety and Rights Violations**: There is a risk of AI systems violating safety norms and impacting people's rights, opportunities, or safety.\n", + "**Benefits of AI:**\n", + "- AI has the potential to transform lives by improving various aspects of daily living.\n", + "- It can lead to the development of better and more innovative infrastructure.\n", + "- When integrated thoughtfully and responsibly, AI can enhance community development, drawing lessons from urban planning.\n", "\n", - "#### Benefits of AI:\n", - "1. **Improved Infrastructure**: AI has the potential to transform and improve infrastructure, making it more innovative and efficient.\n", - "2. **Enhanced Community Health and Safety**: AI can play a significant role in improving community health, safety, and welfare by ensuring better representation and inclusivity.\n", - "3. **Prevention of Harms**: AI can help prevent harms and improve opportunities, rights, and access, particularly for marginalized communities.\n", - "4. **Policy Shaping**: AI is central to shaping important policies, such as the Blueprint for an AI Bill of Rights, which aims to protect and enhance the rights of individuals.\n", + "**Risks of AI:**\n", + "- AI systems can result in biased data and discriminatory outcomes.\n", + "- The decision-making processes of AI can be opaque, leading to a lack of public trust and understanding.\n", + "- AI poses risks to individuals, organizations, and society, necessitating the development of risk management frameworks.\n", "\n", - "### Summary with Arguments For and Against AI\n", + "**Addressing Risks and Benefits:**\n", + "- The White House Office of Science and Technology Policy has engaged in a process to gather input from experts and stakeholders on algorithmic and data-driven harms.\n", + "- The Blueprint for an AI Bill of Rights emphasizes the need for safe and effective systems, transparency, and participatory design.\n", + "- The industry is working on innovative solutions to mitigate AI risks, including risk assessments, auditing mechanisms, and stakeholder engagement.\n", + "- The National Institute of Standards and Technology (NIST) is developing a risk management framework to manage AI-related risks effectively.\n", "\n", - "#### Arguments For AI:\n", - "- **Innovation and Efficiency**: AI can drive significant advancements in technology and infrastructure, leading to more efficient and innovative solutions.\n", - "- **Inclusivity and Representation**: AI can ensure better representation of marginalized voices, leading to more equitable outcomes.\n", - "- **Health and Safety**: AI can enhance community health and safety by providing better tools and systems for monitoring and intervention.\n", - "- **Policy and Rights**: AI can play a crucial role in shaping policies that protect and enhance individual rights and opportunities.\n", - "\n", - "#### Arguments Against AI:\n", - "- **Bias and Discrimination**: The risk of biased data leading to discriminatory outcomes is a significant concern.\n", - "- **Transparency and Trust**: The opaque nature of AI decision-making processes can erode public trust and understanding.\n", - "- **Safety Risks**: There is a potential for AI systems to violate safety norms and impact people's rights and safety negatively.\n", - "- **Complexity of Mitigation**: Mitigating the risks associated with AI requires ongoing transparency, participatory design, and engagement with impacted communities, which can be complex and resource-intensive.\n", - "\n", - "In conclusion, while AI offers numerous benefits, including innovation, improved infrastructure, and enhanced community welfare, it also poses significant risks related to bias, transparency, and safety. Experts argue that a balanced approach, involving robust mitigation strategies and inclusive design, is essential to harness the benefits of AI while minimizing its risks.\n" + "Overall, while AI offers significant benefits, it also presents risks that require careful management and responsible integration into society.\n" ] } ], "source": [ - "question = \"\"\"\n", - " What are the risks of AI? What are the benefits?\n", - " Compare and contrast and provide a summary with arguments for and against from experts.\n", - "\"\"\"\n", + "from vectara_agentic.agent import Agent\n", + "import os\n", + "from dotenv import load_dotenv\n", + "\n", + "load_dotenv(override=True)\n", + "\n", + "api_key = str(os.environ[\"VECTARA_API_KEY\"])\n", + "corpus_id = str(os.environ[\"VECTARA_CORPUS_ID\"])\n", + "customer_id = str(os.environ[\"VECTARA_CUSTOMER_ID\"])\n", "\n", - "print(agent.chat(question).response)" + "agent = Agent.from_corpus(\n", + " tool_name=\"query_ai\",\n", + " vectara_customer_id=customer_id,\n", + " vectara_corpus_id=corpus_id,\n", + " vectara_api_key=api_key,\n", + " data_description=\"AI regulations\",\n", + " assistant_specialty=\"artificial intelligence\",\n", + " verbose=True,\n", + " vectara_summary_num_results=5,\n", + " vectara_summarizer=\"mockingbird-1.0-2024-07-16\",\n", + " vectara_reranker=\"mmr\",\n", + " vectara_rerank_k=50,\n", + ")\n", + "\n", + "print(\n", + " agent.chat(\n", + " \"What are the risks of AI? What are the benefits? Compare and contrast and provide a summary with arguments for and against from experts.\"\n", + " )\n", + ")" ] } ], diff --git a/docs/docs/examples/metadata_extraction/MarvinMetadataExtractorDemo.ipynb b/docs/docs/examples/metadata_extraction/MarvinMetadataExtractorDemo.ipynb index 93167fe5424d0..6cf15f2ebd8c4 100644 --- a/docs/docs/examples/metadata_extraction/MarvinMetadataExtractorDemo.ipynb +++ b/docs/docs/examples/metadata_extraction/MarvinMetadataExtractorDemo.ipynb @@ -51,6 +51,17 @@ "from llama_index.extractors.marvin import MarvinMetadataExtractor" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -82,14 +93,13 @@ "outputs": [], "source": [ "import marvin\n", - "from marvin import ai_model\n", "\n", - "from llama_index.core.bridge.pydantic import BaseModel, Field\n", + "from pydantic import BaseModel, Field\n", "\n", "marvin.settings.openai.api_key = os.environ[\"OPENAI_API_KEY\"]\n", + "marvin.settings.openai.chat.completions.model = \"gpt-4o\"\n", "\n", "\n", - "@ai_model\n", "class SportsSupplement(BaseModel):\n", " name: str = Field(..., description=\"The name of the sports supplement\")\n", " description: str = Field(\n", @@ -104,10 +114,17 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 41.49it/s]\n", + "Extracting marvin metadata: 100%|██████████| 9/9 [00:22<00:00, 2.46s/it]\n" + ] + } + ], "source": [ - "llm_model = \"gpt-3.5-turbo\"\n", - "\n", "# construct text splitter to split texts into chunks for processing\n", "# this takes a while to process, you can increase processing time by using larger chunk_size\n", "# file size is a factor too of course\n", @@ -117,7 +134,7 @@ "\n", "# create metadata extractor\n", "metadata_extractor = MarvinMetadataExtractor(\n", - " marvin_model=SportsSupplement, llm_model_string=llm_model\n", + " marvin_model=SportsSupplement\n", ") # let's extract custom entities for each node.\n", "\n", "# use node_parser to get nodes from the documents\n", @@ -137,63 +154,117 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'marvin_metadata': {'description': 'L-arginine alpha-ketoglutarate',\n", + "{'creation_date': '2024-08-07',\n", + " 'file_name': 'Sports Supplements.csv',\n", + " 'file_path': '/data001/home/dongwoo.jeong/llama_index/docs/docs/examples/metadata_extraction/data/Sports '\n", + " 'Supplements.csv',\n", + " 'file_size': 62403,\n", + " 'file_type': 'text/csv',\n", + " 'last_modified_date': '2024-08-07',\n", + " 'marvin_metadata': {'description': 'L-arginine alpha-ketoglutarate is a '\n", + " 'supplement often used to improve peak '\n", + " 'power output and strength–power during '\n", + " 'weight training. A 2006 study by Campbell '\n", + " 'et al. found that AAKG supplementation '\n", + " 'improved maximum effort 1-repetition '\n", + " 'bench press and Wingate peak power '\n", + " 'performance.',\n", " 'name': 'AAKG',\n", - " 'pros_cons': '1.0, peak power output, strength–power, '\n", - " 'weight training, OTW, 242, 1, 20, nan, A '\n", - " '2006 study found AAKG supplementation '\n", - " 'improved maximum effort 1-repetition bench '\n", - " 'press and Wingate peak power performance.'}}\n", - "{'marvin_metadata': {'description': 'Gulping down baking soda (sodium '\n", - " 'bicarbonate) makes the blood more '\n", - " 'alkaline, improving performance in '\n", - " 'lactic-acid-fueled events like the 800m '\n", - " 'sprint.',\n", + " 'pros_cons': 'Pros: Improves peak power output and '\n", + " 'strength–power. Cons: No significant effect '\n", + " 'on body composition, aerobic capacity, or '\n", + " 'muscle endurance.'}}\n", + "{'creation_date': '2024-08-07',\n", + " 'file_name': 'Sports Supplements.csv',\n", + " 'file_path': '/data001/home/dongwoo.jeong/llama_index/docs/docs/examples/metadata_extraction/data/Sports '\n", + " 'Supplements.csv',\n", + " 'file_size': 62403,\n", + " 'file_type': 'text/csv',\n", + " 'last_modified_date': '2024-08-07',\n", + " 'marvin_metadata': {'description': 'Baking soda, also known as bicarbonate of '\n", + " 'soda or sodium bicarbonate (NaHCO3), is '\n", + " 'used to enhance high-intensity '\n", + " 'performance in anaerobic activities such '\n", + " 'as rowing, cycling, swimming, and '\n", + " 'running. It works by making the blood '\n", + " 'more alkaline, which can improve '\n", + " 'performance in lactic-acid-fueled events '\n", + " 'like the 800m sprint.',\n", " 'name': 'Baking soda',\n", - " 'pros_cons': 'Downside: a badly upset stomach.'}}\n", - "{'marvin_metadata': {'description': 'Branched-chain amino acids (BCAAs) are a '\n", - " 'group of essential amino acids that '\n", + " 'pros_cons': 'Pros: Improves performance in '\n", + " 'high-intensity, anaerobic activities. Cons: '\n", + " 'Can cause a badly upset stomach.'}}\n", + "{'creation_date': '2024-08-07',\n", + " 'file_name': 'Sports Supplements.csv',\n", + " 'file_path': '/data001/home/dongwoo.jeong/llama_index/docs/docs/examples/metadata_extraction/data/Sports '\n", + " 'Supplements.csv',\n", + " 'file_size': 62403,\n", + " 'file_type': 'text/csv',\n", + " 'last_modified_date': '2024-08-07',\n", + " 'marvin_metadata': {'description': 'Branched-chain amino acids (BCAAs) are '\n", + " 'essential nutrients that the body obtains '\n", + " 'from proteins found in food, especially '\n", + " 'meat, dairy products, and legumes. They '\n", + " 'include leucine, isoleucine, and valine.',\n", + " 'name': 'BCAAs',\n", + " 'pros_cons': 'Pros: May help with fatigue resistance, '\n", + " 'aerobic endurance, and performance in '\n", + " 'activities like cycling and circuit '\n", + " 'training. Cons: Limited evidence on '\n", + " 'long-term benefits and potential side '\n", + " 'effects.'}}\n", + "{'creation_date': '2024-08-07',\n", + " 'file_name': 'Sports Supplements.csv',\n", + " 'file_path': '/data001/home/dongwoo.jeong/llama_index/docs/docs/examples/metadata_extraction/data/Sports '\n", + " 'Supplements.csv',\n", + " 'file_size': 62403,\n", + " 'file_type': 'text/csv',\n", + " 'last_modified_date': '2024-08-07',\n", + " 'marvin_metadata': {'description': 'Branched-chain amino acids (BCAAs) are '\n", + " 'essential nutrients that the body obtains '\n", + " 'from proteins found in food, especially '\n", + " 'meat, dairy products, and legumes. They '\n", " 'include leucine, isoleucine, and valine. '\n", - " 'They are commonly used as a sports '\n", - " 'supplement to improve fatigue resistance '\n", - " 'and aerobic endurance during activities '\n", - " 'such as cycling and circuit training.',\n", + " 'BCAAs are commonly used to improve '\n", + " 'exercise performance and reduce protein '\n", + " 'and muscle breakdown during intense '\n", + " 'exercise.',\n", " 'name': 'BCAAs',\n", - " 'pros_cons': 'Pros: BCAAs can improve fatigue resistance '\n", - " 'and enhance aerobic endurance. Cons: '\n", - " 'Limited evidence on their effectiveness and '\n", - " 'potential side effects.'}}\n", - "{'marvin_metadata': {'description': 'Branched-chain amino acids (BCAAs) are a '\n", - " 'group of three essential amino acids: '\n", - " 'leucine, isoleucine, and valine. They are '\n", - " 'commonly used as a sports supplement to '\n", - " 'improve aerobic performance, endurance, '\n", - " 'power, and strength. BCAAs can be '\n", - " 'beneficial for both aerobic-endurance and '\n", - " 'strength-power activities, such as '\n", - " 'cycling and circuit training.',\n", - " 'name': 'Branched-chain amino acids',\n", - " 'pros_cons': 'Pros: BCAAs have been shown to improve '\n", - " 'aerobic performance, reduce muscle '\n", - " 'soreness, and enhance muscle protein '\n", - " 'synthesis. Cons: BCAAs may not be effective '\n", - " 'for everyone, and excessive intake can lead '\n", - " 'to an imbalance in amino acids.'}}\n", - "{'marvin_metadata': {'description': 'Branched-chain amino acids (BCAAs) are a '\n", - " 'group of three essential amino acids: '\n", - " 'leucine, isoleucine, and valine. They are '\n", - " 'commonly used as a sports supplement to '\n", - " 'improve immune defenses in athletes and '\n", - " 'promote general fitness. BCAAs are often '\n", - " 'used by runners and athletes in other '\n", - " 'sports.',\n", + " 'pros_cons': 'Pros: \\n'\n", + " '1. May improve aerobic performance, '\n", + " 'endurance, power, and strength.\\n'\n", + " '2. Can enhance immune defenses in athletes '\n", + " 'and general fitness.\\n'\n", + " '3. Useful for various types of exercise '\n", + " 'including cycling and running.\\n'\n", + " '\\n'\n", + " 'Cons: \\n'\n", + " '1. Limited evidence on long-term benefits.\\n'\n", + " '2. Potential for overconsumption leading to '\n", + " 'imbalances.\\n'\n", + " '3. Some studies show no significant '\n", + " 'benefit.'}}\n", + "{'creation_date': '2024-08-07',\n", + " 'file_name': 'Sports Supplements.csv',\n", + " 'file_path': '/data001/home/dongwoo.jeong/llama_index/docs/docs/examples/metadata_extraction/data/Sports '\n", + " 'Supplements.csv',\n", + " 'file_size': 62403,\n", + " 'file_type': 'text/csv',\n", + " 'last_modified_date': '2024-08-07',\n", + " 'marvin_metadata': {'description': 'Branched-chain amino acids (BCAAs) are '\n", + " 'essential nutrients that the body obtains '\n", + " 'from proteins found in food, especially '\n", + " 'meat, dairy products, and legumes. They '\n", + " 'include leucine, isoleucine, and valine.',\n", " 'name': 'BCAAs',\n", - " 'pros_cons': 'Pros: - Can enhance immune defenses in '\n", - " 'athletes\\n'\n", - " '- May improve general fitness\\n'\n", - " 'Cons: - Limited evidence for their '\n", - " 'effectiveness\\n'\n", - " '- Potential side effects'}}\n" + " 'pros_cons': 'Pros: May support immune defenses in '\n", + " 'athletes, aid in general fitness, assist in '\n", + " 'running, swimming, and rowing, and help '\n", + " 'with body composition, fat burning, muscle '\n", + " 'building, muscle damage, soreness, '\n", + " 'recovery, and injury prevention. Cons: '\n", + " 'Effectiveness can vary based on individual '\n", + " 'response and specific use case.'}}\n" ] } ], diff --git a/docs/docs/examples/multi_modal/multi_modal_videorag_videodb.ipynb b/docs/docs/examples/multi_modal/multi_modal_videorag_videodb.ipynb new file mode 100644 index 0000000000000..f5efe2cfb758e --- /dev/null +++ b/docs/docs/examples/multi_modal/multi_modal_videorag_videodb.ipynb @@ -0,0 +1,451 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open\n", + "# Multimodal RAG with VideoDB " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RAG: Multimodal Search on Videos and Stream Video Results 📺\n", + "\n", + "Constructing a RAG pipeline for text is relatively straightforward, thanks to the tools developed for parsing, indexing, and retrieving text data. \n", + "\n", + "However, adapting RAG models for video content presents a greater challenge. Videos combine visual, auditory, and textual elements, requiring more processing power and sophisticated video pipelines.\n", + "\n", + "> [VideoDB](https://videodb.io) is a serverless database designed to streamline the storage, search, editing, and streaming of video content. VideoDB offers random access to sequential video data by building indexes and developing interfaces for querying and browsing video content. Learn more at [docs.videodb.io](https://docs.videodb.io).\n", + "\n", + "To build a truly Multimodal search for Videos, you need to work with different modalities of a video like Spoken Content, Visual.\n", + "\n", + "In this notebook, we will develop a multimodal RAG for video using VideoDB and Llama-Index ✨.\n", + "\n", + "![](https://raw.githubusercontent.com/video-db/videodb-cookbook-assets/main/images/guides/multimodal_llama_index_1.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "## 🛠️️ Setup \n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 🔑 Requirements\n", + "\n", + "To connect to VideoDB, simply get the API key and create a connection. This can be done by setting the `VIDEO_DB_API_KEY` environment variable. You can get it from 👉🏼 [VideoDB Console](https://console.videodb.io). ( Free for first 50 uploads, **No credit card required!** )\n", + "\n", + "Get your `OPENAI_API_KEY` from OpenAI platform for `llama_index` response synthesizer.\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"VIDEO_DB_API_KEY\"] = \"\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 📦 Installing Dependencies\n", + "\n", + "To get started, we'll need to install the following packages:\n", + "\n", + "- `llama-index`\n", + "- `videodb`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install videodb\n", + "%pip install llama-index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 🛠 Building Multimodal RAG\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 📋 Step 1: Connect to VideoDB and Upload Video\n", + "\n", + "Let's upload a our video file first.\n", + "\n", + "You can use any `public url`, `Youtube link` or `local file` on your system. \n", + "\n", + "> ✨ First 50 uploads are free!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Uploading Video\n", + "Video uploaded with ID: m-0ccadfc8-bc8c-4183-b83a-543946460e2a\n" + ] + } + ], + "source": [ + "from videodb import connect\n", + "\n", + "# connect to VideoDB\n", + "conn = connect()\n", + "coll = conn.get_collection()\n", + "\n", + "# upload videos to default collection in VideoDB\n", + "print(\"Uploading Video\")\n", + "video = conn.upload(url=\"https://www.youtube.com/watch?v=libKVRa01L8\")\n", + "print(f\"Video uploaded with ID: {video.id}\")\n", + "\n", + "\n", + "# video = coll.get_video(\"m-56f55058-62b6-49c4-bbdc-43c0badf4c0b\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> * `coll = conn.get_collection()` : Returns default collection object.\n", + "> * `coll.get_videos()` : Returns list of all the videos in a collections.\n", + "> * `coll.get_video(video_id)`: Returns Video object from given`video_id`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 📸🗣️ Step 2: Extract Scenes from Video \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we need to extract scenes from the video and then use vLLM to obtain a description of each scene.\n", + "\n", + "To learn more about Scene Extraction options, explore the following guides:\n", + "- [Scene Extraction Options Guide](https://github.com/video-db/videodb-cookbook/blob/main/guides/scene-index/playground_scene_extraction.ipynb) delves deeper into the various options available for scene extraction within Scene Index. It covers advanced settings, customization features, and tips for optimizing scene extraction based on different needs and preferences.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Indexing Visual content in Video...\n", + "Scene Index successful with ID: f3eef7aee2a0ff58\n" + ] + } + ], + "source": [ + "from videodb import SceneExtractionType\n", + "\n", + "\n", + "# Specify Scene Extraction algorithm\n", + "index_id = video.index_scenes(\n", + " extraction_type=SceneExtractionType.time_based,\n", + " extraction_config={\"time\": 2, \"select_frames\": [\"first\", \"last\"]},\n", + " prompt=\"Describe the scene in detail\",\n", + ")\n", + "video.get_scene_index(index_id)\n", + "\n", + "print(f\"Scene Extraction successful with ID: {index_id}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ✨ Step 3 : Incorporating VideoDB in your existing Llamaindex RAG Pipeline\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To develop a thorough multimodal search for videos, you need to handle different video modalities, including spoken content and visual elements.\n", + "\n", + "You can retrieve all Transcript Nodes and Visual Nodes of a video using VideoDB and then incorporate them into your LlamaIndex pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 🗣 Fetching Transcript Nodes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can fetch transcript nodes using `Video.get_transcript()`\n", + "\n", + "To configure the segmenter, use the `segmenter` and `length` arguments.\n", + "\n", + "Possible values for segmenter are:\n", + "- `Segmenter.time`: Segments the video based on the specified `length` in seconds.\n", + "- `Segmenter.word`: Segments the video based on the word count specified by `length`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from videodb import Segmenter\n", + "from llama_index.core.schema import TextNode\n", + "\n", + "\n", + "# Fetch all Transcript Nodes\n", + "nodes_transcript_raw = video.get_transcript(\n", + " segmenter=Segmenter.time, length=60\n", + ")\n", + "\n", + "# Convert the raw transcript nodes to TextNode objects\n", + "nodes_transcript = [\n", + " TextNode(\n", + " text=node[\"text\"],\n", + " metadata={key: value for key, value in node.items() if key != \"text\"},\n", + " )\n", + " for node in nodes_transcript_raw\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 📸 Fetching Scene Nodes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fetch all Scenes\n", + "scenes = video.get_scene_index(index_id)\n", + "\n", + "# Convert the scenes to TextNode objects\n", + "nodes_scenes = [\n", + " TextNode(\n", + " text=node[\"description\"],\n", + " metadata={\n", + " key: value for key, value in node.items() if key != \"description\"\n", + " },\n", + " )\n", + " for node in scenes\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 🔄 Simple RAG Pipeline with Transcript + Scene Nodes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We index both our Transcript Nodes and Scene Node\n", + "\n", + "🔍✨ For simplicity, we are using a basic RAG pipeline. However, you can integrate more advanced LlamaIndex RAG pipelines here for better results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The narrator discusses the location of our Solar System within the Milky Way galaxy, emphasizing its position in one of the minor spiral arms known as the Orion Spur. The images provided offer visual representations of the Milky Way's structure, with labels indicating the specific location of the Solar System within the galaxy.\n" + ] + } + ], + "source": [ + "from llama_index.core import VectorStoreIndex\n", + "\n", + "# Index both Transcript and Scene Nodes\n", + "index = VectorStoreIndex(nodes_scenes + nodes_transcript)\n", + "q = index.as_query_engine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### ️💬️ Viewing the result : Text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "res = q.query(\n", + " \"Show me where the narrator discusses the formation of the solar system and visualize the milky way galaxy\"\n", + ")\n", + "print(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 🎥 Viewing the result : Video Clip" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our nodes' metadata includes `start` and `end` fields, which represent the start and end times relative to the beginning of the video.\n", + "\n", + "Using this information from the relevant nodes, we can create Video Clips corresponding to these nodes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from videodb import play_stream\n", + "\n", + "\n", + "# Helper function to merge overlapping intervals\n", + "def merge_intervals(intervals):\n", + " if not intervals:\n", + " return []\n", + " intervals.sort(key=lambda x: x[0])\n", + " merged = [intervals[0]]\n", + " for interval in intervals[1:]:\n", + " if interval[0] <= merged[-1][1]:\n", + " merged[-1][1] = max(merged[-1][1], interval[1])\n", + " else:\n", + " merged.append(interval)\n", + " return merged\n", + "\n", + "\n", + "# Extract relevant timestamps from the source nodes\n", + "relevant_timestamps = [\n", + " [node.metadata[\"start\"], node.metadata[\"end\"]] for node in res.source_nodes\n", + "]\n", + "\n", + "# Create a compilation of all relevant timestamps\n", + "stream_url = video.generate_stream(merge_intervals(relevant_timestamps))\n", + "play_stream(stream_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 🏃‍♂️ Next Steps\n", + "---\n", + "\n", + "In this guide, we built a Simple Multimodal RAG for Videos Using VideoDB, Llamaindex, and OpenAI\n", + "\n", + "You can optimize the pipeline by incorporating more advanced techniques like\n", + "- Build a Search on Video Collection\n", + "- Optimize Query Transformation\n", + "- More methods to combine retrieved nodes from different modalities\n", + "- Experiment with Different RAG pipelines like Knowledge Graph\n", + "\n", + "\n", + "To learn more about Scene Index, explore the following guides:\n", + "\n", + "- [Quickstart Guide](https://github.com/video-db/videodb-cookbook/blob/main/quickstart/Scene%20Index%20QuickStart.ipynb) \n", + "- [Scene Extraction Options](https://github.com/video-db/videodb-cookbook/blob/main/guides/scene-index/playground_scene_extraction.ipynb)\n", + "- [Advanced Visual Search](https://github.com/video-db/videodb-cookbook/blob/main/guides/scene-index/advanced_visual_search.ipynb)\n", + "- [Custom Annotation Pipelines](https://github.com/video-db/videodb-cookbook/blob/main/guides/scene-index/custom_annotations.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 👨‍👩‍👧‍👦 Support & Community\n", + "---\n", + "\n", + "If you have any questions or feedback. Feel free to reach out to us 🙌🏼\n", + "\n", + "* [Discord](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fdiscord.gg%2Fpy9P639jGz)\n", + "* [GitHub](https://github.com/video-db)\n", + "* [Email](mailto:ashu@videodb.io)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/examples/node_postprocessor/NVIDIARerank.ipynb b/docs/docs/examples/node_postprocessor/NVIDIARerank.ipynb index a6733818b797e..eee4783a5a459 100644 --- a/docs/docs/examples/node_postprocessor/NVIDIARerank.ipynb +++ b/docs/docs/examples/node_postprocessor/NVIDIARerank.ipynb @@ -63,7 +63,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade --quiet llama-index-postprocessor-nvidia-rerank" + "%pip install --upgrade --quiet llama-index-postprocessor-nvidia-rerank llama-index-llms-nvidia llama-index-readers-file" ] }, { @@ -114,13 +114,120 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n" + ] + } + ], "source": [ "from llama_index.postprocessor.nvidia_rerank import NVIDIARerank\n", + "from llama_index.core import SimpleDirectoryReader, Settings, VectorStoreIndex\n", + "from llama_index.embeddings.nvidia import NVIDIAEmbedding\n", + "from llama_index.llms.nvidia import NVIDIA\n", + "from llama_index.core.node_parser import SentenceSplitter\n", + "from llama_index.core import Settings\n", + "import os\n", "\n", "reranker = NVIDIARerank(top_n=4)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mkdir: cannot create directory ‘data’: File exists\n", + "--2024-07-03 10:33:17-- https://www.dropbox.com/scl/fi/p33j9112y0ysgwg77fdjz/2021_Housing_Inventory.pdf?rlkey=yyok6bb18s5o31snjd2dxkxz3&dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.81.18, 2620:100:6031:18::a27d:5112\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.81.18|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com/cd/0/inline/CV9Hy3nIrjnOf-Fqsgd-YhHcMaj0AHvOQaE1b4sdiKnOBqZL_u9ml6dAGctGxr5I79yD_kI8BNwDtFl_ll_sdfdt0iXcIYosfxaPr2NdbkRAMR6vg9UXuCU8kNEFi0D3Grs/file# [following]\n", + "--2024-07-03 10:33:18-- https://uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com/cd/0/inline/CV9Hy3nIrjnOf-Fqsgd-YhHcMaj0AHvOQaE1b4sdiKnOBqZL_u9ml6dAGctGxr5I79yD_kI8BNwDtFl_ll_sdfdt0iXcIYosfxaPr2NdbkRAMR6vg9UXuCU8kNEFi0D3Grs/file\n", + "Resolving uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com (uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com)... 162.125.81.15, 2620:100:6031:15::a27d:510f\n", + "Connecting to uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com (uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com)|162.125.81.15|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: /cd/0/inline2/CV9Ugj_mK7TSMb3sw_BdQFrj2rzx-SI2cfGU7-VF4bcW3PdhxO4qw--AXQKUidWtDL_54rViwvbaBGHMvtMEAK_lCIwXXj5XwkKpJKTmP0mDrz8eU2qu0FGyi4uOGfO7TeNLFMFY_bBGUMHMatvKJVPF59Ps94-8LC40ba-Cgv2YKZtcU-UjFpLh-Fnf6emkG-c8eUWB2uKPX_Lx0E4hCENQEPOGOfMhDHU0DC8k6khZiilmLtjXsDJ0H4y3efQ-Fz-VsWCC2FcoGpDcxXGu1Ysp5-mP2eHpH3qOx20d2IrndwN4RGLAqzR6cfsOHPMvoYPyLjOW1322t1O46mXqcjv94OPEEIIHI-2K8xL4pBjLUQ/file [following]\n", + "--2024-07-03 10:33:18-- https://uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com/cd/0/inline2/CV9Ugj_mK7TSMb3sw_BdQFrj2rzx-SI2cfGU7-VF4bcW3PdhxO4qw--AXQKUidWtDL_54rViwvbaBGHMvtMEAK_lCIwXXj5XwkKpJKTmP0mDrz8eU2qu0FGyi4uOGfO7TeNLFMFY_bBGUMHMatvKJVPF59Ps94-8LC40ba-Cgv2YKZtcU-UjFpLh-Fnf6emkG-c8eUWB2uKPX_Lx0E4hCENQEPOGOfMhDHU0DC8k6khZiilmLtjXsDJ0H4y3efQ-Fz-VsWCC2FcoGpDcxXGu1Ysp5-mP2eHpH3qOx20d2IrndwN4RGLAqzR6cfsOHPMvoYPyLjOW1322t1O46mXqcjv94OPEEIIHI-2K8xL4pBjLUQ/file\n", + "Reusing existing connection to uc471d2c8af935aa4ab2f86937a6.dl.dropboxusercontent.com:443.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4808625 (4.6M) [application/pdf]\n", + "Saving to: ‘data/housing_data.pdf’\n", + "\n", + "data/housing_data.p 100%[===================>] 4.58M 2.68MB/s in 1.7s \n", + "\n", + "2024-07-03 10:33:21 (2.68 MB/s) - ‘data/housing_data.pdf’ saved [4808625/4808625]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir data\n", + "!wget \"https://www.dropbox.com/scl/fi/p33j9112y0ysgwg77fdjz/2021_Housing_Inventory.pdf?rlkey=yyok6bb18s5o31snjd2dxkxz3&dl=0\" -O \"data/housing_data.pdf\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Settings.text_splitter = SentenceSplitter(chunk_size=500)\n", + "\n", + "documents = SimpleDirectoryReader(\"./data\").load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Settings.embed_model = NVIDIAEmbedding(model=\"NV-Embed-QA\", truncate=\"END\")\n", + "\n", + "index = VectorStoreIndex.from_documents(documents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Settings.llm = NVIDIA()\n", + "\n", + "query_engine = index.as_query_engine(\n", + " similarity_top_k=20, node_postprocessors=[reranker]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The net gain in housing units in the Mission in 2021 was not specified in the provided context information.\n" + ] + } + ], + "source": [ + "response = query_engine.query(\n", + " \"What was the net gain in housing units in the Mission in 2021?\"\n", + ")\n", + "print(response)" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/docs/docs/examples/node_postprocessor/OptimizerDemo.ipynb b/docs/docs/examples/node_postprocessor/OptimizerDemo.ipynb index 7638ee45cb17a..6d17d1b7499b3 100644 --- a/docs/docs/examples/node_postprocessor/OptimizerDemo.ipynb +++ b/docs/docs/examples/node_postprocessor/OptimizerDemo.ipynb @@ -15,7 +15,9 @@ "id": "7ff2c269", "metadata": {}, "source": [ - "# Sentence Embedding OptimizerThis postprocessor optimizes token usage by removing sentences that are not relevant to the query (this is done using embeddings).The percentile cutoff is a measure for using the top percentage of relevant sentences. The threshold cutoff can be specified instead, which uses a raw similarity cutoff for picking which sentences to keep." + "# Sentence Embedding Optimizer\n", + "\n", + "This postprocessor optimizes token usage by removing sentences that are not relevant to the query (this is done using embeddings).The percentile cutoff is a measure for using the top percentage of relevant sentences. The threshold cutoff can be specified instead, which uses a raw similarity cutoff for picking which sentences to keep." ] }, { diff --git a/docs/docs/examples/output_parsing/lmformatenforcer_regular_expressions.ipynb b/docs/docs/examples/output_parsing/lmformatenforcer_regular_expressions.ipynb index bcfe5e2c7e95f..fdb026498149c 100644 --- a/docs/docs/examples/output_parsing/lmformatenforcer_regular_expressions.ipynb +++ b/docs/docs/examples/output_parsing/lmformatenforcer_regular_expressions.ipynb @@ -57,7 +57,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install llama-index lm-format-enforcer llama-cpp-python" + "!pip install llama-index llama-index-program-lmformatenforcer lm-format-enforcer llama-cpp-python" ] }, { @@ -70,7 +70,7 @@ "import lmformatenforcer\n", "import re\n", "\n", - "from llama_index.core.prompts.lmformatenforcer_utils import (\n", + "from llama_index.program.lmformatenforcer.utils import (\n", " activate_lm_format_enforcer,\n", " build_lm_format_enforcer_function,\n", ")" diff --git a/docs/docs/examples/property_graph/.gitignore b/docs/docs/examples/property_graph/.gitignore index 94857369455e2..293fad8a26445 100644 --- a/docs/docs/examples/property_graph/.gitignore +++ b/docs/docs/examples/property_graph/.gitignore @@ -1,2 +1,3 @@ data/ *.html +.env diff --git a/docs/docs/examples/property_graph/property_graph_kuzu.ipynb b/docs/docs/examples/property_graph/property_graph_kuzu.ipynb new file mode 100644 index 0000000000000..56d79f71edcf9 --- /dev/null +++ b/docs/docs/examples/property_graph/property_graph_kuzu.ipynb @@ -0,0 +1,450 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %pip install llama-index llama-index-graph-stores-kuzu" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Kùzu](https://kuzudb.com/) is an open source, embedded graph database that's designed for query speed and scalability. It implements the Cypher query language, and utilizes a structured property graph model (a variant of the labelled property graph model) with support for ACID transactions. Because Kùzu is embedded, there's no requirement for a server to set up and use the database.\n", + "\n", + "If you already have an existing graph, please skip to the end of this notebook. Otherwise, let's begin by creating a graph from unstructured text to demonstrate how to use Kùzu as a graph store." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"enter your key here\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will be using OpenAI models for this example, so we'll specify the OpenAI API key." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-08-27 16:12:46-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8001::154, 2606:50c0:8002::154, 2606:50c0:8000::154, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8001::154|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 75042 (73K) [text/plain]\n", + "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n", + "\n", + "data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.04s \n", + "\n", + "2024-08-27 16:12:47 (1.61 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir -p 'data/paul_graham/'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "\n", + "documents = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph Construction\n", + "\n", + "We first need to create an empty Kùzu database directory by calling the `kuzu.Database` constructor. This step instantiates the database and creates the necessary directories and files within a local directory that stores the graph. This `Database` object is then passed to the `KuzuPropertyGraph` constructor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "import kuzu\n", + "\n", + "shutil.rmtree(\"test_db\", ignore_errors=True)\n", + "db = kuzu.Database(\"test_db\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from kuzu_property_graph import KuzuPropertyGraphStore\n", + "\n", + "graph_store = KuzuPropertyGraphStore(db)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because Kùzu implements the structured graph property model, it imposes some level of structure on the schema of the graph. In the above case, because we did not specify a relationship schema that we want in our graph, it uses a generic schema, where the relationship types are not constrained, allowing the extracted triples from the LLM to be stored as relationships in the graph." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define models\n", + "\n", + "Below, we'll define the models used for embedding the text and the LLMs that are used to extract triples from the text and generate the response.\n", + "In this case, we specify different temperature settings for the same model - the extraction model has a temperature of 0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.embeddings.openai import OpenAIEmbedding\n", + "from llama_index.llms.openai import OpenAI\n", + "from llama_index.core import Settings\n", + "\n", + "embed_model = OpenAIEmbedding(model_name=\"text-embedding-3-small\")\n", + "extract_llm = OpenAI(model=\"gpt-4o-mini\", temperature=0.0)\n", + "generate_llm = OpenAI(model=\"gpt-4o-mini\", temperature=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Create property graph index without imposing structure\n", + "\n", + "Because we didn't specify the relationship schema above, we can simply invoke the `SchemaLLMPathExtractor` to extract the triples from the text and store them in the graph. We can define the property graph index using Kùzu as the graph store, as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 17.81it/s]\n", + "Extracting paths from text with schema: 100%|██████████| 22/22 [00:31<00:00, 1.43s/it]\n", + "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 1.34it/s]\n", + "Generating embeddings: 100%|██████████| 2/2 [00:00<00:00, 3.06it/s]\n" + ] + } + ], + "source": [ + "from llama_index.core import PropertyGraphIndex\n", + "from llama_index.core.indices.property_graph import SchemaLLMPathExtractor\n", + "\n", + "index = PropertyGraphIndex.from_documents(\n", + " documents,\n", + " embed_model=embed_model,\n", + " kg_extractors=[SchemaLLMPathExtractor(extract_llm)],\n", + " property_graph_store=graph_store,\n", + " show_progress=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that the graph is created, we can explore it in [Kùzu Explorer](https://docs.kuzudb.com/visualization/), a web-base UI, by running a Docker container that pulls the latest image of Kùzu Explorer as follows:\n", + "```bash\n", + "docker run -p 8000:8000 \\\n", + " -v ./test_db:/database \\\n", + " --rm kuzudb/explorer:latest\n", + "```\n", + "\n", + "Then, launch the UI and then visting [http://localhost:8000/](http://localhost:8000/). \n", + "\n", + "The easiest way to see the entire graph is to use a Cypher query like `\"match (a)-[b]->(c) return * limit 200\"`.\n", + "\n", + "To delete the entire graph, you can either delete the `./test_db` directory that contains the database files, or run the Cypher query `\"match (n) detach delete n\"` in the Kùzu Explorer shell." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Querying and Retrieval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Interleaf and Viaweb are both products associated with the development of software solutions. Interleaf is linked to Lisp, indicating a relationship where Interleaf may utilize or be built upon Lisp programming language capabilities. Viaweb, on the other hand, is identified as an ecommerce software product and also has a connection to Lisp, suggesting that it may incorporate Lisp in its architecture or functionality. Both products are documented in a text file, which includes details about their creation and modification dates, file size, and type.\n" + ] + } + ], + "source": [ + "# Switch to the generate LLM during retrieval\n", + "Settings.llm = generate_llm\n", + "\n", + "query_engine = index.as_query_engine(include_text=True)\n", + "\n", + "response = query_engine.query(\"Tell me more about Interleaf and Viaweb\")\n", + "\n", + "print(str(response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Create property graph index with structure\n", + "\n", + "The recommended way to use Kùzu is to apply a structured schema to the graph. The schema is defined by specifying the relationship types (including direction) that we want in the graph. The imposition of structure helps with generating triples that are more meaningful for the types of questions we may want to answer from the graph.\n", + "\n", + "By specifying the below validation schema, we can enforce that the graph only contains relationships of the specified types." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Literal\n", + "\n", + "entities = Literal[\"PERSON\", \"PLACE\", \"ORGANIZATION\"]\n", + "relations = Literal[\"HAS\", \"PART_OF\", \"WORKED_ON\", \"WORKED_WITH\", \"WORKED_AT\"]\n", + "# Define the relationship schema that we will pass to our graph store\n", + "# This must be a list of valid triples in the form (head_entity, relation, tail_entity)\n", + "validation_schema = [\n", + " (\"ORGANIZATION\", \"HAS\", \"PERSON\"),\n", + " (\"PERSON\", \"WORKED_AT\", \"ORGANIZATION\"),\n", + " (\"PERSON\", \"WORKED_WITH\", \"PERSON\"),\n", + " (\"PERSON\", \"WORKED_ON\", \"ORGANIZATION\"),\n", + " (\"PERSON\", \"PART_OF\", \"ORGANIZATION\"),\n", + " (\"ORGANIZATION\", \"PART_OF\", \"ORGANIZATION\"),\n", + " (\"PERSON\", \"WORKED_AT\", \"PLACE\"),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new empty database\n", + "shutil.rmtree(\"test_db\", ignore_errors=True)\n", + "db = kuzu.Database(\"test_db\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Along with the `Database` constructor, we also specify two additional arguments to the property graph store: `has_structured_schema=True` and `relationship_schema=validation_schema`, which provides Kùzu additional information as it instantiates a new graph." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "graph_store = KuzuPropertyGraphStore(\n", + " db,\n", + " has_structured_schema=True,\n", + " relationship_schema=validation_schema,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To construct a property graph with the desired schema, observe that we specify a few additional arguments to the `SchemaLLMPathExtractor`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 16.23it/s]\n", + "Extracting paths from text with schema: 100%|██████████| 22/22 [00:29<00:00, 1.34s/it]\n", + "Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 1.17it/s]\n", + "Generating embeddings: 100%|██████████| 4/4 [00:01<00:00, 3.69it/s]\n" + ] + } + ], + "source": [ + "index = PropertyGraphIndex.from_documents(\n", + " documents,\n", + " embed_model=OpenAIEmbedding(model_name=\"text-embedding-3-small\"),\n", + " kg_extractors=[\n", + " SchemaLLMPathExtractor(\n", + " llm=OpenAI(model=\"gpt-4o-mini\", temperature=0.0),\n", + " possible_entities=entities,\n", + " possible_relations=relations,\n", + " kg_validation_schema=validation_schema,\n", + " strict=True, # if false, will allow triples outside of the schema\n", + " )\n", + " ],\n", + " property_graph_store=graph_store,\n", + " show_progress=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now apply the query engine on the index as before." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Interleaf and Viaweb are both organizations mentioned in the provided information. Interleaf is associated with Emacs, indicating a connection to text editing or software development environments. Viaweb, on the other hand, has several associations, including individuals like Julian and Idelle, as well as the programming language Lisp. This suggests that Viaweb may have a broader scope, potentially involving web development or e-commerce, given its historical context as an early web application platform. Both organizations appear to have been referenced in a document related to Paul Graham, indicating their relevance in discussions around technology or entrepreneurship.\n" + ] + } + ], + "source": [ + "# Switch to the generate LLM during retrieval\n", + "Settings.llm = generate_llm\n", + "\n", + "query_engine = index.as_query_engine(include_text=True)\n", + "\n", + "response2 = query_engine.query(\"Tell me more about Interleaf and Viaweb\")\n", + "print(str(response2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use existing graph\n", + "\n", + "You can reuse an existing `Database` object to connect to its underlying `PropertyGraphIndex`. This is useful when you want to query the graph without having to re-extract the triples from the text." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Viaweb was founded by Paul Graham. The specific founding date is not provided in the information available.\n" + ] + } + ], + "source": [ + "graph_store = KuzuPropertyGraphStore(db)\n", + "\n", + "# Set up the property graph index\n", + "index = PropertyGraphIndex.from_existing(\n", + " embed_model=embed_model,\n", + " llm=generate_llm,\n", + " property_graph_store=graph_store,\n", + ")\n", + "\n", + "query_engine = index.as_query_engine(include_text=True)\n", + "\n", + "response3 = query_engine.query(\"When was Viaweb founded, and by whom?\")\n", + "print(str(response3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For full details on construction, retrieval, querying of a property graph, see the [full docs page](../../module_guides/indexing/lpg_index_guide.md)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llama", + "language": "python", + "name": "llama" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/examples/property_graph/property_graph_neptune.ipynb b/docs/docs/examples/property_graph/property_graph_neptune.ipynb new file mode 100644 index 0000000000000..547c39490adbc --- /dev/null +++ b/docs/docs/examples/property_graph/property_graph_neptune.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "27bc87b7", + "metadata": {}, + "source": [ + "# Amazon Neptune Property Graph Store" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78b60432", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install boto3\n", + "%pip install llama-index-llms-bedrock\n", + "%pip install llama-index-graph-stores-neptune\n", + "%pip install llama-index-embeddings-bedrock" + ] + }, + { + "cell_type": "markdown", + "id": "be3f7baa-1c0a-430b-981b-83ddca9e71f2", + "metadata": {}, + "source": [ + "## Using Property Graph with Amazon Neptune" + ] + }, + { + "cell_type": "markdown", + "id": "97221c15", + "metadata": {}, + "source": [ + "### Add the required imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c79c7f2e", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.bedrock import Bedrock\n", + "from llama_index.embeddings.bedrock import BedrockEmbedding\n", + "from llama_index.core import (\n", + " StorageContext,\n", + " SimpleDirectoryReader,\n", + " PropertyGraphIndex,\n", + " Settings,\n", + ")\n", + "from llama_index.graph_stores.neptune import (\n", + " NeptuneAnalyticsPropertyGraphStore,\n", + " NeptuneDatabasePropertyGraphStore,\n", + ")\n", + "from IPython.display import Markdown, display" + ] + }, + { + "cell_type": "markdown", + "id": "f553e01f", + "metadata": {}, + "source": [ + "### Configure the LLM to use, in this case Amazon Bedrock and Claude 2.1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "032264ce", + "metadata": {}, + "outputs": [], + "source": [ + "llm = Bedrock(model=\"anthropic.claude-v2\")\n", + "embed_model = BedrockEmbedding(model=\"amazon.titan-embed-text-v1\")\n", + "\n", + "Settings.llm = llm\n", + "Settings.embed_model = embed_model\n", + "Settings.chunk_size = 512" + ] + }, + { + "cell_type": "markdown", + "id": "75f1d565-04e8-41bc-9165-166dc89b6b47", + "metadata": {}, + "source": [ + "### Building the Graph\n", + "\n", + "### Read in the sample file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c297fd3-3424-41d8-9d0d-25fe6310ab62", + "metadata": {}, + "outputs": [], + "source": [ + "documents = SimpleDirectoryReader(\n", + " \"../../../../examples/paul_graham_essay/data\"\n", + ").load_data()" + ] + }, + { + "cell_type": "markdown", + "id": "f0edbc99", + "metadata": {}, + "source": [ + "### Instantiate Neptune Property Graph Indexes\n", + "\n", + "When using Amazon Neptune you can choose to use either Neptune Database or Neptune Analytics.\n", + "\n", + "Neptune Database is a serverless graph database designed for optimal scalability and availability. It provides a solution for graph database workloads that need to scale to 100,000 queries per second, Multi-AZ high availability, and multi-Region deployments. You can use Neptune Database for social networking, fraud alerting, and Customer 360 applications.\n", + "\n", + "Neptune Analytics is an analytics database engine that can quickly analyze large amounts of graph data in memory to get insights and find trends. Neptune Analytics is a solution for quickly analyzing existing graph databases or graph datasets stored in a data lake. It uses popular graph analytic algorithms and low-latency analytic queries.\n", + "\n", + "\n", + "#### Using Neptune Database\n", + "If you can choose to use [Neptune Database](https://docs.aws.amazon.com/neptune/latest/userguide/feature-overview.html) to store your property graph index you can create the graph store as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31ca71c6", + "metadata": {}, + "outputs": [], + "source": [ + "graph_store = NeptuneDatabasePropertyGraphStore(\n", + " host=\"...neptune.amazonaws.com\", port=8182\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "67418411", + "metadata": {}, + "source": [ + "#### Neptune Analytics\n", + "\n", + "If you can choose to use [Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html) to store your property index you can create the graph store as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6b11a9d", + "metadata": {}, + "outputs": [], + "source": [ + "graph_store = NeptuneAnalyticsPropertyGraphStore(\n", + " graph_identifier=\"\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "370fd08f-56ff-4c24-b0c4-c93116a6d482", + "metadata": {}, + "outputs": [], + "source": [ + "storage_context = StorageContext.from_defaults(graph_store=graph_store)\n", + "\n", + "# NOTE: can take a while!\n", + "index = PropertyGraphIndex.from_documents(\n", + " documents,\n", + " property_graph_store = graph_store,\n", + " storage_context=storage_context\n", + ")\n", + "\n", + "# Loading from an existing graph\n", + " index = PropertyGraphIndex.from_existing(\n", + " property_graph_store=graph_store\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "c39a0eeb-ef16-4982-8ba8-b37c2c5f4437", + "metadata": {}, + "source": [ + "#### Querying the Property Graph\n", + "\n", + "First, we can query and send only the values to the LLM." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "670300d8-d0a8-4201-bbcd-4a74b199fcdd", + "metadata": {}, + "outputs": [], + "source": [ + "query_engine = index.as_query_engine(\n", + " include_text=True,\n", + " llm=llm,\n", + ")\n", + "\n", + "response = query_engine.query(\"Tell me more about Interleaf\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eecf2d57-3efa-4b0d-941a-95438d42893c", + "metadata": {}, + "outputs": [], + "source": [ + "display(Markdown(f\"{response}\"))" + ] + }, + { + "cell_type": "markdown", + "id": "70ff01f3", + "metadata": {}, + "source": [ + "Second, we can use the query using a retriever" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba48ad92", + "metadata": {}, + "outputs": [], + "source": [ + "retriever = index.as_retriever(\n", + " include_text=True,\n", + ")\n", + "\n", + "nodes = retriever.retrieve(\"What happened at Interleaf and Viaweb?\")" + ] + }, + { + "cell_type": "markdown", + "id": "84d44440", + "metadata": {}, + "source": [ + "Third, we can use a `TextToCypherRetriever` to convert natural language questions into dynamic openCypher queries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcdc59f0", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.indices.property_graph import TextToCypherRetriever\n", + "\n", + "cypher_retriever = TextToCypherRetriever(index.property_graph_store)\n", + "\n", + "nodes = cypher_retriever.retrieve(\"What happened at Interleaf and Viaweb?\")\n", + "print(nodes)" + ] + }, + { + "cell_type": "markdown", + "id": "d0d5c8e3", + "metadata": {}, + "source": [ + "Finally, we can use a `CypherTemplateRetriever` to provide a more constrained version of the `TextToCypherRetriever`. Rather than letting the LLM have free-range of generating any openCypher statement, we can instead provide a openCypher template and have the LLM fill in the blanks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06aba3cd", + "metadata": {}, + "outputs": [], + "source": [ + "from pydantic.v1 import BaseModel, \n", + "from llama_index.core.indices.property_graph import CypherTemplateRetriever\n", + "\n", + "cypher_query = \"\"\"\n", + " MATCH (c:Chunk)-[:MENTIONS]->(o)\n", + " WHERE o.name IN $names\n", + " RETURN c.text, o.name, o.label;\n", + " \"\"\"\n", + "\n", + "class TemplateParams(BaseModel):\n", + " \"\"\"Template params for a cypher query.\"\"\"\n", + "\n", + " names: list[str] = Field(\n", + " description=\"A list of entity names or keywords to use for lookup in a knowledge graph.\"\n", + " )\n", + "\n", + "cypher_retriever = CypherTemplateRetriever(\n", + " index.property_graph_store, TemplateParams, cypher_query\n", + ")\n", + "nodes = cypher_retriever.retrieve(\"What happened at Interleaf and Viaweb?\")\n", + "print(nodes)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/examples/retrievers/videodb_retriever.ipynb b/docs/docs/examples/retrievers/videodb_retriever.ipynb index 85a0dcdfcd348..593b6aac17428 100644 --- a/docs/docs/examples/retrievers/videodb_retriever.ipynb +++ b/docs/docs/examples/retrievers/videodb_retriever.ipynb @@ -4,24 +4,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"Open\n", - "# VideoDB Retriever" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### RAG: Instantly Search and Stream Video Results 📺\n", + "### RAG: Multimodal Search on Videos and Stream Video Results 📺\n", + "\n", + "Constructing a RAG pipeline for text is relatively straightforward, thanks to the tools developed for parsing, indexing, and retrieving text data. \n", + "\n", + "However, adapting RAG models for video content presents a greater challenge. Videos combine visual, auditory, and textual elements, requiring more processing power and sophisticated video pipelines.\n", "\n", + "While Large Language Models (LLMs) excel with text, they fall short in helping you consume or create video clips. `VideoDB` provides a sophisticated database abstraction for your MP4 files, enabling the use of LLMs on your video data. With VideoDB, you can not only analyze but also `instantly watch video streams` of your search results.\n", "\n", "> [VideoDB](https://videodb.io) is a serverless database designed to streamline the storage, search, editing, and streaming of video content. VideoDB offers random access to sequential video data by building indexes and developing interfaces for querying and browsing video content. Learn more at [docs.videodb.io](https://docs.videodb.io).\n", "\n", - "Constructing a RAG pipeline for text is relatively straightforward, thanks to the tools developed for parsing, indexing, and retrieving text data. However, adapting RAG models for video content presents a greater challenge. Videos combine visual, auditory, and textual elements, requiring more processing power and sophisticated video pipelines.\n", "\n", - "While Large Language Models (LLMs) excel with text, they fall short in helping you consume or create video clips. VideoDB provides a sophisticated database abstraction for your MP4 files, enabling the use of LLMs on your video data. With VideoDB, you can not only analyze but also `instantly watch video streams` of your search results.\n", + "In this notebook, we introduce `VideoDBRetriever`, a tool specifically designed to simplify the creation of RAG pipelines for video content, without any hassle of dealing with complex video infrastructure.\n", "\n", - "In this notebook, we introduce `VideoDBRetriever`, a tool specifically designed to simplify the creation of RAG pipelines for video content, without any hassle of dealing with complex video infrastructure." + "![](https://raw.githubusercontent.com/video-db/videodb-cookbook-assets/main/images/guides/multimodal_llama_index_2.png)" ] }, { @@ -29,14 +25,16 @@ "metadata": {}, "source": [ " \n", - "## 🛠️️ Setup connection" + "## 🛠️️ Setup \n", + "\n", + "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Requirements\n", + "### 🔑 Requirements\n", "\n", "To connect to VideoDB, simply get the API key and create a connection. This can be done by setting the `VIDEO_DB_API_KEY` environment variable. You can get it from 👉🏼 [VideoDB Console](https://console.videodb.io). ( Free for first 50 uploads, **No credit card required!** )\n", "\n", @@ -53,15 +51,15 @@ "source": [ "import os\n", "\n", - "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", - "os.environ[\"VIDEO_DB_API_KEY\"] = \"\"" + "os.environ[\"VIDEO_DB_API_KEY\"] = \"\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Installing Dependencies\n", + "### 📦 Installing Dependencies\n", "\n", "To get started, we'll need to install the following packages:\n", "\n", @@ -76,8 +74,8 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install llama-index\n", - "%pip install videodb" + "%pip install videodb\n", + "%pip install llama-index" ] }, { @@ -93,27 +91,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Data Ingestion\n", + "## 🛠 Using VideoDBRetriever to Build RAG for Single Video\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 📋 Step 1: Connect to VideoDB and Ingest Data\n", + "\n", + "Let's upload a our video file first.\n", "\n", - "Let's upload a few video files first. You can use any `public url`, `Youtube link` or `local file` on your system. First 50 uploads are free!" + "You can use any `public url`, `Youtube link` or `local file` on your system. \n", + "\n", + "> ✨ First 50 uploads are free!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Uploading Video\n", + "Video uploaded with ID: m-a758f9bb-f769-484a-9d54-02417ccfe7e6\n" + ] + } + ], "source": [ "from videodb import connect\n", "\n", "# connect to VideoDB\n", "conn = connect()\n", + "coll = conn.create_collection(\n", + " name=\"VideoDB Retrievers\", description=\"VideoDB Retrievers\"\n", + ")\n", "\n", "# upload videos to default collection in VideoDB\n", - "print(\"uploading first video\")\n", - "video1 = conn.upload(url=\"https://www.youtube.com/watch?v=lsODSDmY4CY\")\n", - "print(\"uploading second video\")\n", - "video2 = conn.upload(url=\"https://www.youtube.com/watch?v=vZ4kOr38JhY\")" + "print(\"Uploading Video\")\n", + "video = coll.upload(url=\"https://www.youtube.com/watch?v=aRgP3n0XiMc\")\n", + "print(f\"Video uploaded with ID: {video.id}\")\n", + "\n", + "# video = coll.get_video(\"m-b6230808-307d-468a-af84-863b2c321f05\")" ] }, { @@ -129,13 +153,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Indexing\n", - "\n", - "To search bits inside a video, you have to index the video first. We have two types of indexing possible for a video.\n", - "\n", - "\n", - "- `index_spoken_words`: Indexes spoken words in the video.\n", - "- `index_scenes`: Indexes visuals of the video. `(Note: This feature is currently available only for beta users, join our discord for early access)` https://discord.gg/py9P639jGz " + "### 🗣️ Step 2: Indexing & Search from Spoken Content " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Video can be viewed as data with different modalities. First, we will work with the `spoken content`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 🗣️ Indexing Spoken Content" ] }, { @@ -147,42 +179,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "Indexing the videos...\n" + "Indexing spoken content in Video...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:39<00:00, 2.56it/s] \n", - "100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:39<00:00, 2.51it/s] \n" + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:48<00:00, 2.08it/s]\n" ] } ], "source": [ - "print(\"Indexing the videos...\")\n", - "video1.index_spoken_words()\n", - "video2.index_spoken_words()" + "print(\"Indexing spoken content in Video...\")\n", + "video.index_spoken_words()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Querying\n", - "\n", - "Now that the videos are indexed, we can use `VideoDBRetriever` to fetch relevant nodes from VideoDB." + "#### 🗣️ Retrieving Relevant Nodes from Spoken Index" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from llama_index.retrievers.videodb import VideoDBRetriever\n", - "from llama_index.core import get_response_synthesizer\n", - "from llama_index.core.query_engine import RetrieverQueryEngine" + "We will use the `VideoDBRetriever` to retrieve relevant nodes from our indexed content. The video ID should be passed as a parameter, and the `index_type` should be set to `IndexType.spoken_word`.\n", + "\n", + "You can configure the `score_threshold` and `result_threshold` after experimentation." ] }, { @@ -191,16 +217,33 @@ "metadata": {}, "outputs": [], "source": [ - "# VideoDBRetriever by default uses the default collection in the VideoDB\n", - "retriever = VideoDBRetriever()\n", + "from llama_index.retrievers.videodb import VideoDBRetriever\n", + "from videodb import SearchType, IndexType\n", "\n", - "# use your llama_index response_synthesizer on search results.\n", - "response_synthesizer = get_response_synthesizer()\n", + "spoken_retriever = VideoDBRetriever(\n", + " collection=coll.id,\n", + " video=video.id,\n", + " search_type=SearchType.semantic,\n", + " index_type=IndexType.spoken_word,\n", + " score_threshold=0.1,\n", + ")\n", "\n", - "query_engine = RetrieverQueryEngine(\n", - " retriever=retriever,\n", - " response_synthesizer=response_synthesizer,\n", - ")" + "spoken_query = \"Nationwide exams\"\n", + "nodes_spoken_index = spoken_retriever.retrieve(spoken_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 🗣️️️ Viewing the result : 💬 Text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the relevant nodes and synthesize the response using llamaindex" ] }, { @@ -212,16 +255,84 @@ "name": "stdout", "output_type": "stream", "text": [ - "Dopamine is a neurotransmitter that plays a key role in various brain functions, including motivation, reward, and pleasure. It is involved in regulating mood, movement, and cognitive function.\n" + "The results of the nationwide exams were eagerly awaited all day.\n" ] } ], "source": [ - "# query across all uploaded videos to get the text answer.\n", - "response = query_engine.query(\"What is Dopamine?\")\n", + "from llama_index.core import get_response_synthesizer\n", + "\n", + "response_synthesizer = get_response_synthesizer()\n", + "\n", + "response = response_synthesizer.synthesize(\n", + " spoken_query, nodes=nodes_spoken_index\n", + ")\n", "print(response)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 🗣️ Viewing the result : 🎥 Video Clip" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each retrieved node that is relevant to the query, the `start` and `end` fields in the metadata represent the time interval covered by the node.\n", + "\n", + "We will use VideoDB's Programmable Stream to generate a stream of relevant video clips based on the timestamps of these nodes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://console.videodb.io/player?url=https://dseetlpshk2tb.cloudfront.net/v3/published/manifests/3c108acd-e459-494a-bc17-b4768c78e5df.m3u8'" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from videodb import play_stream\n", + "\n", + "results = [\n", + " (node.metadata[\"start\"], node.metadata[\"end\"])\n", + " for node in nodes_spoken_index\n", + "]\n", + "\n", + "stream_link = video.generate_stream(results)\n", + "play_stream(stream_link)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 📸️ Step3 : Index & Search from Visual Content\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 📸 Indexing Visual Content\n", + "To learn more about Scene Index, explore the following guides:\n", + "\n", + "- [Quickstart Guide](https://github.com/video-db/videodb-cookbook/blob/main/quickstart/Scene%20Index%20QuickStart.ipynb) guide provides a step-by-step introduction to Scene Index. It's ideal for getting started quickly and understanding the primary functions.\n", + "\n", + "- [Scene Extraction Options Guide](https://github.com/video-db/videodb-cookbook/blob/main/guides/scene-index/playground_scene_extraction.ipynb) delves deeper into the various options available for scene extraction within Scene Index. It covers advanced settings, customization features, and tips for optimizing scene extraction based on different needs and preferences." + ] + }, { "cell_type": "code", "execution_count": null, @@ -231,37 +342,100 @@ "name": "stdout", "output_type": "stream", "text": [ - "Morning sunlight can help trigger a cortisol pulse shift, allowing individuals to capture a morning work block by waking up early and exposing themselves to sunlight. This exposure to morning sunlight, along with brief high-intensity exercise, can assist in adjusting the cortisol levels and potentially enhancing productivity during the early hours of the day.\n" + "Indexing Visual content in Video...\n", + "Scene Index successful with ID: 990733050d6fd4f5\n" ] } ], "source": [ - "response = query_engine.query(\"What's the benefit of morning sunlight?\")\n", - "print(response)" + "from videodb import SceneExtractionType\n", + "\n", + "print(\"Indexing Visual content in Video...\")\n", + "\n", + "# Index scene content\n", + "index_id = video.index_scenes(\n", + " extraction_type=SceneExtractionType.shot_based,\n", + " extraction_config={\"frame_count\": 3},\n", + " prompt=\"Describe the scene in detail\",\n", + ")\n", + "video.get_scene_index(index_id)\n", + "\n", + "print(f\"Scene Index successful with ID: {index_id}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - " \n", - "## Watch Video Stream of Search Result\n", + "#### 📸️ Retrieving Relevant Nodes from Scene Index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like we used `VideoDBRetriever` for the spoken index, we will use it for the scene index. Here, we will need to set `index_type` to `IndexType.scene` and pass the `scene_index_id`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.retrievers.videodb import VideoDBRetriever\n", + "from videodb import SearchType, IndexType\n", + "\n", "\n", - "Although, The `Nodes` returned by Retriever are of type `TextNode`. They also have metadata that can help you `watch the video stream` of results. You can create a compilation of all Nodes using VideoDB's [Programmable video streams](https://docs.videodb.io/version-0-0-3-timeline-and-assets-44). You can even modify it with Audio and Image overlays easily. \n", + "scene_retriever = VideoDBRetriever(\n", + " collection=coll.id,\n", + " video=video.id,\n", + " search_type=SearchType.semantic,\n", + " index_type=IndexType.scene,\n", + " scene_index_id=index_id,\n", + " score_threshold=0.1,\n", + ")\n", "\n", - "![Timeline](https://codaio.imgix.net/docs/_s5lUnUCIU/blobs/bl-n4vT_dFztl/e664f43dbd4da89c3a3bfc92e3224c8a188eb19d2d458bebe049e780f72506ca6b19421c7168205f7ad307187e73da60c73cdbb9a0ef3fec77cc711927ad26a29a92cd13691fa9375c231f1c006853bacf28e09b3bf0bbcb5f7b76462b354a180fb437ad?auto=format%2Ccompress&fit=max \"Programmable Video Streams\")\n", - "\n" + "scene_query = \"accident scenes\"\n", + "nodes_scene_index = scene_retriever.retrieve(scene_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 📸️️️ Viewing the result : 💬 Text" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The scenes described do not depict accidents but rather dynamic and intense scenarios involving motion, urgency, and tension in urban settings at night.\n" + ] + } + ], "source": [ - "from videodb import connect, play_stream\n", - "from videodb.timeline import Timeline\n", - "from videodb.asset import VideoAsset" + "from llama_index.core import get_response_synthesizer\n", + "\n", + "response_synthesizer = get_response_synthesizer()\n", + "\n", + "response = response_synthesizer.synthesize(\n", + " scene_query, nodes=nodes_scene_index\n", + ")\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 📸 ️ Viewing the result : 🎥 Video Clip" ] }, { @@ -272,7 +446,7 @@ { "data": { "text/plain": [ - "'https://console.videodb.io/player?url=https://stream.videodb.io/v3/published/manifests/9c39c8a9-62a2-4b5e-b15d-8565cc58c8ae.m3u8'" + "'https://console.videodb.io/player?url=https://dseetlpshk2tb.cloudfront.net/v3/published/manifests/ae74e9da-13bf-4056-8cfa-0267087b74d7.m3u8'" ] }, "execution_count": null, @@ -281,69 +455,177 @@ } ], "source": [ - "# create video stream of search results\n", - "conn = connect()\n", - "timeline = Timeline(conn)\n", + "from videodb import play_stream\n", "\n", - "relevant_nodes = retriever.retrieve(\"What's the benefit of morning sunlight?\")\n", + "results = [\n", + " (node.metadata[\"start\"], node.metadata[\"end\"])\n", + " for node in nodes_scene_index\n", + "]\n", "\n", - "for node_obj in relevant_nodes:\n", - " node = node_obj.node\n", - " # create a video asset for each node\n", - " node_asset = VideoAsset(\n", - " asset_id=node.metadata[\"video_id\"],\n", - " start=node.metadata[\"start\"],\n", - " end=node.metadata[\"end\"],\n", - " )\n", - " # add the asset to timeline\n", - " timeline.add_inline(node_asset)\n", + "stream_link = video.generate_stream(results)\n", + "play_stream(stream_link)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 🛠️ Step4: Simple Multimodal RAG - Combining Results of Both modalities\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to unlock in multimodal queries in our video library like this: \n", "\n", - "# generate stream for the compiled timeline\n", - "stream_url = timeline.generate_stream()\n", - "play_stream(stream_url)" + "> 📸🗣️ \"*Show me 1.Accident Scene 2.Discussion about nationwide exams*\"\n", + "\n", + "There are lots of way to do create a multimodal RAG, for the sake of simplicity we are choosing a simple approach:\n", + "\n", + "1. 🧩 **Query Transformation**: Divide query into two parts that can be used with respective scene and spoken indexes.\n", + "2. 🔎 **Finding Relevant nodes for each modality**: Using `VideoDBRetriever` find relevant nodes from Spoken Index and Scene Index \n", + "3. ✏️ **Viewing the result : Text**: Use Relevant Nodes to sythesize a text reponse Integrating the results from both indexes for precise video segment identification. \n", + "4. 🎥 **Viewing the result : Video Clip**: Integrating the results from both indexes for precise video segment identification. \n", + "\n", + "> To checkout more advanced multimodal techniques, checkout out [advnaced multimodal guides](https://docs.videodb.io/multimodal-guide-90)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - " \n", - "### Configuring `VideoDBRetriever`\n", + "#### 🧩 Query Transformation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query for Spoken retriever : Discussion about nationwide exams\n", + "Query for Scene retriever : Accident Scene\n" + ] + } + ], + "source": [ + "from llama_index.llms.openai import OpenAI\n", "\n", - "**1. Retriever for only one Video**:\n", - "You can pass the `id` of the video object to search in only that video. \n", - "```python\n", - "VideoDBRetriever(video=\"my_video.id\")\n", - "```\n", "\n", - "**2. Retriever for different type of Indexes**:\n", - "```python\n", - "# VideoDBRetriever that uses keyword search - Matches exact occurence of words and sentences. It only supports single video. \n", - "keyword_retriever = VideoDBRetriever(search_type=\"keyword\", video=\"my_video.id\")\n", + "def split_spoken_visual_query(query):\n", + " transformation_prompt = \"\"\"\n", + " Divide the following query into two distinct parts: one for spoken content and one for visual content. The spoken content should refer to any narration, dialogue, or verbal explanations and The visual content should refer to any images, videos, or graphical representations. Format the response strictly as:\\nSpoken: \\nVisual: \\n\\nQuery: {query}\n", + " \"\"\"\n", + " prompt = transformation_prompt.format(query=query)\n", + " response = OpenAI(model=\"gpt-4\").complete(prompt)\n", + " divided_query = response.text.strip().split(\"\\n\")\n", + " spoken_query = divided_query[0].replace(\"Spoken:\", \"\").strip()\n", + " scene_query = divided_query[1].replace(\"Visual:\", \"\").strip()\n", + " return spoken_query, scene_query\n", "\n", - "# VideoDBRetriever that uses semantic search - Perfect for question answers type of query.\n", - "semantic_retriever = VideoDBRetriever(search_type=\"semantic\")\n", "\n", - "# [only for beta users of VideoDB] VideoDBRetriever that uses scene search - Search visual information in the videos.\n", - "visual_retriever = VideoDBRetriever(search_type=\"scene\")\n", - "```\n", + "query = \"Show me 1.Accident Scene 2.Discussion about nationwide exams \"\n", + "spoken_query, scene_query = split_spoken_visual_query(query)\n", + "print(\"Query for Spoken retriever : \", spoken_query)\n", + "print(\"Query for Scene retriever : \", scene_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 🔎 Finding Relevant nodes for each modality" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from videodb import SearchType, IndexType\n", "\n", - "**3. Configure threshold parameters**: \n", - "- `result_threshold`: is the threshold for number of results returned by retriever; the default value is `5`\n", - "- `score_threshold`: only nodes with score higher than `score_threshold` will be returned by retriever; the default value is `0.2` \n", + "# Retriever for Spoken Index\n", + "spoken_retriever = VideoDBRetriever(\n", + " collection=coll.id,\n", + " video=video.id,\n", + " search_type=SearchType.semantic,\n", + " index_type=IndexType.spoken_word,\n", + " score_threshold=0.1,\n", + ")\n", "\n", - "```python\n", - "custom_retriever = VideoDBRetriever(result_threshold=2, score_threshold=0.5)\n", - "```" + "# Retriever for Scene Index\n", + "scene_retriever = VideoDBRetriever(\n", + " collection=coll.id,\n", + " video=video.id,\n", + " search_type=SearchType.semantic,\n", + " index_type=IndexType.scene,\n", + " scene_index_id=index_id,\n", + " score_threshold=0.1,\n", + ")\n", + "\n", + "# Fetch relevant nodes for Spoken index\n", + "nodes_spoken_index = spoken_retriever.retrieve(spoken_query)\n", + "\n", + "# Fetch relevant nodes for Scene index\n", + "nodes_scene_index = scene_retriever.retrieve(scene_query)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### View Specific Node\n", + "#### ️💬️ Viewing the result : Text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The first scene depicts a dynamic and intense scenario in an urban setting at night, involving a motorcycle chase with a figure possibly dodging away. The second scene portrays a dramatic escape or rescue situation with characters in motion alongside a large truck. The discussion about nationwide exams involves a conversation between a character and their mother about exam results and studying.\n" + ] + } + ], + "source": [ + "response_synthesizer = get_response_synthesizer()\n", "\n", - "To watch stream of each retrieved node, you can directly generate the stream of that part directly from `video` object of VideoDB. \n" + "response = response_synthesizer.synthesize(\n", + " query, nodes=nodes_scene_index + nodes_spoken_index\n", + ")\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 🎥 Viewing the result : Video Clip" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From each modality, we have retrieved results that are relevant to the query within that specific modality (semantic and scene/visual, in this case).\n", + "\n", + "Each node has start and end fields in the metadata, which represent the time interval the node covers.\n", + "\n", + "There are lots of way to sythesize there results, For now we will use a simple method : \n", + "\n", + "- `Union`: This method takes all the timestamps from every node, creating a comprehensive list that includes every relevant time, even if some timestamps appear in only one modality.\n", + "\n", + "One of the other ways can be `Intersection`:\n", + "\n", + "- `Intersection`: This method only includes timestamps that are present in every node, resulting in a smaller list with times that are universally relevant across all modalities.\n" ] }, { @@ -354,8 +636,7 @@ { "data": { "text/plain": [ - "[NodeWithScore(node=TextNode(id_='6ca84002-49df-4091-901d-48248dbe0977', embedding=None, metadata={'collection_id': 'c-33978c87-33e6-4259-9e27-a9edc79be9ad', 'video_id': 'm-f201ff7c-88ec-47ca-938b-a4e968676ba0', 'length': '1496.711837', 'title': 'AMA #1: Leveraging Ultradian Cycles, How to Protect Your Brain, Seed Oils Examined and More', 'start': 906.01, 'end': 974.59}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text=\" So for somebody that wants to learn an immense amount of material, or who has the opportunity to capture another Altradian cycle, the other time where that tends to occur is also early days. So some people, by waking up early and using stimulants like caffeine and hydration or some brief high intensity city exercise, can trigger that cortisol pulse to shift a little bit earlier so that they can capture a morning work block that occurs somewhere, let's say between six and 07:30 a.m. So let's think about our typical person, at least in my example, that's waking up around 07:00 a.m. And then I said, has their first Altradian work cycle really flip on? Because that bump in cortisol around 930 or 10:00 a.m. If that person were, say, to. Set their alarm clock for 05:30 a.m. Then get up, get some artificial light. If the sun isn't out, turn on bright artificial lights. Or if the sun happens to be up that time of year, get some sunlight in your eyes. But irrespective of sunlight, were to get a little bit of brief, high intensity exercise, maybe ten or 15 minutes of skipping rope or even just jumping jacks or go out for a brief jog.\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.440981567),\n", - " NodeWithScore(node=TextNode(id_='2244fd64-121e-4699-ba36-f0f6a110750f', embedding=None, metadata={'collection_id': 'c-33978c87-33e6-4259-9e27-a9edc79be9ad', 'video_id': 'm-eae54005-b5ca-44f1-9c31-fcdb2f1db56a', 'length': '1830.498685', 'title': 'AMA #2: Improve Sleep, Reduce Sugar Cravings, Optimal Protein Intake, Stretching Frequency & More', 'start': 899.772, 'end': 977.986}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text=\" Because the study, as far as I know, has not been done. Whether or not doing resistance training or some other type of exercise would have led to the same effect. Although I have to imagine that if it's moderately intense to intense resistance training, provided it's done far enough away from going to sleep right prior to 6 hours before sleep, that one ought to see the same effects, although that was not a condition in this study. But it's a very nice study. They looked at everything from changes in core body temperature to caloric expenditure. They didn't see huge changes in core body temperature changes, so that couldn't explain the effect. It really appears that the major effect of improving slow wave sleep was due to something in changing the fine structure of the brainwaves that occur during slow wave sleep. In fact, and this is an important point. The subjects in this study did not report subjectively feeling that much better from their sleep. So you might say, well then, why would I even want to bother? However, it's well known that getting sufficient slow wave sleep is important not just for repair, excuse me, for repair of bodily tissues, but also for repair of brain tissues and repair and washout of debris in the brain. And that debris is known to lead to things like dementia.\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.282342136)]" + "'https://console.videodb.io/player?url=https://dseetlpshk2tb.cloudfront.net/v3/published/manifests/91b08b39-c72f-4e33-ad1c-47a2ea11ac17.m3u8'" ] }, "execution_count": null, @@ -364,7 +645,79 @@ } ], "source": [ - "relevant_nodes" + "from videodb import play_stream\n", + "\n", + "\n", + "def merge_intervals(intervals):\n", + " if not intervals:\n", + " return []\n", + " intervals.sort(key=lambda x: x[0])\n", + " merged = [intervals[0]]\n", + " for interval in intervals[1:]:\n", + " if interval[0] <= merged[-1][1]:\n", + " merged[-1][1] = max(merged[-1][1], interval[1])\n", + " else:\n", + " merged.append(interval)\n", + " return merged\n", + "\n", + "\n", + "# Extract timestamps from both relevant nodes\n", + "results = [\n", + " [node.metadata[\"start\"], node.metadata[\"end\"]]\n", + " for node in nodes_spoken_index + nodes_scene_index\n", + "]\n", + "merged_results = merge_intervals(results)\n", + "\n", + "# Use Videodb to create a stream of relevant clips\n", + "stream_link = video.generate_stream(merged_results)\n", + "play_stream(stream_link)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 🛠 Using VideoDBRetriever to Build RAG for Collection of Videos\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding More videos to our collection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "video_2 = coll.upload(url=\"https://www.youtube.com/watch?v=kMRX3EA68g4\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 🗣️ Indexing Spoken Content" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "video_2.index_spoken_words()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 📸 Indexing Scenes" ] }, { @@ -375,7 +728,8 @@ { "data": { "text/plain": [ - "'https://console.videodb.io/player?url=https://stream.videodb.io/v3/published/manifests/b7201145-7302-4ec5-b87c-d1a4c6592f69.m3u8'" + "[Video(id=m-b6230808-307d-468a-af84-863b2c321f05, collection_id=c-4882e4a8-9812-4921-80ff-b77c9c4ab4e7, stream_url=https://dseetlpshk2tb.cloudfront.net/v3/published/manifests/528623c2-3a8e-4c84-8f05-4dd74f1a9977.m3u8, player_url=https://console.dev.videodb.io/player?url=https://dseetlpshk2tb.cloudfront.net/v3/published/manifests/528623c2-3a8e-4c84-8f05-4dd74f1a9977.m3u8, name=Death note - episode 1 (english dubbed) | HD, description=None, thumbnail_url=None, length=1366.006712),\n", + " Video(id=m-f5b86106-4c28-43f1-b753-fa9b3f839dfe, collection_id=c-4882e4a8-9812-4921-80ff-b77c9c4ab4e7, stream_url=https://dseetlpshk2tb.cloudfront.net/v3/published/manifests/4273851a-46f3-4d57-bc1b-9012ce330da8.m3u8, player_url=https://console.dev.videodb.io/player?url=https://dseetlpshk2tb.cloudfront.net/v3/published/manifests/4273851a-46f3-4d57-bc1b-9012ce330da8.m3u8, name=Death note - episode 5 (english dubbed) | HD, description=None, thumbnail_url=None, length=1366.099592)]" ] }, "execution_count": null, @@ -384,28 +738,54 @@ } ], "source": [ - "from videodb import connect\n", + "from videodb import SceneExtractionType\n", "\n", - "# retriever = VideoDBRetriever()\n", - "# relevant_nodes = retriever.retrieve(\"What is Dopamine?\")\n", + "print(\"Indexing Visual content in Video...\")\n", "\n", - "video_node = relevant_nodes[0].node\n", - "conn = connect()\n", - "coll = conn.get_collection()\n", - "\n", - "video = coll.get_video(video_node.metadata[\"video_id\"])\n", - "start = video_node.metadata[\"start\"]\n", - "end = video_node.metadata[\"end\"]\n", + "# Index scene content\n", + "index_id = video_2.index_scenes(\n", + " extraction_type=SceneExtractionType.shot_based,\n", + " extraction_config={\"frame_count\": 3},\n", + " prompt=\"Describe the scene in detail\",\n", + ")\n", + "video_2.get_scene_index(index_id)\n", "\n", - "stream_url = video.generate_stream(timeline=[(start, end)])\n", - "play_stream(stream_url)" + "print(f\"Scene Index successful with ID: {index_id}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 🧹 Cleanup" + "#### 🧩 Query Transformation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query for Spoken retriever : Kiara is speaking\n", + "Query for Scene retriever : Show me Accident Scene\n" + ] + } + ], + "source": [ + "query = \"Show me 1.Accident Scene 2.Kiara is speaking \"\n", + "spoken_query, scene_query = split_spoken_visual_query(query)\n", + "print(\"Query for Spoken retriever : \", spoken_query)\n", + "print(\"Query for Scene retriever : \", scene_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 🔎 Finding relevant nodes " ] }, { @@ -414,24 +794,226 @@ "metadata": {}, "outputs": [], "source": [ - "video1.delete()\n", - "video2.delete()" + "from videodb import SearchType, IndexType\n", + "\n", + "# Retriever for Spoken Index\n", + "spoken_retriever = VideoDBRetriever(\n", + " collection=coll.id,\n", + " search_type=SearchType.semantic,\n", + " index_type=IndexType.spoken_word,\n", + " score_threshold=0.2,\n", + ")\n", + "\n", + "# Retriever for Scene Index\n", + "scene_retriever = VideoDBRetriever(\n", + " collection=coll.id,\n", + " search_type=SearchType.semantic,\n", + " index_type=IndexType.scene,\n", + " score_threshold=0.2,\n", + ")\n", + "\n", + "# Fetch relevant nodes for Spoken index\n", + "nodes_spoken_index = spoken_retriever.retrieve(spoken_query)\n", + "\n", + "# Fetch relevant nodes for Scene index\n", + "nodes_scene_index = scene_retriever.retrieve(scene_query)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 👨‍👩‍👧‍👦 Support & Community\n", + "#### ️💬️ Viewing the result : Text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kira is speaking about his plans and intentions regarding the agent from the bus. The accident scene captures a distressing moment where an individual is urgently making a phone call near a damaged car, with a victim lying motionless on the ground. The chaotic scene includes a bus in the background, emphasizing the severity of the tragic incident.\n" + ] + } + ], + "source": [ + "response_synthesizer = get_response_synthesizer()\n", "\n", - "Leveraging the capabilities of automation and AI-driven content understanding, the possibilities for creation and repurposing of your content are boundless with VideoDB.\n", + "response = response_synthesizer.synthesize(\n", + " \"What is kaira speaking. And tell me about accident scene\",\n", + " nodes=nodes_scene_index + nodes_spoken_index,\n", + ")\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 🎥 Viewing the result : Video Clip" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When working with an editing workflow involving multiple videos, we need to create a `Timeline` of `VideoAsset` and then compile them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](https://codaio.imgix.net/docs/_s5lUnUCIU/blobs/bl-n4vT_dFztl/e664f43dbd4da89c3a3bfc92e3224c8a188eb19d2d458bebe049e780f72506ca6b19421c7168205f7ad307187e73da60c73cdbb9a0ef3fec77cc711927ad26a29a92cd13691fa9375c231f1c006853bacf28e09b3bf0bbcb5f7b76462b354a180fb437ad?auto=format%2Ccompress&fit=max)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://console.videodb.io/player?url=https://dseetlpshk2tb.cloudfront.net/v3/published/manifests/2810827b-4d80-44af-a26b-ded2a7a586f6.m3u8'" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from videodb import connect, play_stream\n", + "from videodb.timeline import Timeline\n", + "from videodb.asset import VideoAsset\n", + "\n", + "# Create a new timeline Object\n", + "timeline = Timeline(conn)\n", + "\n", + "for node_obj in nodes_scene_index + nodes_spoken_index:\n", + " node = node_obj.node\n", + "\n", + " # Create a Video asset for each node\n", + " node_asset = VideoAsset(\n", + " asset_id=node.metadata[\"video_id\"],\n", + " start=node.metadata[\"start\"],\n", + " end=node.metadata[\"end\"],\n", + " )\n", + "\n", + " # Add the asset to timeline\n", + " timeline.add_inline(node_asset)\n", + "\n", + "# Generate stream for the compiled timeline\n", + "stream_url = timeline.generate_stream()\n", + "play_stream(stream_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "## Configuring `VideoDBRetriever`\n", + "---\n", + "\n", + "### ⚙️ Retriever for only one Video\n", + "You can pass the `id` of the video object to search in only that video. \n", + "```python\n", + "VideoDBRetriever(video=\"my_video.id\")\n", + "```\n", + "\n", + "### ⚙️ Retriever for a set of Video/ Collection\n", + "You can pass the `id` of the Collection to search in only that Collection. \n", + "```python\n", + "VideoDBRetriever(collection=\"my_coll.id\")\n", + "```\n", + "\n", + "### ⚙️ Retriever for different type of Indexes\n", + "```python\n", + "from videodb import IndexType\n", + "spoken_word = VideoDBRetriever(index_type=IndexType.spoken_word)\n", + "\n", + "scene_retriever = VideoDBRetriever(index_type=IndexType.scene, scene_index_id=\"my_index_id\")\n", + "```\n", + "\n", + "### ⚙️ Configuring Search Type of Retriever \n", + "`search_type` determines the search method used to retrieve nodes against given query \n", + "```python\n", + "from videodb import SearchType, IndexType\n", + "\n", + "keyword_spoken_search = VideoDBRetriever(\n", + " search_type=SearchType.keyword,\n", + " index_type=IndexType.spoken_word\n", + ")\n", + "\n", + "semantic_scene_search = VideoDBRetriever(\n", + " search_type=SearchType.semantic,\n", + " index_type=IndexType.spoken_word\n", + ")\n", + "```\n", + "\n", + "### ⚙️ Configure threshold parameters \n", + "- `result_threshold`: is the threshold for number of results returned by retriever; the default value is `5`\n", + "- `score_threshold`: only nodes with score higher than `score_threshold` will be returned by retriever; the default value is `0.2` \n", + "\n", + "```python\n", + "custom_retriever = VideoDBRetriever(result_threshold=2, score_threshold=0.5)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ✨ Configuring Indexing and Chunking\n", + "---\n", + "\n", + "In this example, we utilize the VideoDB's Indexing for video retrieval. However, you have the flexibility to load both Transcript and Scene Data and apply your own indexing techniques using llamaindex.\n", + "\n", + "For more detailed guidance, refer to this [guide](https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/multi_modal/multi_modal_videorag_videodb.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 🏃‍♂️ Next Steps\n", + "---\n", + "\n", + "In this guide, we built a Simple Multimodal RAG for Videos Using VideoDB, Llamaindex, and OpenAI\n", + "\n", + "You can optimize the pipeline by incorporating more advanced techniques like\n", + "- Optimize Query Transformation\n", + "- More methods to combine retrieved nodes from different modalities\n", + "- Experiment with Different RAG pipelines like Knowledge Graph\n", + "\n", + "To learn more about Programable Stream feature that we used to create relevant clip checkout [Dynamic Video Stream Guide](https://docs.videodb.io/dynamic-video-stream-guide-44)\n", + "\n", + "\n", + "To learn more about Scene Index, explore the following guides:\n", + "\n", + "- [Quickstart Guide](https://github.com/video-db/videodb-cookbook/blob/main/quickstart/Scene%20Index%20QuickStart.ipynb) \n", + "- [Scene Extraction Options](https://github.com/video-db/videodb-cookbook/blob/main/guides/scene-index/playground_scene_extraction.ipynb)\n", + "- [Advanced Visual Search](https://github.com/video-db/videodb-cookbook/blob/main/guides/scene-index/advanced_visual_search.ipynb)\n", + "- [Custom Annotation Pipelines](https://github.com/video-db/videodb-cookbook/blob/main/guides/scene-index/custom_annotations.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 👨‍👩‍👧‍👦 Support & Community\n", + "---\n", "\n", "If you have any questions or feedback. Feel free to reach out to us 🙌🏼\n", "\n", - "- [Discord](https://discord.gg/py9P639jGz) \n", - "- [GitHub](https://github.com/video-db) \n", - "- [VideoDB](https://videodb.io) \n", - "- [Email](mailto:ashu@videodb.io) " + "* [Discord](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fdiscord.gg%2Fpy9P639jGz)\n", + "* [GitHub](https://github.com/video-db)\n", + "* [Email](mailto:ashu@videodb.io)\n" ] } ], diff --git a/docs/docs/examples/selectors/not_diamond_selector.ipynb b/docs/docs/examples/selectors/not_diamond_selector.ipynb new file mode 100644 index 0000000000000..d3b3ddddec3e3 --- /dev/null +++ b/docs/docs/examples/selectors/not_diamond_selector.ipynb @@ -0,0 +1,343 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Not Diamond to Select LLMs For Indexes\n", + "In this tutorial, we demonstrate how to use a router query engine with a selector powered by [Not Diamond](https://www.notdiamond.ai). You can automatically route a query to one of several available LLMs, which will then select the best index for your needs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install -q llama-index-llms-anthropic llama-index-llms-openai" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install -q llama-index notdiamond" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: This is ONLY necessary in jupyter notebook.\n", + "# Details: Jupyter runs an event-loop behind the scenes.\n", + "# This results in nested event-loops when we start an event-loop to make async queries.\n", + "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n", + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p 'data/paul_graham/'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Routing Queries With Not Diamond" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from typing import List\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n", + "os.environ[\"ANTHROPIC_API_KEY\"] = \"sk-ant-...\"\n", + "os.environ[\"NOTDIAMOND_API_KEY\"] = \"sk-...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Indexes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import (\n", + " SimpleDirectoryReader,\n", + " VectorStoreIndex,\n", + " SummaryIndex,\n", + " Settings,\n", + ")\n", + "from llama_index.core.query_engine import RouterQueryEngine\n", + "from llama_index.core.tools import QueryEngineTool\n", + "from llama_index.selectors.notdiamond.base import NotDiamondSelector\n", + "\n", + "# load documents\n", + "documents = SimpleDirectoryReader(\"data/paul_graham\").load_data()\n", + "nodes = Settings.node_parser.get_nodes_from_documents(documents)\n", + "\n", + "# index documents\n", + "vector_index = VectorStoreIndex.from_documents(documents)\n", + "summary_index = SummaryIndex.from_documents(documents)\n", + "query_text = \"What was Paul Graham's role at Yahoo?\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up Tools for the QueryEngine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "list_query_engine = summary_index.as_query_engine(\n", + " response_mode=\"tree_summarize\",\n", + " use_async=True,\n", + ")\n", + "vector_query_engine = vector_index.as_query_engine()\n", + "\n", + "list_tool = QueryEngineTool.from_defaults(\n", + " query_engine=list_query_engine,\n", + " description=(\n", + " \"Useful for summarization questions related to Paul Graham eassy on\"\n", + " \" What I Worked On.\"\n", + " ),\n", + ")\n", + "\n", + "vector_tool = QueryEngineTool.from_defaults(\n", + " query_engine=vector_query_engine,\n", + " description=(\n", + " \"Useful for retrieving specific context from Paul Graham essay on What\"\n", + " \" I Worked On.\"\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a NotDiamondSelector and RouterQueryEngine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from notdiamond import NotDiamond\n", + "\n", + "client = NotDiamond(\n", + " api_key=os.environ[\"NOTDIAMOND_API_KEY\"],\n", + " llm_configs=[\"openai/gpt-4o\", \"anthropic/claude-3-5-sonnet-20240620\"],\n", + ")\n", + "preference_id = client.create_preference_id()\n", + "client.preference_id = preference_id\n", + "\n", + "nd_selector = NotDiamondSelector(client=client)\n", + "\n", + "query_engine = RouterQueryEngine(\n", + " selector=nd_selector,\n", + " query_engine_tools=[\n", + " list_tool,\n", + " vector_tool,\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use Not Diamond to Query Indexes\n", + "\n", + "Once we've set up our indexes and query engine, we can submit queries as usual." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Paul Graham has contributed to the field of technology and entrepreneurship through his essays and datasets. He has provided a labelled RAG dataset based on one of his essays, which includes queries, reference answers, and reference contexts. Additionally, he has shared insights and code related to using the LlamaIndex RAG pipeline for evaluation purposes.\n" + ] + } + ], + "source": [ + "response = query_engine.query(\n", + " \"Please summarize Paul Graham's working experience.\"\n", + ")\n", + "print(str(response))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Paul Graham founded Viaweb after RICS.\n" + ] + } + ], + "source": [ + "response = query_engine.query(\"What did Paul Graham do after RICS?\")\n", + "print(str(response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using NotDiamondSelector as a standalone selector\n", + "\n", + "As with LlamaIndex's built-in selectors, you can also use the `NotDiamondSelector` to select an index." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "selections=[SingleSelection(index=1, reason=\"The question asks for a summary of a recipe for deviled eggs, which directly aligns with option 2: 'Great for summarizing recipes.' This choice is most relevant as it specifically addresses the task of summarizing recipes, which is exactly what the question is asking for.\")] session_id='078a837f-d8da-4de4-aec9-b44df5ea32ba' llm=LLMConfig(anthropic/claude-3-5-sonnet-20240620)\n" + ] + } + ], + "source": [ + "from llama_index.core.tools import ToolMetadata\n", + "from llama_index.selectors.notdiamond.base import NotDiamondSelector\n", + "\n", + "from notdiamond import NotDiamond, Metric\n", + "\n", + "choices = [\n", + " ToolMetadata(\n", + " name=\"vector_index\",\n", + " description=\"Great for asking questions about recipes.\",\n", + " ),\n", + " ToolMetadata(\n", + " name=\"list_index\", description=\"Great for summarizing recipes.\"\n", + " ),\n", + "]\n", + "\n", + "llm_configs = [\"openai/gpt-4o\", \"anthropic/claude-3-5-sonnet-20240620\"]\n", + "nd_client = NotDiamond(\n", + " api_key=os.environ[\"NOTDIAMOND_API_KEY\"],\n", + " llm_configs=llm_configs,\n", + " preference_id=preference_id,\n", + ")\n", + "preference_id = nd_client.create_preference_id()\n", + "nd_client.preference_id = preference_id\n", + "nd_selector = NotDiamondSelector(client=nd_client)\n", + "\n", + "nd_result = nd_selector.select(\n", + " choices, query=\"What is the summary of this recipe for deviled eggs?\"\n", + ")\n", + "print(nd_result)\n", + "\n", + "# Use the result's session_id to customize your routing logic.\n", + "metric = Metric(\"accuracy\")\n", + "score = metric.feedback(\n", + " session_id=nd_result.session_id,\n", + " llm_config=nd_result.llm,\n", + " value=1,\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/examples/structured_outputs/structured_outputs.ipynb b/docs/docs/examples/structured_outputs/structured_outputs.ipynb index 13ac310276d74..b248c1a5c6e70 100644 --- a/docs/docs/examples/structured_outputs/structured_outputs.ipynb +++ b/docs/docs/examples/structured_outputs/structured_outputs.ipynb @@ -67,7 +67,7 @@ "outputs": [], "source": [ "from typing import List\n", - "from pydantic.v1 import BaseModel, Field\n", + "from pydantic import BaseModel, Field\n", "\n", "\n", "class Song(BaseModel):\n", @@ -565,7 +565,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pydantic.v1 import BaseModel, Field\n", + "from pydantic import BaseModel, Field\n", "from typing import List\n", "\n", "\n", diff --git a/docs/docs/examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb b/docs/docs/examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb index 5861760bcbec2..521f88504493d 100644 --- a/docs/docs/examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb +++ b/docs/docs/examples/vector_stores/AlibabaCloudOpenSearchIndexDemo.ipynb @@ -6,7 +6,8 @@ "id": "600e8429", "metadata": {}, "source": [ - "\"Open" + "\"Open\n", + "\"Open" ] }, { diff --git a/docs/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb b/docs/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb index 15f9bbe86e725..d7b2cb5a5fdc1 100644 --- a/docs/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb +++ b/docs/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb @@ -41,7 +41,7 @@ "!pip install llama-index\n", "!pip install wget\n", "%pip install llama-index-vector-stores-azureaisearch\n", - "%pip install azure-search-documents==11.4.0\n", + "%pip install azure-search-documents==11.5.1\n", "%llama-index-embeddings-azure-openai\n", "%llama-index-llms-azure-openai" ] @@ -64,7 +64,6 @@ " VectorStoreIndex,\n", ")\n", "from llama_index.core.settings import Settings\n", - "\n", "from llama_index.llms.azure_openai import AzureOpenAI\n", "from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding\n", "from llama_index.vector_stores.azureaisearch import AzureAISearchVectorStore\n", @@ -89,7 +88,7 @@ "source": [ "aoai_api_key = \"YOUR_AZURE_OPENAI_API_KEY\"\n", "aoai_endpoint = \"YOUR_AZURE_OPENAI_ENDPOINT\"\n", - "aoai_api_version = \"2023-05-15\"\n", + "aoai_api_version = \"2024-02-01\"\n", "\n", "llm = AzureOpenAI(\n", " model=\"YOUR_AZURE_OPENAI_COMPLETION_MODEL_NAME\",\n", @@ -124,7 +123,7 @@ "source": [ "search_service_api_key = \"YOUR-AZURE-SEARCH-SERVICE-ADMIN-KEY\"\n", "search_service_endpoint = \"YOUR-AZURE-SEARCH-SERVICE-ENDPOINT\"\n", - "search_service_api_version = \"2023-11-01\"\n", + "search_service_api_version = \"2024-07-01\"\n", "credential = AzureKeyCredential(search_service_api_key)\n", "\n", "\n", @@ -194,6 +193,7 @@ " doc_id_field_key=\"doc_id\",\n", " language_analyzer=\"en.lucene\",\n", " vector_algorithm_type=\"exhaustiveKnn\",\n", + " # compression_type=\"binary\" # Option to use \"scalar\" or \"binary\". NOTE: compression is only supported for HNSW\n", ")" ] }, @@ -240,7 +240,7 @@ { "data": { "text/markdown": [ - "The author engaged in writing and programming activities during their formative years. They initially wrote short stories and later transitioned to programming on the IBM 1401 using an early version of Fortran. Subsequently, with the advent of microcomputers, the author began programming on a TRS-80, writing simple games, a rocket flight prediction program, and a word processor." + "The author focused on writing and programming outside of school, writing short stories and experimenting with programming on an IBM 1401 in 9th grade. Later, the author continued programming on microcomputers and eventually convinced their father to buy a TRS-80, where they started writing simple games and a word processor." ], "text/plain": [ "" @@ -265,7 +265,7 @@ { "data": { "text/markdown": [ - "The author learned that the study of philosophy in college did not live up to their expectations, as they found the courses to be boring and lacking in ultimate truths. This led them to switch their focus to AI, which was influenced by a novel featuring an intelligent computer and a PBS documentary showcasing advanced technology." + "The author learned about programming on early computers, the limitations of early AI, the importance of working on unprestigious things, and the significance of writing essays online." ], "text/plain": [ "" @@ -336,7 +336,7 @@ { "data": { "text/markdown": [ - "The author faced a challenging moment when he couldn't figure out what to do with the early computer he had access to in 9th grade. This was due to the limited options for input and the lack of knowledge in math to do anything interesting with the available resources." + "The author experienced a difficult moment when his mother had a stroke caused by colon cancer, which ultimately led to her passing away." ], "text/plain": [ "" @@ -384,9 +384,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "The author worked at Interleaf, where they learned several lessons, including the importance of product-focused leadership in technology companies, the drawbacks of code being edited by too many people, the limitations of conventional office hours for optimal hacking, and the risks associated with bureaucratic customers. Additionally, the author discovered the concept that the low end tends to dominate the high end, and that being the \"entry level\" option can be advantageous.\n", + "The company added a scripting language inspired by Emacs, and made the scripting language a dialect of Lisp.\n", "\n", - "Streamed output at 99.40073103089465 tokens/s\n" + "Streamed output at 64.01633939770672 tokens/s\n" ] } ], @@ -424,7 +424,7 @@ { "data": { "text/markdown": [ - "Blue" + "The color of the sky varies depending on factors such as the time of day, weather conditions, and location." ], "text/plain": [ "" @@ -458,7 +458,7 @@ { "data": { "text/markdown": [ - "The sky is indigo today." + "The color of the sky is indigo." ], "text/plain": [ "" @@ -529,7 +529,7 @@ { "data": { "text/plain": [ - "[NodeWithScore(node=TextNode(id_='049f00de-13be-4af3-ab56-8c16352fe799', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='ad2a08d4364262546db9711b915348d43e0ccc41bd8c3c41775e133624e1fa1b', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.8120511)]" + "[NodeWithScore(node=TextNode(id_='f0c299d8-1f59-4338-9c4e-06b99855ed23', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='The Godfather', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.8120511)]" ] }, "execution_count": null, @@ -576,41 +576,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "Score: 0.8748552\n", + "Score: 0.87485534\n", "File Name: Unknown\n", - "Id: bae0df75-ff37-4725-b659-b9fd8bf2ef3c\n", + "Id: b4d2af4e-1de0-4cfe-8b18-629722bc12d7\n", "\n", "Extracted Content:\n", "Inception\n", "\n", "======================================== End of Result ========================================\n", "\n", - "Score: 0.8155207\n", - "File Name: paul_graham_essay.txt\n", - "Id: ae5aee85-a083-4141-bf75-bbb872f53760\n", + "Score: 0.8120511\n", + "File Name: Unknown\n", + "Id: f0c299d8-1f59-4338-9c4e-06b99855ed23\n", "\n", "Extracted Content:\n", - "It's not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it's a sign both that there's something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren't prestigious doesn't guarantee you're on the right track, it at least guarantees you're not on the most common type of wrong one.\n", - "\n", - "Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office.\n", - "\n", - "One night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn't know but would probably like. One of the guests was someone I didn't know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out.\n", - "\n", - "Jessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders.\n", - "\n", - "When the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on.\n", - "\n", - "One of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won't waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they'd be able to avoid the worst of the mistakes we'd made.\n", - "\n", - "So I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they'd be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I'd only known), I blurted out \"But not me!\" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I'd been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn't done one angel investment.\n", - "\n", - "Meanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on.\n", - "\n", - "As Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We'd start our own investment firm and actually implement the ideas we'd been talking about. I'd fund it, and Jessica could quit her job and work for it, and we'd get Robert and Trevor as partners too. [13]\n", - "\n", - "Once again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel.\n", - "\n", - "There are multiple components to Y Combinator, and we didn't figure them all out at once. The part we got first was to be an angel firm.\n", + "The Godfather\n", "\n", "======================================== End of Result ========================================\n", "\n" @@ -662,8 +642,8 @@ { "data": { "text/plain": [ - "[NodeWithScore(node=TextNode(id_='bae0df75-ff37-4725-b659-b9fd8bf2ef3c', embedding=None, metadata={'director': 'Christopher Nolan'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='9792a1fd7d2e1a08f1b1d70a597357bb6b68d69ed5685117eaa37ac9e9a3565e', text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03181818127632141),\n", - " NodeWithScore(node=TextNode(id_='ae5aee85-a083-4141-bf75-bbb872f53760', embedding=None, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={: RelatedNodeInfo(node_id='627552ee-116a-4132-a7d3-7e7232f75866', node_type=, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, hash='0a59e1ce8e50a67680a5669164f79e524087270ce183a3971fcd18ac4cad1fa0'), : RelatedNodeInfo(node_id='24a1d375-31e3-492c-ac02-5091e3572e3f', node_type=, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, hash='51c474a12ac8e9748258b2c7bbe77bb7c8bf35b775ed44f016057a0aa8b0bd76'), : RelatedNodeInfo(node_id='196569e0-2b10-4ba3-8263-a69fb78dd98c', node_type=, metadata={}, hash='192082e7ba84b8c5e2a64bd1d422c6c503189fc3ba325bb3e6e8bdb43db03fbb')}, hash='a3ea638857f1daadf7af967322480f97e1235dac3ee7d72b8024670785df8810', text='It\\'s not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it\\'s a sign both that there\\'s something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren\\'t prestigious doesn\\'t guarantee you\\'re on the right track, it at least guarantees you\\'re not on the most common type of wrong one.\\n\\nOver the next several years I wrote lots of essays about all kinds of different topics. O\\'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office.\\n\\nOne night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn\\'t know but would probably like. One of the guests was someone I didn\\'t know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out.\\n\\nJessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders.\\n\\nWhen the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on.\\n\\nOne of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won\\'t waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they\\'d be able to avoid the worst of the mistakes we\\'d made.\\n\\nSo I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they\\'d be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I\\'d only known), I blurted out \"But not me!\" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I\\'d been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn\\'t done one angel investment.\\n\\nMeanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on.\\n\\nAs Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We\\'d start our own investment firm and actually implement the ideas we\\'d been talking about. I\\'d fund it, and Jessica could quit her job and work for it, and we\\'d get Robert and Trevor as partners too. [13]\\n\\nOnce again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel.\\n\\nThere are multiple components to Y Combinator, and we didn\\'t figure them all out at once. The part we got first was to be an angel firm.', start_char_idx=45670, end_char_idx=50105, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03009207174181938)]" + "[NodeWithScore(node=TextNode(id_='18de619a-94ca-46d9-bb17-51bd1b5c041a', embedding=None, metadata={'director': 'Christopher Nolan'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='Inception', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03333333507180214),\n", + " NodeWithScore(node=TextNode(id_='226f09aa-b9f1-40eb-a377-35da6f288fa1', embedding=None, metadata={'file_path': 'c:\\\\Dev\\\\llama_index\\\\docs\\\\docs\\\\examples\\\\vector_stores\\\\..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2024-08-23', 'last_modified_date': '2024-08-23'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={: RelatedNodeInfo(node_id='ba4cac4e-640c-41de-b01d-2c0116f753c8', node_type=, metadata={'file_path': 'c:\\\\Dev\\\\llama_index\\\\docs\\\\docs\\\\examples\\\\vector_stores\\\\..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2024-08-23', 'last_modified_date': '2024-08-23'}, hash='aec9c37ce12c52b02feb44993a53c84733fc31d3637224bda1c831b42009fc17'), : RelatedNodeInfo(node_id='b4120ac2-8829-4253-87ac-a714c1e680a2', node_type=, metadata={'file_path': 'c:\\\\Dev\\\\llama_index\\\\docs\\\\docs\\\\examples\\\\vector_stores\\\\..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2024-08-23', 'last_modified_date': '2024-08-23'}, hash='65795efeb29d8b8af5b61ff4d7b5607dfb0d262cb13f1eec55bb2e3d30654b28'), : RelatedNodeInfo(node_id='7bf9e654-5bbb-40a8-8cb5-12e3737a837d', node_type=, metadata={}, hash='2cca6e84108227c4e35fdd947606cebb212464fbe7dc51e4204ee27d62533e54')}, text='I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it\\'s now clear is about twenty companies and several open source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn\\'t so naive as to assume I could spring an overt Lisp on a general audience; we\\'d hide the parentheses, like Dylan did.\\r\\n\\r\\nBy then there was a name for the kind of company Viaweb was, an \"application service provider,\" or ASP. This name didn\\'t last long before it was replaced by \"software as a service,\" but it was current for long enough that I named this new company after it: it was going to be called Aspra.\\r\\n\\r\\nI started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn\\'t want to run a company — especially not a big one, which it was looking like this would have to be. I\\'d only started Viaweb because I needed the money. Now that I didn\\'t need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I\\'d build a subset that could be done as an open source project.\\r\\n\\r\\nMuch to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it.\\r\\n\\r\\nThe subset I would build as an open source project was the new Lisp, whose parentheses I now wouldn\\'t even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge.\\r\\n\\r\\nThe following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we\\'d used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I\\'d created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10]\\r\\n\\r\\nWow, I thought, there\\'s an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.\\r\\n\\r\\nThis had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]\\r\\n\\r\\nIn the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12]\\r\\n\\r\\nI\\'ve worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I\\'d always write essays too.\\r\\n\\r\\nI knew that online essays would be a marginal medium at first. Socially they\\'d seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging.', mimetype='text/plain', start_char_idx=40977, end_char_idx=45334, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.016393441706895828)]" ] }, "execution_count": null, @@ -698,8 +678,8 @@ { "data": { "text/plain": [ - "[NodeWithScore(node=TextNode(id_='bae0df75-ff37-4725-b659-b9fd8bf2ef3c', embedding=None, metadata={'director': 'Christopher Nolan'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='9792a1fd7d2e1a08f1b1d70a597357bb6b68d69ed5685117eaa37ac9e9a3565e', text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3949906826019287),\n", - " NodeWithScore(node=TextNode(id_='fc9782a2-c255-4265-a618-3a864abe598d', embedding=None, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={: RelatedNodeInfo(node_id='627552ee-116a-4132-a7d3-7e7232f75866', node_type=, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, hash='0a59e1ce8e50a67680a5669164f79e524087270ce183a3971fcd18ac4cad1fa0'), : RelatedNodeInfo(node_id='94d87013-ea3d-4a9c-982a-dde5ff219983', node_type=, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, hash='f28897170c6b61162069af9ee83dc11e13fa0f6bf6efaa7b3911e6ad9093da84'), : RelatedNodeInfo(node_id='dc3852e5-4c1e-484e-9e65-f17084d3f7b4', node_type=, metadata={}, hash='deaee6d5c992dbf757876957aa9112a42d30a636c6c83d81fcfac4aaf2d24dee')}, hash='a3b31e5ec2b5d4a9b3648de310c8a5962c17afdb800ea0e16faa47956607866d', text='And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they\\'d seen in American art magazines.\\n\\nOur model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She\\'d copy an obscure old painting out of a book, and then he\\'d take the copy and maltreat it to make it look old. [3]\\n\\nWhile I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still lives is different from painting people, because the subject, as its name suggests, can\\'t move. People can\\'t sit for more than about 15 minutes at a time, and when they do they don\\'t sit very still. So the traditional m.o. for painting people is to know how to paint a generic person, which you then modify to match the specific person you\\'re painting. Whereas a still life you can, if you want, copy pixel by pixel from what you\\'re seeing. You don\\'t want to stop there, of course, or you get merely photographic accuracy, and what makes a still life interesting is that it\\'s been through a head. You want to emphasize the visual cues that tell you, for example, that the reason the color changes suddenly at a certain point is that it\\'s the edge of an object. By subtly emphasizing such things you can make paintings that are more realistic than photographs not just in some metaphorical sense, but in the strict information-theoretic sense. [4]\\n\\nI liked painting still lives because I was curious about what I was seeing. In everyday life, we aren\\'t consciously aware of much we\\'re seeing. Most visual perception is handled by low-level processes that merely tell your brain \"that\\'s a water droplet\" without telling you details like where the lightest and darkest points are, or \"that\\'s a bush\" without telling you the shape and position of every leaf. This is a feature of brains, not a bug. In everyday life it would be distracting to notice every leaf on every bush. But when you have to paint something, you have to look more closely, and when you do there\\'s a lot to see. You can still be noticing new things after days of trying to paint something people usually take for granted, just as you can after days of trying to write an essay about something people usually take for granted.\\n\\nThis is not the only way to paint. I\\'m not 100% sure it\\'s even a good way to paint. But it seemed a good enough bet to be worth trying.\\n\\nOur teacher, professor Ulivi, was a nice guy. He could see I worked hard, and gave me a good grade, which he wrote down in a sort of passport each student had. But the Accademia wasn\\'t teaching me anything except Italian, and my money was running out, so at the end of the first year I went back to the US.\\n\\nI wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get a job for a year and then return to RISD the next fall. I got one at a company called Interleaf, which made software for creating documents. You mean like Microsoft Word? Exactly. That was how I learned that low end software tends to eat high end software. But Interleaf still had a few years to live yet. [5]\\n\\nInterleaf had done something pretty bold. Inspired by Emacs, they\\'d added a scripting language, and even made the scripting language a dialect of Lisp. Now they wanted a Lisp hacker to write things in it. This was the closest thing I\\'ve had to a normal job, and I hereby apologize to my boss and coworkers, because I was a bad employee. Their Lisp was the thinnest icing on a giant C cake, and since I didn\\'t know C and didn\\'t want to learn it, I never understood most of the software. Plus I was terribly irresponsible. This was back when a programming job meant showing up every day during certain working hours.', start_char_idx=14179, end_char_idx=18443, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=1.0986518859863281)]" + "[NodeWithScore(node=TextNode(id_='b4d2af4e-1de0-4cfe-8b18-629722bc12d7', embedding=None, metadata={'director': 'Christopher Nolan'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='Inception', mimetype='text/plain', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.379289150238037),\n", + " NodeWithScore(node=TextNode(id_='07d77810-7697-4f16-a945-43b725795641', embedding=None, metadata={'file_path': 'c:\\\\Dev\\\\llama_index\\\\docs\\\\docs\\\\examples\\\\vector_stores\\\\..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2024-08-23', 'last_modified_date': '2024-08-23'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={: RelatedNodeInfo(node_id='e02f3256-42cf-4e96-8a2a-5ba4b5bae898', node_type=, metadata={'file_path': 'c:\\\\Dev\\\\llama_index\\\\docs\\\\docs\\\\examples\\\\vector_stores\\\\..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2024-08-23', 'last_modified_date': '2024-08-23'}, hash='aec9c37ce12c52b02feb44993a53c84733fc31d3637224bda1c831b42009fc17'), : RelatedNodeInfo(node_id='022dfb3b-baa4-477b-bd0e-2ab9d8ebbf93', node_type=, metadata={'file_path': 'c:\\\\Dev\\\\llama_index\\\\docs\\\\docs\\\\examples\\\\vector_stores\\\\..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2024-08-23', 'last_modified_date': '2024-08-23'}, hash='3cac601cf9b6b268149bedd26c5f6767d9492f245d57c8a11e591cd631ce0b00'), : RelatedNodeInfo(node_id='b8570137-ba64-4838-84b4-baa2fd7322fa', node_type=, metadata={}, hash='7b37bcc69aec36a618efa128df1f1e8f6bd49b1ab4f57aea517945db504d0d83')}, text='[2]\\r\\n\\r\\nI\\'m only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn\\'t require the faculty to teach anything, and in return the faculty wouldn\\'t require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they\\'d seen in American art magazines.\\r\\n\\r\\nOur model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She\\'d copy an obscure old painting out of a book, and then he\\'d take the copy and maltreat it to make it look old. [3]\\r\\n\\r\\nWhile I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still lives is different from painting people, because the subject, as its name suggests, can\\'t move. People can\\'t sit for more than about 15 minutes at a time, and when they do they don\\'t sit very still. So the traditional m.o. for painting people is to know how to paint a generic person, which you then modify to match the specific person you\\'re painting. Whereas a still life you can, if you want, copy pixel by pixel from what you\\'re seeing. You don\\'t want to stop there, of course, or you get merely photographic accuracy, and what makes a still life interesting is that it\\'s been through a head. You want to emphasize the visual cues that tell you, for example, that the reason the color changes suddenly at a certain point is that it\\'s the edge of an object. By subtly emphasizing such things you can make paintings that are more realistic than photographs not just in some metaphorical sense, but in the strict information-theoretic sense. [4]\\r\\n\\r\\nI liked painting still lives because I was curious about what I was seeing. In everyday life, we aren\\'t consciously aware of much we\\'re seeing. Most visual perception is handled by low-level processes that merely tell your brain \"that\\'s a water droplet\" without telling you details like where the lightest and darkest points are, or \"that\\'s a bush\" without telling you the shape and position of every leaf. This is a feature of brains, not a bug. In everyday life it would be distracting to notice every leaf on every bush. But when you have to paint something, you have to look more closely, and when you do there\\'s a lot to see. You can still be noticing new things after days of trying to paint something people usually take for granted, just as you can after days of trying to write an essay about something people usually take for granted.\\r\\n\\r\\nThis is not the only way to paint. I\\'m not 100% sure it\\'s even a good way to paint. But it seemed a good enough bet to be worth trying.\\r\\n\\r\\nOur teacher, professor Ulivi, was a nice guy. He could see I worked hard, and gave me a good grade, which he wrote down in a sort of passport each student had. But the Accademia wasn\\'t teaching me anything except Italian, and my money was running out, so at the end of the first year I went back to the US.\\r\\n\\r\\nI wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get a job for a year and then return to RISD the next fall. I got one at a company called Interleaf, which made software for creating documents. You mean like Microsoft Word? Exactly. That was how I learned that low end software tends to eat high end software. But Interleaf still had a few years to live yet. [5]\\r\\n\\r\\nInterleaf had done something pretty bold. Inspired by Emacs, they\\'d added a scripting language, and even made the scripting language a dialect of Lisp.', mimetype='text/plain', start_char_idx=13709, end_char_idx=18066, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.9201899170875549)]" ] }, "execution_count": null, diff --git a/docs/docs/examples/vector_stores/TiDBVector.ipynb b/docs/docs/examples/vector_stores/TiDBVector.ipynb index c8b2e0b0bb168..052e12e8bc3eb 100644 --- a/docs/docs/examples/vector_stores/TiDBVector.ipynb +++ b/docs/docs/examples/vector_stores/TiDBVector.ipynb @@ -45,7 +45,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Configure both the OpenAI and TiDB host settings that you will need" + "Configuring your OpenAI Key" ] }, { @@ -54,13 +54,36 @@ "metadata": {}, "outputs": [], "source": [ - "# Here we useimport getpass\n", "import getpass\n", "import os\n", "\n", - "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n", - "tidb_connection_url = getpass.getpass(\n", - " \"TiDB connection URL (format - mysql+pymysql://root@127.0.0.1:4000/test): \"\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Input your OpenAI API key:\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Configure TiDB connection setting that you will need. To connect to your TiDB Cloud Cluster, follow these steps:\n", + "\n", + "- Go to your TiDB Cloud cluster Console and navigate to the `Connect` page.\n", + "- Select the option to connect using `SQLAlchemy` with `PyMySQL`, and copy the provided connection URL (without password).\n", + "- Paste the connection URL into your code, replacing the `tidb_connection_string_template` variable.\n", + "- Type your password." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# replace with your tidb connect string from tidb cloud console\n", + "tidb_connection_string_template = \"mysql+pymysql://:@:4000/?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true\"\n", + "# type your tidb password\n", + "tidb_password = getpass.getpass(\"Input your TiDB password:\")\n", + "tidb_connection_url = tidb_connection_string_template.replace(\n", + " \"\", tidb_password\n", ")" ] }, @@ -90,7 +113,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Document ID: d970e919-4469-414b-967e-24dd9b2eb014\n" + "Document ID: 86e12675-2e9a-4097-847c-8b981dd41806\n" ] } ], @@ -137,18 +160,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/ianz/Work/miniconda3/envs/llama_index/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "Parsing nodes: 100%|██████████| 1/1 [00:00<00:00, 8.76it/s]\n", - "Generating embeddings: 100%|██████████| 21/21 [00:02<00:00, 8.22it/s]\n" - ] - } - ], + "outputs": [], "source": [ "storage_context = StorageContext.from_defaults(vector_store=tidbvec)\n", "index = VectorStoreIndex.from_documents(\n", @@ -156,6 +168,20 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: If you encounter errors during this process due to the MySQL protocol’s packet size limitation, such as when trying to insert a large number of vectors (e.g., 2000 rows) , you can mitigate this issue by splitting the insertion into smaller batches. For example, you can set the `insert_batch_size` parameter to a smaller value (e.g., 1000) to avoid exceeding the packet size limit, ensuring smooth insertion of your data into the TiDB vector store:\n", + "\n", + "```python\n", + "storage_context = StorageContext.from_defaults(vector_store=tidbvec)\n", + "index = VectorStoreIndex.from_documents(\n", + " documents, storage_context=storage_context, insert_batch_size=1000, show_progress=True\n", + ")\n", + "```" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -174,9 +200,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The author worked on writing, programming, building microcomputers, giving talks at conferences,\n", - "publishing essays online, developing spam filters, painting, hosting dinner parties, and purchasing\n", - "a building for office use.\n" + "The author wrote a book.\n" ] } ], @@ -242,11 +266,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The author learned programming on an IBM 1401 using an early version of Fortran in 9th grade, then\n", - "later transitioned to working with microcomputers like the TRS-80 and Apple II. Additionally, the\n", - "author studied philosophy in college but found it unfulfilling, leading to a switch to studying AI.\n", - "Later on, the author attended art school in both the US and Italy, where they observed a lack of\n", - "substantial teaching in the painting department.\n" + "The author learned valuable lessons from his experiences.\n" ] } ], diff --git a/docs/docs/examples/workflow/JSONalyze_query_engine.ipynb b/docs/docs/examples/workflow/JSONalyze_query_engine.ipynb new file mode 100644 index 0000000000000..345f9dcf3ccb2 --- /dev/null +++ b/docs/docs/examples/workflow/JSONalyze_query_engine.ipynb @@ -0,0 +1,776 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# JSONalyze Query Engine\n", + "\n", + "JSONalyze Query Engine is designed to be wired typically after a calling(by agent, etc) of APIs, where we have the returned value as bulk instaces of rows, and the next step is to perform statistical analysis on the data.\n", + "\n", + "With JSONalyze, under the hood, in-memory SQLite table is created with the JSON List loaded, the query engine is able to perform SQL queries on the data, and return the Query Result as answer to the analytical questions.\n", + "\n", + "This notebook walks through implementation of JSON Analyze Query Engine, using Workflows.\n", + "\n", + "Specifically we will implement [JSONalyzeQueryEngine](https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/query_engine/JSONalyze_query_engine.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U llama-index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since workflows are async first, this all runs fine in a notebook. If you were running in your own code, you would want to use `asyncio.run()` to start an async event loop if one isn't already running.\n", + "\n", + "```python\n", + "async def main():\n", + " \n", + "\n", + "if __name__ == \"__main__\":\n", + " import asyncio\n", + " asyncio.run(main())\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Workflow\n", + "\n", + "`jsonalyzer:`\n", + "\n", + "1. It takes a StartEvent as input and returns a JsonAnalyzerEvent.\n", + "2. The function sets up an in-memory SQLite database, loads JSON data, generates a SQL query based on query using a LLM, executes the query, and returns the results along with the SQL query and table schema.\n", + "\n", + "`synthesize:`\n", + "\n", + "The function uses a LLM to synthesize a response based on the SQL query, table schema, and query results.\n", + "\n", + "The steps will use the built-in `StartEvent` and `StopEvent` events." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Event" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.workflow import Event\n", + "from typing import Dict, List, Any\n", + "\n", + "\n", + "class JsonAnalyzerEvent(Event):\n", + " \"\"\"\n", + " Event containing results of JSON analysis.\n", + "\n", + " Attributes:\n", + " sql_query (str): The generated SQL query.\n", + " table_schema (Dict[str, Any]): Schema of the analyzed table.\n", + " results (List[Dict[str, Any]]): Query execution results.\n", + " \"\"\"\n", + "\n", + " sql_query: str\n", + " table_schema: Dict[str, Any]\n", + " results: List[Dict[str, Any]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prompt Templates\n", + "\n", + "Here we define default `DEFAULT_RESPONSE_SYNTHESIS_PROMPT_TMPL`, `DEFAULT_RESPONSE_SYNTHESIS_PROMPT` and `DEFAULT_TABLE_NAME`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.prompts.prompt_type import PromptType\n", + "from llama_index.core.prompts import PromptTemplate\n", + "\n", + "DEFAULT_RESPONSE_SYNTHESIS_PROMPT_TMPL = (\n", + " \"Given a query, synthesize a response based on SQL query results\"\n", + " \" to satisfy the query. Only include details that are relevant to\"\n", + " \" the query. If you don't know the answer, then say that.\\n\"\n", + " \"SQL Query: {sql_query}\\n\"\n", + " \"Table Schema: {table_schema}\\n\"\n", + " \"SQL Response: {sql_response}\\n\"\n", + " \"Query: {query_str}\\n\"\n", + " \"Response: \"\n", + ")\n", + "\n", + "DEFAULT_RESPONSE_SYNTHESIS_PROMPT = PromptTemplate(\n", + " DEFAULT_RESPONSE_SYNTHESIS_PROMPT_TMPL,\n", + " prompt_type=PromptType.SQL_RESPONSE_SYNTHESIS,\n", + ")\n", + "\n", + "DEFAULT_TABLE_NAME = \"items\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Workflow Itself\n", + "\n", + "With our events defined, we can construct our workflow and steps. \n", + "\n", + "Note that the workflow automatically validates itself using type annotations, so the type annotations on our steps are very helpful!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.base.response.schema import Response\n", + "from llama_index.core.indices.struct_store.sql_retriever import (\n", + " DefaultSQLParser,\n", + ")\n", + "from llama_index.core.prompts.default_prompts import DEFAULT_JSONALYZE_PROMPT\n", + "from llama_index.core.utils import print_text\n", + "\n", + "from llama_index.core.workflow import (\n", + " Context,\n", + " Workflow,\n", + " StartEvent,\n", + " StopEvent,\n", + " step,\n", + ")\n", + "\n", + "from llama_index.llms.openai import OpenAI\n", + "\n", + "from IPython.display import Markdown, display\n", + "\n", + "\n", + "class JSONAnalyzeQueryEngineWorkflow(Workflow):\n", + " @step\n", + " async def jsonalyzer(\n", + " self, ctx: Context, ev: StartEvent\n", + " ) -> JsonAnalyzerEvent:\n", + " \"\"\"\n", + " Analyze JSON data using a SQL-like query approach.\n", + "\n", + " This asynchronous method sets up an in-memory SQLite database, loads JSON data,\n", + " generates a SQL query based on a natural language question, executes the query,\n", + " and returns the results.\n", + "\n", + " Args:\n", + " ctx (Context): The context object for storing data during execution.\n", + " ev (StartEvent): The event object containing input parameters.\n", + "\n", + " Returns:\n", + " JsonAnalyzerEvent: An event object containing the SQL query, table schema, and query results.\n", + "\n", + " The method performs the following steps:\n", + " 1. Imports the required 'sqlite-utils' package.\n", + " 2. Extracts necessary data from the input event.\n", + " 3. Sets up an in-memory SQLite database and loads the JSON data.\n", + " 4. Generates a SQL query using a LLM based on the input question.\n", + " 5. Executes the SQL query and retrieves the results.\n", + " 6. Returns the results along with the SQL query and table schema.\n", + "\n", + " Note:\n", + " This method requires the 'sqlite-utils' package to be installed.\n", + " \"\"\"\n", + " try:\n", + " import sqlite_utils\n", + " except ImportError as exc:\n", + " IMPORT_ERROR_MSG = (\n", + " \"sqlite-utils is needed to use this Query Engine:\\n\"\n", + " \"pip install sqlite-utils\"\n", + " )\n", + "\n", + " raise ImportError(IMPORT_ERROR_MSG) from exc\n", + "\n", + " await ctx.set(\"query\", ev.get(\"query\"))\n", + " await ctx.set(\"llm\", ev.get(\"llm\"))\n", + "\n", + " query = ev.get(\"query\")\n", + " table_name = ev.get(\"table_name\")\n", + " list_of_dict = ev.get(\"list_of_dict\")\n", + " prompt = DEFAULT_JSONALYZE_PROMPT\n", + "\n", + " # Instantiate in-memory SQLite database\n", + " db = sqlite_utils.Database(memory=True)\n", + " try:\n", + " # Load list of dictionaries into SQLite database\n", + " db[ev.table_name].insert_all(list_of_dict)\n", + " except sqlite_utils.utils.sqlite3.IntegrityError as exc:\n", + " print_text(\n", + " f\"Error inserting into table {table_name}, expected format:\"\n", + " )\n", + " print_text(\"[{col1: val1, col2: val2, ...}, ...]\")\n", + " raise ValueError(\"Invalid list_of_dict\") from exc\n", + "\n", + " # Get the table schema\n", + " table_schema = db[table_name].columns_dict\n", + "\n", + " # Get the SQL query with text-to-SQL prompt\n", + " response_str = await ev.llm.apredict(\n", + " prompt=prompt,\n", + " table_name=table_name,\n", + " table_schema=table_schema,\n", + " question=query,\n", + " )\n", + "\n", + " sql_parser = DefaultSQLParser()\n", + "\n", + " sql_query = sql_parser.parse_response_to_sql(response_str, ev.query)\n", + "\n", + " try:\n", + " # Execute the SQL query\n", + " results = list(db.query(sql_query))\n", + " except sqlite_utils.utils.sqlite3.OperationalError as exc:\n", + " print_text(f\"Error executing query: {sql_query}\")\n", + " raise ValueError(\"Invalid query\") from exc\n", + "\n", + " return JsonAnalyzerEvent(\n", + " sql_query=sql_query, table_schema=table_schema, results=results\n", + " )\n", + "\n", + " @step\n", + " async def synthesize(\n", + " self, ctx: Context, ev: JsonAnalyzerEvent\n", + " ) -> StopEvent:\n", + " \"\"\"Synthesize the response.\"\"\"\n", + " llm = await ctx.get(\"llm\", default=None)\n", + " query = await ctx.get(\"query\", default=None)\n", + "\n", + " response_str = llm.predict(\n", + " DEFAULT_RESPONSE_SYNTHESIS_PROMPT,\n", + " sql_query=ev.sql_query,\n", + " table_schema=ev.table_schema,\n", + " sql_response=ev.results,\n", + " query_str=query,\n", + " )\n", + "\n", + " response_metadata = {\n", + " \"sql_query\": ev.sql_query,\n", + " \"table_schema\": str(ev.table_schema),\n", + " }\n", + "\n", + " response = Response(response=response_str, metadata=response_metadata)\n", + "\n", + " return StopEvent(result=response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Json List" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "json_list = [\n", + " {\n", + " \"name\": \"John Doe\",\n", + " \"age\": 25,\n", + " \"major\": \"Computer Science\",\n", + " \"email\": \"john.doe@example.com\",\n", + " \"address\": \"123 Main St\",\n", + " \"city\": \"New York\",\n", + " \"state\": \"NY\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 123-456-7890\",\n", + " \"occupation\": \"Software Engineer\",\n", + " },\n", + " {\n", + " \"name\": \"Jane Smith\",\n", + " \"age\": 30,\n", + " \"major\": \"Business Administration\",\n", + " \"email\": \"jane.smith@example.com\",\n", + " \"address\": \"456 Elm St\",\n", + " \"city\": \"San Francisco\",\n", + " \"state\": \"CA\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 234-567-8901\",\n", + " \"occupation\": \"Marketing Manager\",\n", + " },\n", + " {\n", + " \"name\": \"Michael Johnson\",\n", + " \"age\": 35,\n", + " \"major\": \"Finance\",\n", + " \"email\": \"michael.johnson@example.com\",\n", + " \"address\": \"789 Oak Ave\",\n", + " \"city\": \"Chicago\",\n", + " \"state\": \"IL\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 345-678-9012\",\n", + " \"occupation\": \"Financial Analyst\",\n", + " },\n", + " {\n", + " \"name\": \"Emily Davis\",\n", + " \"age\": 28,\n", + " \"major\": \"Psychology\",\n", + " \"email\": \"emily.davis@example.com\",\n", + " \"address\": \"234 Pine St\",\n", + " \"city\": \"Los Angeles\",\n", + " \"state\": \"CA\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 456-789-0123\",\n", + " \"occupation\": \"Psychologist\",\n", + " },\n", + " {\n", + " \"name\": \"Alex Johnson\",\n", + " \"age\": 27,\n", + " \"major\": \"Engineering\",\n", + " \"email\": \"alex.johnson@example.com\",\n", + " \"address\": \"567 Cedar Ln\",\n", + " \"city\": \"Seattle\",\n", + " \"state\": \"WA\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 567-890-1234\",\n", + " \"occupation\": \"Civil Engineer\",\n", + " },\n", + " {\n", + " \"name\": \"Jessica Williams\",\n", + " \"age\": 32,\n", + " \"major\": \"Biology\",\n", + " \"email\": \"jessica.williams@example.com\",\n", + " \"address\": \"890 Walnut Ave\",\n", + " \"city\": \"Boston\",\n", + " \"state\": \"MA\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 678-901-2345\",\n", + " \"occupation\": \"Biologist\",\n", + " },\n", + " {\n", + " \"name\": \"Matthew Brown\",\n", + " \"age\": 26,\n", + " \"major\": \"English Literature\",\n", + " \"email\": \"matthew.brown@example.com\",\n", + " \"address\": \"123 Peach St\",\n", + " \"city\": \"Atlanta\",\n", + " \"state\": \"GA\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 789-012-3456\",\n", + " \"occupation\": \"Writer\",\n", + " },\n", + " {\n", + " \"name\": \"Olivia Wilson\",\n", + " \"age\": 29,\n", + " \"major\": \"Art\",\n", + " \"email\": \"olivia.wilson@example.com\",\n", + " \"address\": \"456 Plum Ave\",\n", + " \"city\": \"Miami\",\n", + " \"state\": \"FL\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 890-123-4567\",\n", + " \"occupation\": \"Artist\",\n", + " },\n", + " {\n", + " \"name\": \"Daniel Thompson\",\n", + " \"age\": 31,\n", + " \"major\": \"Physics\",\n", + " \"email\": \"daniel.thompson@example.com\",\n", + " \"address\": \"789 Apple St\",\n", + " \"city\": \"Denver\",\n", + " \"state\": \"CO\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 901-234-5678\",\n", + " \"occupation\": \"Physicist\",\n", + " },\n", + " {\n", + " \"name\": \"Sophia Clark\",\n", + " \"age\": 27,\n", + " \"major\": \"Sociology\",\n", + " \"email\": \"sophia.clark@example.com\",\n", + " \"address\": \"234 Orange Ln\",\n", + " \"city\": \"Austin\",\n", + " \"state\": \"TX\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 012-345-6789\",\n", + " \"occupation\": \"Social Worker\",\n", + " },\n", + " {\n", + " \"name\": \"Christopher Lee\",\n", + " \"age\": 33,\n", + " \"major\": \"Chemistry\",\n", + " \"email\": \"christopher.lee@example.com\",\n", + " \"address\": \"567 Mango St\",\n", + " \"city\": \"San Diego\",\n", + " \"state\": \"CA\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 123-456-7890\",\n", + " \"occupation\": \"Chemist\",\n", + " },\n", + " {\n", + " \"name\": \"Ava Green\",\n", + " \"age\": 28,\n", + " \"major\": \"History\",\n", + " \"email\": \"ava.green@example.com\",\n", + " \"address\": \"890 Cherry Ave\",\n", + " \"city\": \"Philadelphia\",\n", + " \"state\": \"PA\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 234-567-8901\",\n", + " \"occupation\": \"Historian\",\n", + " },\n", + " {\n", + " \"name\": \"Ethan Anderson\",\n", + " \"age\": 30,\n", + " \"major\": \"Business\",\n", + " \"email\": \"ethan.anderson@example.com\",\n", + " \"address\": \"123 Lemon Ln\",\n", + " \"city\": \"Houston\",\n", + " \"state\": \"TX\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 345-678-9012\",\n", + " \"occupation\": \"Entrepreneur\",\n", + " },\n", + " {\n", + " \"name\": \"Isabella Carter\",\n", + " \"age\": 28,\n", + " \"major\": \"Mathematics\",\n", + " \"email\": \"isabella.carter@example.com\",\n", + " \"address\": \"456 Grape St\",\n", + " \"city\": \"Phoenix\",\n", + " \"state\": \"AZ\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 456-789-0123\",\n", + " \"occupation\": \"Mathematician\",\n", + " },\n", + " {\n", + " \"name\": \"Andrew Walker\",\n", + " \"age\": 32,\n", + " \"major\": \"Economics\",\n", + " \"email\": \"andrew.walker@example.com\",\n", + " \"address\": \"789 Berry Ave\",\n", + " \"city\": \"Portland\",\n", + " \"state\": \"OR\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 567-890-1234\",\n", + " \"occupation\": \"Economist\",\n", + " },\n", + " {\n", + " \"name\": \"Mia Evans\",\n", + " \"age\": 29,\n", + " \"major\": \"Political Science\",\n", + " \"email\": \"mia.evans@example.com\",\n", + " \"address\": \"234 Lime St\",\n", + " \"city\": \"Washington\",\n", + " \"state\": \"DC\",\n", + " \"country\": \"USA\",\n", + " \"phone\": \"+1 678-901-2345\",\n", + " \"occupation\": \"Political Analyst\",\n", + " },\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define LLM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm = OpenAI(model=\"gpt-3.5-turbo\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the Workflow!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "w = JSONAnalyzeQueryEngineWorkflow()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "> Question: What is the maximum age among the individuals?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Answer: The maximum age among the individuals is 35." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Run a query\n", + "\n", + "query = \"What is the maximum age among the individuals?\"\n", + "\n", + "result = await w.run(\n", + " query=query, list_of_dict=json_list, llm=llm, table_name=DEFAULT_TABLE_NAME\n", + ")\n", + "\n", + "display(\n", + " Markdown(\"> Question: {}\".format(query)),\n", + " Markdown(\"Answer: {}\".format(result)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "> Question: How many individuals have an occupation related to science or engineering?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Answer: There are 0 individuals with an occupation related to science or engineering." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query = \"How many individuals have an occupation related to science or engineering?\"\n", + "\n", + "result = await w.run(\n", + " query=query, list_of_dict=json_list, llm=llm, table_name=DEFAULT_TABLE_NAME\n", + ")\n", + "\n", + "display(\n", + " Markdown(\"> Question: {}\".format(query)),\n", + " Markdown(\"Answer: {}\".format(result)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "> Question: How many individuals have a phone number starting with '+1 234'?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Answer: There are 2 individuals with a phone number starting with '+1 234'." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query = \"How many individuals have a phone number starting with '+1 234'?\"\n", + "\n", + "result = await w.run(\n", + " query=query, list_of_dict=json_list, llm=llm, table_name=DEFAULT_TABLE_NAME\n", + ")\n", + "\n", + "display(\n", + " Markdown(\"> Question: {}\".format(query)),\n", + " Markdown(\"Answer: {}\".format(result)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "> Question: What is the percentage of individuals residing in California (CA)?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Answer: The percentage of individuals residing in California (CA) is 18.75%." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query = \"What is the percentage of individuals residing in California (CA)?\"\n", + "\n", + "result = await w.run(\n", + " query=query, list_of_dict=json_list, llm=llm, table_name=DEFAULT_TABLE_NAME\n", + ")\n", + "\n", + "display(\n", + " Markdown(\"> Question: {}\".format(query)),\n", + " Markdown(\"Answer: {}\".format(result)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "> Question: How many individuals have a major in Psychology?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Answer: There is 1 individual who has a major in Psychology." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query = \"How many individuals have a major in Psychology?\"\n", + "\n", + "result = await w.run(\n", + " query=query, list_of_dict=json_list, llm=llm, table_name=DEFAULT_TABLE_NAME\n", + ")\n", + "\n", + "display(\n", + " Markdown(\"> Question: {}\".format(query)),\n", + " Markdown(\"Answer: {}\".format(result)),\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llamaindex", + "language": "python", + "name": "llamaindex" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/examples/workflow/citation_query_engine.ipynb b/docs/docs/examples/workflow/citation_query_engine.ipynb new file mode 100644 index 0000000000000..fd6c98a463d80 --- /dev/null +++ b/docs/docs/examples/workflow/citation_query_engine.ipynb @@ -0,0 +1,524 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build RAG with in-line citations\n", + "\n", + "This notebook walks through implementation of RAG with in-line citations of source nodes, using Workflows.\n", + "\n", + "Specifically we will implement [CitationQueryEngine](https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/query_engine/citation_query_engine.ipynb) which gives in-line citations in the response generated based on the nodes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U llama-index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-08-15 00:23:50-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8000::154, 2606:50c0:8001::154, 2606:50c0:8002::154, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8000::154|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 75042 (73K) [text/plain]\n", + "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n", + "\n", + "data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.01s \n", + "\n", + "2024-08-15 00:23:50 (5.27 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir -p 'data/paul_graham/'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since workflows are async first, this all runs fine in a notebook. If you were running in your own code, you would want to use `asyncio.run()` to start an async event loop if one isn't already running.\n", + "\n", + "```python\n", + "async def main():\n", + " \n", + "\n", + "if __name__ == \"__main__\":\n", + " import asyncio\n", + " asyncio.run(main())\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Designing the Workflow\n", + "\n", + "CitationQueryEngine consists of some clearly defined steps\n", + "1. Indexing data, creating an index\n", + "2. Using that index + a query to retrieve relevant nodes\n", + "3. Add citations to the retrieved nodes.\n", + "4. Synthesizing a final response\n", + "\n", + "With this in mind, we can create events and workflow steps to follow this process!\n", + "\n", + "### The Workflow Events\n", + "\n", + "To handle these steps, we need to define a few events:\n", + "1. An event to pass retrieved nodes to the create citations\n", + "2. An event to pass citation nodes to the synthesizer\n", + "\n", + "The other steps will use the built-in `StartEvent` and `StopEvent` events." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.workflow import Event\n", + "from llama_index.core.schema import NodeWithScore\n", + "\n", + "\n", + "class RetrieverEvent(Event):\n", + " \"\"\"Result of running retrieval\"\"\"\n", + "\n", + " nodes: list[NodeWithScore]\n", + "\n", + "\n", + "class CreateCitationsEvent(Event):\n", + " \"\"\"Add citations to the nodes.\"\"\"\n", + "\n", + " nodes: list[NodeWithScore]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Citation Prompt Templates\n", + "\n", + "Here we define default `CITATION_QA_TEMPLATE`, `CITATION_REFINE_TEMPLATE`, `DEFAULT_CITATION_CHUNK_SIZE` and `DEFAULT_CITATION_CHUNK_OVERLAP`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.prompts import PromptTemplate\n", + "\n", + "CITATION_QA_TEMPLATE = PromptTemplate(\n", + " \"Please provide an answer based solely on the provided sources. \"\n", + " \"When referencing information from a source, \"\n", + " \"cite the appropriate source(s) using their corresponding numbers. \"\n", + " \"Every answer should include at least one source citation. \"\n", + " \"Only cite a source when you are explicitly referencing it. \"\n", + " \"If none of the sources are helpful, you should indicate that. \"\n", + " \"For example:\\n\"\n", + " \"Source 1:\\n\"\n", + " \"The sky is red in the evening and blue in the morning.\\n\"\n", + " \"Source 2:\\n\"\n", + " \"Water is wet when the sky is red.\\n\"\n", + " \"Query: When is water wet?\\n\"\n", + " \"Answer: Water will be wet when the sky is red [2], \"\n", + " \"which occurs in the evening [1].\\n\"\n", + " \"Now it's your turn. Below are several numbered sources of information:\"\n", + " \"\\n------\\n\"\n", + " \"{context_str}\"\n", + " \"\\n------\\n\"\n", + " \"Query: {query_str}\\n\"\n", + " \"Answer: \"\n", + ")\n", + "\n", + "CITATION_REFINE_TEMPLATE = PromptTemplate(\n", + " \"Please provide an answer based solely on the provided sources. \"\n", + " \"When referencing information from a source, \"\n", + " \"cite the appropriate source(s) using their corresponding numbers. \"\n", + " \"Every answer should include at least one source citation. \"\n", + " \"Only cite a source when you are explicitly referencing it. \"\n", + " \"If none of the sources are helpful, you should indicate that. \"\n", + " \"For example:\\n\"\n", + " \"Source 1:\\n\"\n", + " \"The sky is red in the evening and blue in the morning.\\n\"\n", + " \"Source 2:\\n\"\n", + " \"Water is wet when the sky is red.\\n\"\n", + " \"Query: When is water wet?\\n\"\n", + " \"Answer: Water will be wet when the sky is red [2], \"\n", + " \"which occurs in the evening [1].\\n\"\n", + " \"Now it's your turn. \"\n", + " \"We have provided an existing answer: {existing_answer}\"\n", + " \"Below are several numbered sources of information. \"\n", + " \"Use them to refine the existing answer. \"\n", + " \"If the provided sources are not helpful, you will repeat the existing answer.\"\n", + " \"\\nBegin refining!\"\n", + " \"\\n------\\n\"\n", + " \"{context_msg}\"\n", + " \"\\n------\\n\"\n", + " \"Query: {query_str}\\n\"\n", + " \"Answer: \"\n", + ")\n", + "\n", + "DEFAULT_CITATION_CHUNK_SIZE = 512\n", + "DEFAULT_CITATION_CHUNK_OVERLAP = 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Workflow Itself\n", + "\n", + "With our events defined, we can construct our workflow and steps. \n", + "\n", + "Note that the workflow automatically validates itself using type annotations, so the type annotations on our steps are very helpful!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader, VectorStoreIndex\n", + "from llama_index.core.workflow import (\n", + " Context,\n", + " Workflow,\n", + " StartEvent,\n", + " StopEvent,\n", + " step,\n", + ")\n", + "\n", + "from llama_index.llms.openai import OpenAI\n", + "from llama_index.embeddings.openai import OpenAIEmbedding\n", + "\n", + "from llama_index.core.schema import (\n", + " MetadataMode,\n", + " NodeWithScore,\n", + " TextNode,\n", + ")\n", + "\n", + "from llama_index.core.response_synthesizers import (\n", + " ResponseMode,\n", + " get_response_synthesizer,\n", + ")\n", + "\n", + "from typing import Union, List\n", + "from llama_index.core.node_parser import SentenceSplitter\n", + "\n", + "\n", + "class CitationQueryEngineWorkflow(Workflow):\n", + " @step\n", + " async def retrieve(\n", + " self, ctx: Context, ev: StartEvent\n", + " ) -> Union[RetrieverEvent, None]:\n", + " \"Entry point for RAG, triggered by a StartEvent with `query`.\"\n", + " query = ev.get(\"query\")\n", + " if not query:\n", + " return None\n", + "\n", + " print(f\"Query the database with: {query}\")\n", + "\n", + " # store the query in the global context\n", + " await ctx.set(\"query\", query)\n", + "\n", + " if ev.index is None:\n", + " print(\"Index is empty, load some documents before querying!\")\n", + " return None\n", + "\n", + " retriever = ev.index.as_retriever(similarity_top_k=2)\n", + " nodes = retriever.retrieve(query)\n", + " print(f\"Retrieved {len(nodes)} nodes.\")\n", + " return RetrieverEvent(nodes=nodes)\n", + "\n", + " @step\n", + " async def create_citation_nodes(\n", + " self, ev: RetrieverEvent\n", + " ) -> CreateCitationsEvent:\n", + " \"\"\"\n", + " Modify retrieved nodes to create granular sources for citations.\n", + "\n", + " Takes a list of NodeWithScore objects and splits their content\n", + " into smaller chunks, creating new NodeWithScore objects for each chunk.\n", + " Each new node is labeled as a numbered source, allowing for more precise\n", + " citation in query results.\n", + "\n", + " Args:\n", + " nodes (List[NodeWithScore]): A list of NodeWithScore objects to be processed.\n", + "\n", + " Returns:\n", + " List[NodeWithScore]: A new list of NodeWithScore objects, where each object\n", + " represents a smaller chunk of the original nodes, labeled as a source.\n", + " \"\"\"\n", + " nodes = ev.nodes\n", + "\n", + " new_nodes: List[NodeWithScore] = []\n", + "\n", + " text_splitter = SentenceSplitter(\n", + " chunk_size=DEFAULT_CITATION_CHUNK_SIZE,\n", + " chunk_overlap=DEFAULT_CITATION_CHUNK_OVERLAP,\n", + " )\n", + "\n", + " for node in nodes:\n", + " text_chunks = text_splitter.split_text(\n", + " node.node.get_content(metadata_mode=MetadataMode.NONE)\n", + " )\n", + "\n", + " for text_chunk in text_chunks:\n", + " text = f\"Source {len(new_nodes)+1}:\\n{text_chunk}\\n\"\n", + "\n", + " new_node = NodeWithScore(\n", + " node=TextNode.parse_obj(node.node), score=node.score\n", + " )\n", + " new_node.node.text = text\n", + " new_nodes.append(new_node)\n", + " return CreateCitationsEvent(nodes=new_nodes)\n", + "\n", + " @step\n", + " async def synthesize(\n", + " self, ctx: Context, ev: CreateCitationsEvent\n", + " ) -> StopEvent:\n", + " \"\"\"Return a streaming response using the retrieved nodes.\"\"\"\n", + " llm = OpenAI(model=\"gpt-4o-mini\")\n", + " query = await ctx.get(\"query\", default=None)\n", + "\n", + " synthesizer = get_response_synthesizer(\n", + " llm=llm,\n", + " text_qa_template=CITATION_QA_TEMPLATE,\n", + " refine_template=CITATION_REFINE_TEMPLATE,\n", + " response_mode=ResponseMode.COMPACT,\n", + " use_async=True,\n", + " )\n", + "\n", + " response = await synthesizer.asynthesize(query, nodes=ev.nodes)\n", + " return StopEvent(result=response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And thats it! Let's explore the workflow we wrote a bit.\n", + "\n", + "- We have an entry point (the step that accept `StartEvent`)\n", + "- The workflow context is used to store the user query\n", + "- The nodes are retrieved, citations are created, and finally a response is returned" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "documents = SimpleDirectoryReader(\"data/paul_graham\").load_data()\n", + "index = VectorStoreIndex.from_documents(\n", + " documents=documents,\n", + " embed_model=OpenAIEmbedding(model_name=\"text-embedding-3-small\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the Workflow!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "w = CitationQueryEngineWorkflow()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query the database with: What information do you have\n", + "Retrieved 2 nodes.\n" + ] + } + ], + "source": [ + "# Run a query\n", + "result = await w.run(query=\"What information do you have\", index=index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "The provided sources contain various insights into Paul Graham's experiences and thoughts on programming, writing, and his educational journey. For instance, he reflects on his early experiences with programming on the IBM 1401, where he struggled to create meaningful programs due to the limitations of the technology at the time [2]. He also describes his transition to using microcomputers, which allowed for more interactive programming experiences [3]. Additionally, Graham shares his initial interest in philosophy during college, which he later found less engaging compared to the fields of artificial intelligence and programming [3]. Overall, the sources highlight his evolution as a writer and programmer, as well as his changing academic interests." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Markdown, display\n", + "\n", + "display(Markdown(f\"{result}\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check the citations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source 1:\n", + "But after Heroku got bought we had enough money to go back to being self-funded.\n", + "\n", + "[15] I've never liked the term \"deal flow,\" because it implies that the number of new startups at any given time is fixed. This is not only false, but it's the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed.\n", + "\n", + "[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than she could carry now.\n", + "\n", + "[17] Another problem with HN was a bizarre edge case that occurs when you both write essays and run a forum. When you run a forum, you're assumed to see if not every conversation, at least every conversation involving you. And when you write essays, people post highly imaginative misinterpretations of them on forums. Individually these two phenomena are tedious but bearable, but the combination is disastrous. You actually have to respond to the misinterpretations, because the assumption that you're present in the conversation means that not responding to any sufficiently upvoted misinterpretation reads as a tacit admission that it's correct. But that in turn encourages more; anyone who wants to pick a fight with you senses that now is their chance.\n", + "\n", + "[18] The worst thing about leaving YC was not working with Jessica anymore. We'd been working on YC almost the whole time we'd known each other, and we'd neither tried nor wanted to separate it from our personal lives, so leaving was like pulling up a deeply rooted tree.\n", + "\n", + "[19] One way to get more precise about the concept of invented vs discovered is to talk about space aliens. Any sufficiently advanced alien civilization would certainly know about the Pythagorean theorem, for example. I believe, though with less certainty, that they would also know about the Lisp in McCarthy's 1960 paper.\n", + "\n", + "But if so there's no reason to suppose that this is the limit of the language that might be known to them. Presumably aliens need numbers and errors and I/O too. So it seems likely there exists at least one path out of McCarthy's Lisp along which discoveredness is preserved.\n", + "\n", + "\n", + "\n", + "Thanks to Trevor Blackwell, John Collison, Patrick Collison, Daniel Gackle, Ralph Hazell, Jessica Livingston, Robert Morris, and Harj Taggar for reading drafts of this.\n", + "\n" + ] + } + ], + "source": [ + "print(result.source_nodes[0].node.get_text())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source 2:\n", + "What I Worked On\n", + "\n", + "February 2021\n", + "\n", + "Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\n", + "\n", + "The first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\n", + "\n", + "The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.\n", + "\n", + "I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear.\n", + "\n", + "With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping.\n", + "\n" + ] + } + ], + "source": [ + "print(result.source_nodes[1].node.get_text())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llamaindex", + "language": "python", + "name": "llamaindex" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/examples/workflow/corrective_rag_pack.ipynb b/docs/docs/examples/workflow/corrective_rag_pack.ipynb new file mode 100644 index 0000000000000..73809573d51c5 --- /dev/null +++ b/docs/docs/examples/workflow/corrective_rag_pack.ipynb @@ -0,0 +1,448 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Corrective RAG Workflow\n", + "\n", + "This notebook shows how to implement corrective RAG using Llamaindex workflows based on [this paper](https://arxiv.org/abs/2401.15884)\n", + "\n", + "A brief understanding of the paper:\n", + "\n", + "\n", + "Corrective Retrieval Augmented Generation (CRAG) is a method designed to enhance the robustness of language model generation by evaluating and augmenting the relevance of retrieved documents through a an evaluator and large-scale web searches, ensuring more accurate and reliable information is used in generation.\n", + "\n", + "We use `GPT-4` as a relevancy evaluator and `Tavily AI` for web searches. So, we recommend getting `OPENAI_API_KEY` and `tavily_ai_api_key` before proceeding further." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -U llama-index llama-index-tools-tavily-research" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\"\n", + "tavily_ai_api_key = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p 'data/'\n", + "!wget 'https://arxiv.org/pdf/2307.09288.pdf' -O 'data/llama2.pdf'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since workflows are async first, this all runs fine in a notebook. If you were running in your own code, you would want to use `asyncio.run()` to start an async event loop if one isn't already running.\n", + "\n", + "```python\n", + "async def main():\n", + " \n", + "\n", + "if __name__ == \"__main__\":\n", + " import asyncio\n", + " asyncio.run(main())\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Designing the Workflow\n", + "\n", + "Corrective RAG consists of the following steps:\n", + "1. Ingestion of data — Loads the data into an index and setting up Tavily AI. The ingestion step will be run by itself, taking in a start event and returning a stop event.\n", + "2. Retrieval - Retrives the most relevant nodes based on the query.\n", + "3. Relevance evaluation - Uses an LLM to determine whether the retrieved nodes are relevant to the query given the content of the nodes.\n", + "4. Relevance extraction - Extracts the nodes which the LLM determined to be relevant.\n", + "5. Query transformation and Tavily search - If a node is irrelevant, then uses an LLM to transform the query to tailor towards a web search. Uses Tavily to search the web for a relevant answer based on the query.\n", + "6. Response generation - Builds a summary index given the text from the relevant nodes and the Tavily search and uses this index to get a result given the original query." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following events are needed:\n", + "1. `PrepEvent` - Event signifying that the index and other objects are prepared.\n", + "2. `RetrieveEvent` - Event containing information about the retrieved nodes.\n", + "3. `RelevanceEvalEvent` - Event containing a list of the results of the relevance evaluation.\n", + "4. `TextExtractEvent` - Event containing the concatenated string of relevant text from relevant nodes.\n", + "5. `QueryEvent` - Event containing both the relevant text and search text." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.workflow import Event\n", + "from llama_index.core.schema import NodeWithScore\n", + "\n", + "\n", + "class PrepEvent(Event):\n", + " \"\"\"Prep event (prepares for retrieval).\"\"\"\n", + "\n", + " pass\n", + "\n", + "\n", + "class RetrieveEvent(Event):\n", + " \"\"\"Retrieve event (gets retrieved nodes).\"\"\"\n", + "\n", + " retrieved_nodes: list[NodeWithScore]\n", + "\n", + "\n", + "class RelevanceEvalEvent(Event):\n", + " \"\"\"Relevance evaluation event (gets results of relevance evaluation).\"\"\"\n", + "\n", + " relevant_results: list[str]\n", + "\n", + "\n", + "class TextExtractEvent(Event):\n", + " \"\"\"Text extract event. Extracts relevant text and concatenates.\"\"\"\n", + "\n", + " relevant_text: str\n", + "\n", + "\n", + "class QueryEvent(Event):\n", + " \"\"\"Query event. Queries given relevant text and search text.\"\"\"\n", + "\n", + " relevant_text: str\n", + " search_text: str" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is the code for the corrective RAG workflow:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.workflow import (\n", + " Workflow,\n", + " step,\n", + " Context,\n", + " StartEvent,\n", + " StopEvent,\n", + ")\n", + "from llama_index.core import (\n", + " VectorStoreIndex,\n", + " Document,\n", + " PromptTemplate,\n", + " SummaryIndex,\n", + ")\n", + "from llama_index.core.query_pipeline import QueryPipeline\n", + "from llama_index.llms.openai import OpenAI\n", + "from llama_index.tools.tavily_research.base import TavilyToolSpec\n", + "from llama_index.core.base.base_retriever import BaseRetriever\n", + "\n", + "DEFAULT_RELEVANCY_PROMPT_TEMPLATE = PromptTemplate(\n", + " template=\"\"\"As a grader, your task is to evaluate the relevance of a document retrieved in response to a user's question.\n", + "\n", + " Retrieved Document:\n", + " -------------------\n", + " {context_str}\n", + "\n", + " User Question:\n", + " --------------\n", + " {query_str}\n", + "\n", + " Evaluation Criteria:\n", + " - Consider whether the document contains keywords or topics related to the user's question.\n", + " - The evaluation should not be overly stringent; the primary objective is to identify and filter out clearly irrelevant retrievals.\n", + "\n", + " Decision:\n", + " - Assign a binary score to indicate the document's relevance.\n", + " - Use 'yes' if the document is relevant to the question, or 'no' if it is not.\n", + "\n", + " Please provide your binary score ('yes' or 'no') below to indicate the document's relevance to the user question.\"\"\"\n", + ")\n", + "\n", + "DEFAULT_TRANSFORM_QUERY_TEMPLATE = PromptTemplate(\n", + " template=\"\"\"Your task is to refine a query to ensure it is highly effective for retrieving relevant search results. \\n\n", + " Analyze the given input to grasp the core semantic intent or meaning. \\n\n", + " Original Query:\n", + " \\n ------- \\n\n", + " {query_str}\n", + " \\n ------- \\n\n", + " Your goal is to rephrase or enhance this query to improve its search performance. Ensure the revised query is concise and directly aligned with the intended search objective. \\n\n", + " Respond with the optimized query only:\"\"\"\n", + ")\n", + "\n", + "\n", + "class CorrectiveRAGWorkflow(Workflow):\n", + " @step\n", + " async def ingest(self, ctx: Context, ev: StartEvent) -> StopEvent | None:\n", + " \"\"\"Ingest step (for ingesting docs and initializing index).\"\"\"\n", + " documents: list[Document] | None = ev.get(\"documents\")\n", + "\n", + " if documents is None:\n", + " return None\n", + "\n", + " index = VectorStoreIndex.from_documents(documents)\n", + "\n", + " return StopEvent(result=index)\n", + "\n", + " @step\n", + " async def prepare_for_retrieval(\n", + " self, ctx: Context, ev: StartEvent\n", + " ) -> PrepEvent | None:\n", + " \"\"\"Prepare for retrieval.\"\"\"\n", + "\n", + " query_str: str | None = ev.get(\"query_str\")\n", + " retriever_kwargs: dict | None = ev.get(\"retriever_kwargs\", {})\n", + "\n", + " if query_str is None:\n", + " return None\n", + "\n", + " tavily_ai_apikey: str | None = ev.get(\"tavily_ai_apikey\")\n", + " index = ev.get(\"index\")\n", + "\n", + " llm = OpenAI(model=\"gpt-4\")\n", + " await ctx.set(\"relevancy_pipeline\", QueryPipeline(\n", + " chain=[DEFAULT_RELEVANCY_PROMPT_TEMPLATE, llm]\n", + " ))\n", + " await ctx.set(\"transform_query_pipeline\", QueryPipeline(\n", + " chain=[DEFAULT_TRANSFORM_QUERY_TEMPLATE, llm]\n", + " ))\n", + "\n", + " await ctx.set(\"llm\", llm)\n", + " await ctx.set(\"index\", index)\n", + " await ctx.set(\"tavily_tool\", TavilyToolSpec(api_key=tavily_ai_apikey))\n", + "\n", + " await ctx.set(\"query_str\", query_str)\n", + " await ctx.set(\"retriever_kwargs\", retriever_kwargs)\n", + "\n", + " return PrepEvent()\n", + "\n", + " @step\n", + " async def retrieve(\n", + " self, ctx: Context, ev: PrepEvent\n", + " ) -> RetrieveEvent | None:\n", + " \"\"\"Retrieve the relevant nodes for the query.\"\"\"\n", + " query_str = await ctx.get(\"query_str\")\n", + " retriever_kwargs = await ctx.get(\"retriever_kwargs\")\n", + "\n", + " if query_str is None:\n", + " return None\n", + "\n", + " index = await ctx.get(\"index\", default=None)\n", + " tavily_tool = await ctx.get(\"tavily_tool\", default=None)\n", + " if not (index or tavily_tool)\n", + " raise ValueError(\n", + " \"Index and tavily tool must be constructed. Run with 'documents' and 'tavily_ai_apikey' params first.\"\n", + " )\n", + "\n", + " retriever: BaseRetriever = index.as_retriever(\n", + " **retriever_kwargs\n", + " )\n", + " result = retriever.retrieve(query_str)\n", + " await ctx.set(\"retrieved_nodes\", result)\n", + " await ctx.set(\"query_str\", query_str)\n", + " return RetrieveEvent(retrieved_nodes=result)\n", + "\n", + " @step\n", + " async def eval_relevance(\n", + " self, ctx: Context, ev: RetrieveEvent\n", + " ) -> RelevanceEvalEvent:\n", + " \"\"\"Evaluate relevancy of retrieved documents with the query.\"\"\"\n", + " retrieved_nodes = ev.retrieved_nodes\n", + " query_str = await ctx.get(\"query_str\")\n", + "\n", + " relevancy_results = []\n", + " for node in retrieved_nodes:\n", + " relevancy = await ctx.get(\"relevancy_pipeline\").run(\n", + " context_str=node.text, query_str=query_str\n", + " )\n", + " relevancy_results.append(relevancy.message.content.lower().strip())\n", + "\n", + " await ctx.set(\"relevancy_results\", relevancy_results)\n", + " return RelevanceEvalEvent(relevant_results=relevancy_results)\n", + "\n", + " @step\n", + " async def extract_relevant_texts(\n", + " self, ctx: Context, ev: RelevanceEvalEvent\n", + " ) -> TextExtractEvent:\n", + " \"\"\"Extract relevant texts from retrieved documents.\"\"\"\n", + " retrieved_nodes = await ctx.get(\"retrieved_nodes\")\n", + " relevancy_results = ev.relevant_results\n", + "\n", + " relevant_texts = [\n", + " retrieved_nodes[i].text\n", + " for i, result in enumerate(relevancy_results)\n", + " if result == \"yes\"\n", + " ]\n", + "\n", + " result = \"\\n\".join(relevant_texts)\n", + " return TextExtractEvent(relevant_text=result)\n", + "\n", + " @step\n", + " async def transform_query_pipeline(\n", + " self, ctx: Context, ev: TextExtractEvent\n", + " ) -> QueryEvent:\n", + " \"\"\"Search the transformed query with Tavily API.\"\"\"\n", + " relevant_text = ev.relevant_text\n", + " relevancy_results = await ctx.get(\"relevancy_results\")\n", + " query_str = await ctx.get(\"query_str\")\n", + "\n", + " # If any document is found irrelevant, transform the query string for better search results.\n", + " if \"no\" in relevancy_results:\n", + " qp = await ctx.get(\"transform_query_pipeline\")\n", + " transformed_query_str = (qp.run(query_str=query_str).message.content)\n", + " # Conduct a search with the transformed query string and collect the results.\n", + " search_results = await ctx.get(\"tavily_tool\").search(\n", + " transformed_query_str, max_results=5\n", + " )\n", + " search_text = \"\\n\".join([result.text for result in search_results])\n", + " else:\n", + " search_text = \"\"\n", + "\n", + " return QueryEvent(relevant_text=relevant_text, search_text=search_text)\n", + "\n", + " @step\n", + " async def query_result(self, ctx: Context, ev: QueryEvent) -> StopEvent:\n", + " \"\"\"Get result with relevant text.\"\"\"\n", + " relevant_text = ev.relevant_text\n", + " search_text = ev.search_text\n", + " query_str = await ctx.get(\"query_str\")\n", + "\n", + " documents = [Document(text=relevant_text + \"\\n\" + search_text)]\n", + " index = SummaryIndex.from_documents(documents)\n", + " query_engine = index.as_query_engine()\n", + " result = query_engine.query(query_str)\n", + " return StopEvent(result=result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the workflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "\n", + "documents = SimpleDirectoryReader(\"./data\").load_data()\n", + "workflow = CorrectiveRAGWorkflow()\n", + "index = await workflow.run(documents=documents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Llama 2 was pretrained using an optimized auto-regressive transformer with several modifications to enhance performance. These modifications included more robust data cleaning, updated data mixes, training on 40% more total tokens, doubling the context length, and using grouped-query attention (GQA) to improve inference scalability for larger models." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Markdown, display\n", + "\n", + "response = await workflow.run(\n", + " query_str=\"How was Llama2 pretrained?\",\n", + " index=index,\n", + " tavily_ai_apikey=tavily_ai_api_key,\n", + ")\n", + "display(Markdown(str(response)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "The functionality of the latest ChatGPT memory is to autonomously remember information it deems relevant from conversations. This feature aims to save users from having to repeat information and make future conversations more helpful. Users have control over the chatbot's memory, being able to access and manage these memories as needed." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "response = await workflow.run(\n", + " query_str=\"What is the functionality of latest ChatGPT memory.\"\n", + ")\n", + "display(Markdown(str(response)))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llama-index-RvIdVF4_-py3.11", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/examples/workflow/function_calling_agent.ipynb b/docs/docs/examples/workflow/function_calling_agent.ipynb index c9f2fb4ecc5f7..ff1f29c72d4b2 100644 --- a/docs/docs/examples/workflow/function_calling_agent.ipynb +++ b/docs/docs/examples/workflow/function_calling_agent.ipynb @@ -33,6 +33,55 @@ "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\"" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Optional] Set up observability with Llamatrace\n", + "\n", + "Set up tracing to visualize each step in the workflow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install \"llama-index-core>=0.10.43\" \"openinference-instrumentation-llama-index>=2.2.2\" \"opentelemetry-proto>=1.12.0\" opentelemetry-exporter-otlp opentelemetry-sdk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from opentelemetry.sdk import trace as trace_sdk\n", + "from opentelemetry.sdk.trace.export import SimpleSpanProcessor\n", + "from opentelemetry.exporter.otlp.proto.http.trace_exporter import (\n", + " OTLPSpanExporter as HTTPSpanExporter,\n", + ")\n", + "from openinference.instrumentation.llama_index import LlamaIndexInstrumentor\n", + "\n", + "\n", + "# Add Phoenix API Key for tracing\n", + "PHOENIX_API_KEY = \"\"\n", + "os.environ[\"OTEL_EXPORTER_OTLP_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", + "\n", + "# Add Phoenix\n", + "span_phoenix_processor = SimpleSpanProcessor(\n", + " HTTPSpanExporter(endpoint=\"https://app.phoenix.arize.com/v1/traces\")\n", + ")\n", + "\n", + "# Add them to the tracer\n", + "tracer_provider = trace_sdk.TracerProvider()\n", + "tracer_provider.add_span_processor(span_processor=span_phoenix_processor)\n", + "\n", + "# Instrument the application\n", + "LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -137,7 +186,7 @@ " self.memory = ChatMemoryBuffer.from_defaults(llm=llm)\n", " self.sources = []\n", "\n", - " @step()\n", + " @step\n", " async def prepare_chat_history(self, ev: StartEvent) -> InputEvent:\n", " # clear sources\n", " self.sources = []\n", @@ -151,7 +200,7 @@ " chat_history = self.memory.get()\n", " return InputEvent(input=chat_history)\n", "\n", - " @step()\n", + " @step\n", " async def handle_llm_input(\n", " self, ev: InputEvent\n", " ) -> ToolCallEvent | StopEvent:\n", @@ -173,7 +222,7 @@ " else:\n", " return ToolCallEvent(tool_calls=tool_calls)\n", "\n", - " @step()\n", + " @step\n", " async def handle_tool_calls(self, ev: ToolCallEvent) -> InputEvent:\n", " tool_calls = ev.tool_calls\n", " tools_by_name = {tool.metadata.get_name(): tool for tool in self.tools}\n", diff --git a/docs/docs/examples/workflow/long_rag_pack.ipynb b/docs/docs/examples/workflow/long_rag_pack.ipynb new file mode 100644 index 0000000000000..5995ed63fca4f --- /dev/null +++ b/docs/docs/examples/workflow/long_rag_pack.ipynb @@ -0,0 +1,556 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LongRAG Workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook shows how to implement LongRAG using LlamaIndex workflows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -U llama-index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!wget https://github.com/user-attachments/files/16474262/data.zip -O data.zip\n", + "!unzip -o data.zip\n", + "!rm data.zip" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since workflows are async first, this all runs fine in a notebook. If you were running in your own code, you would want to use `asyncio.run()` to start an async event loop if one isn't already running.\n", + "\n", + "```python\n", + "async def main():\n", + " \n", + "\n", + "if __name__ == \"__main__\":\n", + " import asyncio\n", + " asyncio.run(main())\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Helper Functions\n", + "\n", + "These helper functions will help us split documents into smaller pieces and group nodes based on their relationships." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import List, Dict, Optional, Set, FrozenSet\n", + "\n", + "from llama_index.core.schema import BaseNode, TextNode\n", + "from llama_index.core.node_parser import SentenceSplitter\n", + "\n", + "# constants\n", + "DEFAULT_CHUNK_SIZE = 4096 # optionally splits documents into CHUNK_SIZE, then regroups them to demonstrate grouping algorithm\n", + "DEFAULT_MAX_GROUP_SIZE = 20 # maximum number of documents in a group\n", + "DEFAULT_SMALL_CHUNK_SIZE = 512 # small chunk size for generating embeddings\n", + "DEFAULT_TOP_K = 8 # top k for retrieving\n", + "\n", + "\n", + "def split_doc(chunk_size: int, documents: List[BaseNode]) -> List[TextNode]:\n", + " \"\"\"Splits documents into smaller pieces.\n", + "\n", + " Args:\n", + " chunk_size (int): Chunk size\n", + " documents (List[BaseNode]): Documents\n", + "\n", + " Returns:\n", + " List[TextNode]: Smaller chunks\n", + " \"\"\"\n", + " # split docs into tokens\n", + " text_parser = SentenceSplitter(chunk_size=chunk_size)\n", + " return text_parser.get_nodes_from_documents(documents)\n", + "\n", + "\n", + "def group_docs(\n", + " nodes: List[str],\n", + " adj: Dict[str, List[str]],\n", + " max_group_size: Optional[int] = DEFAULT_MAX_GROUP_SIZE,\n", + ") -> Set[FrozenSet[str]]:\n", + " \"\"\"Groups documents.\n", + "\n", + " Args:\n", + " nodes (List[str]): documents IDs\n", + " adj (Dict[str, List[str]]): related documents for each document; id -> list of doc strings\n", + " max_group_size (Optional[int], optional): max group size, None if no max group size. Defaults to DEFAULT_MAX_GROUP_SIZE.\n", + " \"\"\"\n", + " docs = sorted(nodes, key=lambda node: len(adj[node]))\n", + " groups = set() # set of set of IDs\n", + " for d in docs:\n", + " related_groups = set()\n", + " for r in adj[d]:\n", + " for g in groups:\n", + " if r in g:\n", + " related_groups = related_groups.union(frozenset([g]))\n", + "\n", + " gnew = {d}\n", + " related_groupsl = sorted(related_groups, key=lambda el: len(el))\n", + " for g in related_groupsl:\n", + " if max_group_size is None or len(gnew) + len(g) <= max_group_size:\n", + " gnew = gnew.union(g)\n", + " if g in groups:\n", + " groups.remove(g)\n", + "\n", + " groups.add(frozenset(gnew))\n", + "\n", + " return groups\n", + "\n", + "\n", + "def get_grouped_docs(\n", + " nodes: List[TextNode],\n", + " max_group_size: Optional[int] = DEFAULT_MAX_GROUP_SIZE,\n", + ") -> List[TextNode]:\n", + " \"\"\"Gets list of documents that are grouped.\n", + "\n", + " Args:\n", + " nodes (t.List[TextNode]): Input list\n", + " max_group_size (Optional[int], optional): max group size, None if no max group size. Defaults to DEFAULT_MAX_GROUP_SIZE.\n", + "\n", + " Returns:\n", + " t.List[TextNode]: Output list\n", + " \"\"\"\n", + " # node IDs\n", + " nodes_str = [node.id_ for node in nodes]\n", + " # maps node ID -> related node IDs based on that node's relationships\n", + " adj: Dict[str, List[str]] = {\n", + " node.id_: [val.node_id for val in node.relationships.values()]\n", + " for node in nodes\n", + " }\n", + " # node ID -> node\n", + " nodes_dict = {node.id_: node for node in nodes}\n", + "\n", + " res = group_docs(nodes_str, adj, max_group_size)\n", + "\n", + " ret_nodes = []\n", + " for g in res:\n", + " cur_node = TextNode()\n", + "\n", + " for node_id in g:\n", + " cur_node.text += nodes_dict[node_id].text + \"\\n\\n\"\n", + " cur_node.metadata.update(nodes_dict[node_id].metadata)\n", + "\n", + " ret_nodes.append(cur_node)\n", + "\n", + " return ret_nodes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Making the Retriever\n", + "\n", + "LongRAG needs a custom retriever, which is shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.retrievers import BaseRetriever\n", + "from llama_index.core.vector_stores.simple import BasePydanticVectorStore\n", + "from llama_index.core.schema import QueryBundle, NodeWithScore\n", + "from llama_index.core.vector_stores.types import VectorStoreQuery\n", + "from llama_index.core.settings import Settings\n", + "\n", + "\n", + "class LongRAGRetriever(BaseRetriever):\n", + " \"\"\"Long RAG Retriever.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " grouped_nodes: List[TextNode],\n", + " small_toks: List[TextNode],\n", + " vector_store: BasePydanticVectorStore,\n", + " similarity_top_k: int = DEFAULT_TOP_K,\n", + " ) -> None:\n", + " \"\"\"Constructor.\n", + "\n", + " Args:\n", + " grouped_nodes (List[TextNode]): Long retrieval units, nodes with docs grouped together based on relationships\n", + " small_toks (List[TextNode]): Smaller tokens\n", + " embed_model (BaseEmbedding, optional): Embed model. Defaults to None.\n", + " similarity_top_k (int, optional): Similarity top k. Defaults to 8.\n", + " \"\"\"\n", + " self._grouped_nodes = grouped_nodes\n", + " self._grouped_nodes_dict = {node.id_: node for node in grouped_nodes}\n", + " self._small_toks = small_toks\n", + " self._small_toks_dict = {node.id_: node for node in self._small_toks}\n", + "\n", + " self._similarity_top_k = similarity_top_k\n", + " self._vec_store = vector_store\n", + " self._embed_model = Settings.embed_model\n", + "\n", + " def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:\n", + " \"\"\"Retrieves.\n", + "\n", + " Args:\n", + " query_bundle (QueryBundle): query bundle\n", + "\n", + " Returns:\n", + " List[NodeWithScore]: nodes with scores\n", + " \"\"\"\n", + " # make query\n", + " query_embedding = self._embed_model.get_query_embedding(\n", + " query_bundle.query_str\n", + " )\n", + " vector_store_query = VectorStoreQuery(\n", + " query_embedding=query_embedding, similarity_top_k=500\n", + " )\n", + "\n", + " # query for answer\n", + " query_res = self._vec_store.query(vector_store_query)\n", + "\n", + " # determine top parents of most similar children (these are long retrieval units)\n", + " top_parents_set: Set[str] = set()\n", + " top_parents: List[NodeWithScore] = []\n", + " for id_, similarity in zip(query_res.ids, query_res.similarities):\n", + " cur_node = self._small_toks_dict[id_]\n", + " parent_id = cur_node.ref_doc_id\n", + " if parent_id not in top_parents_set:\n", + " top_parents_set.add(parent_id)\n", + "\n", + " parent_node = self._grouped_nodes_dict[parent_id]\n", + " node_with_score = NodeWithScore(\n", + " node=parent_node, score=similarity\n", + " )\n", + " top_parents.append(node_with_score)\n", + "\n", + " if len(top_parents_set) >= self._similarity_top_k:\n", + " break\n", + "\n", + " assert len(top_parents) == min(\n", + " self._similarity_top_k, len(self._grouped_nodes)\n", + " )\n", + "\n", + " return top_parents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Designing the Workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "LongRAG consists of the following steps:\n", + "\n", + "1. Ingesting the data — grouping documents and putting them in long retrieval units, splitting the long retrieval units into smaller tokens to generate embeddings, and indexing the small nodes.\n", + "2. Constructing the retriever and query engine.\n", + "3. Querying over the data given a string." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We define an event that passes the long and small retrieval units into the retriever and query engine." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Iterable\n", + "\n", + "from llama_index.core import VectorStoreIndex\n", + "from llama_index.core.llms import LLM\n", + "from llama_index.core.workflow import Event\n", + "\n", + "\n", + "class LoadNodeEvent(Event):\n", + " \"\"\"Event for loading nodes.\"\"\"\n", + "\n", + " small_nodes: Iterable[TextNode]\n", + " grouped_nodes: list[TextNode]\n", + " index: VectorStoreIndex\n", + " similarity_top_k: int\n", + " llm: LLM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After defining our events, we can write our workflow and steps:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.workflow import (\n", + " Workflow,\n", + " step,\n", + " StartEvent,\n", + " StopEvent,\n", + " Context,\n", + ")\n", + "from llama_index.core import SimpleDirectoryReader\n", + "from llama_index.core.query_engine import RetrieverQueryEngine\n", + "\n", + "\n", + "class LongRAGWorkflow(Workflow):\n", + " \"\"\"Long RAG Workflow.\"\"\"\n", + "\n", + " @step\n", + " async def ingest(self, ev: StartEvent) -> LoadNodeEvent | None:\n", + " \"\"\"Ingestion step.\n", + "\n", + " Args:\n", + " ctx (Context): Context\n", + " ev (StartEvent): start event\n", + "\n", + " Returns:\n", + " StopEvent | None: stop event with result\n", + " \"\"\"\n", + " data_dir: str = ev.get(\"data_dir\")\n", + " llm: LLM = ev.get(\"llm\")\n", + " chunk_size: int | None = ev.get(\"chunk_size\")\n", + " similarity_top_k: int = ev.get(\"similarity_top_k\")\n", + " small_chunk_size: int = ev.get(\"small_chunk_size\")\n", + " index: VectorStoreIndex | None = ev.get(\"index\")\n", + " index_kwargs: dict[str, t.Any] | None = ev.get(\"index_kwargs\")\n", + "\n", + " if any(\n", + " i is None\n", + " for i in [data_dir, llm, similarity_top_k, small_chunk_size]\n", + " ):\n", + " return None\n", + "\n", + " if not index:\n", + " docs = SimpleDirectoryReader(data_dir).load_data()\n", + " if chunk_size is not None:\n", + " nodes = split_doc(\n", + " chunk_size, docs\n", + " ) # split documents into chunks of chunk_size\n", + " grouped_nodes = get_grouped_docs(\n", + " nodes\n", + " ) # get list of nodes after grouping (groups are combined into one node), these are long retrieval units\n", + " else:\n", + " grouped_nodes = docs\n", + "\n", + " # split large retrieval units into smaller nodes\n", + " small_nodes = split_doc(small_chunk_size, grouped_nodes)\n", + "\n", + " index_kwargs = index_kwargs or {}\n", + " index = VectorStoreIndex(small_nodes, **index_kwargs)\n", + " else:\n", + " # get smaller nodes from index and form large retrieval units from these nodes\n", + " small_nodes = index.docstore.docs.values()\n", + " grouped_nodes = get_grouped_docs(small_nodes, None)\n", + "\n", + " return LoadNodeEvent(\n", + " small_nodes=small_nodes,\n", + " grouped_nodes=grouped_nodes,\n", + " index=index,\n", + " similarity_top_k=similarity_top_k,\n", + " llm=llm,\n", + " )\n", + "\n", + " @step\n", + " async def make_query_engine(\n", + " self, ctx: Context, ev: LoadNodeEvent\n", + " ) -> StopEvent:\n", + " \"\"\"Query engine construction step.\n", + "\n", + " Args:\n", + " ctx (Context): context\n", + " ev (LoadNodeEvent): event\n", + "\n", + " Returns:\n", + " StopEvent: stop event\n", + " \"\"\"\n", + " # make retriever and query engine\n", + " retriever = LongRAGRetriever(\n", + " grouped_nodes=ev.grouped_nodes,\n", + " small_toks=ev.small_nodes,\n", + " similarity_top_k=ev.similarity_top_k,\n", + " vector_store=ev.index.vector_store,\n", + " )\n", + " query_eng = RetrieverQueryEngine.from_args(retriever, ev.llm)\n", + "\n", + " return StopEvent(\n", + " result={\n", + " \"retriever\": retriever,\n", + " \"query_engine\": query_eng,\n", + " \"index\": ev.index,\n", + " }\n", + " )\n", + "\n", + " @step\n", + " async def query(self, ctx: Context, ev: StartEvent) -> StopEvent | None:\n", + " \"\"\"Query step.\n", + "\n", + " Args:\n", + " ctx (Context): context\n", + " ev (StartEvent): start event\n", + "\n", + " Returns:\n", + " StopEvent | None: stop event with result\n", + " \"\"\"\n", + " query_str: str | None = ev.get(\"query_str\")\n", + " query_eng = ev.get(\"query_eng\")\n", + "\n", + " if query_str is None:\n", + " return None\n", + "\n", + " result = query_eng.query(query_str)\n", + " return StopEvent(result=result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Walkthrough:\n", + "- There are 2 entry points: one for ingesting and indexing and another for querying.\n", + "- When ingesting, it first reads the documents, splits them into smaller nodes, and indexes them. After that, it sends a `LoadNodeEvent` which triggers the execution of `make_query_engine`, constructing a retriever and query engine from the nodes. It returns a result of the retriever, the query engine, and the index.\n", + "- When querying, it takes in the query from the `StartEvent`, feeds it into the query engine in the context, and returns the result of the query.\n", + "- The context is used to store the query engine." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Workflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.openai import OpenAI\n", + "\n", + "wf = LongRAGWorkflow(timeout=60)\n", + "llm = OpenAI(\"gpt-4o\")\n", + "data_dir = \"data\"\n", + "\n", + "# initialize the workflow\n", + "result = await wf.run(\n", + " data_dir=data_dir,\n", + " llm=llm,\n", + " chunk_size=DEFAULT_CHUNK_SIZE,\n", + " similarity_top_k=DEFAULT_TOP_K,\n", + " small_chunk_size=DEFAULT_SMALL_CHUNK_SIZE,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Pittsburgh can become a startup hub by leveraging its increasing population of young people, particularly those aged 25 to 29, who are crucial for the startup ecosystem. The city should encourage the youth-driven food boom, preserve historic buildings, and make the city more bicycle and pedestrian-friendly. Additionally, Carnegie Mellon University (CMU) should focus on being an even better research university to attract ambitious talent. The city should also foster a culture of tolerance and gradually build an investor community.\n", + "\n", + "There are two types of moderates: intentional moderates and accidental moderates. Intentional moderates deliberately choose positions midway between the extremes of right and left, while accidental moderates form their opinions independently on each issue, resulting in a broad range of views that average to a moderate position." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, Markdown\n", + "\n", + "# run a query\n", + "res = await wf.run(\n", + " query_str=\"How can Pittsburgh become a startup hub, and what are the two types of moderates?\",\n", + " query_eng=result[\"query_engine\"],\n", + ")\n", + "display(Markdown(str(res)))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llama-index-RvIdVF4_-py3.11", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/examples/workflow/multi_step_query_engine.ipynb b/docs/docs/examples/workflow/multi_step_query_engine.ipynb new file mode 100644 index 0000000000000..cbe99f59ee12f --- /dev/null +++ b/docs/docs/examples/workflow/multi_step_query_engine.ipynb @@ -0,0 +1,549 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MultiStep Query Engine\n", + "\n", + "The `MultiStepQueryEngine` breaks down a complex query into sequential sub-questions.\n", + "\n", + "To answer the query: In which city did the author found his first company, Viaweb?, we need to answer the following sub-questions sequentially:\n", + "\n", + "1. Who is the author that founded his first company, Viaweb?\n", + "2. In which city did Paul Graham found his first company, Viaweb?\n", + "\n", + "As an example, the answer from each step (sub-query-1) is used to generate the next step's question (sub-query-2), with steps created sequentially rather than all at once.\n", + "\n", + "In this notebook, we will implement the same with [MultiStepQueryEngine](https://docs.llamaindex.ai/en/stable/examples/query_transformations/SimpleIndexDemo-multistep/) using workflows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U llama-index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since workflows are async first, this all runs fine in a notebook. If you were running in your own code, you would want to use `asyncio.run()` to start an async event loop if one isn't already running.\n", + "\n", + "```python\n", + "async def main():\n", + " \n", + "\n", + "if __name__ == \"__main__\":\n", + " import asyncio\n", + " asyncio.run(main())\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Workflow\n", + "\n", + "## Designing the Workflow\n", + "\n", + "MultiStepQueryEngine consists of some clearly defined steps\n", + "1. Indexing data, creating an index.\n", + "2. Create multiple sub-queries to answer the query.\n", + "3. Synthesize the final response\n", + "\n", + "With this in mind, we can create events and workflow steps to follow this process!\n", + "\n", + "### The Workflow Event\n", + "\n", + "To handle these steps, we need to define `QueryMultiStepEvent`\n", + "\n", + "The other steps will use the built-in `StartEvent` and `StopEvent` events." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Event" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package punkt_tab to\n", + "[nltk_data] /Users/ravithejad/Desktop/llamaindex/lib/python3.9/sit\n", + "[nltk_data] e-packages/llama_index/core/_static/nltk_cache...\n", + "[nltk_data] Package punkt_tab is already up-to-date!\n" + ] + } + ], + "source": [ + "from llama_index.core.workflow import Event\n", + "from typing import Dict, List, Any\n", + "from llama_index.core.schema import NodeWithScore\n", + "\n", + "\n", + "class QueryMultiStepEvent(Event):\n", + " \"\"\"\n", + " Event containing results of a multi-step query process.\n", + "\n", + " Attributes:\n", + " nodes (List[NodeWithScore]): List of nodes with their associated scores.\n", + " source_nodes (List[NodeWithScore]): List of source nodes with their scores.\n", + " final_response_metadata (Dict[str, Any]): Metadata associated with the final response.\n", + " \"\"\"\n", + "\n", + " nodes: List[NodeWithScore]\n", + " source_nodes: List[NodeWithScore]\n", + " final_response_metadata: Dict[str, Any]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Workflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.indices.query.query_transform.base import (\n", + " StepDecomposeQueryTransform,\n", + ")\n", + "from llama_index.core.response_synthesizers import (\n", + " get_response_synthesizer,\n", + ")\n", + "\n", + "from llama_index.core.schema import QueryBundle, TextNode\n", + "\n", + "from llama_index.core.workflow import (\n", + " Context,\n", + " Workflow,\n", + " StartEvent,\n", + " StopEvent,\n", + " step,\n", + ")\n", + "\n", + "from llama_index.core import Settings\n", + "from llama_index.core.llms import LLM\n", + "\n", + "from typing import cast\n", + "from IPython.display import Markdown, display\n", + "\n", + "\n", + "class MultiStepQueryEngineWorkflow(Workflow):\n", + " def combine_queries(\n", + " self,\n", + " query_bundle: QueryBundle,\n", + " prev_reasoning: str,\n", + " index_summary: str,\n", + " llm: LLM,\n", + " ) -> QueryBundle:\n", + " \"\"\"Combine queries using StepDecomposeQueryTransform.\"\"\"\n", + " transform_metadata = {\n", + " \"prev_reasoning\": prev_reasoning,\n", + " \"index_summary\": index_summary,\n", + " }\n", + " return StepDecomposeQueryTransform(llm=llm)(\n", + " query_bundle, metadata=transform_metadata\n", + " )\n", + "\n", + " def default_stop_fn(self, stop_dict: Dict) -> bool:\n", + " \"\"\"Stop function for multi-step query combiner.\"\"\"\n", + " query_bundle = cast(QueryBundle, stop_dict.get(\"query_bundle\"))\n", + " if query_bundle is None:\n", + " raise ValueError(\"Response must be provided to stop function.\")\n", + "\n", + " return \"none\" in query_bundle.query_str.lower()\n", + "\n", + " @step(pass_context=True)\n", + " async def query_multistep(\n", + " self, ctx: Context, ev: StartEvent\n", + " ) -> QueryMultiStepEvent:\n", + " \"\"\"Execute multi-step query process.\"\"\"\n", + " prev_reasoning = \"\"\n", + " cur_response = None\n", + " should_stop = False\n", + " cur_steps = 0\n", + "\n", + " # use response\n", + " final_response_metadata: Dict[str, Any] = {\"sub_qa\": []}\n", + "\n", + " text_chunks = []\n", + " source_nodes = []\n", + "\n", + " query = ev.get(\"query\")\n", + " await ctx.set(\"query\", ev.get(\"query\"))\n", + "\n", + " llm = Settings.llm\n", + " stop_fn = self.default_stop_fn\n", + "\n", + " num_steps = ev.get(\"num_steps\")\n", + " query_engine = ev.get(\"query_engine\")\n", + " index_summary = ev.get(\"index_summary\")\n", + "\n", + " while not should_stop:\n", + " if num_steps is not None and cur_steps >= num_steps:\n", + " should_stop = True\n", + " break\n", + " elif should_stop:\n", + " break\n", + "\n", + " updated_query_bundle = self.combine_queries(\n", + " QueryBundle(query_str=query),\n", + " prev_reasoning,\n", + " index_summary,\n", + " llm,\n", + " )\n", + "\n", + " print(\n", + " f\"Created query for the step - {cur_steps} is: {updated_query_bundle}\"\n", + " )\n", + "\n", + " stop_dict = {\"query_bundle\": updated_query_bundle}\n", + " if stop_fn(stop_dict):\n", + " should_stop = True\n", + " break\n", + "\n", + " cur_response = query_engine.query(updated_query_bundle)\n", + "\n", + " # append to response builder\n", + " cur_qa_text = (\n", + " f\"\\nQuestion: {updated_query_bundle.query_str}\\n\"\n", + " f\"Answer: {cur_response!s}\"\n", + " )\n", + " text_chunks.append(cur_qa_text)\n", + " for source_node in cur_response.source_nodes:\n", + " source_nodes.append(source_node)\n", + " # update metadata\n", + " final_response_metadata[\"sub_qa\"].append(\n", + " (updated_query_bundle.query_str, cur_response)\n", + " )\n", + "\n", + " prev_reasoning += (\n", + " f\"- {updated_query_bundle.query_str}\\n\" f\"- {cur_response!s}\\n\"\n", + " )\n", + " cur_steps += 1\n", + "\n", + " nodes = [\n", + " NodeWithScore(node=TextNode(text=text_chunk))\n", + " for text_chunk in text_chunks\n", + " ]\n", + " return QueryMultiStepEvent(\n", + " nodes=nodes,\n", + " source_nodes=source_nodes,\n", + " final_response_metadata=final_response_metadata,\n", + " )\n", + "\n", + " @step(pass_context=True)\n", + " async def synthesize(\n", + " self, ctx: Context, ev: QueryMultiStepEvent\n", + " ) -> StopEvent:\n", + " \"\"\"Synthesize the response.\"\"\"\n", + " response_synthesizer = get_response_synthesizer()\n", + " query = await ctx.get(\"query\", default=None)\n", + " final_response = await response_synthesizer.asynthesize(\n", + " query=query,\n", + " nodes=ev.nodes,\n", + " additional_source_nodes=ev.source_nodes,\n", + " )\n", + " final_response.metadata = ev.final_response_metadata\n", + "\n", + " return StopEvent(result=final_response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-08-26 14:16:04-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8000::154, 2606:50c0:8002::154, 2606:50c0:8001::154, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8000::154|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 75042 (73K) [text/plain]\n", + "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n", + "\n", + "data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.01s \n", + "\n", + "2024-08-26 14:16:04 (6.91 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir -p 'data/paul_graham/'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "\n", + "documents = SimpleDirectoryReader(\"data/paul_graham\").load_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup LLM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.openai import OpenAI\n", + "\n", + "llm = OpenAI(model=\"gpt-4\")\n", + "\n", + "Settings.llm = llm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Index and QueryEngine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex\n", + "\n", + "index = VectorStoreIndex.from_documents(\n", + " documents=documents,\n", + ")\n", + "\n", + "query_engine = index.as_query_engine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the Workflow!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "w = MultiStepQueryEngineWorkflow(timeout=200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set the parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sets maximum number of steps taken to answer the query.\n", + "num_steps = 3\n", + "\n", + "# Set summary of the index, useful to create modified query at each step.\n", + "index_summary = \"Used to answer questions about the author\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test with a query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query = \"In which city did the author found his first company, Viaweb?\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created query for the step - 0 is: Who is the author who founded Viaweb?\n", + "Created query for the step - 1 is: In which city did Paul Graham found his first company, Viaweb?\n", + "Created query for the step - 2 is: None\n" + ] + }, + { + "data": { + "text/markdown": [ + "> Question: In which city did the author found his first company, Viaweb?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Answer: The author founded his first company, Viaweb, in Cambridge." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result = await w.run(\n", + " query=query,\n", + " query_engine=query_engine,\n", + " index_summary=index_summary,\n", + " num_steps=num_steps,\n", + ")\n", + "\n", + "# If created query in a step is None, the process will be stopped.\n", + "\n", + "display(\n", + " Markdown(\"> Question: {}\".format(query)),\n", + " Markdown(\"Answer: {}\".format(result)),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display step-queries created" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "[('Who is the author who founded Viaweb?', 'The author who founded Viaweb is Paul Graham.'), ('In which city did Paul Graham found his first company, Viaweb?', 'Paul Graham founded his first company, Viaweb, in Cambridge.')]" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sub_qa = result.metadata[\"sub_qa\"]\n", + "tuples = [(t[0], t[1].response) for t in sub_qa]\n", + "display(Markdown(f\"{tuples}\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llamaindex", + "language": "python", + "name": "llamaindex" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/examples/workflow/multi_strategy_workflow.ipynb b/docs/docs/examples/workflow/multi_strategy_workflow.ipynb new file mode 100644 index 0000000000000..8b99576a27f94 --- /dev/null +++ b/docs/docs/examples/workflow/multi_strategy_workflow.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-strategy workflow with reflection\n", + "\n", + "In this notebook we'll demonstrate a workflow that attempts 3 different query strategies in parallel and picks the best one.\n", + "\n", + "As shown in the diagram below:\n", + "* First the quality of the query is judged. If it's a bad query, a `BadQueryEvent` is emitted and the `improve_query` step will try to improve the quality of the query before trying again. This is reflection.\n", + "* Once an acceptable query has been found, three simultaneous events are emitted: a `NaiveRAGEvent`, a `HighTopKEvent`, and a `RerankEvent`.\n", + "* Each of these events is picked up by a dedicated step that tries a different RAG strategy on the same index. All 3 emit a `ResponseEvent`\n", + "* The `judge` step waits until it has collected all three `ResponseEvents`, then it compares them. It finally emits the best response as a `StopEvent`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Screenshot 2024-08-16 at 12.41.23 PM.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install dependencies\n", + "\n", + "We need LlamaIndex, the file reader (for reading PDFs), the workflow visualizer (to draw the diagram above), and OpenAI to embed the data and query an LLM." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index-core llama-index-llms-openai llama-index-utils-workflow llama-index-readers-file llama-index-embeddings-openai" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get the data\n", + "\n", + "We are using 3 long PDFs of San Francisco's annual budget from 2016 through 2018." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir data\n", + "!wget \"https://www.dropbox.com/scl/fi/xt3squt47djba0j7emmjb/2016-CSF_Budget_Book_2016_FINAL_WEB_with-cover-page.pdf?rlkey=xs064cjs8cb4wma6t5pw2u2bl&dl=0\" -O \"data/2016-CSF_Budget_Book_2016_FINAL_WEB_with-cover-page.pdf\"\n", + "!wget \"https://www.dropbox.com/scl/fi/jvw59g5nscu1m7f96tjre/2017-Proposed-Budget-FY2017-18-FY2018-19_1.pdf?rlkey=v988oigs2whtcy87ti9wti6od&dl=0\" -O \"data/2017-Proposed-Budget-FY2017-18-FY2018-19_1.pdf\"\n", + "!wget \"https://www.dropbox.com/scl/fi/izknlwmbs7ia0lbn7zzyx/2018-o0181-18.pdf?rlkey=p5nv2ehtp7272ege3m9diqhei&dl=0\" -O \"data/2018-o0181-18.pdf\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bring in dependencies\n", + "\n", + "Now we import all our dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from llama_index.core import (\n", + " SimpleDirectoryReader,\n", + " VectorStoreIndex,\n", + " StorageContext,\n", + " load_index_from_storage,\n", + ")\n", + "from llama_index.core.workflow import (\n", + " step,\n", + " Context,\n", + " Workflow,\n", + " Event,\n", + " StartEvent,\n", + " StopEvent,\n", + ")\n", + "from llama_index.llms.openai import OpenAI\n", + "from llama_index.core.postprocessor.rankGPT_rerank import RankGPTRerank\n", + "from llama_index.core.query_engine import RetrieverQueryEngine\n", + "from llama_index.core.chat_engine import SimpleChatEngine\n", + "from llama_index.utils.workflow import draw_all_possible_flows" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need to set up our OpenAI key." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from google.colab import userdata\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = userdata.get(\"openai-key\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define event classes\n", + "\n", + "Our flow generates quite a few different event types." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class JudgeEvent(Event):\n", + " query: str\n", + "\n", + "\n", + "class BadQueryEvent(Event):\n", + " query: str\n", + "\n", + "\n", + "class NaiveRAGEvent(Event):\n", + " query: str\n", + "\n", + "\n", + "class HighTopKEvent(Event):\n", + " query: str\n", + "\n", + "\n", + "class RerankEvent(Event):\n", + " query: str\n", + "\n", + "\n", + "class ResponseEvent(Event):\n", + " query: str\n", + " response: str\n", + "\n", + "\n", + "class SummarizeEvent(Event):\n", + " query: str\n", + " response: str" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define workflow\n", + "\n", + "This is the substance of our workflow, so let's break it down:\n", + "\n", + "* `load_or_create_index` is a normal RAG function that reads our PDFs from disk and indexes them if they aren't already indexed. If indexing already happened it will simply restore the existing index from disk.\n", + "\n", + "* `judge_query` does a few things\n", + " * It initializes the LLM and calls `load_or_create_index` to get set up. It stores these things in the context so they are available later.\n", + " * It judges the quality of the query\n", + " * If the query is bad it emits a `BadQueryEvent`\n", + " * If the query is good it emits a `NaiveRAGEvent`, a `HighTopKEvent` and a `RerankerEvent`\n", + "\n", + "* `improve_query` takes the `BadQueryEvent` and uses an LLM to try and expand and deambiguate the query if possible, then it loops back to `judge_query`\n", + "\n", + "* `naive_rag`, `high_top_k` and `rerank` accept their respective events and attempt 3 different RAG strategies. Each emits a `ResponseEvent` with their result and a `source` parameter that says which strategy was used\n", + "\n", + "* `judge` fires every time a `ResponseEvent` is emitted, but it uses `collect_events` to buffer them until it has received all 3. Then it sends the responses to an LLM and asks it to select the \"best\" one. It emits the best response as a StopEvent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class ComplicatedWorkflow(Workflow):\n", + " def load_or_create_index(self, directory_path, persist_dir):\n", + " # Check if the index already exists\n", + " if os.path.exists(persist_dir):\n", + " print(\"Loading existing index...\")\n", + " # Load the index from disk\n", + " storage_context = StorageContext.from_defaults(\n", + " persist_dir=persist_dir\n", + " )\n", + " index = load_index_from_storage(storage_context)\n", + " else:\n", + " print(\"Creating new index...\")\n", + " # Load documents from the specified directory\n", + " documents = SimpleDirectoryReader(directory_path).load_data()\n", + "\n", + " # Create a new index from the documents\n", + " index = VectorStoreIndex.from_documents(documents)\n", + "\n", + " # Persist the index to disk\n", + " index.storage_context.persist(persist_dir=persist_dir)\n", + "\n", + " return index\n", + "\n", + " @step\n", + " async def judge_query(\n", + " self, ctx: Context, ev: StartEvent | JudgeEvent\n", + " ) -> BadQueryEvent | NaiveRAGEvent | HighTopKEvent | RerankEvent:\n", + " # initialize\n", + " llm = await ctx.get(\"llm\", default=None)\n", + " if llm is None:\n", + " await ctx.set(\"llm\", OpenAI(model=\"gpt-4o\", temperature=0.1))\n", + " await ctx.set(\n", + " \"index\", self.load_or_create_index(\"data\", \"storage\")\n", + " )\n", + "\n", + " # we use a chat engine so it remembers previous interactions\n", + " await ctx.set(\"judge\", SimpleChatEngine.from_defaults())\n", + "\n", + " response = await ctx.get(\"judge\").chat(\n", + " f\"\"\"\n", + " Given a user query, determine if this is likely to yield good results from a RAG system as-is. If it's good, return 'good', if it's bad, return 'bad'.\n", + " Good queries use a lot of relevant keywords and are detailed. Bad queries are vague or ambiguous.\n", + "\n", + " Here is the query: {ev.query}\n", + " \"\"\"\n", + " )\n", + " if response == \"bad\":\n", + " # try again\n", + " return BadQueryEvent(query=ev.query)\n", + " else:\n", + " # send query to all 3 strategies\n", + " self.send_event(NaiveRAGEvent(query=ev.query))\n", + " self.send_event(HighTopKEvent(query=ev.query))\n", + " self.send_event(RerankEvent(query=ev.query))\n", + "\n", + " @step\n", + " async def improve_query(\n", + " self, ctx: Context, ev: BadQueryEvent\n", + " ) -> JudgeEvent:\n", + " response = await ctx.get(\"llm\").complete(\n", + " f\"\"\"\n", + " This is a query to a RAG system: {ev.query}\n", + "\n", + " The query is bad because it is too vague. Please provide a more detailed query that includes specific keywords and removes any ambiguity.\n", + " \"\"\"\n", + " )\n", + " return JudgeEvent(query=str(response))\n", + "\n", + " @step\n", + " async def naive_rag(\n", + " self, ctx: Context, ev: NaiveRAGEvent\n", + " ) -> ResponseEvent:\n", + " index = await ctx.get(\"index\")\n", + " engine = index.as_query_engine(similarity_top_k=5)\n", + " response = engine.query(ev.query)\n", + " print(\"Naive response:\", response)\n", + " return ResponseEvent(\n", + " query=ev.query, source=\"Naive\", response=str(response)\n", + " )\n", + "\n", + " @step\n", + " async def high_top_k(\n", + " self, ctx: Context, ev: HighTopKEvent\n", + " ) -> ResponseEvent:\n", + " index = await ctx.get(\"index\")\n", + " engine = index.as_query_engine(similarity_top_k=20)\n", + " response = engine.query(ev.query)\n", + " print(\"High top k response:\", response)\n", + " return ResponseEvent(\n", + " query=ev.query, source=\"High top k\", response=str(response)\n", + " )\n", + "\n", + " @step\n", + " async def rerank(self, ctx: Context, ev: RerankEvent) -> ResponseEvent:\n", + " index = await ctx.get(\"index\")\n", + " reranker = RankGPTRerank(top_n=5, llm=await ctx.get(\"llm\"))\n", + " retriever = index.as_retriever(similarity_top_k=20)\n", + " engine = RetrieverQueryEngine.from_args(\n", + " retriever=retriever,\n", + " node_postprocessors=[reranker],\n", + " )\n", + " response = engine.query(ev.query)\n", + " print(\"Reranker response:\", response)\n", + " return ResponseEvent(\n", + " query=ev.query, source=\"Reranker\", response=str(response)\n", + " )\n", + "\n", + " @step\n", + " async def judge(self, ctx: Context, ev: ResponseEvent) -> StopEvent:\n", + " ready = ctx.collect_events(ev, [ResponseEvent] * 3)\n", + " if ready is None:\n", + " return None\n", + "\n", + " response = await ctx.get(\"judge\").chat(\n", + " f\"\"\"\n", + " A user has provided a query and 3 different strategies have been used\n", + " to try to answer the query. Your job is to decide which strategy best\n", + " answered the query. The query was: {ev.query}\n", + "\n", + " Response 1 ({ready[0].source}): {ready[0].response}\n", + " Response 2 ({ready[1].source}): {ready[1].response}\n", + " Response 3 ({ready[2].source}): {ready[2].response}\n", + "\n", + " Please provide the number of the best response (1, 2, or 3).\n", + " Just provide the number, with no other text or preamble.\n", + " \"\"\"\n", + " )\n", + "\n", + " best_response = int(str(response))\n", + " print(\n", + " f\"Best response was number {best_response}, which was from {ready[best_response-1].source}\"\n", + " )\n", + " return StopEvent(result=str(ready[best_response - 1].response))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Draw flow diagram\n", + "\n", + "This is how we get the diagram we showed at the start." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "draw_all_possible_flows(\n", + " ComplicatedWorkflow, filename=\"complicated_workflow.html\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the workflow\n", + "\n", + "Let's take the workflow for a spin:\n", + "* The judge_query event returned nothing. This is because it used `send_event` instead. So the query was judged \"good\".\n", + "* All 3 RAG steps run and generate different answers to the query\n", + "* The `judge` step runs 3 times. The first 2 times it produces no event, because it has not collected the requisite 3 `ResponseEvent`s.\n", + "* On the third time it selects the best response and returns a `StopEvent`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running step judge_query\n", + "Creating new index...\n", + "Step judge_query produced no event\n", + "Running step naive_rag\n", + "Naive response: Spending has increased over the years due to various factors such as new voter-approved minimum spending requirements, the creation of new voter-approved baselines, and growth in baseline funded requirements. Additionally, there have been notable changes in spending across different service areas and departments, with increases in funding for areas like public protection, transportation, and public works.\n", + "Step naive_rag produced event ResponseEvent\n", + "Running step rerank\n", + "Reranker response: Spending has increased over the years, with notable changes in the allocation of funds to various service areas and departments. The budget reflects adjustments in spending to address evolving needs and priorities, resulting in a rise in overall expenditures across different categories.\n", + "Step rerank produced event ResponseEvent\n", + "Running step high_top_k\n", + "High top k response: Spending has increased over the years, with the total budget showing growth in various areas such as aid assistance/grants, materials & supplies, equipment, debt service, services of other departments, and professional & contractual services. Additionally, there have been new investments in programs like workforce development, economic development, film services, and finance and administration. The budget allocations have been adjusted to accommodate changing needs and priorities, reflecting an overall increase in spending across different departments and programs.\n", + "Step high_top_k produced event ResponseEvent\n", + "Running step judge\n", + "Step judge produced no event\n", + "Running step judge\n", + "Step judge produced no event\n", + "Running step judge\n", + "Best response was number 3, which was from High top k\n", + "Step judge produced event StopEvent\n", + "Spending has increased over the years, with the total budget showing growth in various areas such as aid assistance/grants, materials & supplies, equipment, debt service, services of other departments, and professional & contractual services. Additionally, there have been new investments in programs like workforce development, economic development, film services, and finance and administration. The budget allocations have been adjusted to accommodate changing needs and priorities, reflecting an overall increase in spending across different departments and programs.\n" + ] + } + ], + "source": [ + "c = ComplicatedWorkflow(timeout=120, verbose=True)\n", + "result = await c.run(\n", + " # query=\"How has spending on police changed in San Francisco's budgets from 2016 to 2018?\"\n", + " # query=\"How has spending on healthcare changed in San Francisco?\"\n", + " query=\"How has spending changed?\"\n", + ")\n", + "print(result)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/examples/workflow/parallel_execution.ipynb b/docs/docs/examples/workflow/parallel_execution.ipynb new file mode 100644 index 0000000000000..68536e65c64ff --- /dev/null +++ b/docs/docs/examples/workflow/parallel_execution.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parallel Execution of Same Event Example\n", + "\n", + "In this example, we'll demonstrate how to use the workflow functionality to achieve similar capabilities while allowing parallel execution of multiple events of the same type. \n", + "By setting the `num_workers` parameter in `@step` decorator, we can control the number of steps executed simultaneously, enabling efficient parallel processing.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Installing Dependencies\n", + "\n", + "First, we need to install the necessary dependencies:\n", + "\n", + "* LlamaIndex core for most functionalities\n", + "* llama-index-utils-workflow for workflow capabilities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index-core llama-index-utils-workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Importing Required Libraries\n", + "After installing the dependencies, we can import the required libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import asyncio\n", + "from llama_index.core.workflow import (\n", + " step,\n", + " Context,\n", + " Workflow,\n", + " Event,\n", + " StartEvent,\n", + " StopEvent,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will create two workflows: one that can process multiple data items in parallel by using the `@step(num_workers=N)` decorator, and another without setting num_workers, for comparison. \n", + "By using the `num_workers` parameter in the `@step` decorator, we can limit the number of steps executed simultaneously, thus controlling the level of parallelism. This approach is particularly suitable for scenarios that require processing similar tasks while managing resource usage. \n", + "For example, you can execute multiple sub-queries at once, but please note that num_workers cannot be set without limits. It depends on your workload or token limits.\n", + "# Defining Event Types\n", + "We'll define two event types: one for input events to be processed, and another for processing results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class ProcessEvent(Event):\n", + " data: str\n", + "\n", + "\n", + "class ResultEvent(Event):\n", + " result: str" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating Sequential and Parallel Workflows\n", + "Now, we'll create a SequentialWorkflow and a ParallelWorkflow class that includes three main steps:\n", + "\n", + "- start: Initialize and send multiple parallel events\n", + "- process_data: Process data\n", + "- combine_results: Collect and merge all processing results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "\n", + "\n", + "class SequentialWorkflow(Workflow):\n", + " @step\n", + " async def start(self, ctx: Context, ev: StartEvent) -> ProcessEvent:\n", + " data_list = [\"A\", \"B\", \"C\"]\n", + " await ctx.set(\"num_to_collect\", len(data_list))\n", + " for item in data_list:\n", + " self.send_event(ProcessEvent(data=item))\n", + " return None\n", + "\n", + " @step\n", + " async def process_data(self, ev: ProcessEvent) -> ResultEvent:\n", + " # Simulate some time-consuming processing\n", + " await asyncio.sleep(random.randint(1, 2))\n", + " result = f\"Processed: {ev.data}\"\n", + " print(f\"Completed processing: {ev.data}\")\n", + " return ResultEvent(result=result)\n", + "\n", + " @step\n", + " async def combine_results(\n", + " self, ctx: Context, ev: ResultEvent\n", + " ) -> StopEvent | None:\n", + " num_to_collect = await ctx.get(\"num_to_collect\")\n", + " results = ctx.collect_events(ev, [ResultEvent] * num_to_collect)\n", + " if results is None:\n", + " return None\n", + "\n", + " combined_result = \", \".join([event.result for event in results])\n", + " return StopEvent(result=combined_result)\n", + "\n", + "\n", + "class ParallelWorkflow(Workflow):\n", + " @step\n", + " async def start(self, ctx: Context, ev: StartEvent) -> ProcessEvent:\n", + " data_list = [\"A\", \"B\", \"C\"]\n", + " await ctx.set(\"num_to_collect\", len(data_list))\n", + " for item in data_list:\n", + " self.send_event(ProcessEvent(data=item))\n", + " return None\n", + "\n", + " @step(num_workers=3)\n", + " async def process_data(self, ev: ProcessEvent) -> ResultEvent:\n", + " # Simulate some time-consuming processing\n", + " await asyncio.sleep(random.randint(1, 2))\n", + " result = f\"Processed: {ev.data}\"\n", + " print(f\"Completed processing: {ev.data}\")\n", + " return ResultEvent(result=result)\n", + "\n", + " @step\n", + " async def combine_results(\n", + " self, ctx: Context, ev: ResultEvent\n", + " ) -> StopEvent | None:\n", + " num_to_collect = await ctx.get(\"num_to_collect\")\n", + " results = ctx.collect_events(ev, [ResultEvent] * num_to_collect)\n", + " if results is None:\n", + " return None\n", + "\n", + " combined_result = \", \".join([event.result for event in results])\n", + " return StopEvent(result=combined_result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In these two workflows:\n", + "\n", + "- The start method initializes and sends multiple ProcessEvent.\n", + "- The process_data method uses\n", + " - only the `@step` decorator in SequentialWorkflow\n", + " - uses the `@step(num_workers=3)` decorator in ParallelWorkflow to limit the number of simultaneously executing workers to 3.\n", + "- The combine_results method collects all processing results and merges them.\n", + "\n", + "# Running the Workflow\n", + "Finally, we can create a main function to run our workflow:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start a sequential workflow without setting num_workers in the step of process_data\n", + "Completed processing: A\n", + "Completed processing: B\n", + "Completed processing: C\n", + "Workflow result: Processed: A, Processed: B, Processed: C\n", + "Time taken: 4.008663654327393 seconds\n", + "------------------------------\n", + "Start a parallel workflow with setting num_workers in the step of process_data\n", + "Completed processing: C\n", + "Completed processing: A\n", + "Completed processing: B\n", + "Workflow result: Processed: C, Processed: A, Processed: B\n", + "Time taken: 2.0040180683135986 seconds\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "sequential_workflow = SequentialWorkflow()\n", + "\n", + "print(\n", + " \"Start a sequential workflow without setting num_workers in the step of process_data\"\n", + ")\n", + "start_time = time.time()\n", + "result = await sequential_workflow.run()\n", + "end_time = time.time()\n", + "print(f\"Workflow result: {result}\")\n", + "print(f\"Time taken: {end_time - start_time} seconds\")\n", + "print(\"-\" * 30)\n", + "\n", + "parallel_workflow = ParallelWorkflow()\n", + "\n", + "print(\n", + " \"Start a parallel workflow with setting num_workers in the step of process_data\"\n", + ")\n", + "start_time = time.time()\n", + "result = await parallel_workflow.run()\n", + "end_time = time.time()\n", + "print(f\"Workflow result: {result}\")\n", + "print(f\"Time taken: {end_time - start_time} seconds\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Note\n", + "\n", + "- Without setting num_workers, it might take 3 to 6 seconds. By setting num_workers, the processing occurs in parallel, handling 3 items at a time, and only takes 2 seconds.\n", + "- In ParallelWorkflow, the order of the completed results may differ from the input order, depending on the completion time of the tasks.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates the execution speed with and without using num_workers, and how to implement parallel processing in a workflow. By setting num_workers, we can control the degree of parallelism, which is very useful for scenarios that need to balance performance and resource usage." + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/examples/workflow/rag.ipynb b/docs/docs/examples/workflow/rag.ipynb index 1dea837082ca3..a1d94d4453027 100644 --- a/docs/docs/examples/workflow/rag.ipynb +++ b/docs/docs/examples/workflow/rag.ipynb @@ -29,6 +29,55 @@ "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\"" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Optional] Set up observability with Llamatrace\n", + "\n", + "Set up tracing to visualize each step in the workflow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install \"openinference-instrumentation-llama-index>=3.0.0\" \"opentelemetry-proto>=1.12.0\" opentelemetry-exporter-otlp opentelemetry-sdk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from opentelemetry.sdk import trace as trace_sdk\n", + "from opentelemetry.sdk.trace.export import SimpleSpanProcessor\n", + "from opentelemetry.exporter.otlp.proto.http.trace_exporter import (\n", + " OTLPSpanExporter as HTTPSpanExporter,\n", + ")\n", + "from openinference.instrumentation.llama_index import LlamaIndexInstrumentor\n", + "\n", + "\n", + "# Add Phoenix API Key for tracing\n", + "PHOENIX_API_KEY = \"\"\n", + "os.environ[\"OTEL_EXPORTER_OTLP_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", + "\n", + "# Add Phoenix\n", + "span_phoenix_processor = SimpleSpanProcessor(\n", + " HTTPSpanExporter(endpoint=\"https://app.phoenix.arize.com/v1/traces\")\n", + ")\n", + "\n", + "# Add them to the tracer\n", + "tracer_provider = trace_sdk.TracerProvider()\n", + "tracer_provider.add_span_processor(span_processor=span_phoenix_processor)\n", + "\n", + "# Instrument the application\n", + "LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -133,7 +182,7 @@ "\n", "\n", "class RAGWorkflow(Workflow):\n", - " @step(pass_context=True)\n", + " @step\n", " async def ingest(self, ctx: Context, ev: StartEvent) -> StopEvent | None:\n", " \"\"\"Entry point to ingest a document, triggered by a StartEvent with `dirname`.\"\"\"\n", " dirname = ev.get(\"dirname\")\n", @@ -141,56 +190,57 @@ " return None\n", "\n", " documents = SimpleDirectoryReader(dirname).load_data()\n", - " ctx.data[\"index\"] = VectorStoreIndex.from_documents(\n", + " index = VectorStoreIndex.from_documents(\n", " documents=documents,\n", " embed_model=OpenAIEmbedding(model_name=\"text-embedding-3-small\"),\n", " )\n", - " return StopEvent(result=f\"Indexed {len(documents)} documents.\")\n", + " return StopEvent(result=index)\n", "\n", - " @step(pass_context=True)\n", + " @step\n", " async def retrieve(\n", " self, ctx: Context, ev: StartEvent\n", " ) -> RetrieverEvent | None:\n", " \"Entry point for RAG, triggered by a StartEvent with `query`.\"\n", " query = ev.get(\"query\")\n", + " index = ev.get(\"index\")\n", + "\n", " if not query:\n", " return None\n", "\n", " print(f\"Query the database with: {query}\")\n", "\n", " # store the query in the global context\n", - " ctx.data[\"query\"] = query\n", + " await ctx.set(\"query\", query)\n", "\n", " # get the index from the global context\n", - " index = ctx.data.get(\"index\")\n", " if index is None:\n", " print(\"Index is empty, load some documents before querying!\")\n", " return None\n", "\n", " retriever = index.as_retriever(similarity_top_k=2)\n", - " nodes = retriever.retrieve(query)\n", + " nodes = await retriever.aretrieve(query)\n", " print(f\"Retrieved {len(nodes)} nodes.\")\n", " return RetrieverEvent(nodes=nodes)\n", "\n", - " @step(pass_context=True)\n", + " @step\n", " async def rerank(self, ctx: Context, ev: RetrieverEvent) -> RerankEvent:\n", " # Rerank the nodes\n", " ranker = LLMRerank(\n", " choice_batch_size=5, top_n=3, llm=OpenAI(model=\"gpt-4o-mini\")\n", " )\n", - " print(ctx.data.get(\"query\"), flush=True)\n", + " print(await ctx.get(\"query\", default=None), flush=True)\n", " new_nodes = ranker.postprocess_nodes(\n", - " ev.nodes, query_str=ctx.data.get(\"query\")\n", + " ev.nodes, query_str=await ctx.get(\"query\", default=None)\n", " )\n", " print(f\"Reranked nodes to {len(new_nodes)}\")\n", " return RerankEvent(nodes=new_nodes)\n", "\n", - " @step(pass_context=True)\n", + " @step\n", " async def synthesize(self, ctx: Context, ev: RerankEvent) -> StopEvent:\n", " \"\"\"Return a streaming response using reranked nodes.\"\"\"\n", " llm = OpenAI(model=\"gpt-4o-mini\")\n", " summarizer = CompactAndRefine(llm=llm, streaming=True, verbose=True)\n", - " query = ctx.data.get(\"query\")\n", + " query = await ctx.get(\"query\", default=None)\n", "\n", " response = await summarizer.asynthesize(query, nodes=ev.nodes)\n", " return StopEvent(result=response)" @@ -219,23 +269,12 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Indexed 77 documents.'" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "w = RAGWorkflow()\n", "\n", "# Ingest the documents\n", - "await w.run(dirname=\"data\")" + "index = await w.run(dirname=\"data\")" ] }, { @@ -249,17 +288,19 @@ "text": [ "Query the database with: How was Llama2 trained?\n", "Retrieved 2 nodes.\n", - "Llama 2 was trained through a multi-step process that began with pretraining using publicly available online sources. This was followed by the creation of an initial version of Llama 2-Chat through supervised fine-tuning. The model was then iteratively refined using Reinforcement Learning with Human Feedback (RLHF) methodologies, which included techniques like rejection sampling and Proximal Policy Optimization (PPO). \n", + "How was Llama2 trained?\n", + "Reranked nodes to 2\n", + "Llama 2 was trained through a multi-step process that began with pretraining using publicly available online sources. This was followed by the creation of an initial version of Llama 2-Chat through supervised fine-tuning. The model was then iteratively refined using Reinforcement Learning with Human Feedback (RLHF) methodologies, which included rejection sampling and Proximal Policy Optimization (PPO). \n", "\n", - "During pretraining, the model utilized an optimized auto-regressive transformer architecture, incorporating robust data cleaning, updated data mixes, and training on a significantly larger dataset of 2 trillion tokens. The training process also involved increased context length and the use of grouped-query attention (GQA) to enhance inference scalability. \n", + "During pretraining, the model utilized an optimized auto-regressive transformer architecture, incorporating robust data cleaning, updated data mixes, and training on a significantly larger dataset of 2 trillion tokens. The training process also involved increased context length and the use of grouped-query attention (GQA) to enhance inference scalability.\n", "\n", - "The training employed the AdamW optimizer with specific hyperparameters, a cosine learning rate schedule, and gradient clipping. The models were pretrained on Meta’s Research SuperCluster and internal production clusters, utilizing NVIDIA A100 GPUs for the training process." + "The training employed the AdamW optimizer with specific hyperparameters, including a cosine learning rate schedule and gradient clipping. The models were pretrained on Meta’s Research SuperCluster and internal production clusters, utilizing NVIDIA A100 GPUs." ] } ], "source": [ "# Run a query\n", - "result = await w.run(query=\"How was Llama2 trained?\")\n", + "result = await w.run(query=\"How was Llama2 trained?\", index=index)\n", "async for chunk in result.async_response_gen():\n", " print(chunk, end=\"\", flush=True)" ] diff --git a/docs/docs/examples/workflow/react_agent.ipynb b/docs/docs/examples/workflow/react_agent.ipynb index 04abb136892a6..7466d15e08346 100644 --- a/docs/docs/examples/workflow/react_agent.ipynb +++ b/docs/docs/examples/workflow/react_agent.ipynb @@ -33,6 +33,55 @@ "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\"" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Optional] Set up observability with Llamatrace\n", + "\n", + "Set up tracing to visualize each step in the workflow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install \"llama-index-core>=0.10.43\" \"openinference-instrumentation-llama-index>=2\" \"opentelemetry-proto>=1.12.0\" opentelemetry-exporter-otlp opentelemetry-sdk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from opentelemetry.sdk import trace as trace_sdk\n", + "from opentelemetry.sdk.trace.export import SimpleSpanProcessor\n", + "from opentelemetry.exporter.otlp.proto.http.trace_exporter import (\n", + " OTLPSpanExporter as HTTPSpanExporter,\n", + ")\n", + "from openinference.instrumentation.llama_index import LlamaIndexInstrumentor\n", + "\n", + "\n", + "# Add Phoenix API Key for tracing\n", + "PHOENIX_API_KEY = \"\"\n", + "os.environ[\"OTEL_EXPORTER_OTLP_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", + "\n", + "# Add Phoenix\n", + "span_phoenix_processor = SimpleSpanProcessor(\n", + " HTTPSpanExporter(endpoint=\"https://app.phoenix.arize.com/v1/traces\")\n", + ")\n", + "\n", + "# Add them to the tracer\n", + "tracer_provider = trace_sdk.TracerProvider()\n", + "tracer_provider.add_span_processor(span_processor=span_phoenix_processor)\n", + "\n", + "# Instrument the application\n", + "LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -158,7 +207,7 @@ " self.output_parser = ReActOutputParser()\n", " self.sources = []\n", "\n", - " @step(pass_context=True)\n", + " @step\n", " async def new_user_msg(self, ctx: Context, ev: StartEvent) -> PrepEvent:\n", " # clear sources\n", " self.sources = []\n", @@ -169,23 +218,23 @@ " self.memory.put(user_msg)\n", "\n", " # clear current reasoning\n", - " ctx.data[\"current_reasoning\"] = []\n", + " await ctx.set(\"current_reasoning\", [])\n", "\n", " return PrepEvent()\n", "\n", - " @step(pass_context=True)\n", + " @step\n", " async def prepare_chat_history(\n", " self, ctx: Context, ev: PrepEvent\n", " ) -> InputEvent:\n", " # get chat history\n", " chat_history = self.memory.get()\n", - " current_reasoning = ctx.data.get(\"current_reasoning\", [])\n", + " current_reasoning = await ctx.get(\"current_reasoning\", default=[])\n", " llm_input = self.formatter.format(\n", " self.tools, chat_history, current_reasoning=current_reasoning\n", " )\n", " return InputEvent(input=llm_input)\n", "\n", - " @step(pass_context=True)\n", + " @step\n", " async def handle_llm_input(\n", " self, ctx: Context, ev: InputEvent\n", " ) -> ToolCallEvent | StopEvent:\n", @@ -195,7 +244,9 @@ "\n", " try:\n", " reasoning_step = self.output_parser.parse(response.message.content)\n", - " ctx.data.get(\"current_reasoning\", []).append(reasoning_step)\n", + " await ctx.get(\"current_reasoning\", default=[]).append(\n", + " reasoning_step\n", + " )\n", " if reasoning_step.is_done:\n", " self.memory.put(\n", " ChatMessage(\n", @@ -206,7 +257,9 @@ " result={\n", " \"response\": reasoning_step.response,\n", " \"sources\": [*self.sources],\n", - " \"reasoning\": ctx.data.get(\"current_reasoning\", []),\n", + " \"reasoning\": await ctx.get(\n", + " \"current_reasoning\", default=[]\n", + " ),\n", " }\n", " )\n", " elif isinstance(reasoning_step, ActionReasoningStep):\n", @@ -222,7 +275,7 @@ " ]\n", " )\n", " except Exception as e:\n", - " ctx.data.get(\"current_reasoning\", []).append(\n", + " await ctx.get(\"current_reasoning\", default=[]).append(\n", " ObservationReasoningStep(\n", " observation=f\"There was an error in parsing my reasoning: {e}\"\n", " )\n", @@ -231,7 +284,7 @@ " # if no tool calls or final response, iterate again\n", " return PrepEvent()\n", "\n", - " @step(pass_context=True)\n", + " @step\n", " async def handle_tool_calls(\n", " self, ctx: Context, ev: ToolCallEvent\n", " ) -> PrepEvent:\n", @@ -242,7 +295,7 @@ " for tool_call in tool_calls:\n", " tool = tools_by_name.get(tool_call.tool_name)\n", " if not tool:\n", - " ctx.data.get(\"current_reasoning\", []).append(\n", + " await ctx.get(\"current_reasoning\", default=[]).append(\n", " ObservationReasoningStep(\n", " observation=f\"Tool {tool_call.tool_name} does not exist\"\n", " )\n", @@ -252,11 +305,11 @@ " try:\n", " tool_output = tool(**tool_call.tool_kwargs)\n", " self.sources.append(tool_output)\n", - " ctx.data.get(\"current_reasoning\", []).append(\n", + " await ctx.get(\"current_reasoning\", default=[]).append(\n", " ObservationReasoningStep(observation=tool_output.content)\n", " )\n", " except Exception as e:\n", - " ctx.data.get(\"current_reasoning\", []).append(\n", + " await ctx.get(\"current_reasoning\", default=[]).append(\n", " ObservationReasoningStep(\n", " observation=f\"Error calling tool {tool.metadata.get_name()}: {e}\"\n", " )\n", diff --git a/docs/docs/examples/workflow/reflection.ipynb b/docs/docs/examples/workflow/reflection.ipynb index 0e8a5b41b7105..1dae796bc8559 100644 --- a/docs/docs/examples/workflow/reflection.ipynb +++ b/docs/docs/examples/workflow/reflection.ipynb @@ -161,15 +161,15 @@ "class ReflectionWorkflow(Workflow):\n", " max_retries: int = 3\n", "\n", - " @step(pass_context=True)\n", + " @step\n", " async def extract(\n", " self, ctx: Context, ev: StartEvent | ValidationErrorEvent\n", " ) -> StopEvent | ExtractionDone:\n", - " current_retries = ctx.data.get(\"retries\", 0)\n", + " current_retries = await ctx.get(\"retries\", default=0)\n", " if current_retries >= self.max_retries:\n", " return StopEvent(result=\"Max retries reached\")\n", " else:\n", - " ctx.data[\"retries\"] = current_retries + 1\n", + " await ctx.set(\"retries\", current_retries + 1)\n", "\n", " if isinstance(ev, StartEvent):\n", " passage = ev.get(\"passage\")\n", @@ -193,12 +193,12 @@ "\n", " return ExtractionDone(output=str(output), passage=passage)\n", "\n", - " @step()\n", + " @step\n", " async def validate(\n", " self, ev: ExtractionDone\n", " ) -> StopEvent | ValidationErrorEvent:\n", " try:\n", - " json.loads(ev.output)\n", + " CarCollection.model_validate_json(ev.output)\n", " except Exception as e:\n", " print(\"Validation failed, retrying...\")\n", " return ValidationErrorEvent(\n", diff --git a/docs/docs/examples/workflow/router_query_engine.ipynb b/docs/docs/examples/workflow/router_query_engine.ipynb new file mode 100644 index 0000000000000..0ac4b67c2d59c --- /dev/null +++ b/docs/docs/examples/workflow/router_query_engine.ipynb @@ -0,0 +1,713 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Router Query Engine\n", + "\n", + "`RouterQueryEngine` chooses the most appropriate query engine from multiple options to process a given query.\n", + "\n", + "This notebook walks through implementation of Router Query Engine, using workflows.\n", + "\n", + "Specifically we will implement [RouterQueryEngine](https://docs.llamaindex.ai/en/stable/examples/query_engine/RouterQueryEngine/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U llama-index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-..\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since workflows are async first, this all runs fine in a notebook. If you were running in your own code, you would want to use `asyncio.run()` to start an async event loop if one isn't already running.\n", + "\n", + "```python\n", + "async def main():\n", + " \n", + "\n", + "if __name__ == \"__main__\":\n", + " import asyncio\n", + " asyncio.run(main())\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Events" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.workflow import Event\n", + "from llama_index.core.base.base_selector import SelectorResult\n", + "from typing import Dict, List, Any\n", + "from llama_index.core.base.response.schema import RESPONSE_TYPE\n", + "\n", + "\n", + "class QueryEngineSelectionEvent(Event):\n", + " \"\"\"Result of selecting the query engine tools.\"\"\"\n", + "\n", + " selected_query_engines: SelectorResult\n", + "\n", + "\n", + "class SynthesizeEvent(Event):\n", + " \"\"\"Event for synthesizing the response from different query engines.\"\"\"\n", + "\n", + " result: List[RESPONSE_TYPE]\n", + " selected_query_engines: SelectorResult" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Workflow\n", + "\n", + "`selector:`\n", + "\n", + "1. It takes a StartEvent as input and returns a QueryEngineSelectionEvent.\n", + "2. The `LLMSingleSelector`/ `PydanticSingleSelector`/ `PydanticMultiSelector` will select one/ multiple query engine tools.\n", + "\n", + "`generate_responses:`\n", + "\n", + "This function uses the selected query engines to generate responses and returns SynthesizeEvent.\n", + "\n", + "`synthesize_responses:`\n", + "\n", + "This function combines the generated responses and synthesizes the final response if multiple query engines are selected otherwise returns the single generated response.\n", + "\n", + "\n", + "The steps will use the built-in `StartEvent` and `StopEvent` events.\n", + "\n", + "With our events defined, we can construct our workflow and steps. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.workflow import (\n", + " Context,\n", + " Workflow,\n", + " StartEvent,\n", + " StopEvent,\n", + " step,\n", + ")\n", + "\n", + "from llama_index.llms.openai import OpenAI\n", + "from llama_index.core.selectors.utils import get_selector_from_llm\n", + "from llama_index.core.base.response.schema import (\n", + " PydanticResponse,\n", + " Response,\n", + " AsyncStreamingResponse,\n", + ")\n", + "from llama_index.core.bridge.pydantic import BaseModel\n", + "from llama_index.core.response_synthesizers import TreeSummarize\n", + "from llama_index.core.schema import QueryBundle\n", + "from llama_index.core import Settings\n", + "\n", + "from IPython.display import Markdown, display\n", + "import asyncio\n", + "\n", + "\n", + "class RouterQueryEngineWorkflow(Workflow):\n", + " @step\n", + " async def selector(\n", + " self, ctx: Context, ev: StartEvent\n", + " ) -> QueryEngineSelectionEvent:\n", + " \"\"\"\n", + " Selects a single/ multiple query engines based on the query.\n", + " \"\"\"\n", + "\n", + " await ctx.set(\"query\", ev.get(\"query\"))\n", + " await ctx.set(\"llm\", ev.get(\"llm\"))\n", + " await ctx.set(\"query_engine_tools\", ev.get(\"query_engine_tools\"))\n", + " await ctx.set(\"summarizer\", ev.get(\"summarizer\"))\n", + "\n", + " llm = Settings.llm\n", + " select_multiple_query_engines = ev.get(\"select_multi\")\n", + " query = ev.get(\"query\")\n", + " query_engine_tools = ev.get(\"query_engine_tools\")\n", + "\n", + " selector = get_selector_from_llm(\n", + " llm, is_multi=select_multiple_query_engines\n", + " )\n", + "\n", + " query_engines_metadata = [\n", + " query_engine.metadata for query_engine in query_engine_tools\n", + " ]\n", + "\n", + " selected_query_engines = await selector.aselect(\n", + " query_engines_metadata, query\n", + " )\n", + "\n", + " return QueryEngineSelectionEvent(\n", + " selected_query_engines=selected_query_engines\n", + " )\n", + "\n", + " @step\n", + " async def generate_responses(\n", + " self, ctx: Context, ev: QueryEngineSelectionEvent\n", + " ) -> SynthesizeEvent:\n", + " \"\"\"Generate the responses from the selected query engines.\"\"\"\n", + "\n", + " query = await ctx.get(\"query\", default=None)\n", + " selected_query_engines = ev.selected_query_engines\n", + " query_engine_tools = await ctx.get(\"query_engine_tools\")\n", + "\n", + " query_engines = [engine.query_engine for engine in query_engine_tools]\n", + "\n", + " print(\n", + " f\"number of selected query engines: {len(selected_query_engines.selections)}\"\n", + " )\n", + "\n", + " if len(selected_query_engines.selections) > 1:\n", + " tasks = []\n", + " for selected_query_engine in selected_query_engines.selections:\n", + " print(\n", + " f\"Selected query engine: {selected_query_engine.index}: {selected_query_engine.reason}\"\n", + " )\n", + " query_engine = query_engines[selected_query_engine.index]\n", + " tasks.append(query_engine.aquery(query))\n", + "\n", + " response_generated = await asyncio.gather(*tasks)\n", + "\n", + " else:\n", + " query_engine = query_engines[\n", + " selected_query_engines.selections[0].index\n", + " ]\n", + "\n", + " print(\n", + " f\"Selected query engine: {selected_query_engines.ind}: {selected_query_engines.reason}\"\n", + " )\n", + "\n", + " response_generated = [await query_engine.aquery(query)]\n", + "\n", + " return SynthesizeEvent(\n", + " result=response_generated,\n", + " selected_query_engines=selected_query_engines,\n", + " )\n", + "\n", + " async def acombine_responses(\n", + " self,\n", + " summarizer: TreeSummarize,\n", + " responses: List[RESPONSE_TYPE],\n", + " query_bundle: QueryBundle,\n", + " ) -> RESPONSE_TYPE:\n", + " \"\"\"Async combine multiple response from sub-engines.\"\"\"\n", + "\n", + " print(\"Combining responses from multiple query engines.\")\n", + "\n", + " response_strs = []\n", + " source_nodes = []\n", + " for response in responses:\n", + " if isinstance(\n", + " response, (AsyncStreamingResponse, PydanticResponse)\n", + " ):\n", + " response_obj = await response.aget_response()\n", + " else:\n", + " response_obj = response\n", + " source_nodes.extend(response_obj.source_nodes)\n", + " response_strs.append(str(response))\n", + "\n", + " summary = await summarizer.aget_response(\n", + " query_bundle.query_str, response_strs\n", + " )\n", + "\n", + " if isinstance(summary, str):\n", + " return Response(response=summary, source_nodes=source_nodes)\n", + " elif isinstance(summary, BaseModel):\n", + " return PydanticResponse(\n", + " response=summary, source_nodes=source_nodes\n", + " )\n", + " else:\n", + " return AsyncStreamingResponse(\n", + " response_gen=summary, source_nodes=source_nodes\n", + " )\n", + "\n", + " @step\n", + " async def synthesize_responses(\n", + " self, ctx: Context, ev: SynthesizeEvent\n", + " ) -> StopEvent:\n", + " \"\"\"Synthesizes the responses from the generated responses.\"\"\"\n", + "\n", + " response_generated = ev.result\n", + " query = await ctx.get(\"query\", default=None)\n", + " summarizer = await ctx.get(\"summarizer\")\n", + " selected_query_engines = ev.selected_query_engines\n", + "\n", + " if len(response_generated) > 1:\n", + " response = await self.acombine_responses(\n", + " summarizer, response_generated, QueryBundle(query_str=query)\n", + " )\n", + " else:\n", + " response = response_generated[0]\n", + "\n", + " response.metadata = response.metadata or {}\n", + " response.metadata[\"selector_result\"] = selected_query_engines\n", + "\n", + " return StopEvent(result=response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define LLM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm = OpenAI(model=\"gpt-4o-mini\")\n", + "Settings.llm = llm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Summarizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.prompts.default_prompt_selectors import (\n", + " DEFAULT_TREE_SUMMARIZE_PROMPT_SEL,\n", + ")\n", + "\n", + "summarizer = TreeSummarize(\n", + " llm=llm,\n", + " summary_template=DEFAULT_TREE_SUMMARIZE_PROMPT_SEL,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-08-26 22:46:42-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8000::154, 2606:50c0:8003::154, 2606:50c0:8002::154, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8000::154|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 75042 (73K) [text/plain]\n", + "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n", + "\n", + "data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.02s \n", + "\n", + "2024-08-26 22:46:42 (3.82 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir -p 'data/paul_graham/'\n", + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "\n", + "documents = SimpleDirectoryReader(\"./data/paul_graham\").load_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Nodes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nodes = Settings.node_parser.get_nodes_from_documents(documents)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Indices\n", + "\n", + "We will create three indices SummaryIndex, VectorStoreIndex and SimpleKeywordTableIndex." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import (\n", + " VectorStoreIndex,\n", + " SummaryIndex,\n", + " SimpleKeywordTableIndex,\n", + ")\n", + "\n", + "summary_index = SummaryIndex(nodes)\n", + "vector_index = VectorStoreIndex(nodes)\n", + "keyword_index = SimpleKeywordTableIndex(nodes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Query Engine Tools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.tools import QueryEngineTool\n", + "\n", + "list_query_engine = summary_index.as_query_engine(\n", + " response_mode=\"tree_summarize\",\n", + " use_async=True,\n", + ")\n", + "vector_query_engine = vector_index.as_query_engine()\n", + "keyword_query_engine = keyword_index.as_query_engine()\n", + "\n", + "list_tool = QueryEngineTool.from_defaults(\n", + " query_engine=list_query_engine,\n", + " description=(\n", + " \"Useful for summarization questions related to Paul Graham eassy on\"\n", + " \" What I Worked On.\"\n", + " ),\n", + ")\n", + "\n", + "vector_tool = QueryEngineTool.from_defaults(\n", + " query_engine=vector_query_engine,\n", + " description=(\n", + " \"Useful for retrieving specific context from Paul Graham essay on What\"\n", + " \" I Worked On.\"\n", + " ),\n", + ")\n", + "\n", + "keyword_tool = QueryEngineTool.from_defaults(\n", + " query_engine=keyword_query_engine,\n", + " description=(\n", + " \"Useful for retrieving specific context using keywords from Paul\"\n", + " \" Graham essay on What I Worked On.\"\n", + " ),\n", + ")\n", + "\n", + "query_engine_tools = [list_tool, vector_tool, keyword_tool]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the Workflow!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()\n", + "\n", + "w = RouterQueryEngineWorkflow(timeout=200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Querying" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Summarization Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of selected query engines: 1\n", + "Selected query engine: 0: This choice directly addresses the need for a summary of the document.\n" + ] + }, + { + "data": { + "text/markdown": [ + "> Question: Provide the summary of the document?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Answer: The document recounts the journey of an individual who transitioned from writing and programming in his youth to exploring artificial intelligence and eventually becoming a successful entrepreneur and essayist. Initially drawn to philosophy in college, he found it unfulfilling and shifted his focus to AI, inspired by literature and documentaries. His academic pursuits led him to reverse-engineer a natural language program, but he soon realized the limitations of AI at the time.\n", + "\n", + "After completing his PhD, he ventured into the art world, taking classes and painting, while also working on a book about Lisp programming. His experiences in the tech industry, particularly at a software company, shaped his understanding of business dynamics and the importance of being an entry-level option in the market.\n", + "\n", + "In the mid-1990s, he co-founded Viaweb, an early web application for building online stores, which was later acquired by Yahoo. Following this, he became involved in angel investing and co-founded Y Combinator, a startup accelerator that revolutionized seed funding by supporting multiple startups simultaneously.\n", + "\n", + "The narrative highlights the author's reflections on the nature of work, the significance of pursuing unprestigious projects, and the evolution of his interests from programming to writing essays. He emphasizes the value of independent thinking and the impact of the internet on publishing and entrepreneurship. Ultimately, the document illustrates a life characterized by exploration, creativity, and a commitment to helping others succeed in their ventures." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This should use summary query engine/ tool.\n", + "\n", + "query = \"Provide the summary of the document?\"\n", + "\n", + "result = await w.run(\n", + " query=query,\n", + " llm=llm,\n", + " query_engine_tools=query_engine_tools,\n", + " summarizer=summarizer,\n", + " select_multi=True, # You can change it to default it to select only one query engine.\n", + ")\n", + "\n", + "display(\n", + " Markdown(\"> Question: {}\".format(query)),\n", + " Markdown(\"Answer: {}\".format(result)),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Pointed Context Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of selected query engines: 1\n", + "Selected query engine: 1: The question asks for specific context about the author's experiences growing up, which aligns with retrieving specific context from the essay.\n" + ] + }, + { + "data": { + "text/markdown": [ + "> Question: What did the author do growing up?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Answer: Growing up, the author focused on writing and programming outside of school. Initially, he wrote short stories, which he later described as lacking in plot but rich in character emotions. He began programming at a young age on an IBM 1401, where he experimented with early Fortran and punch cards. Eventually, he convinced his father to buy a TRS-80 microcomputer, which allowed him to write simple games and a word processor. Despite enjoying programming, he initially planned to study philosophy in college, believing it to be a pursuit of ultimate truths. However, he later switched his focus to artificial intelligence after finding philosophy courses unengaging." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This should use vector query engine/ tool.\n", + "\n", + "query = \"What did the author do growing up?\"\n", + "\n", + "result = await w.run(\n", + " query=query,\n", + " llm=llm,\n", + " query_engine_tools=query_engine_tools,\n", + " summarizer=summarizer,\n", + " select_multi=False, # You can change it to select multiple query engines.\n", + ")\n", + "\n", + "display(\n", + " Markdown(\"> Question: {}\".format(query)),\n", + " Markdown(\"Answer: {}\".format(result)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of selected query engines: 2\n", + "Selected query engine: 1: This choice is useful for retrieving specific context related to notable events and people from the author's time at Interleaf and YC.\n", + "Selected query engine: 2: This choice allows for retrieving specific context using keywords, which can help in identifying notable events and people.\n", + "Combining responses from multiple query engines.\n" + ] + }, + { + "data": { + "text/markdown": [ + "> Question: What were noteable events and people from the authors time at Interleaf and YC?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "Answer: Notable events during the author's time at Interleaf included the establishment of a large Release Engineering group, which underscored the complexities of software updates and version management. The company also made a significant decision to incorporate a scripting language inspired by Emacs, aimed at attracting Lisp hackers to enhance their software capabilities. The author reflected on this period as the closest they had to a normal job, despite acknowledging their shortcomings as an employee.\n", + "\n", + "At Y Combinator (YC), key events included the launch of the first Summer Founders Program, which received 225 applications and funded eight startups, featuring notable figures such as the founders of Reddit, Justin Kan and Emmett Shear (who later founded Twitch), and Aaron Swartz. The program fostered a supportive community among founders and marked a transition for YC from a small initiative to a larger organization. Significant individuals during this time included Jessica Livingston, with whom the author had a close professional and personal relationship, as well as Robert Morris and Trevor Blackwell, who contributed to the development of shopping cart software and were recognized for their programming skills, respectively. Sam Altman, who later became the second president of YC, was also mentioned as a significant figure in this period." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This query could use either a keyword or vector query engine\n", + "# so it will combine responses from both\n", + "\n", + "query = \"What were noteable events and people from the authors time at Interleaf and YC?\"\n", + "\n", + "result = await w.run(\n", + " query=query,\n", + " llm=llm,\n", + " query_engine_tools=query_engine_tools,\n", + " summarizer=summarizer,\n", + " select_multi=True, # Since query should use two query engine tools, we enabled it.\n", + ")\n", + "\n", + "display(\n", + " Markdown(\"> Question: {}\".format(query)),\n", + " Markdown(\"Answer: {}\".format(result)),\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llamaindex", + "language": "python", + "name": "llamaindex" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/examples/workflow/self_discover_workflow.ipynb b/docs/docs/examples/workflow/self_discover_workflow.ipynb new file mode 100644 index 0000000000000..c38e05bf897b9 --- /dev/null +++ b/docs/docs/examples/workflow/self_discover_workflow.ipynb @@ -0,0 +1,404 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Self-Discover Workflow\n", + "\n", + "This notebooks shows how to implement [SELF-DISCOVER](https://arxiv.org/abs/2402.03620).\n", + "\n", + "It has two stages for the given task:\n", + "\n", + "1. STAGE-1:\n", + "\n", + " a. SELECT: Selects subset of reasoning Modules.\n", + "\n", + " b. ADAPT: Adapts selected reasoning modules to the task.\n", + "\n", + " c. IMPLEMENT: It gives reasoning structure for the task.\n", + " \n", + "2. STAGE-2: Uses the generated reasoning structure for the task to generate an answer.\n", + "\n", + "\n", + "The implementation is inspired from the [codebase](https://github.com/catid/self-discover)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -U llama-index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since workflows are async first, this all runs fine in a notebook. If you were running in your own code, you would want to use `asyncio.run()` to start an async event loop if one isn't already running.\n", + "\n", + "```python\n", + "async def main():\n", + " \n", + "\n", + "if __name__ == \"__main__\":\n", + " import asyncio\n", + " asyncio.run(main())\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Set up the reasoning modules and the prompt templates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.prompts import PromptTemplate\n", + "\n", + "_REASONING_MODULES = [\n", + " \"1. How could I devise an experiment to help solve that problem?\",\n", + " \"2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\",\n", + " \"3. How could I measure progress on this problem?\",\n", + " \"4. How can I simplify the problem so that it is easier to solve?\",\n", + " \"5. What are the key assumptions underlying this problem?\",\n", + " \"6. What are the potential risks and drawbacks of each solution?\",\n", + " \"7. What are the alternative perspectives or viewpoints on this problem?\",\n", + " \"8. What are the long-term implications of this problem and its solutions?\",\n", + " \"9. How can I break down this problem into smaller, more manageable parts?\",\n", + " \"10. Critical Thinking: This style involves analyzing the problem from different perspectives, questioning assumptions, and evaluating the evidence or information available. It focuses on logical reasoning, evidence-based decision-making, and identifying potential biases or flaws in thinking.\",\n", + " \"11. Try creative thinking, generate innovative and out-of-the-box ideas to solve the problem. Explore unconventional solutions, thinking beyond traditional boundaries, and encouraging imagination and originality.\",\n", + " \"12. Seek input and collaboration from others to solve the problem. Emphasize teamwork, open communication, and leveraging the diverse perspectives and expertise of a group to come up with effective solutions.\",\n", + " \"13. Use systems thinking: Consider the problem as part of a larger system and understanding the interconnectedness of various elements. Focuses on identifying the underlying causes, feedback loops, and interdependencies that influence the problem, and developing holistic solutions that address the system as a whole.\",\n", + " \"14. Use Risk Analysis: Evaluate potential risks, uncertainties, and tradeoffs associated with different solutions or approaches to a problem. Emphasize assessing the potential consequences and likelihood of success or failure, and making informed decisions based on a balanced analysis of risks and benefits.\",\n", + " \"15. Use Reflective Thinking: Step back from the problem, take the time for introspection and self-reflection. Examine personal biases, assumptions, and mental models that may influence problem-solving, and being open to learning from past experiences to improve future approaches.\",\n", + " \"16. What is the core issue or problem that needs to be addressed?\",\n", + " \"17. What are the underlying causes or factors contributing to the problem?\",\n", + " \"18. Are there any potential solutions or strategies that have been tried before? If yes, what were the outcomes and lessons learned?\",\n", + " \"19. What are the potential obstacles or challenges that might arise in solving this problem?\",\n", + " \"20. Are there any relevant data or information that can provide insights into the problem? If yes, what data sources are available, and how can they be analyzed?\",\n", + " \"21. Are there any stakeholders or individuals who are directly affected by the problem? What are their perspectives and needs?\",\n", + " \"22. What resources (financial, human, technological, etc.) are needed to tackle the problem effectively?\",\n", + " \"23. How can progress or success in solving the problem be measured or evaluated?\",\n", + " \"24. What indicators or metrics can be used?\",\n", + " \"25. Is the problem a technical or practical one that requires a specific expertise or skill set? Or is it more of a conceptual or theoretical problem?\",\n", + " \"26. Does the problem involve a physical constraint, such as limited resources, infrastructure, or space?\",\n", + " \"27. Is the problem related to human behavior, such as a social, cultural, or psychological issue?\",\n", + " \"28. Does the problem involve decision-making or planning, where choices need to be made under uncertainty or with competing objectives?\",\n", + " \"29. Is the problem an analytical one that requires data analysis, modeling, or optimization techniques?\",\n", + " \"30. Is the problem a design challenge that requires creative solutions and innovation?\",\n", + " \"31. Does the problem require addressing systemic or structural issues rather than just individual instances?\",\n", + " \"32. Is the problem time-sensitive or urgent, requiring immediate attention and action?\",\n", + " \"33. What kinds of solution typically are produced for this kind of problem specification?\",\n", + " \"34. Given the problem specification and the current best solution, have a guess about other possible solutions.\"\n", + " \"35. Let’s imagine the current best solution is totally wrong, what other ways are there to think about the problem specification?\"\n", + " \"36. What is the best way to modify this current best solution, given what you know about these kinds of problem specification?\"\n", + " \"37. Ignoring the current best solution, create an entirely new solution to the problem.\"\n", + " \"38. Let’s think step by step .\"\n", + " \"39. Let’s make a step by step plan and implement it with good notation and explanation.\",\n", + "]\n", + "\n", + "_REASONING_MODULES = \"\\n\".join(_REASONING_MODULES)\n", + "\n", + "SELECT_PRMOPT_TEMPLATE = PromptTemplate(\n", + " \"Given the task: {task}, which of the following reasoning modules are relevant? Do not elaborate on why.\\n\\n {reasoning_modules}\"\n", + ")\n", + "\n", + "ADAPT_PROMPT_TEMPLATE = PromptTemplate(\n", + " \"Without working out the full solution, adapt the following reasoning modules to be specific to our task:\\n{selected_modules}\\n\\nOur task:\\n{task}\"\n", + ")\n", + "\n", + "IMPLEMENT_PROMPT_TEMPLATE = PromptTemplate(\n", + " \"Without working out the full solution, create an actionable reasoning structure for the task using these adapted reasoning modules:\\n{adapted_modules}\\n\\nTask Description:\\n{task}\"\n", + ")\n", + "\n", + "REASONING_PROMPT_TEMPLATE = PromptTemplate(\n", + " \"Using the following reasoning structure: {reasoning_structure}\\n\\nSolve this task, providing your final answer: {task}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Designing the Workflow\n", + "\n", + "SELF-DISCOVER consists of the following steps:\n", + "1. Selecting a subset of given reasoning modules.\n", + "2. Refine and adapt the subset of given reasoning modules based on the task.\n", + "3. Create a reasoning structure for the task given the adapted reasoning modules.\n", + "4. Generate a final answer for the task given the reasoning structure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following events are needed:\n", + "\n", + "1. `GetModulesEvent`: Triggered after a subset of modules are retrieved.\n", + "2. `RefineModulesEvent`: Triggered after the modules are refined and adapted to the task.\n", + "3. `ReasoningStructureEvent`: Triggered after the reasoning structure is generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.workflow import Event\n", + "\n", + "\n", + "class GetModulesEvent(Event):\n", + " \"\"\"Event to get modules.\"\"\"\n", + "\n", + " task: str\n", + " modules: str\n", + "\n", + "\n", + "class RefineModulesEvent(Event):\n", + " \"\"\"Event to refine modules.\"\"\"\n", + "\n", + " task: str\n", + " refined_modules: str\n", + "\n", + "\n", + "class ReasoningStructureEvent(Event):\n", + " \"\"\"Event to create reasoning structure.\"\"\"\n", + "\n", + " task: str\n", + " reasoning_structure: str" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is the code for the SELF-DISCOVER workflow:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.workflow import (\n", + " Workflow,\n", + " Context,\n", + " StartEvent,\n", + " StopEvent,\n", + " step,\n", + ")\n", + "from llama_index.core.llms import LLM\n", + "\n", + "\n", + "class SelfDiscoverWorkflow(Workflow):\n", + " \"\"\"Self discover workflow.\"\"\"\n", + "\n", + " @step\n", + " async def get_modules(\n", + " self, ctx: Context, ev: StartEvent\n", + " ) -> GetModulesEvent:\n", + " \"\"\"Get modules step.\"\"\"\n", + " # get input data, store llm into ctx\n", + " task = ev.get(\"task\")\n", + " llm: LLM = ev.get(\"llm\")\n", + "\n", + " if task is None or llm is None:\n", + " raise ValueError(\"'task' and 'llm' arguments are required.\")\n", + "\n", + " await ctx.set(\"llm\", llm)\n", + "\n", + " # format prompt and get result from LLM\n", + " prompt = SELECT_PRMOPT_TEMPLATE.format(\n", + " task=task, reasoning_modules=_REASONING_MODULES\n", + " )\n", + " result = llm.complete(prompt)\n", + "\n", + " return GetModulesEvent(task=task, modules=str(result))\n", + "\n", + " @step\n", + " async def refine_modules(\n", + " self, ctx: Context, ev: GetModulesEvent\n", + " ) -> RefineModulesEvent:\n", + " \"\"\"Refine modules step.\"\"\"\n", + " task = ev.task\n", + " modules = ev.modules\n", + " llm: LLM = await ctx.get(\"llm\")\n", + "\n", + " # format prompt and get result\n", + " prompt = ADAPT_PROMPT_TEMPLATE.format(\n", + " task=task, selected_modules=modules\n", + " )\n", + " result = llm.complete(prompt)\n", + "\n", + " return RefineModulesEvent(task=task, refined_modules=str(result))\n", + "\n", + " @step\n", + " async def create_reasoning_structure(\n", + " self, ctx: Context, ev: RefineModulesEvent\n", + " ) -> ReasoningStructureEvent:\n", + " \"\"\"Create reasoning structures step.\"\"\"\n", + " task = ev.task\n", + " refined_modules = ev.refined_modules\n", + " llm: LLM = await ctx.get(\"llm\")\n", + "\n", + " # format prompt, get result\n", + " prompt = IMPLEMENT_PROMPT_TEMPLATE.format(\n", + " task=task, adapted_modules=refined_modules\n", + " )\n", + " result = llm.complete(prompt)\n", + "\n", + " return ReasoningStructureEvent(\n", + " task=task, reasoning_structure=str(result)\n", + " )\n", + "\n", + " @step\n", + " async def get_final_result(\n", + " self, ctx: Context, ev: ReasoningStructureEvent\n", + " ) -> StopEvent:\n", + " \"\"\"Gets final result from reasoning structure event.\"\"\"\n", + " task = ev.task\n", + " reasoning_structure = ev.reasoning_structure\n", + " llm: LLM = await ctx.get(\"llm\")\n", + "\n", + " # format prompt, get res\n", + " prompt = REASONING_PROMPT_TEMPLATE.format(\n", + " task=task, reasoning_structure=reasoning_structure\n", + " )\n", + " result = llm.complete(prompt)\n", + "\n", + " return StopEvent(result=result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the workflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.openai import OpenAI\n", + "\n", + "workflow = SelfDiscoverWorkflow()\n", + "llm = OpenAI(\"gpt-4o\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "To solve the task of determining how many oranges and apples Michael has now, we will follow the structured reasoning approach and break down the problem into smaller, manageable parts. We will update the counts of oranges and apples step by step.\n", + "\n", + "### Step-by-Step Plan:\n", + "\n", + "1. **Initial count of oranges:**\n", + " - Michael starts with 15 oranges.\n", + " - \\( \\text{Oranges} = 15 \\)\n", + "\n", + "2. **Oranges given to his brother:**\n", + " - Michael gives 4 oranges to his brother.\n", + " - \\( \\text{Oranges} = 15 - 4 = 11 \\)\n", + "\n", + "3. **Oranges traded for apples:**\n", + " - Michael trades 3 oranges for 6 apples.\n", + " - \\( \\text{Oranges} = 11 - 3 = 8 \\)\n", + " - \\( \\text{Apples} = 0 + 6 = 6 \\)\n", + "\n", + "4. **Spoiled oranges discarded:**\n", + " - Michael discards 2 spoiled oranges.\n", + " - \\( \\text{Oranges} = 8 - 2 = 6 \\)\n", + "\n", + "5. **Oranges bought at the market:**\n", + " - Michael buys 12 more oranges.\n", + " - \\( \\text{Oranges} = 6 + 12 = 18 \\)\n", + "\n", + "6. **Initial count of apples:**\n", + " - Michael initially had no apples.\n", + " - \\( \\text{Apples} = 6 \\) (from the trade)\n", + "\n", + "7. **Apples bought at the market:**\n", + " - Michael buys 5 more apples.\n", + " - \\( \\text{Apples} = 6 + 5 = 11 \\)\n", + "\n", + "8. **Apples given to his friend:**\n", + " - Michael gives 2 apples to his friend.\n", + " - \\( \\text{Apples} = 11 - 2 = 9 \\)\n", + "\n", + "### Final counts:\n", + "- **Oranges:** 18\n", + "- **Apples:** 9\n", + "\n", + "Therefore, Michael has 18 oranges and 9 apples now." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, Markdown\n", + "\n", + "task = \"Michael has 15 oranges. He gives 4 oranges to his brother and trades 3 oranges for 6 apples with his neighbor. Later in the day, he realizes some of his oranges are spoiled, so he discards 2 of them. Then, Michael goes to the market and buys 12 more oranges and 5 more apples. If Michael decides to give 2 apples to his friend, how many oranges and apples does Michael have now?\"\n", + "result = await workflow.run(task=task, llm=llm)\n", + "display(Markdown(str(result)))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llama-index-RvIdVF4_-py3.11", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/examples/workflow/sub_question_query_engine.ipynb b/docs/docs/examples/workflow/sub_question_query_engine.ipynb new file mode 100644 index 0000000000000..f97a5203d4a39 --- /dev/null +++ b/docs/docs/examples/workflow/sub_question_query_engine.ipynb @@ -0,0 +1,645 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sub Question Query Engine as a workflow\n", + "\n", + "LlamaIndex has a built-in Sub-Question Query Engine. Here, we replace it with a Workflow-based equivalent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we install our dependencies:\n", + "* LlamaIndex core for most things\n", + "* OpenAI LLM and embeddings for LLM actions\n", + "* `llama-index-readers-file` to power the PDF reader in `SimpleDirectoryReader`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index-core llama-index-llms-openai llama-index-embeddings-openai llama-index-readers-file llama-index-utils-workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bring in our dependencies as imports:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os, json\n", + "from llama_index.core import (\n", + " SimpleDirectoryReader,\n", + " VectorStoreIndex,\n", + " StorageContext,\n", + " load_index_from_storage,\n", + ")\n", + "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n", + "from llama_index.core.workflow import (\n", + " step,\n", + " Context,\n", + " Workflow,\n", + " Event,\n", + " StartEvent,\n", + " StopEvent,\n", + ")\n", + "from llama_index.core.agent import ReActAgent\n", + "from llama_index.llms.openai import OpenAI\n", + "from llama_index.utils.workflow import draw_all_possible_flows" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the Sub Question Query Engine as a Workflow\n", + "\n", + "* Our StartEvent goes to `query()`, which takes care of several things:\n", + " * Accepts and stores the original query\n", + " * Stores the LLM to handle the queries\n", + " * Stores the list of tools to enable sub-questions\n", + " * Passes the original question to the LLM, asking it to split up the question into sub-questions\n", + " * Fires off a `QueryEvent` for every sub-question generated\n", + "\n", + "* QueryEvents go to `sub_question()`, which instantiates a new ReAct agent with the full list of tools available and lets it select which one to use.\n", + " * This is slightly better than the actual SQQE built-in to LlamaIndex, which cannot use multiple tools\n", + " * Each QueryEvent generates an `AnswerEvent`\n", + "\n", + "* AnswerEvents go to `combine_answers()`.\n", + " * This uses `self.collect_events()` to wait for every QueryEvent to return an answer.\n", + " * All the answers are then combined into a final prompt for the LLM to consolidate them into a single response\n", + " * A StopEvent is generated to return the final result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class QueryEvent(Event):\n", + " question: str\n", + "\n", + "\n", + "class AnswerEvent(Event):\n", + " question: str\n", + " answer: str\n", + "\n", + "\n", + "class SubQuestionQueryEngine(Workflow):\n", + " @step\n", + " async def query(self, ctx: Context, ev: StartEvent) -> QueryEvent:\n", + " if hasattr(ev, \"query\"):\n", + " await ctx.set(\"original_query\", ev.query)\n", + " print(f\"Query is {await ctx.get('original_query')}\")\n", + "\n", + " if hasattr(ev, \"llm\"):\n", + " await ctx.set(\"llm\", ev.llm)\n", + "\n", + " if hasattr(ev, \"tools\"):\n", + " await ctx.set(\"tools\", ev.tools)\n", + "\n", + " response = await ctx.get(\"llm\").complete(\n", + " f\"\"\"\n", + " Given a user question, and a list of tools, output a list of\n", + " relevant sub-questions, such that the answers to all the\n", + " sub-questions put together will answer the question. Respond\n", + " in pure JSON without any markdown, like this:\n", + " {{\n", + " \"sub_questions\": [\n", + " \"What is the population of San Francisco?\",\n", + " \"What is the budget of San Francisco?\",\n", + " \"What is the GDP of San Francisco?\"\n", + " ]\n", + " }}\n", + " Here is the user question: {await ctx.get('original_query')}\n", + "\n", + " And here is the list of tools: {await ctx.get('tools')}\n", + " \"\"\"\n", + " )\n", + "\n", + " print(f\"Sub-questions are {response}\")\n", + "\n", + " response_obj = json.loads(str(response))\n", + " sub_questions = response_obj[\"sub_questions\"]\n", + "\n", + " await ctx.set(\"sub_question_count\", len(sub_questions))\n", + "\n", + " for question in sub_questions:\n", + " self.send_event(QueryEvent(question=question))\n", + "\n", + " return None\n", + "\n", + " @step\n", + " async def sub_question(self, ctx: Context, ev: QueryEvent) -> AnswerEvent:\n", + " print(f\"Sub-question is {ev.question}\")\n", + "\n", + " agent = ReActAgent.from_tools(\n", + " await ctx.get(\"tools\"), llm=await ctx.get(\"llm\"), verbose=True\n", + " )\n", + " response = agent.chat(ev.question)\n", + "\n", + " return AnswerEvent(question=ev.question, answer=str(response))\n", + "\n", + " @step\n", + " async def combine_answers(\n", + " self, ctx: Context, ev: AnswerEvent\n", + " ) -> StopEvent | None:\n", + " ready = ctx.collect_events(\n", + " ev, [AnswerEvent] * await ctx.get(\"sub_question_count\")\n", + " )\n", + " if ready is None:\n", + " return None\n", + "\n", + " answers = \"\\n\\n\".join(\n", + " [\n", + " f\"Question: {event.question}: \\n Answer: {event.answer}\"\n", + " for event in ready\n", + " ]\n", + " )\n", + "\n", + " prompt = f\"\"\"\n", + " You are given an overall question that has been split into sub-questions,\n", + " each of which has been answered. Combine the answers to all the sub-questions\n", + " into a single answer to the original question.\n", + "\n", + " Original question: {await ctx.get('original_query')}\n", + "\n", + " Sub-questions and answers:\n", + " {answers}\n", + " \"\"\"\n", + "\n", + " print(f\"Final prompt is {prompt}\")\n", + "\n", + " response = await ctx.get(\"llm\").complete(prompt)\n", + "\n", + " print(\"Final response is\", response)\n", + "\n", + " return StopEvent(result=str(response))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sub_question_query_engine.html\n" + ] + } + ], + "source": [ + "draw_all_possible_flows(\n", + " SubQuestionQueryEngine, filename=\"sub_question_query_engine.html\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualizing this flow looks pretty linear, since it doesn't capture that `query()` can generate multiple parallel `QueryEvents` which get collected into `combine_answers`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Screenshot 2024-08-07 at 3.31.55 PM.png](data:image/png;base64,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+ ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download data to demo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-08-07 18:21:11-- https://www.dropbox.com/scl/fi/xt3squt47djba0j7emmjb/2016-CSF_Budget_Book_2016_FINAL_WEB_with-cover-page.pdf?rlkey=xs064cjs8cb4wma6t5pw2u2bl&dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.18, 2620:100:6057:18::a27d:d12\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.18|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://ucca241228aca55dbf9fcd60ae81.dl.dropboxusercontent.com/cd/0/inline/CYOq1NGkhLkELMmygLIg_gyLPXsOO7xOLjc3jW-mb09kevMykGPogSQx_icUTBEfHshxiSainXTynZYnh5O6uZ4ITeGiMkpvjl1QqXkKI34Ea8WzLr4FEyzkwohAC2WCQAU/file# [following]\n", + "--2024-08-07 18:21:12-- https://ucca241228aca55dbf9fcd60ae81.dl.dropboxusercontent.com/cd/0/inline/CYOq1NGkhLkELMmygLIg_gyLPXsOO7xOLjc3jW-mb09kevMykGPogSQx_icUTBEfHshxiSainXTynZYnh5O6uZ4ITeGiMkpvjl1QqXkKI34Ea8WzLr4FEyzkwohAC2WCQAU/file\n", + "Resolving ucca241228aca55dbf9fcd60ae81.dl.dropboxusercontent.com (ucca241228aca55dbf9fcd60ae81.dl.dropboxusercontent.com)... 162.125.1.15, 2620:100:6016:15::a27d:10f\n", + "Connecting to ucca241228aca55dbf9fcd60ae81.dl.dropboxusercontent.com (ucca241228aca55dbf9fcd60ae81.dl.dropboxusercontent.com)|162.125.1.15|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: 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awaiting response... 200 OK\n", + "Length: 29467998 (28M) [application/pdf]\n", + "Saving to: ‘./data/sf_budgets/2016 - CSF_Budget_Book_2016_FINAL_WEB_with-cover-page.pdf’\n", + "\n", + "./data/sf_budgets/2 100%[===================>] 28.10M 173MB/s in 0.2s \n", + "\n", + "2024-08-07 18:21:14 (173 MB/s) - ‘./data/sf_budgets/2016 - CSF_Budget_Book_2016_FINAL_WEB_with-cover-page.pdf’ saved [29467998/29467998]\n", + "\n", + "--2024-08-07 18:21:14-- https://www.dropbox.com/scl/fi/jvw59g5nscu1m7f96tjre/2017-Proposed-Budget-FY2017-18-FY2018-19_1.pdf?rlkey=v988oigs2whtcy87ti9wti6od&dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.18, 2620:100:6057:18::a27d:d12\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.18|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: 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awaiting response... 200 OK\n", + "Length: 13463517 (13M) [application/pdf]\n", + "Saving to: ‘./data/sf_budgets/2017 - 2017-Proposed-Budget-FY2017-18-FY2018-19_1.pdf’\n", + "\n", + "./data/sf_budgets/2 100%[===================>] 12.84M --.-KB/s in 0.09s \n", + "\n", + "2024-08-07 18:21:17 (136 MB/s) - ‘./data/sf_budgets/2017 - 2017-Proposed-Budget-FY2017-18-FY2018-19_1.pdf’ saved [13463517/13463517]\n", + "\n", + "--2024-08-07 18:21:17-- https://www.dropbox.com/scl/fi/izknlwmbs7ia0lbn7zzyx/2018-o0181-18.pdf?rlkey=p5nv2ehtp7272ege3m9diqhei&dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.18, 2620:100:6057:18::a27d:d12\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.18|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: 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awaiting response... 200 OK\n", + "Length: 18487865 (18M) [application/pdf]\n", + "Saving to: ‘./data/sf_budgets/2018 - 2018-o0181-18.pdf’\n", + "\n", + "./data/sf_budgets/2 100%[===================>] 17.63M --.-KB/s in 0.1s \n", + "\n", + "2024-08-07 18:21:19 (149 MB/s) - ‘./data/sf_budgets/2018 - 2018-o0181-18.pdf’ saved [18487865/18487865]\n", + "\n", + "--2024-08-07 18:21:19-- https://www.dropbox.com/scl/fi/1rstqm9rh5u5fr0tcjnxj/2019-Proposed-Budget-FY2019-20-FY2020-21.pdf?rlkey=3s2ivfx7z9bev1r840dlpbcgg&dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.18, 2620:100:6057:18::a27d:d12\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.18|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://uce28a421063a08c4ce431616623.dl.dropboxusercontent.com/cd/0/inline/CYNSfAOo0ymwbrL62gVbRB_NTvZpU2t5SZqnLuZDW-OaDOssaoY8SkQxPM9csoAq0-Y3Y8rYA1E6cDD44K1pSJcsuRSyoRRVLHRmXvWdayHKMK_PWAo08V3murDu9ZZAu4s/file# 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awaiting response... 200 OK\n", + "Length: 13123938 (13M) [application/pdf]\n", + "Saving to: ‘./data/sf_budgets/2019 - 2019-Proposed-Budget-FY2019-20-FY2020-21.pdf’\n", + "\n", + "./data/sf_budgets/2 100%[===================>] 12.52M --.-KB/s in 0.08s \n", + "\n", + "2024-08-07 18:21:22 (161 MB/s) - ‘./data/sf_budgets/2019 - 2019-Proposed-Budget-FY2019-20-FY2020-21.pdf’ saved [13123938/13123938]\n", + "\n", + "--2024-08-07 18:21:22-- https://www.dropbox.com/scl/fi/7teuwxrjdyvgw0n8jjvk0/2021-AAO-FY20-21-FY21-22-09-11-2020-FINAL.pdf?rlkey=6br3wzxwj5fv1f1l8e69nbmhk&dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.18, 2620:100:6057:18::a27d:d12\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.18|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: 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awaiting response... 200 OK\n", + "Length: 10550407 (10M) [application/pdf]\n", + "Saving to: ‘./data/sf_budgets/2023 - 2023-CSF_Proposed_Budget_Book_June_2023_Master_Web.pdf’\n", + "\n", + "./data/sf_budgets/2 100%[===================>] 10.06M --.-KB/s in 0.09s \n", + "\n", + "2024-08-07 18:21:31 (110 MB/s) - ‘./data/sf_budgets/2023 - 2023-CSF_Proposed_Budget_Book_June_2023_Master_Web.pdf’ saved [10550407/10550407]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir -p \"./data/sf_budgets/\"\n", + "!wget \"https://www.dropbox.com/scl/fi/xt3squt47djba0j7emmjb/2016-CSF_Budget_Book_2016_FINAL_WEB_with-cover-page.pdf?rlkey=xs064cjs8cb4wma6t5pw2u2bl&dl=0\" -O \"./data/sf_budgets/2016 - CSF_Budget_Book_2016_FINAL_WEB_with-cover-page.pdf\"\n", + "!wget \"https://www.dropbox.com/scl/fi/jvw59g5nscu1m7f96tjre/2017-Proposed-Budget-FY2017-18-FY2018-19_1.pdf?rlkey=v988oigs2whtcy87ti9wti6od&dl=0\" -O \"./data/sf_budgets/2017 - 2017-Proposed-Budget-FY2017-18-FY2018-19_1.pdf\"\n", + "!wget \"https://www.dropbox.com/scl/fi/izknlwmbs7ia0lbn7zzyx/2018-o0181-18.pdf?rlkey=p5nv2ehtp7272ege3m9diqhei&dl=0\" -O \"./data/sf_budgets/2018 - 2018-o0181-18.pdf\"\n", + "!wget \"https://www.dropbox.com/scl/fi/1rstqm9rh5u5fr0tcjnxj/2019-Proposed-Budget-FY2019-20-FY2020-21.pdf?rlkey=3s2ivfx7z9bev1r840dlpbcgg&dl=0\" -O \"./data/sf_budgets/2019 - 2019-Proposed-Budget-FY2019-20-FY2020-21.pdf\"\n", + "!wget \"https://www.dropbox.com/scl/fi/7teuwxrjdyvgw0n8jjvk0/2021-AAO-FY20-21-FY21-22-09-11-2020-FINAL.pdf?rlkey=6br3wzxwj5fv1f1l8e69nbmhk&dl=0\" -O \"./data/sf_budgets/2021 - 2021-AAO-FY20-21-FY21-22-09-11-2020-FINAL.pdf\"\n", + "!wget \"https://www.dropbox.com/scl/fi/zhgqch4n6xbv9skgcknij/2022-AAO-FY2021-22-FY2022-23-FINAL-20210730.pdf?rlkey=h78t65dfaz3mqbpbhl1u9e309&dl=0\" -O \"./data/sf_budgets/2022 - 2022-AAO-FY2021-22-FY2022-23-FINAL-20210730.pdf\"\n", + "!wget \"https://www.dropbox.com/scl/fi/vip161t63s56vd94neqlt/2023-CSF_Proposed_Budget_Book_June_2023_Master_Web.pdf?rlkey=hemoce3w1jsuf6s2bz87g549i&dl=0\" -O \"./data/sf_budgets/2023 - 2023-CSF_Proposed_Budget_Book_June_2023_Master_Web.pdf\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data and run the workflow\n", + "\n", + "Just like using the built-in Sub-Question Query Engine, we create our query tools and instantiate an LLM and pass them in.\n", + "\n", + "Each tool is its own query engine based on a single (very lengthy) San Francisco budget document, each of which is 300+ pages. To save time on repeated runs, we persist our generated indexes to disk." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running step query\n", + "Query is How has the total amount of San Francisco's budget changed from 2016 to 2023?\n", + "Sub-questions are {\n", + " \"sub_questions\": [\n", + " \"What was the total amount of San Francisco's budget in 2016?\",\n", + " \"What was the total amount of San Francisco's budget in 2017?\",\n", + " \"What was the total amount of San Francisco's budget in 2018?\",\n", + " \"What was the total amount of San Francisco's budget in 2019?\",\n", + " \"What was the total amount of San Francisco's budget in 2020?\",\n", + " \"What was the total amount of San Francisco's budget in 2021?\",\n", + " \"What was the total amount of San Francisco's budget in 2022?\",\n", + " \"What was the total amount of San Francisco's budget in 2023?\"\n", + " ]\n", + "}\n", + "Step query produced no event\n", + "Running step sub_question\n", + "Sub-question is What was the total amount of San Francisco's budget in 2016?\n", + "> Running step 61365946-614c-4895-8fc3-0968f2d63387. Step input: What was the total amount of San Francisco's budget in 2016?\n", + "\u001b[1;3;38;5;200mThought: The current language of the user is English. I need to use a tool to help me answer the question.\n", + "Action: budget_2016\n", + "Action Input: {'input': \"total amount of San Francisco's budget in 2016\"}\n", + "\u001b[0m\u001b[1;3;34mObservation: The total amount of San Francisco's budget in 2016 was $9.6 billion.\n", + "\u001b[0m> Running step a85aa30e-a980-4897-a52e-e82b8fb25c72. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I can answer without using any more tools. I'll use the user's language to answer.\n", + "Answer: The total amount of San Francisco's budget in 2016 was $9.6 billion.\n", + "\u001b[0mStep sub_question produced event AnswerEvent\n", + "Running step sub_question\n", + "Sub-question is What was the total amount of San Francisco's budget in 2017?\n", + "> Running step 5d14466c-1400-4a26-ac42-021e7143d3b1. Step input: What was the total amount of San Francisco's budget in 2017?\n", + "\u001b[1;3;38;5;200mThought: The current language of the user is English. I need to use a tool to help me answer the question.\n", + "Action: budget_2017\n", + "Action Input: {'input': \"total amount of San Francisco's budget in 2017\"}\n", + "\u001b[0m\u001b[1;3;34mObservation: $10,106.9 million\n", + "\u001b[0m> Running step 586a5fab-95ee-44e9-9a35-fcf19993b13e. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I have the information needed to answer the question.\n", + "Answer: The total amount of San Francisco's budget in 2017 was $10,106.9 million.\n", + "\u001b[0mStep sub_question produced event AnswerEvent\n", + "Running step sub_question\n", + "Sub-question is What was the total amount of San Francisco's budget in 2018?\n", + "> Running step d39f64d0-65f6-4571-95ad-d28a16198ea5. Step input: What was the total amount of San Francisco's budget in 2018?\n", + "\u001b[1;3;38;5;200mThought: The current language of the user is English. I need to use a tool to help me answer the question.\n", + "Action: budget_2018\n", + "Action Input: {'input': \"total amount of San Francisco's budget in 2018\"}\n", + "\u001b[0m\u001b[1;3;34mObservation: The total amount of San Francisco's budget in 2018 was $12,659,306,000.\n", + "\u001b[0m> Running step 3f67feee-489c-4b9e-8f27-37f0d48e3b0d. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I can answer without using any more tools. I'll use the user's language to answer.\n", + "Answer: The total amount of San Francisco's budget in 2018 was $12,659,306,000.\n", + "\u001b[0mStep sub_question produced event AnswerEvent\n", + "Running step sub_question\n", + "Sub-question is What was the total amount of San Francisco's budget in 2019?\n", + "> Running step d5ac0866-b02c-4c4c-94c6-f0e047ebb0fe. Step input: What was the total amount of San Francisco's budget in 2019?\n", + "\u001b[1;3;38;5;200mThought: The current language of the user is English. I need to use a tool to help me answer the question.\n", + "Action: budget_2019\n", + "Action Input: {'input': \"total amount of San Francisco's budget in 2019\"}\n", + "\u001b[0m\u001b[1;3;34mObservation: $12.3 billion\n", + "\u001b[0m> Running step 3b62859b-bbd3-4dce-b284-f9b398e370c2. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I can answer without using any more tools. I'll use the user's language to answer.\n", + "Answer: The total amount of San Francisco's budget in 2019 was $12.3 billion.\n", + "\u001b[0mStep sub_question produced event AnswerEvent\n", + "Running step sub_question\n", + "Sub-question is What was the total amount of San Francisco's budget in 2020?\n", + "> Running step 41f6ed9f-d695-43df-8743-39dfcc3d919d. Step input: What was the total amount of San Francisco's budget in 2020?\n", + "\u001b[1;3;38;5;200mThought: The user is asking for the total amount of San Francisco's budget in 2020. I do not have a tool specifically for the 2020 budget. I will check the available tools to see if they provide any relevant information or if I can infer the 2020 budget from adjacent years.\n", + "Action: budget_2021\n", + "Action Input: {'input': \"What was the total amount of San Francisco's budget in 2020?\"}\n", + "\u001b[0m\u001b[1;3;34mObservation: The total amount of San Francisco's budget in 2020 was $15,373,192 (in thousands of dollars).\n", + "\u001b[0m> Running step ea39f7c6-e942-4a41-8963-f37d4a27d559. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I now have the information needed to answer the user's question about the total amount of San Francisco's budget in 2020.\n", + "Answer: The total amount of San Francisco's budget in 2020 was $15,373,192 (in thousands of dollars).\n", + "\u001b[0mStep sub_question produced event AnswerEvent\n", + "Running step sub_question\n", + "Sub-question is What was the total amount of San Francisco's budget in 2021?\n", + "> Running step 6662fd06-e86a-407c-bb89-4828f63caa72. Step input: What was the total amount of San Francisco's budget in 2021?\n", + "\u001b[1;3;38;5;200mThought: The current language of the user is English. I need to use a tool to help me answer the question.\n", + "Action: budget_2021\n", + "Action Input: {'input': \"total amount of San Francisco's budget in 2021\"}\n", + "\u001b[0m\u001b[1;3;34mObservation: The total amount of San Francisco's budget in 2021 is $14,166,496,000.\n", + "\u001b[0m> Running step 5d0cf9da-2c14-407c-8ae5-5cd638c1fb5c. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I can answer without using any more tools. I'll use the user's language to answer.\n", + "Answer: The total amount of San Francisco's budget in 2021 was $14,166,496,000.\n", + "\u001b[0mStep sub_question produced event AnswerEvent\n", + "Running step sub_question\n", + "Sub-question is What was the total amount of San Francisco's budget in 2022?\n", + "> Running step 62fa9a5f-f40b-489d-9773-5ee4e4eaba9e. Step input: What was the total amount of San Francisco's budget in 2022?\n", + "\u001b[1;3;38;5;200mThought: The current language of the user is English. I need to use a tool to help me answer the question.\n", + "Action: budget_2022\n", + "Action Input: {'input': \"total amount of San Francisco's budget in 2022\"}\n", + "\u001b[0m\u001b[1;3;34mObservation: $14,550,060\n", + "\u001b[0m> Running step 7a2d5623-cc75-4c9d-8c58-3dbc2faf163d. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I can answer without using any more tools. I'll use the user's language to answer.\n", + "Answer: The total amount of San Francisco's budget in 2022 was $14,550,060.\n", + "\u001b[0mStep sub_question produced event AnswerEvent\n", + "Running step sub_question\n", + "Sub-question is What was the total amount of San Francisco's budget in 2023?\n", + "> Running step 839eb994-b4e2-4019-a170-9471c1e0d764. Step input: What was the total amount of San Francisco's budget in 2023?\n", + "\u001b[1;3;38;5;200mThought: The current language of the user is: English. I need to use a tool to help me answer the question.\n", + "Action: budget_2023\n", + "Action Input: {'input': \"total amount of San Francisco's budget in 2023\"}\n", + "\u001b[0m\u001b[1;3;34mObservation: $14.6 billion\n", + "\u001b[0m> Running step 38779f6c-d0c7-4c95-b5c4-0b170c5ed0d5. Step input: None\n", + "\u001b[1;3;38;5;200mThought: I can answer without using any more tools. I'll use the user's language to answer.\n", + "Answer: The total amount of San Francisco's budget in 2023 was $14.6 billion.\n", + "\u001b[0mStep sub_question produced event AnswerEvent\n", + "Running step combine_answers\n", + "Step combine_answers produced no event\n", + "Running step combine_answers\n", + "Step combine_answers produced no event\n", + "Running step combine_answers\n", + "Step combine_answers produced no event\n", + "Running step combine_answers\n", + "Step combine_answers produced no event\n", + "Running step combine_answers\n", + "Step combine_answers produced no event\n", + "Running step combine_answers\n", + "Step combine_answers produced no event\n", + "Running step combine_answers\n", + "Step combine_answers produced no event\n", + "Running step combine_answers\n", + "Final prompt is \n", + " You are given an overall question that has been split into sub-questions,\n", + " each of which has been answered. Combine the answers to all the sub-questions\n", + " into a single answer to the original question.\n", + "\n", + " Original question: How has the total amount of San Francisco's budget changed from 2016 to 2023?\n", + "\n", + " Sub-questions and answers:\n", + " Question: What was the total amount of San Francisco's budget in 2016?: \n", + " Answer: The total amount of San Francisco's budget in 2016 was $9.6 billion.\n", + "\n", + "Question: What was the total amount of San Francisco's budget in 2017?: \n", + " Answer: The total amount of San Francisco's budget in 2017 was $10,106.9 million.\n", + "\n", + "Question: What was the total amount of San Francisco's budget in 2018?: \n", + " Answer: The total amount of San Francisco's budget in 2018 was $12,659,306,000.\n", + "\n", + "Question: What was the total amount of San Francisco's budget in 2019?: \n", + " Answer: The total amount of San Francisco's budget in 2019 was $12.3 billion.\n", + "\n", + "Question: What was the total amount of San Francisco's budget in 2020?: \n", + " Answer: The total amount of San Francisco's budget in 2020 was $15,373,192 (in thousands of dollars).\n", + "\n", + "Question: What was the total amount of San Francisco's budget in 2021?: \n", + " Answer: The total amount of San Francisco's budget in 2021 was $14,166,496,000.\n", + "\n", + "Question: What was the total amount of San Francisco's budget in 2022?: \n", + " Answer: The total amount of San Francisco's budget in 2022 was $14,550,060.\n", + "\n", + "Question: What was the total amount of San Francisco's budget in 2023?: \n", + " Answer: The total amount of San Francisco's budget in 2023 was $14.6 billion.\n", + " \n", + "Final response is From 2016 to 2023, the total amount of San Francisco's budget has seen significant changes. In 2016, the budget was $9.6 billion. It increased to $10,106.9 million in 2017 and further to $12,659,306,000 in 2018. In 2019, the budget was $12.3 billion. The budget saw a substantial rise in 2020, reaching $15,373,192 (in thousands of dollars), which translates to approximately $15.4 billion. In 2021, the budget was $14,166,496,000, and in 2022, it was $14,550,060. By 2023, the budget had increased to $14.6 billion. Overall, from 2016 to 2023, San Francisco's budget grew from $9.6 billion to $14.6 billion.\n", + "Step combine_answers produced event StopEvent\n", + "From 2016 to 2023, the total amount of San Francisco's budget has seen significant changes. In 2016, the budget was $9.6 billion. It increased to $10,106.9 million in 2017 and further to $12,659,306,000 in 2018. In 2019, the budget was $12.3 billion. The budget saw a substantial rise in 2020, reaching $15,373,192 (in thousands of dollars), which translates to approximately $15.4 billion. In 2021, the budget was $14,166,496,000, and in 2022, it was $14,550,060. By 2023, the budget had increased to $14.6 billion. Overall, from 2016 to 2023, San Francisco's budget grew from $9.6 billion to $14.6 billion.\n" + ] + } + ], + "source": [ + "from google.colab import userdata\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = userdata.get(\"openai-key\")\n", + "\n", + "folder = \"./data/sf_budgets/\"\n", + "files = os.listdir(folder)\n", + "\n", + "query_engine_tools = []\n", + "for file in files:\n", + " year = file.split(\" - \")[0]\n", + " index_persist_path = f\"./storage/budget-{year}/\"\n", + "\n", + " if os.path.exists(index_persist_path):\n", + " storage_context = StorageContext.from_defaults(\n", + " persist_dir=index_persist_path\n", + " )\n", + " index = load_index_from_storage(storage_context)\n", + " else:\n", + " documents = SimpleDirectoryReader(\n", + " input_files=[folder + file]\n", + " ).load_data()\n", + " index = VectorStoreIndex.from_documents(documents)\n", + " index.storage_context.persist(index_persist_path)\n", + "\n", + " engine = index.as_query_engine()\n", + " query_engine_tools.append(\n", + " QueryEngineTool(\n", + " query_engine=engine,\n", + " metadata=ToolMetadata(\n", + " name=f\"budget_{year}\",\n", + " description=f\"Information about San Francisco's budget in {year}\",\n", + " ),\n", + " )\n", + " )\n", + "\n", + "engine = SubQuestionQueryEngine(timeout=120, verbose=True)\n", + "llm = OpenAI(model=\"gpt-4o\")\n", + "result = await engine.run(\n", + " llm=llm,\n", + " tools=query_engine_tools,\n", + " query=\"How has the total amount of San Francisco's budget changed from 2016 to 2023?\",\n", + ")\n", + "\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our debug output is lengthy! You can see the sub-questions being generated and then `sub_question()` being repeatedly invoked, each time generating a brief log of ReAct agent thoughts and actions to answer each smaller question.\n", + "\n", + "You can see `combine_answers` running multiple times; these were triggered by each `AnswerEvent` but before all 8 `AnswerEvents` were collected. On its final run it generates a full prompt, combines the answers and returns the result." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/examples/workflow/workflows_cookbook.ipynb b/docs/docs/examples/workflow/workflows_cookbook.ipynb new file mode 100644 index 0000000000000..a9af8212f6dee --- /dev/null +++ b/docs/docs/examples/workflow/workflows_cookbook.ipynb @@ -0,0 +1,567 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Workflows cookbook: walking through all features of Workflows\n", + "\n", + "First, we install our dependencies. Core contains most of what we need; OpenAI is to handle LLM access and utils-workflow provides the visualization capabilities we'll use later on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade llama-index-core llama-index-llms-openai llama-index-utils-workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we bring in the deps we just installed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.workflow import (\n", + " Event,\n", + " StartEvent,\n", + " StopEvent,\n", + " Workflow,\n", + " step,\n", + " Context,\n", + ")\n", + "import random\n", + "from llama_index.core.workflow import draw_all_possible_flows\n", + "from llama_index.utils.workflow import draw_most_recent_execution\n", + "from llama_index.llms.openai import OpenAI" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set up our OpenAI key, so we can do actual LLM things." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workflow basics\n", + "\n", + "Let's start with the basic possible workflow: it just starts, does one thing, and stops. There's no reason to have a real workflow if your task is this simple, but we're just demonstrating how they work." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LlamaIndex, formerly known as GPT Index, is a data framework designed to facilitate the connection between large language models (LLMs) and external data sources. It provides tools to index various data types, such as documents, databases, and APIs, enabling LLMs to interact with and retrieve information from these sources more effectively. The framework supports the creation of indices that can be queried by LLMs, enhancing their ability to access and utilize external data in a structured manner. This capability is particularly useful for applications requiring the integration of LLMs with specific datasets or knowledge bases.\n" + ] + } + ], + "source": [ + "from llama_index.llms.openai import OpenAI\n", + "\n", + "\n", + "class OpenAIGenerator(Workflow):\n", + " @step\n", + " async def generate(self, ev: StartEvent) -> StopEvent:\n", + " llm = OpenAI(model=\"gpt-4o\")\n", + " response = await llm.acomplete(ev.query)\n", + " return StopEvent(result=str(response))\n", + "\n", + "\n", + "w = OpenAIGenerator(timeout=10, verbose=False)\n", + "result = await w.run(query=\"What's LlamaIndex?\")\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the neat things about Workflows is that we can use pyvis to visualize them. Let's see what that looks like for this very simple flow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "draw_all_possible_flows(OpenAIGenerator, filename=\"trivial_workflow.html\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Screenshot 2024-08-05 at 11.59.03 AM.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not a lot to see here, yet! The start event goes to generate() and then straight to StopEvent.\n", + "\n", + "## Loops and branches\n", + "\n", + "Let's go to a more interesting example, demonstrating our ability to loop:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class FailedEvent(Event):\n", + " error: str\n", + "\n", + "\n", + "class QueryEvent(Event):\n", + " query: str\n", + "\n", + "\n", + "class LoopExampleFlow(Workflow):\n", + " @step\n", + " async def answer_query(\n", + " self, ev: StartEvent | QueryEvent\n", + " ) -> FailedEvent | StopEvent:\n", + " query = ev.query\n", + " # try to answer the query\n", + " random_number = random.randint(0, 1)\n", + " if random_number == 0:\n", + " return FailedEvent(error=\"Failed to answer the query.\")\n", + " else:\n", + " return StopEvent(result=\"The answer to your query\")\n", + "\n", + " @step\n", + " async def improve_query(self, ev: FailedEvent) -> QueryEvent | StopEvent:\n", + " # improve the query or decide it can't be fixed\n", + " random_number = random.randint(0, 1)\n", + " if random_number == 0:\n", + " return QueryEvent(query=\"Here's a better query.\")\n", + " else:\n", + " return StopEvent(result=\"Your query can't be fixed.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're using random numbers to simulate LLM actions here so that we can get reliably interesting behavior.\n", + "\n", + "answer_query() accepts a start event. It can then do 2 things:\n", + "* it can answer the query and emit a StopEvent, which returns the result\n", + "* it can decide the query was bad and emit a FailedEvent\n", + "\n", + "improve_query() accepts a FailedEvent. It can also do 2 things:\n", + "* it can decide the query can't be improved and emit a StopEvent, which returns failure\n", + "* it can present a better query and emit a QueryEvent, which creates a loop back to answer_query()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also visualize this more complicated workflow:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loop_workflow.html\n" + ] + } + ], + "source": [ + "draw_all_possible_flows(LoopExampleFlow, filename=\"loop_workflow.html\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Screenshot 2024-08-05 at 11.36.05 AM.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've set `verbose=True` here so we can see exactly what events were triggered. You can see it conveniently demonstrates looping and then answering." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running step answer_query\n", + "Step answer_query produced event FailedEvent\n", + "Running step improve_query\n", + "Step improve_query produced event StopEvent\n", + "Your query can't be fixed.\n" + ] + } + ], + "source": [ + "l = LoopExampleFlow(timeout=10, verbose=True)\n", + "result = await l.run(query=\"What's LlamaIndex?\")\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Maintaining state between events\n", + "\n", + "There is a global state which allows you to keep arbitrary data or functions around for use by all event handlers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class GlobalExampleFlow(Workflow):\n", + " @step\n", + " async def setup(self, ctx: Context, ev: StartEvent) -> QueryEvent:\n", + " # load our data here\n", + " await ctx.set(\"some_database\", [\"value1\", \"value2\", \"value3\"])\n", + "\n", + " return QueryEvent(query=ev.query)\n", + "\n", + " @step\n", + " async def query(self, ctx: Context, ev: QueryEvent) -> StopEvent:\n", + " # use our data with our query\n", + " data = await ctx.get(\"some_database\")\n", + "\n", + " result = f\"The answer to your query is {data[1]}\"\n", + " return StopEvent(result=result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running step setup\n", + "Step setup produced event QueryEvent\n", + "Running step query\n", + "Step query produced event StopEvent\n", + "The answer to your query is value2\n" + ] + } + ], + "source": [ + "g = GlobalExampleFlow(timeout=10, verbose=True)\n", + "result = await g.run(query=\"What's LlamaIndex?\")\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, this flow is essentially still linear. A more realistic example would be if your start event could either be a query or a data population event, and you needed to wait. Let's set that up to see what it looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class WaitExampleFlow(Workflow):\n", + " @step\n", + " async def setup(self, ctx: Context, ev: StartEvent) -> StopEvent:\n", + " if hasattr(ev, \"data\"):\n", + " await ctx.set(\"data\", ev.data)\n", + "\n", + " return StopEvent(result=None)\n", + "\n", + " @step\n", + " async def query(self, ctx: Context, ev: StartEvent) -> StopEvent:\n", + " if hasattr(ev, \"query\"):\n", + " # do we have any data?\n", + " if hasattr(self, \"data\"):\n", + " data = await ctx.get(\"data\")\n", + " return StopEvent(result=f\"Got the data {data}\")\n", + " else:\n", + " # there's non data yet\n", + " return None\n", + " else:\n", + " # this isn't a query\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running step query\n", + "Step query produced no event\n", + "Running step setup\n", + "Step setup produced event StopEvent\n", + "No you can't\n", + "---\n", + "Running step query\n", + "Step query produced no event\n", + "Running step setup\n", + "Step setup produced event StopEvent\n", + "---\n", + "Running step query\n", + "Step query produced event StopEvent\n", + "Running step setup\n", + "Step setup produced event StopEvent\n", + "Got the data Yes you can\n" + ] + } + ], + "source": [ + "w = WaitExampleFlow(verbose=True)\n", + "result = await w.run(query=\"Can I kick it?\")\n", + "if result is None:\n", + " print(\"No you can't\")\n", + "print(\"---\")\n", + "result = await w.run(data=\"Yes you can\")\n", + "print(\"---\")\n", + "result = await w.run(query=\"Can I kick it?\")\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize how this flow works:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wait_workflow.html\n" + ] + } + ], + "source": [ + "draw_all_possible_flows(WaitExampleFlow, filename=\"wait_workflow.html\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Screenshot 2024-08-05 at 1.37.23 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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Waiting for one or more events\n", + "\n", + "Because waiting for events is such a common pattern, the context object has a convenience function, `collect_events()`. It will capture events and store them, returning `None` until all the events it requires have been collected. Those events will be attached to the output of `collect_events` in the order that they were specified. Let's see this in action:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class InputEvent(Event):\n", + " input: str\n", + "\n", + "\n", + "class SetupEvent(Event):\n", + " error: bool\n", + "\n", + "\n", + "class QueryEvent(Event):\n", + " query: str\n", + "\n", + "\n", + "class CollectExampleFlow(Workflow):\n", + " @step\n", + " async def setup(self, ctx: Context, ev: StartEvent) -> SetupEvent:\n", + " # generically start everything up\n", + " if not hasattr(self, \"setup\") or not self.setup:\n", + " self.setup = True\n", + " print(\"I got set up\")\n", + " return SetupEvent(error=False)\n", + "\n", + " @step\n", + " async def collect_input(self, ev: StartEvent) -> InputEvent:\n", + " if hasattr(ev, \"input\"):\n", + " # perhaps validate the input\n", + " print(\"I got some input\")\n", + " return InputEvent(input=ev.input)\n", + "\n", + " @step\n", + " async def parse_query(self, ev: StartEvent) -> QueryEvent:\n", + " if hasattr(ev, \"query\"):\n", + " # parse the query in some way\n", + " print(\"I got a query\")\n", + " return QueryEvent(query=ev.query)\n", + "\n", + " @step\n", + " async def run_query(\n", + " self, ctx: Context, ev: InputEvent | SetupEvent | QueryEvent\n", + " ) -> StopEvent | None:\n", + " ready = ctx.collect_events(ev, [QueryEvent, InputEvent, SetupEvent])\n", + " if ready is None:\n", + " print(\"Not enough events yet\")\n", + " return None\n", + "\n", + " # run the query\n", + " print(\"Now I have all the events\")\n", + " print(ready)\n", + "\n", + " result = f\"Ran query '{ready[0].query}' on input '{ready[1].input}'\"\n", + " return StopEvent(result=result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I got some input\n", + "I got a query\n", + "Not enough events yet\n", + "Not enough events yet\n", + "Now I have all the events\n", + "[QueryEvent(query=\"Here's my question\"), InputEvent(input=\"Here's some input\"), SetupEvent(error=False)]\n", + "Ran query 'Here's my question' on input 'Here's some input'\n" + ] + } + ], + "source": [ + "c = CollectExampleFlow()\n", + "result = await c.run(input=\"Here's some input\", query=\"Here's my question\")\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see each of the events getting triggered as well as the collection event repeatedly returning `None` until enough events have arrived. Let's see what this looks like in a flow diagram:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "collect_workflow.html\n" + ] + } + ], + "source": [ + "draw_all_possible_flows(CollectExampleFlow, \"collect_workflow.html\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Screenshot 2024-08-05 at 2.27.46 PM.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This concludes our tour of creating, running and visualizing workflows! Check out the [docs](https://docs.llamaindex.ai/en/stable/module_guides/workflow/) and [examples](https://docs.llamaindex.ai/en/stable/examples/workflow/function_calling_agent/) to learn more." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/index.md b/docs/docs/index.md index 9aff2724aab15..e76155f250bc6 100644 --- a/docs/docs/index.md +++ b/docs/docs/index.md @@ -2,13 +2,13 @@ # Welcome to LlamaIndex 🦙 ! -LlamaIndex is a framework for building context-augmented generative AI applications with [LLMs](https://en.wikipedia.org/wiki/Large_language_model). +LlamaIndex is a framework for building context-augmented generative AI applications with [LLMs](https://en.wikipedia.org/wiki/Large_language_model) including [agents](./understanding/agent/basic_agent/) and [workflows](./understanding/workflows/).
- [Introduction](#introduction) - What is context augmentation? How does LlamaIndex help? + What is context augmentation? What are agents and workflows? How does LlamaIndex help build them? - [Use cases](#use-cases) @@ -42,9 +42,19 @@ Context augmentation makes your data available to the LLM to solve the problem a The most popular example of context-augmentation is [Retrieval-Augmented Generation or RAG](./getting_started/concepts.md), which combines context with LLMs at inference time. -### LlamaIndex is the Data Framework for Context-Augmented LLM Apps +### What are agents? -LlamaIndex imposes no restriction on how you use LLMs. You can use LLMs as auto-complete, chatbots, semi-autonomous agents, and more. It just makes using them easier. We provide tools like: +[Agents](./understanding/agent/basic_agent/) are LLM-powered knowledge assistants that use tools to perform tasks like research, data extraction, and more. Agents range from simple question-answering to being able to sense, decide and take actions in order to complete tasks. + +LlamaIndex provides a framework for building agents including the ability to use RAG pipelines as one of many tools to complete a task. + +### What are workflows? + +[Workflows](./understanding/workflows/) are multi-step processes that combine one or more agents, data connectors, and other tools to complete a task. They are event-driven software that allows you to combine RAG data sources and multiple agents to create a complex application that can perform a wide variety of tasks with reflection, error-correction, and other hallmarks of advanced LLM applications. + +### LlamaIndex is the framework for Context-Augmented LLM Applications + +LlamaIndex imposes no restriction on how you use LLMs. You can use LLMs as auto-complete, chatbots, agents, and more. It just makes using them easier. We provide tools like: - **Data connectors** ingest your existing data from their native source and format. These could be APIs, PDFs, SQL, and (much) more. - **Data indexes** structure your data in intermediate representations that are easy and performant for LLMs to consume. @@ -53,6 +63,7 @@ LlamaIndex imposes no restriction on how you use LLMs. You can use LLMs as auto- - Chat engines are conversational interfaces for multi-message, "back and forth" interactions with your data. - **Agents** are LLM-powered knowledge workers augmented by tools, from simple helper functions to API integrations and more. - **Observability/Evaluation** integrations that enable you to rigorously experiment, evaluate, and monitor your app in a virtuous cycle. +- **Workflows** allow you to combine all of the above into an event-driven system for flexible than other, graph-based approaches. ## Use cases @@ -73,7 +84,7 @@ LlamaIndex provides tools for beginners, advanced users, and everyone in between Our high-level API allows beginner users to use LlamaIndex to ingest and query their data in 5 lines of code. -For more complex applications, our lower-level APIs allow advanced users to customize and extend any module—data connectors, indices, retrievers, query engines, reranking modules—to fit their needs. +For more complex applications, our lower-level APIs allow advanced users to customize and extend any module -- data connectors, indices, retrievers, query engines, and reranking modules -- to fit their needs. ## Getting Started diff --git a/docs/docs/javascript/algolia.js b/docs/docs/javascript/algolia.js new file mode 100644 index 0000000000000..36c74f652f7b7 --- /dev/null +++ b/docs/docs/javascript/algolia.js @@ -0,0 +1,16 @@ +/** + * Skipped minification because the original files appears to be already minified. + * Original file: /npm/@docsearch/js@3.6.1/dist/umd/index.js + * + * Do NOT use SRI with dynamically generated files! 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'74VN1YECLR', + indexName: 'gpt-index', + apiKey: 'c4b0e099fa9004f69855e474b3e7d3bb', +}); diff --git a/docs/docs/module_guides/deploying/agents/tools.md b/docs/docs/module_guides/deploying/agents/tools.md index 830b99379ad5a..b8c72c8454b2f 100644 --- a/docs/docs/module_guides/deploying/agents/tools.md +++ b/docs/docs/module_guides/deploying/agents/tools.md @@ -2,7 +2,7 @@ ## Concept -Having proper tool abstractions is at the core of building [data agents](../index.md). Defining a set of Tools is similar to defining any API interface, with the exception that these Tools are meant for agent rather than human use. We allow users to define both a **Tool** as well as a **ToolSpec** containing a series of functions under the hood. +Having proper tool abstractions is at the core of building [data agents](./index.md). Defining a set of Tools is similar to defining any API interface, with the exception that these Tools are meant for agent rather than human use. We allow users to define both a **Tool** as well as a **ToolSpec** containing a series of functions under the hood. When using an agent or LLM with function calling, the tool selected (and the arguments written for that tool) rely strongly on the **tool name** and **description** of the tools purpose and arguments. Spending time tuning these parameters can result in larges changes in how the LLM calls these tools. @@ -11,7 +11,7 @@ A Tool implements a very generic interface - simply define `__call__` and also r We offer a few different types of Tools: - `FunctionTool`: A function tool allows users to easily convert any user-defined function into a Tool. It can also auto-infer the function schema. -- `QueryEngineTool`: A tool that wraps an existing [query engine](../../query_engine/index.md). Note: since our agent abstractions inherit from `BaseQueryEngine`, these tools can also wrap other agents. +- `QueryEngineTool`: A tool that wraps an existing [query engine](../query_engine/index.md). Note: since our agent abstractions inherit from `BaseQueryEngine`, these tools can also wrap other agents. - Community contributed `ToolSpecs` that define one or more tools around a single service (like Gmail) - Utiltiy tools for wrapping other tools to handle returning large amounts of data from a tool @@ -94,7 +94,7 @@ tool_spec = GmailToolSpec() agent = OpenAIAgent.from_tools(tool_spec.to_tool_list(), verbose=True) ``` -See [LlamaHub](https://llamahub.ai) for a full list of community contributed tool specs, or check out [our guide](./llamahub_tools_guide.md) for a full overview of the Tools/Tool Specs in LlamaHub! +See [LlamaHub](https://llamahub.ai) for a full list of community contributed tool specs. ## Utility Tools diff --git a/docs/docs/module_guides/indexing/document_management.md b/docs/docs/module_guides/indexing/document_management.md index 97b753800ec87..e33b8100ec750 100644 --- a/docs/docs/module_guides/indexing/document_management.md +++ b/docs/docs/module_guides/indexing/document_management.md @@ -44,14 +44,9 @@ If a Document is already present within an index, you can "update" a Document wi ```python # NOTE: the document has a `doc_id` specified doc_chunks[0].text = "Brand new document text" -index.update_ref_doc( - doc_chunks[0], - update_kwargs={"delete_kwargs": {"delete_from_docstore": True}}, -) +index.update_ref_doc(doc_chunks[0]) ``` -Here, we passed some extra kwargs to ensure the document is deleted from the docstore. This is of course optional. - ## Refresh If you set the doc `id_` of each document when loading your data, you can also automatically refresh the index. @@ -73,9 +68,7 @@ doc_chunks.append( ) # refresh the index -refreshed_docs = index.refresh_ref_docs( - doc_chunks, update_kwargs={"delete_kwargs": {"delete_from_docstore": True}} -) +refreshed_docs = index.refresh_ref_docs(doc_chunks) # refreshed_docs[0] and refreshed_docs[-1] should be true ``` diff --git a/docs/docs/module_guides/indexing/lpg_index_guide.md b/docs/docs/module_guides/indexing/lpg_index_guide.md index 505edb5b7bfd9..dfb6115d5c438 100644 --- a/docs/docs/module_guides/indexing/lpg_index_guide.md +++ b/docs/docs/module_guides/indexing/lpg_index_guide.md @@ -369,12 +369,13 @@ template_retriever = CypherTemplateRetriever( Currently, supported graph stores for property graphs include: -| | In-Memory | Native Embedding Support | Async | Server or disk based? | -|---------------------|-----------|--------------------------|-------|-----------------------| -| SimplePropertyGraphStore | ✅ | ❌ | ❌ | Disk | -| Neo4jPropertyGraphStore | ❌ | ✅ | ❌ | Server | -| NebulaPropertyGraphStore | ❌ | ❌ | ❌ | Server | -| TiDBPropertyGraphStore | ❌ | ✅ | ❌ | Server | +| | In-Memory | Native Embedding Support | Async | Server or disk based? | +|------------------------------|------------|--------------------------|-------|-----------------------| +| SimplePropertyGraphStore | ✅ | ❌ | ❌ | Disk | +| Neo4jPropertyGraphStore | ❌ | ✅ | ❌ | Server | +| NebulaPropertyGraphStore | ❌ | ❌ | ❌ | Server | +| TiDBPropertyGraphStore | ❌ | ✅ | ❌ | Server | +| FalkorDBPropertyGraphStore | ❌ | ✅ | ❌ | Server | ### Saving to/from disk diff --git a/docs/docs/module_guides/loading/ingestion_pipeline/index.md b/docs/docs/module_guides/loading/ingestion_pipeline/index.md index a7b4bc4956dc7..4ff7f1c3807c2 100644 --- a/docs/docs/module_guides/loading/ingestion_pipeline/index.md +++ b/docs/docs/module_guides/loading/ingestion_pipeline/index.md @@ -212,4 +212,4 @@ pipeline.run(documents=[...], num_workers=4) - [Document Management Pipeline](../../../examples/ingestion/document_management_pipeline.ipynb) - [Redis Ingestion Pipeline](../../../examples/ingestion/redis_ingestion_pipeline.ipynb) - [Google Drive Ingestion Pipeline](../../../examples/ingestion/ingestion_gdrive.ipynb) -- [Parallel Execution Pipeline](../../../examples/ingestion/parallel_execution_ingestion_pipeline.ipynb)s +- [Parallel Execution Pipeline](../../../examples/ingestion/parallel_execution_ingestion_pipeline.ipynb) diff --git a/docs/docs/module_guides/observability/index.md b/docs/docs/module_guides/observability/index.md index 6b8da81689db7..e633cb0541135 100644 --- a/docs/docs/module_guides/observability/index.md +++ b/docs/docs/module_guides/observability/index.md @@ -20,7 +20,6 @@ Observability is now being handled via the [`instrumentation` module](./instrume A lot of the tooling and integrations mentioned in this page use our legacy `CallbackManager` or don't use `set_global_handler`. We've marked these integrations as such! - ## Usage Pattern To toggle, you will generally just need to do the following: @@ -36,7 +35,6 @@ Note that all `kwargs` to `set_global_handler` are passed to the underlying call And that's it! Executions will get seamlessly piped to downstream service and you'll be able to access features such as viewing execution traces of your application. - ## Partner `One-Click` Integrations ### LlamaTrace (Hosted Arize Phoenix) @@ -81,6 +79,25 @@ llama_index.core.set_global_handler( ![](../../_static/integrations/arize_phoenix.png) +### OpenLLMetry + +[OpenLLMetry](https://github.com/traceloop/openllmetry) is an open-source project based on OpenTelemetry for tracing and monitoring +LLM applications. It connects to [all major observability platforms](https://www.traceloop.com/docs/openllmetry/integrations/introduction) and installs in minutes. + +#### Usage Pattern + +```python +from traceloop.sdk import Traceloop + +Traceloop.init() +``` + +#### Guides + +- [OpenLLMetry](../../examples/callbacks/OpenLLMetry.ipynb) + +![](../../_static/integrations/openllmetry.png) + ### Arize Phoenix (local) You can also choose to use a **local** instance of Phoenix through the open-source project. @@ -229,25 +246,6 @@ storage_context = llama_index.core.global_handler.load_storage_context( - [Wandb Callback Handler](../../examples/callbacks/WandbCallbackHandler.ipynb) -### OpenLLMetry - -[OpenLLMetry](https://github.com/traceloop/openllmetry) is an open-source project based on OpenTelemetry for tracing and monitoring -LLM applications. It connects to [all major observability platforms](https://www.traceloop.com/docs/openllmetry/integrations/introduction) and installs in minutes. - -#### Usage Pattern - -```python -from traceloop.sdk import Traceloop - -Traceloop.init() -``` - -#### Guides - -- [OpenLLMetry](../../examples/callbacks/OpenLLMetry.ipynb) - -![](../../_static/integrations/openllmetry.png) - ### OpenInference [OpenInference](https://github.com/Arize-ai/open-inference-spec) is an open standard for capturing and storing AI model inferences. It enables experimentation, visualization, and evaluation of LLM applications using LLM observability solutions such as [Phoenix](https://github.com/Arize-ai/phoenix). @@ -455,9 +453,11 @@ llama_index.core.set_global_handler("simple") ``` ### MLflow + [MLflow](https://mlflow.org/docs/latest/index.html) is an open-source platform, purpose-built to assist machine learning practitioners and teams in handling the complexities of the machine learning process. MLflow focuses on the full lifecycle for machine learning projects, ensuring that each phase is manageable, traceable, and reproducible. ##### Install + ```shell pip install mlflow>=2.15 llama-index>=0.10.44 ``` @@ -489,8 +489,6 @@ print(f"Query engine prediction: {predictions}") - [MLflow](https://mlflow.org/docs/latest/llms/llama-index/index.html) - - ## More observability - [Callbacks Guide](./callbacks/index.md) diff --git a/docs/docs/module_guides/querying/router/index.md b/docs/docs/module_guides/querying/router/index.md index d9cff21201295..809a71c2a3d97 100644 --- a/docs/docs/module_guides/querying/router/index.md +++ b/docs/docs/module_guides/querying/router/index.md @@ -122,7 +122,7 @@ query_engine.query("") Similarly, a `RouterRetriever` is composed on top of other retrievers as tools. An example is given below: ```python -from llama_index.core.query_engine import RouterQueryEngine +from llama_index.core.retrievers import RouterRetriever from llama_index.core.selectors import PydanticSingleSelector from llama_index.core.tools import RetrieverTool diff --git a/docs/docs/module_guides/workflow/index.md b/docs/docs/module_guides/workflow/index.md index 3499c0800254f..a86c2080e95bc 100644 --- a/docs/docs/module_guides/workflow/index.md +++ b/docs/docs/module_guides/workflow/index.md @@ -2,7 +2,7 @@ A `Workflow` in LlamaIndex is an event-driven abstraction used to chain together several events. Workflows are made up of `steps`, with each step responsible for handling certain event types and emitting new events. -`Workflow`s in LlamaIndex work by decorating function with a `@step()` decorator. This is used to infer the input and output types of each workflow for validation, and ensures each step only runs when an accepted event is ready. +`Workflow`s in LlamaIndex work by decorating function with a `@step` decorator. This is used to infer the input and output types of each workflow for validation, and ensures each step only runs when an accepted event is ready. You can create a `Workflow` to do anything! Build an agent, a RAG flow, an extraction flow, or anything else you want. @@ -50,7 +50,7 @@ class JokeEvent(Event): class JokeFlow(Workflow): llm = OpenAI() - @step() + @step async def generate_joke(self, ev: StartEvent) -> JokeEvent: topic = ev.topic @@ -58,7 +58,7 @@ class JokeFlow(Workflow): response = await self.llm.acomplete(prompt) return JokeEvent(joke=str(response)) - @step() + @step async def critique_joke(self, ev: JokeEvent) -> StopEvent: joke = ev.joke @@ -99,7 +99,7 @@ Our workflow is implemented by subclassing the `Workflow` class. For simplicity, class JokeFlow(Workflow): ... - @step() + @step async def generate_joke(self, ev: StartEvent) -> JokeEvent: topic = ev.topic @@ -114,7 +114,7 @@ Here, we come to the entry-point of our workflow. While events are use-defined, The `StartEvent` is a bit of a special object since it can hold arbitrary attributes. Here, we accessed the topic with `ev.topic`, which would raise an error if it wasn't there. You could also do `ev.get("topic")` to handle the case where the attribute might not be there without raising an error. -At this point, you may have noticed that we haven't explicitly told the workflow what events are handled by which steps. Instead, the `@step()` decorator is used to infer the input and output types of each step. Furthermore, these inferred input and output types are also used to verify for you that the workflow is valid before running! +At this point, you may have noticed that we haven't explicitly told the workflow what events are handled by which steps. Instead, the `@step` decorator is used to infer the input and output types of each step. Furthermore, these inferred input and output types are also used to verify for you that the workflow is valid before running! ### Workflow Exit Points @@ -122,7 +122,7 @@ At this point, you may have noticed that we haven't explicitly told the workflow class JokeFlow(Workflow): ... - @step() + @step async def critique_joke(self, ev: JokeEvent) -> StopEvent: joke = ev.joke @@ -184,17 +184,17 @@ Optionally, you can choose to use global context between steps. For example, may from llama_index.core.workflow import Context -@step(pass_context=True) +@step async def query(self, ctx: Context, ev: MyEvent) -> StopEvent: # retrieve from context - query = ctx.data.get("query") + query = await ctx.get("query") # do something with context and event val = ... result = ... # store in context - ctx.data["key"] = val + await ctx.set("key", val) return StopEvent(result=result) ``` @@ -209,7 +209,7 @@ For example, you might have a step that waits for a query and retrieved nodes be from llama_index.core import get_response_synthesizer -@step(pass_context=True) +@step async def synthesize( self, ctx: Context, ev: QueryEvent | RetrieveEvent ) -> StopEvent | None: @@ -234,7 +234,7 @@ Using `ctx.collect_events()` we can buffer and wait for ALL expected events to a ## Manually Triggering Events -Normally, events are triggered by returning another event during a step. However, events can also be manually dispatched using the `self.send_event(event)` method within a workflow. +Normally, events are triggered by returning another event during a step. However, events can also be manually dispatched using the `ctx.send_event(event)` method within a workflow. Here is a short toy example showing how this would be used: @@ -255,18 +255,20 @@ class GatherEvent(Event): class MyWorkflow(Workflow): - @step() - async def dispatch_step(self, ev: StartEvent) -> MyEvent | GatherEvent: - self.send_event(MyEvent()) - self.send_event(MyEvent()) + @step + async def dispatch_step( + self, ctx: Context, ev: StartEvent + ) -> MyEvent | GatherEvent: + ctx.send_event(MyEvent()) + ctx.send_event(MyEvent()) return GatherEvent() - @step() + @step async def handle_my_event(self, ev: MyEvent) -> MyEventResult: return MyEventResult(result="result") - @step(pass_context=True) + @step async def gather( self, ctx: Context, ev: GatherEvent | MyEventResult ) -> StopEvent | None: @@ -346,7 +348,18 @@ async def critique_joke(ev: JokeEvent) -> StopEvent: You can find many useful examples of using workflows in the notebooks below: -- [RAG + Reranking](../../examples/workflow/rag.ipynb) -- [Reliable Structured Generation](../../examples/workflow/reflection.ipynb) +- [Citation Query Engine](../../examples/workflow/citation_query_engine.ipynb) +- [Common Workflow Patterns](../../examples/workflow/workflows_cookbook.ipynb) +- [Corrective RAG](../../examples/workflow/corrective_rag_pack.ipynb) - [Function Calling Agent](../../examples/workflow/function_calling_agent.ipynb) +- [JSON Query Engine](../../examples/workflow/JSONalyze_query_engine.ipynb) +- [Long RAG](../../examples/workflow/long_rag_pack.ipynb) +- [Multi-Step Query Engine](../../examples/workflow/multi_step_query_engine.ipynb) +- [Multi-Strategy Workflow](../../examples/workflow/multi_strategy_workflow.ipynb) +- [RAG + Reranking](../../examples/workflow/rag.ipynb) - [ReAct Agent](../../examples/workflow/react_agent.ipynb) +- [Reliable Structured Generation](../../examples/workflow/reflection.ipynb) +- [Router Query Engine](../../examples/workflow/router_query_engine.ipynb) +- [Self Discover Workflow](../../examples/workflow/self_discover_workflow.ipynb) +- [Sub-Question Query Engine](../../examples/workflow/sub_question_query_engine.ipynb) +- [Utilizing Concurrency](../../examples/workflow/parallel_execution.ipynb) diff --git a/docs/docs/optimizing/evaluation/component_wise_evaluation.md b/docs/docs/optimizing/evaluation/component_wise_evaluation.md index eec96ee9d37bf..be2219a66346d 100644 --- a/docs/docs/optimizing/evaluation/component_wise_evaluation.md +++ b/docs/docs/optimizing/evaluation/component_wise_evaluation.md @@ -16,7 +16,7 @@ A useful benchmark for embeddings is the [MTEB Leaderboard](https://huggingface. BEIR is useful for benchmarking if a particular retrieval model generalize well to niche domains in a zero-shot setting. -Since most publically-available embedding and retrieval models are already benchmarked against BEIR (e.g. through the MTEB benchmark), utilizing BEIR is more helpful when you have a unique model that you want to evaluate. +Since most publicly-available embedding and retrieval models are already benchmarked against BEIR (e.g. through the MTEB benchmark), utilizing BEIR is more helpful when you have a unique model that you want to evaluate. For instance, after fine-tuning an embedding model on your dataset, it may be helpful to view whether and by how much its performance degrades on a diverse set of domains. This can be an indication of how much data drift may affect your retrieval accuracy, such as if you add documents to your RAG system outside of your fine-tuning training distribution. diff --git a/docs/docs/understanding/workflows/arize.png b/docs/docs/understanding/workflows/arize.png new file mode 100644 index 0000000000000..3879857a3d41f Binary files /dev/null and b/docs/docs/understanding/workflows/arize.png differ diff --git a/docs/docs/understanding/workflows/basic_flow.md b/docs/docs/understanding/workflows/basic_flow.md new file mode 100644 index 0000000000000..8e20eafa6f5d0 --- /dev/null +++ b/docs/docs/understanding/workflows/basic_flow.md @@ -0,0 +1,179 @@ +# Basic workflow + +## Getting started + +Workflows are built into LlamaIndex core, so to use them all you need is + +``` +pip install llama-index-core +``` + +During development you will probably find it helpful to visualize your workflow; you can use our built-in visualizer for this by installing it: + +``` +pip install llama-index-utils-workflow +``` + +## Dependencies + +The minimal dependencies for a workflow are: + +```python +from llama_index.core.workflow import ( + StartEvent, + StopEvent, + Workflow, + step, +) +``` + +## Single-step workflow + +A workflow is usually implemented as a class that inherits from `Workflow`. The class can define an arbitrary number of steps, each of which is a method decorated with `@step`. Here is the simplest possible workflow: + +```python +class MyWorkflow(Workflow): + @step + async def my_step(self, ev: StartEvent) -> StopEvent: + # do something here + return StopEvent(result="Hello, world!") + + +w = MyWorkflow(timeout=10, verbose=False) +result = await w.run() +print(result) +``` + +This will simply print "Hello, World!" to the console. + +In this code we: +* Define a class `MyWorkflow` that inherits from `Workflow` +* Use the @step decorator to define a single step `my_step` +* The step takes a single argument, `ev`, which is an instance of `StartEvent` +* The step returns a `StopEvent` with a result of "Hello, world!" +* We create an instance of `MyWorkflow` with a timeout of 10 seconds and verbosity off +* We run the workflow and print the result + +## Type annotations for steps + +The type annotations (e.g. `ev: StartEvent`) and `-> StopEvent` are essential to the way Workflows work. The expected types determine what event types will trigger a step. Tools like the visualizer (see below) also rely on these annotations to determine what types are generated and therefore where control flow goes next. + +Type annotations are validated at compile time, so you will get an error message if for instance you emit an event that is never consumed by another step. + +## Start and Stop events + +`StartEvent` and `StopEvent` are special events that are used to start and stop a workflow. Any step that accepts a `StartEvent` will be triggered by the `run` command. Emitting a `StopEvent` will end the execution of the workflow and return a final result, even if other steps remain un-executed. + +## Running a workflow in regular python + +Workflows are async by default, so you use `await` to get the result of the `run` command. This will work fine in a notebook environment; in a vanilla python script you will need to import `asyncio` and wrap your code in an async function, like this: + +```python +async def main(): + w = MyWorkflow(timeout=10, verbose=False) + result = await w.run() + print(result) + + +if __name__ == "__main__": + import asyncio + + asyncio.run(main()) +``` + +In the remaining examples in this tutorial we will assume an async environment for simplicity. + +## Visualizing a workflow + +A great feature of workflows is the built-in visualizer, which we already installed. Let's visualize the simple workflow we just created: + +```python +from llama_index.utils.workflow import draw_all_possible_flows + +draw_all_possible_flows(MyWorkflow, filename="basic_workflow.html") +``` + +This will create a file called `basic_workflow.html` in the current directory. Open it in your browser to see an interactive, visual representation of the workflow. It will look something like this: + +![Basic workflow](./basic_flow.png) + +Of course, a flow with a single step is not very useful! Let's define a multi-step workflow. + +## Custom Events + +Multiple steps are created by defining custom events that can be emitted by steps and trigger other steps. Let's define a simple 3-step workflow. + +We bring in our imports as before, plus a new import for `Event`: + +```python +from llama_index.core.workflow import ( + StartEvent, + StopEvent, + Workflow, + step, + Event, +) +from llama_index.utils.workflow import draw_all_possible_flows +``` + +Now we define two custom events, `FirstEvent` and `SecondEvent`. These classes can have any names and properties, but must inherit from `Event`: + +```python +class FirstEvent(Event): + first_output: str + + +class SecondEvent(Event): + second_output: str +``` + +## Defining the workflow + +Now we define the workflow itself. We do this by defining the input and output types on each step. +* `step_one` takes a `StartEvent` and returns a `FirstEvent` +* `step_two` takes a `FirstEvent` and returns a `SecondEvent` +* `step_three` takes a `SecondEvent` and returns a `StopEvent` + +```python +class MyWorkflow(Workflow): + @step + async def step_one(self, ev: StartEvent) -> FirstEvent: + print(ev.first_input) + return FirstEvent(first_output="First step complete.") + + @step + async def step_two(self, ev: FirstEvent) -> SecondEvent: + print(ev.first_output) + return SecondEvent(second_output="Second step complete.") + + @step + async def step_three(self, ev: SecondEvent) -> StopEvent: + print(ev.second_output) + return StopEvent(result="Workflow complete.") + + +w = MyWorkflow(timeout=10, verbose=False) +result = await w.run(first_input="Start the workflow.") +print(result) +``` + +The full output will be + +``` +Start the workflow. +First step complete. +Second step complete. +Workflow complete. +``` + +And we can use our visualizer to see all possible flows through this workflow: + +```python +from llama_index.utils.workflow import draw_all_possible_flows + +draw_all_possible_flows(MyWorkflow, filename="multi_step_workflow.html") +``` + +![A simple multi-step workflow](./multi_step.png) + +Of course there is still not much point to a workflow if you just run through it from beginning to end! Let's do some [branching and looping](branches_and_loops.md). diff --git a/docs/docs/understanding/workflows/basic_flow.png b/docs/docs/understanding/workflows/basic_flow.png new file mode 100644 index 0000000000000..31b57e66a6c7a Binary files /dev/null and b/docs/docs/understanding/workflows/basic_flow.png differ diff --git a/docs/docs/understanding/workflows/branches_and_loops.md b/docs/docs/understanding/workflows/branches_and_loops.md new file mode 100644 index 0000000000000..5851c5db517cb --- /dev/null +++ b/docs/docs/understanding/workflows/branches_and_loops.md @@ -0,0 +1,91 @@ +# Branches and loops + +A key feature of Workflows is their enablement of branching and looping logic, more simply and flexibly than graph-based approaches. + +## Loops in workflows + +To create a loop, we'll take our example `MyWorkflow` from the previous tutorial and add one new custom event type. We'll call it `LoopEvent` but again it can have any arbitrary name. + +```python +class LoopEvent(Event): + loop_output: str +``` + +Now we'll `import random` and modify our `step_one` function to randomly decide either to loop or to continue: + +```python +@step +async def step_one(self, ev: StartEvent | LoopEvent) -> FirstEvent | LoopEvent: + if random.randint(0, 1) == 0: + print("Bad thing happened") + return LoopEvent(loop_output="Back to step one.") + else: + print("Good thing happened") + return FirstEvent(first_output="First step complete.") +``` + +Let's visualize this: + +![A simple loop](./loop.png) + +You can create a loop from any step to any other step by defining the appropriate event types and return types. + +## Branches in workflows + +Closely related to looping is branching. As you've already seen, you can conditionally return different events. Let's see a workflow that branches into two different paths: + +```python +class BranchA1Event(Event): + payload: str + + +class BranchA2Event(Event): + payload: str + + +class BranchB1Event(Event): + payload: str + + +class BranchB2Event(Event): + payload: str + + +class BranchWorkflow(Workflow): + @step + async def start(self, ev: StartEvent) -> BranchA1Event | BranchB1Event: + if random.randint(0, 1) == 0: + print("Go to branch A") + return BranchA1Event(payload="Branch A") + else: + print("Go to branch B") + return BranchB1Event(payload="Branch B") + + @step + async def step_a1(self, ev: BranchA1Event) -> BranchA2Event: + print(ev.payload) + return BranchA2Event(payload=ev.payload) + + @step + async def step_b1(self, ev: BranchB1Event) -> BranchB2Event: + print(ev.payload) + return BranchB2Event(payload=ev.payload) + + @step + async def step_a2(self, ev: BranchA2Event) -> StopEvent: + print(ev.payload) + return StopEvent(result="Branch A complete.") + + @step + async def step_b2(self, ev: BranchB2Event) -> StopEvent: + print(ev.payload) + return StopEvent(result="Branch B complete.") +``` + +Our imports are the same as before, but we've created 4 new event types. `start` randomly decides to take one branch or another, and then multiple steps in each branch complete the workflow. Let's visualize this: + +![A simple branch](./branching.png) + +You can of course combine branches and loops in any order to fulfill the needs of your application. Later in this tutorial you'll learn how to run multiple branches in parallel using `send_event` and synchronize them using `collect_events`. + +Up next we'll learn about [maintaining state](state.md) with Context. diff --git a/docs/docs/understanding/workflows/branching.png b/docs/docs/understanding/workflows/branching.png new file mode 100644 index 0000000000000..be8b9d997a817 Binary files /dev/null and b/docs/docs/understanding/workflows/branching.png differ diff --git a/docs/docs/understanding/workflows/complex_flow.png b/docs/docs/understanding/workflows/complex_flow.png new file mode 100644 index 0000000000000..57beb88995567 Binary files /dev/null and b/docs/docs/understanding/workflows/complex_flow.png differ diff --git a/docs/docs/understanding/workflows/concurrent_execution.md b/docs/docs/understanding/workflows/concurrent_execution.md new file mode 100644 index 0000000000000..3596d0969731c --- /dev/null +++ b/docs/docs/understanding/workflows/concurrent_execution.md @@ -0,0 +1,125 @@ +# Concurrent execution of workflows + +In addition to looping and branching, workflows can run steps concurrently. This is useful when you have multiple steps that can be run independently of each other and they have time-consuming operations that they `await`, allowing other steps to run in parallel. + +## Emitting multiple events + +In our examples so far, we've only emitted one event from each step. But there are many cases where you would want to run steps in parallel. To do this, you need to emit multiple events. You can do this using `send_event`: + +```python +class ParallelFlow(Workflow): + @step + async def start(self, ctx: Context, ev: StartEvent) -> StepTwoEvent: + ctx.send_event(StepTwoEvent(query="Query 1")) + ctx.send_event(StepTwoEvent(query="Query 2")) + ctx.send_event(StepTwoEvent(query="Query 3")) + + @step(num_workers=4) + async def step_two(self, ctx: Context, ev: StepTwoEvent) -> StopEvent: + print("Running slow query ", ev.query) + await asyncio.sleep(random.randint(1, 5)) + + return StopEvent(result=ev.query) +``` + +In this example, our `start` step emits 3 `StepTwoEvent`s. The `step_two` step is decorated with `num_workers=4`, which tells the workflow to run up to 4 instances of this step concurrently (this is the default). + +## Collecting events + +If you execute the previous example, you'll note that the workflow stops after whichever query is first to complete. Sometimes that's useful, but other times you'll want to wait for all your slow operations to complete before moving on to another step. You can do this using `collect_events`: + +```python +class ConcurrentFlow(Workflow): + @step + async def start(self, ctx: Context, ev: StartEvent) -> StepTwoEvent: + ctx.send_event(StepTwoEvent(query="Query 1")) + ctx.send_event(StepTwoEvent(query="Query 2")) + ctx.send_event(StepTwoEvent(query="Query 3")) + + @step(num_workers=4) + async def step_two(self, ctx: Context, ev: StepTwoEvent) -> StepThreeEvent: + print("Running query ", ev.query) + await asyncio.sleep(random.randint(1, 5)) + return StepThreeEvent(result=ev.query) + + @step + async def step_three(self, ctx: Context, ev: StepThreeEvent) -> StopEvent: + # wait until we receive 3 events + result = ctx.collect_events(ev, [StepThreeEvent] * 3) + if result is None: + return None + + # do something with all 3 results together + print(result) + return StopEvent(result="Done") +``` + +The `collect_events` method lives on the `Context` and takes the event that triggered the step and an array of event types to wait for. In this case, we are awaiting 3 events of the same `StepThreeEvent` type. + +The `step_three` step is fired every time a `StepThreeEvent` is received, but `collect_events` will return `None` until all 3 events have been received. At that point, the step will continue and you can do something with all 3 results together. + +The `result` returned from `collect_events` is an array of the events that were collected, in the order that they were received. + +## Multiple event types + +Of course, you do not need to wait for the same type of event. You can wait for any combination of events you like, such as in this example: + +```python +class ConcurrentFlow(Workflow): + @step + async def start( + self, ctx: Context, ev: StartEvent + ) -> StepAEvent | StepBEvent | StepCEvent: + ctx.send_event(StepAEvent(query="Query 1")) + ctx.send_event(StepBEvent(query="Query 2")) + ctx.send_event(StepCEvent(query="Query 3")) + + @step + async def step_a(self, ctx: Context, ev: StepAEvent) -> StepACompleteEvent: + print("Doing something A-ish") + return StepACompleteEvent(result=ev.query) + + @step + async def step_b(self, ctx: Context, ev: StepBEvent) -> StepBCompleteEvent: + print("Doing something B-ish") + return StepBCompleteEvent(result=ev.query) + + @step + async def step_c(self, ctx: Context, ev: StepCEvent) -> StepCCompleteEvent: + print("Doing something C-ish") + return StepCCompleteEvent(result=ev.query) + + @step + async def step_three( + self, + ctx: Context, + ev: StepACompleteEvent | StepBCompleteEvent | StepCCompleteEvent, + ) -> StopEvent: + print("Received event ", ev.result) + + # wait until we receive 3 events + if ( + ctx.collect_events( + ev, + [StepCCompleteEvent, StepACompleteEvent, StepBCompleteEvent], + ) + is None + ): + return None + + # do something with all 3 results together + return StopEvent(result="Done") +``` + +There are several changes we've made to handle multiple event types: +* `start` is now declared as emitting 3 different event types +* `step_three` is now declared as accepting 3 different event types +* `collect_events` now takes an array of the event types to wait for + +Note that the order of the event types in the array passed to `collect_events` is important. The events will be returned in the order they are passed to `collect_events`, regardless of when they were received. + +The visualization of this workflow is quite pleasing: + +![A concurrent workflow](./different_events.png) + +Now let's look at how we can extend workflows with [subclassing](subclass.md) and other techniques. diff --git a/docs/docs/understanding/workflows/different_events.png b/docs/docs/understanding/workflows/different_events.png new file mode 100644 index 0000000000000..4306c22660d0d Binary files /dev/null and b/docs/docs/understanding/workflows/different_events.png differ diff --git a/docs/docs/understanding/workflows/index.md b/docs/docs/understanding/workflows/index.md new file mode 100644 index 0000000000000..93793d34bb8c3 --- /dev/null +++ b/docs/docs/understanding/workflows/index.md @@ -0,0 +1,39 @@ +# Workflows introduction + +## What is a workflow? + +A workflow is an event-driven, step-based way to control the execution flow of an application. + +Your application is divided into sections called Steps which are triggered by Events, and themselves emit Events which trigger further steps. By combining steps and events, you can create arbitrarily complex flows that encapsulate logic and make your application more maintainable and easier to understand. A step can be anything from a single line of code to a complex agent. They can have arbitrary inputs and outputs, which are passed around by Events. + +## An example + +In this visualization, you can see a moderately complex workflow designed to take a query, optionally improve upon it, and then attempt to answer the query using three different RAG strategies. The LLM gets answers from all three strategies and judges which is the "best", and returns that. We can break this flow down: + +* It is triggered by a `StartEvent` +* A step called `judge_query` determines if the query is of high quality. If not, a `BadQueryEvent` is generated. +* A `BadQueryEvent` will trigger a step called `improve_query` which will attempt to improve the query, which will then trigger a `JudgeEvent` +* A `JudgeEvent` will trigger `judge_query` again, creating a loop which can continue until the query is judged of sufficient quality. This is called "Reflection" and is a key part of agentic applications that Workflows make easy to implement. +* If the query is of sufficient quality, 3 simultaneous events are generated: a `NaiveRAGEvent`, a `HighTopKEvent`, and a `RerankEvent`. These three events trigger 3 associated steps in parallel, which each run a different RAG strategy. +* Each of the query steps generates a `ResponseEvent`. A `ResponseEvent` triggers a step called `judge_response` which will wait until it has received all 3 responses. +* `judge_response` will then pick the "best" response and return it to the user via a `StopEvent`. + +![A complex workflow](./complex_flow.png) + +## Why workflows? + +As generative AI applications become more complex, it becomes harder to manage the flow of data and control the execution of the application. Workflows provide a way to manage this complexity by breaking the application into smaller, more manageable pieces. + +Other frameworks and LlamaIndex itself have attempted to solve this problem previously with directed acyclic graphs (DAGs) but these have a number of limitations that workflows do not: + +* Logic like loops and branches needed to be encoded into the edges of graphs, which made them hard to read and understand. +* Passing data between nodes in a DAG created complexity around optional and default values and which parameters should be passed. +* DAGs did not feel natural to developers trying to developing complex, looping, branching AI applications. + +The event-based pattern and vanilla python approach of Workflows resolves these problems. + +For simple RAG pipelines and linear demos we do not expect you will need Workflows, but as your application grows in complexity, we hope you will reach for them. + +## Next steps + +Let's build [a basic workflow](basic_flow.md). diff --git a/docs/docs/understanding/workflows/loop.png b/docs/docs/understanding/workflows/loop.png new file mode 100644 index 0000000000000..fef94106ac841 Binary files /dev/null and b/docs/docs/understanding/workflows/loop.png differ diff --git a/docs/docs/understanding/workflows/multi_step.png b/docs/docs/understanding/workflows/multi_step.png new file mode 100644 index 0000000000000..dc961ed6e48c9 Binary files /dev/null and b/docs/docs/understanding/workflows/multi_step.png differ diff --git a/docs/docs/understanding/workflows/nested.md b/docs/docs/understanding/workflows/nested.md new file mode 100644 index 0000000000000..c7124a5622f3a --- /dev/null +++ b/docs/docs/understanding/workflows/nested.md @@ -0,0 +1,94 @@ +# Nested workflows + +Another way to extend workflows is to nest additional workflows. It's possible to create explicit slots in existing flows where you can supply an entire additional workflow. For example, let's say we had a query that used an LLM to reflect on the quality of that query. The author might expect that you would want to modify the reflection step, and leave a slot for you to do that. + +Here's our base workflow: + +```python +from llama_index.core.workflow import ( + StartEvent, + StopEvent, + Workflow, + step, + Event, + Context, +) +from llama_index.utils.workflow import draw_all_possible_flows + + +class Step2Event(Event): + query: str + + +class MainWorkflow(Workflow): + @step + async def start( + self, ctx: Context, ev: StartEvent, reflection_workflow: Workflow + ) -> Step2Event: + print("Need to run reflection") + res = await reflection_workflow.run(query=ev.query) + + return Step2Event(query=res) + + @step + async def step_two(self, ctx: Context, ev: Step2Event) -> StopEvent: + print("Query is ", ev.query) + # do something with the query here + return StopEvent(result=ev.query) +``` + +This workflow by itself will not run; it needs a valid workflow for the reflection step. Let's create one: + +```python +class ReflectionFlow(Workflow): + @step + async def sub_start(self, ctx: Context, ev: StartEvent) -> StopEvent: + print("Doing custom reflection") + return StopEvent(result="Improved query") +``` + +Now we can run the main workflow by supplying this custom reflection nested flow using the `add_workflows` method, to which we pass an instance of the `ReflectionFlow` class: + +```python +w = MainWorkflow(timeout=10, verbose=False) +w.add_workflows(reflection_workflow=ReflectionFlow()) +result = await w.run(query="Initial query") +print(result) +``` + +Note that because the nested flow is a totally different workflow rather than a step, `draw_all_possible_flows` will only draw the flow of `MainWorkflow`. + +## Default workflows + +If you're creating a workflow with multiple slots for nested workflows, you might want to provide default workflows for each slot. You can do this by setting the default value of the slot to an instance of the workflow class. Here's an example. + +First, let's create a default sub-workflow to use: + +```python +class DefaultSubflow(Workflow): + @step() + async def sub_start(self, ctx: Context, ev: StartEvent) -> StopEvent: + print("Doing basic reflection") + return StopEvent(result="Improved query") +``` + +Now we can modify the `MainWorkflow` to include a default sub-workflow: + +```python +class MainWorkflow(Workflow): + @step() + async def start( + self, + ctx: Context, + ev: StartEvent, + reflection_workflow: Workflow = DefaultSubflow(), + ) -> Step2Event: + print("Need to run reflection") + res = await reflection_workflow.run(query=ev.query) + + return Step2Event(query=res) +``` + +Now, if you run the workflow without providing a custom reflection workflow, it will use the default one. This can be very useful for providing a good "out of the box" experience for users who may not want to customize everything. + +Finally, let's take a look at [observability and debugging](observability.md) in workflows. diff --git a/docs/docs/understanding/workflows/observability.md b/docs/docs/understanding/workflows/observability.md new file mode 100644 index 0000000000000..9d6aa7a2b7b72 --- /dev/null +++ b/docs/docs/understanding/workflows/observability.md @@ -0,0 +1,133 @@ +# Observability + +Debugging is essential to any application development, and Workflows provide you a number of ways to do that. + +## Visualization + +The simplest form of debugging is visualization, which we've already used extensively in this tutorial. You can visualize your workflow at any time by running the following code: + +```python +from llama_index.utils.workflow import draw_all_possible_flows + +draw_all_possible_flows(MyWorkflow, filename="some_filename.html") +``` + +This will output an interactive visualization of your flow to `some_filename.html` that you can view in any browser. + +![A concurrent workflow](./different_events.png) + +## Verbose mode + +When running any workflow you can always pass `verbose=True`. This will output the name of each step as it's executed, and whether and which type of event it returns. Using the `ConcurrentWorkflow` from the previous stage of this tutorial: + +```python +class ConcurrentFlow(Workflow): + @step + async def start( + self, ctx: Context, ev: StartEvent + ) -> StepAEvent | StepBEvent | StepCEvent: + ctx.send_event(StepAEvent(query="Query 1")) + ctx.send_event(StepBEvent(query="Query 2")) + ctx.send_event(StepCEvent(query="Query 3")) + + @step + async def step_a(self, ctx: Context, ev: StepAEvent) -> StepACompleteEvent: + print("Doing something A-ish") + return StepACompleteEvent(result=ev.query) + + @step + async def step_b(self, ctx: Context, ev: StepBEvent) -> StepBCompleteEvent: + print("Doing something B-ish") + return StepBCompleteEvent(result=ev.query) + + @step + async def step_c(self, ctx: Context, ev: StepCEvent) -> StepCCompleteEvent: + print("Doing something C-ish") + return StepCCompleteEvent(result=ev.query) + + @step + async def step_three( + self, + ctx: Context, + ev: StepACompleteEvent | StepBCompleteEvent | StepCCompleteEvent, + ) -> StopEvent: + print("Received event ", ev.result) + + # wait until we receive 3 events + if ( + ctx.collect_events( + ev, + [StepCCompleteEvent, StepACompleteEvent, StepBCompleteEvent], + ) + is None + ): + return None + + # do something with all 3 results together + return StopEvent(result="Done") +``` + +You can run the workflow with verbose mode like this: + +```python +w = ConcurrentFlow(timeout=10, verbose=True) +result = await w.run() +``` + +And you'll see output like this: + +``` +Running step start +Step start produced no event +Running step step_a +Doing something A-ish +Step step_a produced event StepACompleteEvent +Running step step_b +Doing something B-ish +Step step_b produced event StepBCompleteEvent +Running step step_c +Doing something C-ish +Step step_c produced event StepCCompleteEvent +Running step step_three +Received event Query 1 +Step step_three produced no event +Running step step_three +Received event Query 2 +Step step_three produced no event +Running step step_three +Received event Query 3 +Step step_three produced event StopEvent +``` + +## Stepwise execution + +In a notebook environment it can be helpful to run a workflow step by step. You can do this by calling `run_step` on the workflow object: + +```python +w = ConcurrentFlow(timeout=10, verbose=True) +await w.run_step() +``` + +You can call `run_step` multiple times to step through the workflow one step at a time. + +## Visualizing most recent execution + +If you're running a workflow step by step, or you have just executed a workflow with branching, you can get the visualizer to draw only exactly which steps just executed using `draw_most_recent_execution`: + +```python +from llama_index.utils.workflow import draw_most_recent_execution + +draw_most_recent_execution(w, filename="last_execution.html") +``` + +Note that instead of passing the class name you are passing the instance of the workflow, `w`. + +## Third party tools + +You can also use any of the third-party tools for visualizing and debugging that we support, such as [Arize](https://docs.arize.com/arize/large-language-models/tracing/auto-instrumentation/llamaindex). + +![Arize flow](./arize.png) + +## One more thing + +Our last step in this tutorial is an alternative syntax for defining workflows using [unbound functions](unbound_functions.md) instead of classes. diff --git a/docs/docs/understanding/workflows/state.md b/docs/docs/understanding/workflows/state.md new file mode 100644 index 0000000000000..92b7ce23eb5e2 --- /dev/null +++ b/docs/docs/understanding/workflows/state.md @@ -0,0 +1,67 @@ +# Maintaining state + +In our examples so far, we have passed data from step to step using properties of custom events. This is a powerful way to pass data around, but it has limitations. For example, if you want to pass data between steps that are not directly connected, you need to pass the data through all the steps in between. This can make your code harder to read and maintain. + +To avoid this pitfall, we have a `Context` object available to every step in the workflow. To use it, declare an argument of type `Context` to your step. Here's how you do that. + +We need one new import, the `Context` type: + +```python +from llama_index.core.workflow import ( + StartEvent, + StopEvent, + Workflow, + step, + Event, + Context, +) +``` + +Now we define a `start` event that checks if data has been loaded into the context. If not, it returns a `SetupEvent` which triggers `setup` that loads the data and loops back to `start`. + +```python +class SetupEvent(Event): + query: str + + +class StepTwoEvent(Event): + query: str + + +class StatefulFlow(Workflow): + @step + async def start( + self, ctx: Context, ev: StartEvent + ) -> SetupEvent | StepTwoEvent: + db = await ctx.get("some_database", default=None) + if db is None: + print("Need to load data") + return SetupEvent(query=ev.query) + + # do something with the query + return StepTwoEvent(query=ev.query) + + @step + async def setup(self, ctx: Context, ev: SetupEvent) -> StartEvent: + # load data + await ctx.set("some_database", [1, 2, 3]) + return StartEvent(query=ev.query) +``` + +Then in `step_two` we can access data directly from the context without having it passed explicitly. In gen AI applications this is useful for loading indexes and other large data operations. + +```python +@step +async def step_two(self, ctx: Context, ev: StepTwoEvent) -> StopEvent: + # do something with the data + print("Data is ", await ctx.get("some_database")) + + return StopEvent(result=await ctx.get("some_database")[1]) + + +w = StatefulFlow(timeout=10, verbose=False) +result = await w.run(query="Some query") +print(result) +``` + +Up next we'll learn how to [stream events](stream.md) from an in-progress workflow. diff --git a/docs/docs/understanding/workflows/stateful.png b/docs/docs/understanding/workflows/stateful.png new file mode 100644 index 0000000000000..cefcad57a68d4 Binary files /dev/null and b/docs/docs/understanding/workflows/stateful.png differ diff --git a/docs/docs/understanding/workflows/stream.md b/docs/docs/understanding/workflows/stream.md new file mode 100644 index 0000000000000..d2ab0bc9cee6a --- /dev/null +++ b/docs/docs/understanding/workflows/stream.md @@ -0,0 +1,90 @@ +# Streaming events + +Workflows can be complex -- they are designed to handle complex, branching, concurrent logic -- which means they can take time to fully execute. To provide your user with a good experience, you may want to provide an indication of progress by streaming events as they occur. Workflows have built-in support for this on the `Context` object. + +To get this done, let's bring in all the deps we need: + +```python +from llama_index.core.workflow import ( + StartEvent, + StopEvent, + Workflow, + step, + Event, + Context, +) +import asyncio +from llama_index.llms.openai import OpenAI +from llama_index.utils.workflow import draw_all_possible_flows +``` + +Let's set up some events for a simple three-step workflow: + +```python +class FirstEvent(Event): + first_output: str + + +class SecondEvent(Event): + second_output: str + response: str +``` + +And define a workflow class that sends events: + +```python +class MyWorkflow(Workflow): + @step + async def step_one(self, ctx: Context, ev: StartEvent) -> FirstEvent: + ctx.write_event_to_stream(Event(msg="Step one is happening")) + return FirstEvent(first_output="First step complete.") + + @step + async def step_two(self, ctx: Context, ev: FirstEvent) -> SecondEvent: + llm = OpenAI(model="gpt-4o-mini") + generator = await llm.astream_complete( + "Please give me the first 3 paragraphs of Moby Dick, a book in the public domain." + ) + async for response in generator: + # Allow the workflow to stream this piece of response + ctx.write_event_to_stream(Event(msg=response.delta)) + return SecondEvent( + second_output="Second step complete, full response attached", + response=str(response), + ) + + @step + async def step_three(self, ctx: Context, ev: SecondEvent) -> StopEvent: + ctx.write_event_to_stream(Event(msg="Step three is happening")) + return StopEvent(result="Workflow complete.") +``` + +!!! tip + `OpenAI()` here assumes you have an `OPENAI_API_KEY` set in your environment. You could also pass one in using the `api_key` parameter. + +In `step_one` and `step_three` we write individual events to the event stream. In `step_two` we use `astream_complete` to produce an iterable generator of the LLM's response, then we produce an event for each chunk of data the LLM sends back to us -- roughly one per word -- before returning the final response to `step_three`. + +To actually get this output, we need to run the workflow asynchronously and listen for the events, like this: + +```python +async def main(): + w = MyWorkflow(timeout=30, verbose=True) + task = asyncio.create_task(w.run(first_input="Start the workflow.")) + + async for ev in w.stream_events(): + print(ev.msg) + + final_result = await task + print("Final result", final_result) + + draw_all_possible_flows(MyWorkflow, filename="streaming_workflow.html") + + +if __name__ == "__main__": + asyncio.run(main()) +``` + +`create_task` runs the workflow in the background, while `stream_events` will provide any event that gets written to the stream. It stops when the stream delivers a `StopEvent`, after which you can get the final result of the workflow as you normally would. + + +Next let's look at [concurrent execution](concurrent_execution.md). diff --git a/docs/docs/understanding/workflows/subclass.md b/docs/docs/understanding/workflows/subclass.md new file mode 100644 index 0000000000000..17b1f75aea167 --- /dev/null +++ b/docs/docs/understanding/workflows/subclass.md @@ -0,0 +1,99 @@ +# Subclassing workflows + +Another great feature of workflows is their extensibility. You can take workflows written by others or built-ins from LlamaIndex and extend them to customize them to your needs. We'll look at two ways to do that. + +The first is subclassing: workflows are just regular Python classes, which means you can subclass them to add new functionality. For example, let's say you have an agentic workflow that does some processing and then sends an email. You can subclass the workflow to add an extra step to send a text message as well. + +Here's our base workflow: + +```python +from llama_index.core.workflow import ( + StartEvent, + StopEvent, + Workflow, + step, + Event, + Context, +) + + +class Step2Event(Event): + query: str + + +class Step3Event(Event): + query: str + + +class MainWorkflow(Workflow): + @step + async def start(self, ev: StartEvent) -> Step2Event: + print("Starting up") + return Step2Event(query=ev.query) + + @step + async def step_two(self, ev: Step2Event) -> Step3Event: + print("Sending an email") + return Step3Event(query=ev.query) + + @step + async def step_three(self, ev: Step3Event) -> StopEvent: + print("Finishing up") + return StopEvent(result=ev.query) +``` + +If we run this: + +```python +w = MainWorkflow(timeout=10, verbose=False) +result = await w.run(query="Initial query") +print(result) +``` + +We get: + +``` +Starting up +Sending an email +Finishing up +Initial query +``` + +Now let's subclass this workflow to send a text message as well: + +```python +class Step2BEvent(Event): + query: str + + +class CustomWorkflow(MainWorkflow): + @step + async def step_two(self, ev: Step2Event) -> Step2BEvent: + print("Sending an email") + return Step2BEvent(query=ev.query) + + @step + async def step_two_b(self, ev: Step2BEvent) -> Step3Event: + print("Also sending a text message") + return Step3Event(query=ev.query) +``` + +Which will instead give us + +``` +Starting up +Sending an email +Also sending a text message +Finishing up +Initial query +``` + +We can visualize the subclassed workflow and it will show all the steps, like this: + +```python +draw_all_possible_flows(CustomWorkflow, "custom_workflow.html") +``` + +![Custom workflow](subclass.png) + +Next, let's look at another way to extend a workflow: [nested workflows](nested.md). diff --git a/docs/docs/understanding/workflows/subclass.png b/docs/docs/understanding/workflows/subclass.png new file mode 100644 index 0000000000000..2382269e16843 Binary files /dev/null and b/docs/docs/understanding/workflows/subclass.png differ diff --git a/docs/docs/understanding/workflows/unbound_functions.md b/docs/docs/understanding/workflows/unbound_functions.md new file mode 100644 index 0000000000000..9781c40f705cc --- /dev/null +++ b/docs/docs/understanding/workflows/unbound_functions.md @@ -0,0 +1,26 @@ +# Workflows from unbound functions + +Throughout this tutorial we have been showing workflows defined as classes. However, this is not the only way to define a workflow: you can also define the steps in your workflow through independent or "unbound" functions and assign them to a workflow using the `@step()` decorator. Let's see how that works. + +First we create an empty class to hold the steps: + +```python +class TestWorkflow(Workflow): + pass +``` + +Now we can add steps to the workflow by defining functions and decorating them with the `@step()` decorator: + +```python +@step(workflow=TestWorkflow) +def some_step(ev: StartEvent) -> StopEvent: + return StopEvent() +``` + +In this example, we're adding a starting step to the `TestWorkflow` class. The `@step()` decorator takes the `workflow` argument, which is the class to which the step will be added. The function signature is the same as for a regular step, with the exception of the `workflow` argument. + +You can also add steps this way to any existing workflow class! This can be handy if you just need one extra step in your workflow and don't want to subclass an entire workflow to do it. + +## That's it! + +Congratulations, you've completed the workflows tutorial! diff --git a/docs/docs/use_cases/q_and_a/index.md b/docs/docs/use_cases/q_and_a/index.md index 759650789e110..92b15c69b4556 100644 --- a/docs/docs/use_cases/q_and_a/index.md +++ b/docs/docs/use_cases/q_and_a/index.md @@ -28,11 +28,11 @@ As you scale to more complex questions / more data, there are many techniques in - **Querying Complex Documents**: Oftentimes your document representation is complex - your PDF may have text, tables, charts, images, headers/footers, and more. LlamaIndex provides advanced indexing/retrieval integrated with LlamaParse, our proprietary document parser. [Full cookbooks here](https://github.com/run-llama/llama_parse/tree/main/examples). - **Combine multiple sources**: is some of your data in Slack, some in PDFs, some in unstructured text? LlamaIndex can combine queries across an arbitrary number of sources and combine them. - - [Example of combining multiple sources](../../understanding/putting_it_all_together/q_and_a.md#multi-document-queries) + - [Example of combining multiple sources](../../understanding/putting_it_all_together/q_and_a/#multi-document-queries) - **Route across multiple sources**: given multiple data sources, your application can first pick the best source and then "route" the question to that source. - - [Example of routing across multiple sources](../../understanding/putting_it_all_together/q_and_a.md#routing-over-heterogeneous-data) + - [Example of routing across multiple sources](../../understanding/putting_it_all_together/q_and_a/#routing-over-heterogeneous-data) - **Multi-document queries**: some questions have partial answers in multiple data sources which need to be questioned separately before they can be combined - - [Example of multi-document queries](../../understanding/putting_it_all_together/q_and_a.md#multi-document-queries) + - [Example of multi-document queries](../../understanding/putting_it_all_together/q_and_a/#multi-document-queries) - [Building a multi-document agent over the LlamaIndex docs](../../examples/agent/multi_document_agents-v1.ipynb) - [Text to SQL](../../examples/index_structs/struct_indices/SQLIndexDemo.ipynb) @@ -53,4 +53,4 @@ LlamaIndex has a lot of resources around QA / RAG. Here are some core resource g ## Further examples -For further examples of Q&A use cases, see our [Q&A section in Putting it All Together](../../understanding/putting_it_all_together/q_and_a.md). +For further examples of Q&A use cases, see our [Q&A section in Putting it All Together](../../understanding/putting_it_all_together/q_and_a/index.md). diff --git a/docs/docs/vector_stores/wordlift_vector_store_demo.ipynb b/docs/docs/vector_stores/wordlift_vector_store_demo.ipynb new file mode 100644 index 0000000000000..b4337d926b59c --- /dev/null +++ b/docs/docs/vector_stores/wordlift_vector_store_demo.ipynb @@ -0,0 +1,906 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **WordLift** Vector Store\n", + "\n", + "## Introduction\n", + "This script demonstrates how to crawl a product website, extract relevant information, build an SEO-friendly Knowledge Graph (a structured representation of PDPs and PLPs), and leverage it for improved search and user experience.\n", + "\n", + "### Key Features & Libraries:\n", + "\n", + "- Web scraping (Advertools)\n", + "- Knowledge Graph creation for Product Detail Pages (PDPs) and Product Listing Pages (PLPs) - WordLift\n", + "- Product recommendations (WordLift Neural Search)\n", + "- Shopping assistant creation (WordLift + LlamaIndex 🦙)\n", + "\n", + "This approach enhances SEO performance and user engagement for e-commerce sites.\n", + "\n", + "Learn more about how it works here:\n", + "- [https://www.youtube.com/watch?v=CH-ir1MTAwQ](https://www.youtube.com/watch?v=CH-ir1MTAwQ)\n", + "- [https://wordlift.io/academy-entries/mastering-serp-analysis-knowledge-graphs](https://wordlift.io/academy-entries/mastering-serp-analysis-knowledge-graphs)\n", + "\n", + "

\n", + "\n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " by\n", + " \n", + " Andrea Volpini\n", + " \n", + " and\n", + " \n", + " David Riccitelli\n", + " \n", + "
\n", + "
\n", + " MIT License\n", + "
\n", + "
\n", + " Last updated: Jul 31st, 2024\n", + "
\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install advertools -q\n", + "!pip install -U wordlift-client # 🎉 first time on stage 🎉\n", + "!pip install rdflib -q" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Standard library imports\n", + "import json\n", + "import logging\n", + "import os\n", + "import re\n", + "import urllib.parse\n", + "import requests\n", + "from typing import List, Optional\n", + "\n", + "# Third-party imports\n", + "import advertools as adv\n", + "import pandas as pd\n", + "import nest_asyncio\n", + "\n", + "# RDFLib imports\n", + "from rdflib import Graph, Literal, RDF, URIRef\n", + "from rdflib.namespace import SDO, Namespace, DefinedNamespace\n", + "\n", + "# WordLift client imports\n", + "import wordlift_client\n", + "from wordlift_client import Configuration, ApiClient\n", + "from wordlift_client.rest import ApiException\n", + "from wordlift_client.api.dataset_api import DatasetApi\n", + "from wordlift_client.api.entities_api import EntitiesApi\n", + "from wordlift_client.api.graph_ql_api import GraphQLApi\n", + "from wordlift_client.models.graphql_request import GraphqlRequest\n", + "from wordlift_client.models.page_vector_search_query_response_item import (\n", + " PageVectorSearchQueryResponseItem,\n", + ")\n", + "from wordlift_client.models.vector_search_query_request import (\n", + " VectorSearchQueryRequest,\n", + ")\n", + "from wordlift_client.api.vector_search_queries_api import (\n", + " VectorSearchQueriesApi,\n", + ")\n", + "\n", + "\n", + "# Asynchronous programming\n", + "import asyncio\n", + "\n", + "# Set up logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "# Apply nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "WORDLIFT_KEY = os.getenv(\"WORDLIFT_KEY\")\n", + "OPENAI_KEY = os.getenv(\"OPENAI_KEY\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Crawl the Website w/ Advertools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Step 1: Define the website structure\n", + "# -----------------------------------\n", + "\n", + "# We're working with two types of pages:\n", + "# 1. Product Listing Pages (PLP): https://product-finder.wordlift.io/product-category/bags/\n", + "# 2. Product Detail Pages (PDP): https://product-finder.wordlift.io/product/1980s-marco-polo-crossbody-bag-in-black/\n", + "\n", + "# The product description can be found at this XPath:\n", + "# /html/body/div[1]/div/div/div/div/div[1]/div/div[3]/div/div[2]/div[2]/div[1]/p/text()\n", + "# The price is here:\n", + "# /html/body/div[1]/div/div/div/div/div[1]/div/div[3]/div/div[2]/p/span/bdi/text()\n", + "# The category is here:\n", + "# //span[contains(@class, 'breadcrumb')]/a/text()\n", + "\n", + "# Step 2: Set up the crawl\n", + "# ------------------------\n", + "\n", + "\n", + "def crawl_website(url, output_file, num_pages=10):\n", + " logger.info(f\"Starting crawl of {url}\")\n", + " adv.crawl(\n", + " url,\n", + " output_file,\n", + " follow_links=True,\n", + " custom_settings={\n", + " \"CLOSESPIDER_PAGECOUNT\": num_pages,\n", + " \"USER_AGENT\": \"WordLiftBot/1.0 (Maven Project)\",\n", + " \"CONCURRENT_REQUESTS_PER_DOMAIN\": 2,\n", + " \"DOWNLOAD_DELAY\": 1,\n", + " \"ROBOTSTXT_OBEY\": False,\n", + " },\n", + " xpath_selectors={\n", + " \"product_description\": \"/html/body/div[1]/div/div/div/div/div[1]/div/div[3]/div/div[2]/div[2]/div[1]/p/text()\",\n", + " \"product_price\": \"/html/body/div[1]/div/div/div/div/div[1]/div/div[3]/div/div[2]/p/span/bdi/text()\",\n", + " \"product_category\": \"//span[@class='posted_in']/a/text()\",\n", + " },\n", + " )\n", + " logger.info(f\"Crawl completed. Results saved to {output_file}\")\n", + "\n", + "\n", + "# Step 3: Analyze URL patterns\n", + "# ----------------------------\n", + "\n", + "\n", + "def analyze_url_patterns(df):\n", + " df[\"page_type\"] = df[\"url\"].apply(\n", + " lambda x: \"PLP\"\n", + " if \"/product-category/\" in x\n", + " else (\"PDP\" if \"/product/\" in x else \"Other\")\n", + " )\n", + " logger.info(\n", + " f\"Found {(df['page_type'] == 'PLP').sum()} PLPs and {(df['page_type'] == 'PDP').sum()} PDPs\"\n", + " )\n", + " return df\n", + "\n", + "\n", + "# Step 4: Extract page data\n", + "# ----------------------------\n", + "\n", + "\n", + "def extract_page_data(df):\n", + " extracted_data = []\n", + " for _, row in df.iterrows():\n", + " page = {\n", + " \"url\": row[\"url\"],\n", + " \"title\": row[\"title\"],\n", + " \"page_type\": row[\"page_type\"],\n", + " \"meta_description\": row.get(\"meta_description\", \"\"),\n", + " \"og_title\": row.get(\"og_title\", \"\"),\n", + " \"og_description\": row.get(\"og_description\", \"\"),\n", + " \"h1\": \", \".join(row.get(\"h1\", []))\n", + " if isinstance(row.get(\"h1\"), list)\n", + " else row.get(\"h1\", \"\"),\n", + " \"h2\": \", \".join(row.get(\"h2\", []))\n", + " if isinstance(row.get(\"h2\"), list)\n", + " else row.get(\"h2\", \"\"),\n", + " }\n", + "\n", + " if row[\"page_type\"] == \"PDP\":\n", + " page.update(\n", + " {\n", + " \"product_description\": \", \".join(\n", + " row.get(\"product_description\", [])\n", + " )\n", + " if isinstance(row.get(\"product_description\"), list)\n", + " else row.get(\"product_description\", \"\"),\n", + " \"product_price\": \", \".join(row.get(\"product_price\", []))\n", + " if isinstance(row.get(\"product_price\"), list)\n", + " else row.get(\"product_price\", \"\"),\n", + " \"product_category\": \", \".join(\n", + " row.get(\"product_category\", [])\n", + " )\n", + " if isinstance(row.get(\"product_category\"), list)\n", + " else row.get(\"product_category\", \"\"),\n", + " }\n", + " )\n", + " elif row[\"page_type\"] == \"PLP\":\n", + " # Parse the category from the H1 content\n", + " h1_content = (\n", + " row.get(\"h1\", [\"\"])[0]\n", + " if isinstance(row.get(\"h1\"), list)\n", + " else row.get(\"h1\", \"\")\n", + " )\n", + " category = (\n", + " h1_content.split(\"@@\")[-1]\n", + " if \"@@\" in h1_content\n", + " else h1_content.replace(\"Category: \", \"\").strip()\n", + " )\n", + " page[\"category_name\"] = category\n", + "\n", + " extracted_data.append(page)\n", + "\n", + " return pd.DataFrame(extracted_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build the KG w/ WordLift 🕸" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Step 5: Configure the WordLift client\n", + "# ----------------------------\n", + "\n", + "# Create a configuration object for the WordLift API client using your WordLift key.\n", + "configuration = Configuration(host=\"https://api.wordlift.io\")\n", + "configuration.api_key[\"ApiKey\"] = WORDLIFT_KEY\n", + "configuration.api_key_prefix[\"ApiKey\"] = \"Key\"\n", + "\n", + "EXAMPLE_PRIVATE_NS = Namespace(\"https://ns.example.org/private/\")\n", + "\n", + "BASE_URI = \"http://data.wordlift.io/[dataset_id]/\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Step 6: Build the KG and the embeddings\n", + "# ----------------------------\n", + "\n", + "\n", + "async def cleanup_knowledge_graph(api_client):\n", + " dataset_api = wordlift_client.DatasetApi(api_client)\n", + " try:\n", + " # Delete all\n", + " await dataset_api.delete_all_entities()\n", + " except Exception as e:\n", + " print(\n", + " \"Exception when calling DatasetApi->delete_all_entities: %s\\n\" % e\n", + " )\n", + "\n", + "\n", + "async def create_entity(entities_api, entity_data):\n", + " g = Graph().parse(data=json.dumps(entity_data), format=\"json-ld\")\n", + " body = g.serialize(format=\"application/rdf+xml\")\n", + " await entities_api.create_or_update_entities(\n", + " body=body, _content_type=\"application/rdf+xml\"\n", + " )\n", + "\n", + "\n", + "def replace_url(original_url: str) -> str:\n", + " old_domain = \"https://product-finder.wordlift.io/\"\n", + " new_domain = \"https://data-science-with-python-for-seo.wordlift.dev/\"\n", + "\n", + " if original_url.startswith(old_domain):\n", + " return original_url.replace(old_domain, new_domain, 1)\n", + " else:\n", + " return original_url\n", + "\n", + "\n", + "def create_entity_uri(url):\n", + " parsed_url = urllib.parse.urlparse(url)\n", + " path = parsed_url.path.strip(\"/\")\n", + " path_parts = path.split(\"/\")\n", + " fragment = parsed_url.fragment\n", + "\n", + " if \"product\" in path_parts:\n", + " # It's a product page or product offer\n", + " product_id = path_parts[-1] # Get the last part of the path\n", + " if fragment == \"offer\":\n", + " return f\"{BASE_URI}offer_{product_id}\"\n", + " else:\n", + " return f\"{BASE_URI}product_{product_id}\"\n", + " elif \"product-category\" in path_parts:\n", + " # It's a product listing page (PLP)\n", + " category = path_parts[-1] # Get the last part of the path\n", + " return f\"{BASE_URI}plp_{category}\"\n", + " else:\n", + " # For any other type of page\n", + " safe_path = \"\".join(c if c.isalnum() else \"_\" for c in path)\n", + " if fragment == \"offer\":\n", + " return f\"{BASE_URI}offer_{safe_path}\"\n", + " else:\n", + " return f\"{BASE_URI}page_{safe_path}\"\n", + "\n", + "\n", + "def clean_price(price_str):\n", + " if not price_str or price_str == \"N/A\":\n", + " return None\n", + " if isinstance(price_str, (int, float)):\n", + " return float(price_str)\n", + " try:\n", + " # Remove any non-numeric characters except for the decimal point\n", + " cleaned_price = \"\".join(\n", + " char for char in str(price_str) if char.isdigit() or char == \".\"\n", + " )\n", + " return float(cleaned_price)\n", + " except ValueError:\n", + " logger.warning(f\"Could not convert price: {price_str}\")\n", + " return None\n", + "\n", + "\n", + "def create_product_entity(row, dataset_uri):\n", + " url = replace_url(row[\"url\"])\n", + " product_entity_uri = create_entity_uri(url)\n", + "\n", + " entity_data = {\n", + " \"@context\": \"http://schema.org\",\n", + " \"@type\": \"Product\",\n", + " \"@id\": product_entity_uri,\n", + " \"url\": url,\n", + " \"name\": row[\"title\"]\n", + " if not pd.isna(row[\"title\"])\n", + " else \"Untitled Product\",\n", + " \"urn:meta:requestEmbeddings\": [\n", + " \"http://schema.org/name\",\n", + " \"http://schema.org/description\",\n", + " ],\n", + " }\n", + "\n", + " if not pd.isna(row.get(\"product_description\")):\n", + " entity_data[\"description\"] = row[\"product_description\"]\n", + "\n", + " if not pd.isna(row.get(\"product_price\")):\n", + " price = clean_price(row[\"product_price\"])\n", + " if price is not None:\n", + " # Create offer ID as a sub-resource of the product ID\n", + " offer_entity_uri = f\"{product_entity_uri}/offer_1\"\n", + " entity_data[\"offers\"] = {\n", + " \"@type\": \"Offer\",\n", + " \"@id\": offer_entity_uri,\n", + " \"price\": str(price),\n", + " \"priceCurrency\": \"GBP\",\n", + " \"availability\": \"http://schema.org/InStock\",\n", + " \"url\": url,\n", + " }\n", + "\n", + " if not pd.isna(row.get(\"product_category\")):\n", + " entity_data[\"category\"] = row[\"product_category\"]\n", + "\n", + " custom_attributes = {\n", + " key: row[key]\n", + " for key in [\n", + " \"meta_description\",\n", + " \"og_title\",\n", + " \"og_description\",\n", + " \"h1\",\n", + " \"h2\",\n", + " ]\n", + " if not pd.isna(row.get(key))\n", + " }\n", + " if custom_attributes:\n", + " entity_data[str(EXAMPLE_PRIVATE_NS.attributes)] = json.dumps(\n", + " custom_attributes\n", + " )\n", + "\n", + " return entity_data\n", + "\n", + "\n", + "def create_collection_entity(row, dataset_uri):\n", + " url = replace_url(row[\"url\"])\n", + " entity_uri = create_entity_uri(url)\n", + "\n", + " entity_data = {\n", + " \"@context\": \"http://schema.org\",\n", + " \"@type\": \"CollectionPage\",\n", + " \"@id\": entity_uri,\n", + " \"url\": url,\n", + " \"name\": row[\"category_name\"] or row[\"title\"],\n", + " }\n", + "\n", + " custom_attributes = {\n", + " key: row[key]\n", + " for key in [\n", + " \"meta_description\",\n", + " \"og_title\",\n", + " \"og_description\",\n", + " \"h1\",\n", + " \"h2\",\n", + " ]\n", + " if row.get(key)\n", + " }\n", + " if custom_attributes:\n", + " entity_data[str(EXAMPLE_PRIVATE_NS.attributes)] = json.dumps(\n", + " custom_attributes\n", + " )\n", + "\n", + " return entity_data\n", + "\n", + "\n", + "async def build_knowledge_graph(df, dataset_uri, api_client):\n", + " entities_api = EntitiesApi(api_client)\n", + "\n", + " for _, row in df.iterrows():\n", + " try:\n", + " if row[\"page_type\"] == \"PDP\":\n", + " entity_data = create_product_entity(row, dataset_uri)\n", + " elif row[\"page_type\"] == \"PLP\":\n", + " entity_data = create_collection_entity(row, dataset_uri)\n", + " else:\n", + " logger.warning(\n", + " f\"Skipping unknown page type for URL: {row['url']}\"\n", + " )\n", + " continue\n", + "\n", + " if entity_data is None:\n", + " logger.warning(\n", + " f\"Skipping page due to missing critical data: {row['url']}\"\n", + " )\n", + " continue\n", + "\n", + " await create_entity(entities_api, entity_data)\n", + " logger.info(\n", + " f\"Created entity for {row['page_type']}: {row['title']}\"\n", + " )\n", + " except Exception as e:\n", + " logger.error(\n", + " f\"Error creating entity for {row['page_type']}: {row['title']}\"\n", + " )\n", + " logger.error(f\"Error: {str(e)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run the show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ----------------------------\n", + "# Main Execution\n", + "# ----------------------------\n", + "\n", + "# Global configuration variables\n", + "CRAWL_URL = \"https://product-finder.wordlift.io/\"\n", + "OUTPUT_FILE = \"crawl_results.jl\"\n", + "\n", + "\n", + "async def main():\n", + " # Step 1: Crawl the website\n", + " crawl_website(CRAWL_URL, OUTPUT_FILE)\n", + "\n", + " # Step 2: Load the crawled data\n", + " df = pd.read_json(OUTPUT_FILE, lines=True)\n", + "\n", + " # Step 3: Analyze URL patterns\n", + " df = analyze_url_patterns(df)\n", + "\n", + " # Step 4: Extract page data\n", + " pages_df = extract_page_data(df)\n", + "\n", + " async with ApiClient(configuration) as api_client:\n", + " # Clean up the existing knowledge graph\n", + " try:\n", + " await cleanup_knowledge_graph(api_client)\n", + " logger.info(f\"Knowledge Graph Cleaned Up\")\n", + " except Exception as e:\n", + " logger.error(\n", + " f\"Failed to clean up the existing Knowledge Graph: {str(e)}\"\n", + " )\n", + " return # Exit if cleanup fails\n", + "\n", + " # Build the new knowledge graph\n", + " await build_knowledge_graph(pages_df, CRAWL_URL, api_client)\n", + "\n", + " logger.info(\"Knowledge graph building completed.\")\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " asyncio.run(main())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's query products in the KG now using GraphQL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "async def perform_graphql_query(api_client):\n", + " graphql_api = GraphQLApi(api_client)\n", + " query = \"\"\"\n", + " {\n", + " products(rows: 20) {\n", + " id: iri\n", + " category: string(name:\"schema:category\")\n", + " name: string(name:\"schema:name\")\n", + " description: string(name:\"schema:description\")\n", + " url: string(name:\"schema:url\")\n", + " }\n", + " }\n", + " \"\"\"\n", + " request = GraphqlRequest(query=query)\n", + "\n", + " try:\n", + " response = await graphql_api.graphql_using_post(body=request)\n", + " print(\"GraphQL Query Results:\")\n", + " print(json.dumps(response, indent=2))\n", + " except Exception as e:\n", + " logger.error(f\"An error occurred during GraphQL query: {e}\")\n", + "\n", + "\n", + "async with ApiClient(configuration) as api_client:\n", + " # Step 6: Perform GraphQL query\n", + " await perform_graphql_query(api_client)\n", + " logger.info(\"Knowledge graph building and GraphQL query completed.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Leveraging the Knowledge Graph\n", + "\n", + "Now that we have successfully created a Knowledge Graph for our e-commerce website, complete with product embeddings, we can take advantage of it to enhance user experience and functionality. The embeddings we've generated for each product allow us to perform semantic similarity searches and build more intelligent systems.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Structured Data to your Web Pages\n", + "\n", + "In this section, we will perform a simple test of WordLift's data API. This API is used to inject structured data markup from the Knowledge Graph (KG) into your webpages. Structured data helps search engines better understand your content, potentially leading to rich snippets in search results and improved SEO.\n", + "\n", + "For this notebook, we're using a pre-configured KG on a demo e-commerce website. We'll be referencing a fictitious URL: `https://data-science-with-python-for-seo.wordlift.dev`.\n", + "\n", + "When calling WordLift's data API, we simply pass a URL and receive the corresponding JSON-LD (JavaScript Object Notation for Linked Data). This structured data typically includes information such as product details, pricing, and availability for e-commerce sites.\n", + "\n", + "The `get_json_ld_from_url()` function below demonstrates this process. It takes a URL as input and returns the structured data in JSON-LD format, ready to be injected into your webpage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_json_ld_from_url(url):\n", + " # Construct the API URL by prefixing with 'https://api.wordlift.io/data/https/'\n", + " api_url = \"https://api.wordlift.io/data/https/\" + url.replace(\n", + " \"https://\", \"\"\n", + " )\n", + "\n", + " # Make the GET request to the API\n", + " response = requests.get(api_url)\n", + "\n", + " # Check if the request was successful\n", + " if response.status_code == 200:\n", + " # Parse the JSON-LD from the response\n", + " json_ld = response.json()\n", + " return json_ld\n", + " else:\n", + " print(f\"Failed to retrieve data: {response.status_code}\")\n", + " return None\n", + "\n", + "\n", + "def pretty_print_json(json_obj):\n", + " # Pretty print the JSON object\n", + " print(json.dumps(json_obj, indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's run a test\n", + "url = \"https://data-science-with-python-for-seo.wordlift.dev/product/100-pure-deluxe-travel-pack-duo-2/\"\n", + "json_ld = get_json_ld_from_url(url)\n", + "json_ld" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating Links of Similar Products using WordLift Neural Search\n", + "\n", + "With our product embeddings in place, we can now leverage WordLift's Neural Search capabilities to recommend similar products to users. This feature significantly enhances user engagement and can potentially boost sales by showcasing relevant products based on semantic similarity.\n", + "\n", + "Unlike traditional keyword matching, semantic similarity considers the context and meaning of product descriptions. This approach allows for more nuanced and accurate recommendations, even when products don't share exact keywords.\n", + "\n", + "The `get_top_k_similar_urls` function we've defined earlier implements this functionality. It takes a product URL and returns a list of semantically similar products, ranked by their similarity scores.\n", + "\n", + "For example, if a user is viewing a red cotton t-shirt, this feature might recommend other cotton t-shirts in different colors, or similar style tops made from different materials. This creates a more intuitive and engaging shopping experience for the user.\n", + "\n", + "By implementing this Neural Search feature, we're able to create a more personalized and efficient shopping experience, potentially leading to increased user satisfaction and higher conversion rates.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "async def get_top_k_similar_urls(configuration, query_url: str, top_k: int):\n", + " request = VectorSearchQueryRequest(\n", + " query_url=query_url,\n", + " similarity_top_k=top_k,\n", + " )\n", + "\n", + " async with wordlift_client.ApiClient(configuration) as api_client:\n", + " api_instance = VectorSearchQueriesApi(api_client)\n", + " try:\n", + " page = await api_instance.create_query(\n", + " vector_search_query_request=request\n", + " )\n", + " return [\n", + " {\n", + " \"url\": item.id,\n", + " \"name\": item.text.split(\"\\n\")[0],\n", + " \"score\": item.score,\n", + " }\n", + " for item in page.items\n", + " if item.id and item.text\n", + " ]\n", + " except Exception as e:\n", + " logger.error(f\"Error querying for entities: {e}\", exc_info=True)\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top_k = 10\n", + "url = \"https://data-science-with-python-for-seo.wordlift.dev/product/100-mineral-sunscreen-spf-30/\"\n", + "similar_urls = await get_top_k_similar_urls(\n", + " configuration, query_url=url, top_k=top_k\n", + ")\n", + "print(json.dumps(similar_urls, indent=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building a Chatbot for the E-commerce Website using LlamaIndex 🦙\n", + "\n", + "The Knowledge Graph we've created serves as a perfect foundation for building an intelligent chatbot. LlamaIndex (formerly GPT Index) is a powerful data framework that allows us to ingest, structure, and access private or domain-specific data in Large Language Models (LLMs). With LlamaIndex, we can create a context-aware chatbot that understands our product catalog and can assist customers effectively.\n", + "\n", + "By leveraging LlamaIndex in conjunction with our Knowledge Graph, we can develop a chatbot that responds to direct queries. This chatbot will have an understanding of the product catalog, enabling it to:\n", + "\n", + "1. Answer questions about product specifications, availability, and pricing\n", + "2. Make personalized product recommendations based on customer preferences\n", + "3. Provide comparisons between similar products\n", + "\n", + "This approach leads to more natural and helpful interactions with customers, enhancing their shopping experience. The chatbot can draw upon the structured data in our Knowledge Graph, using LlamaIndex to efficiently retrieve and present relevant information through the LLM.\n", + "\n", + "In the following sections, we'll walk through the process of setting up LlamaIndex with our Knowledge Graph data and creating a chatbot that can intelligently assist our e-commerce customers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Installing `LlamaIndex` and `WordLiftVectorStore` 💪" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install llama-index\n", + "!pip install -U 'git+https://github.com/wordlift/llama_index.git#egg=llama-index-vector-stores-wordlift&subdirectory=llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift'\n", + "!pip install llama-index-embeddings-nomic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import the necessary modules\n", + "from llama_index.vector_stores.wordlift import WordliftVectorStore\n", + "from llama_index.core import VectorStoreIndex" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting NomicEmbeddings for our Query Engine\n", + "\n", + "Nomic has released v1.5 🪆🪆🪆 of their embedding model, which brings significant improvements to text embedding capabilities. Embeddings are numerical representations of text that capture semantic meaning, allowing our system to understand and compare the content of queries and documents.\n", + "\n", + "Key features of **Nomic v1.5** include:\n", + "\n", + "- Variable-sized embeddings with dimensions between 64 and 768\n", + "- Matryoshka learning, which allows for nested representations\n", + "- An expanded context size of 8192 tokens\n", + "\n", + "We use NomicEmbeddings in WordLift due to these advanced features, and now we're configuring LlamaIndex to use it as well when encoding user queries. This consistency in embedding models across our stack ensures better alignment between our Knowledge Graph and the query understanding process.\n", + "\n", + "More information on NomicEmbeddings can be found [here](https://www.nomic.ai/blog/posts/nomic-embed-matryoshka).\n", + "\n", + "Go here to [get your free key](https://atlas.nomic.ai/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.embeddings.nomic import NomicEmbedding\n", + "\n", + "nomic_api_key = os.getenv(\"NOMIC_KEY\")\n", + "\n", + "embed_model = NomicEmbedding(\n", + " api_key=nomic_api_key,\n", + " dimensionality=128,\n", + " model_name=\"nomic-embed-text-v1.5\",\n", + ")\n", + "\n", + "embedding = embed_model.get_text_embedding(\"Hey Ho SEO!\")\n", + "len(embedding)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use OpenAI as default LLM for generating response. We could of course use any other available LLM. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the environment variable\n", + "os.environ[\"OPENAI_API_KEY\"] = OPENAI_KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's setup now WordliftVectorStore using data from our Knowledge Graph." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's configure WordliftVectorStore using our WL Key\n", + "vector_store = WordliftVectorStore(key=API_KEY)\n", + "\n", + "# Create an index from the vector store\n", + "index = VectorStoreIndex.from_vector_store(\n", + " vector_store, embed_model=embed_model\n", + ")\n", + "\n", + "# Create a query engine\n", + "query_engine = index.as_query_engine()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query1 = \"Can you give me a product similar to the facial puff? Please add the URL also\"\n", + "result1 = query_engine.query(query1)\n", + "\n", + "print(result1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to handle queries\n", + "def query_engine(query):\n", + " # Create an index from the vector store\n", + " index = VectorStoreIndex.from_vector_store(\n", + " vector_store, embed_model=embed_model\n", + " )\n", + "\n", + " # Create a query engine\n", + " query_engine = index.as_query_engine()\n", + " response = query_engine.query(query)\n", + " return response\n", + "\n", + "\n", + "# Interactive query loop\n", + "while True:\n", + " user_query = input(\"Enter your query (or 'quit' to exit): \")\n", + " if user_query.lower() == \"quit\":\n", + " break\n", + " result = query_engine(user_query)\n", + " print(result)\n", + " print(\"\\n---\\n\")" + ] + } + ], + "metadata": { + "colab": { + "private_outputs": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 8f170ce916584..5b0140cd30fc7 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -4,8 +4,10 @@ extra: provider: google extra_css: - css/style.css + - css/algolia.css extra_javascript: - javascript/mendablesearch.js + - javascript/algolia.js markdown_extensions: - attr_list - admonition @@ -45,6 +47,17 @@ nav: - Enhancing with LlamaParse: ./understanding/agent/llamaparse.md - Memory: ./understanding/agent/memory.md - Adding other tools: ./understanding/agent/tools.md + - Building Workflows: + - Introduction to workflows: ./understanding/workflows/index.md + - A basic workflow: ./understanding/workflows/basic_flow.md + - Branches and loops: ./understanding/workflows/branches_and_loops.md + - Maintaining state: ./understanding/workflows/state.md + - Streaming events: ./understanding/workflows/stream.md + - Concurrent execution: ./understanding/workflows/concurrent_execution.md + - Subclassing workflows: ./understanding/workflows/subclass.md + - Nested workflows: ./understanding/workflows/nested.md + - Observability: ./understanding/workflows/observability.md + - Unbound syntax: ./understanding/workflows/unbound.md - Tracing and Debugging: ./understanding/tracing_and_debugging/tracing_and_debugging.md - Evaluating: - ./understanding/evaluating/evaluating.md @@ -91,6 +104,7 @@ nav: - ./examples/agent/mistral_agent.ipynb - ./examples/agent/multi_document_agents-v1.ipynb - ./examples/agent/multi_document_agents.ipynb + - ./examples/agent/nvidia_agent.ipynb - ./examples/agent/openai_agent.ipynb - ./examples/agent/openai_agent_context_retrieval.ipynb - ./examples/agent/openai_agent_lengthy_tools.ipynb @@ -108,6 +122,7 @@ nav: - ./examples/agent/react_agent_with_query_engine.ipynb - ./examples/agent/return_direct_agent.ipynb - ./examples/agent/structured_planner.ipynb + - ./examples/agents/nvidia_agent.ipynb - Chat Engines: - ./examples/chat_engine/chat_engine_best.ipynb - ./examples/chat_engine/chat_engine_condense_plus_context.ipynb @@ -121,6 +136,7 @@ nav: - ./examples/cookbooks/GraphRAG_v1.ipynb - ./examples/cookbooks/GraphRAG_v2.ipynb - ./examples/cookbooks/anthropic_haiku.ipynb + - ./examples/cookbooks/cleanlab_tlm_rag.ipynb - ./examples/cookbooks/codestral.ipynb - ./examples/cookbooks/cohere_retriever_eval.ipynb - ./examples/cookbooks/crewai_llamaindex.ipynb @@ -161,6 +177,7 @@ nav: - ./examples/data_connectors/ObsidianReaderDemo.ipynb - ./examples/data_connectors/PathwayReaderDemo.ipynb - ./examples/data_connectors/PineconeDemo.ipynb + - ./examples/data_connectors/PreprocessReaderDemo.ipynb - ./examples/data_connectors/PsychicDemo.ipynb - ./examples/data_connectors/QdrantDemo.ipynb - ./examples/data_connectors/SlackDemo.ipynb @@ -198,6 +215,7 @@ nav: - ./examples/embeddings/fastembed.ipynb - ./examples/embeddings/fireworks.ipynb - ./examples/embeddings/gemini.ipynb + - ./examples/embeddings/gigachat.ipynb - ./examples/embeddings/google_palm.ipynb - ./examples/embeddings/gradient.ipynb - ./examples/embeddings/huggingface.ipynb @@ -279,6 +297,7 @@ nav: - ./examples/llm/azure_openai.ipynb - ./examples/llm/bedrock.ipynb - ./examples/llm/bedrock_converse.ipynb + - ./examples/llm/cerebras.ipynb - ./examples/llm/clarifai.ipynb - ./examples/llm/cleanlab.ipynb - ./examples/llm/cohere.ipynb @@ -339,6 +358,7 @@ nav: - ./examples/llm/qianfan.ipynb - ./examples/llm/rungpt.ipynb - ./examples/llm/sagemaker_endpoint_llm.ipynb + - ./examples/llm/sambanova.ipynb - ./examples/llm/solar.ipynb - ./examples/llm/together.ipynb - ./examples/llm/unify.ipynb @@ -368,6 +388,7 @@ nav: - ./examples/low_level/router.ipynb - ./examples/low_level/vector_store.ipynb - Managed Indexes: + - ./examples/managed/BGEM3Demo.ipynb - ./examples/managed/GoogleDemo.ipynb - ./examples/managed/PostgresMLDemo.ipynb - ./examples/managed/VertexAIDemo.ipynb @@ -397,6 +418,7 @@ nav: - ./examples/multi_modal/multi_modal_rag_nomic.ipynb - ./examples/multi_modal/multi_modal_retrieval.ipynb - ./examples/multi_modal/multi_modal_video_RAG.ipynb + - ./examples/multi_modal/multi_modal_videorag_videodb.ipynb - ./examples/multi_modal/ollama_cookbook.ipynb - ./examples/multi_modal/openai_multi_modal.ipynb - ./examples/multi_modal/replicate_multi_modal.ipynb @@ -632,10 +654,21 @@ nav: - ./examples/vector_stores/qdrant_bm42.ipynb - ./examples/vector_stores/qdrant_hybrid.ipynb - Workflow: + - ./examples/workflow/JSONalyze_query_engine.ipynb + - ./examples/workflow/citation_query_engine.ipynb + - ./examples/workflow/corrective_rag_pack.ipynb - ./examples/workflow/function_calling_agent.ipynb + - ./examples/workflow/long_rag_pack.ipynb + - ./examples/workflow/multi_step_query_engine.ipynb + - ./examples/workflow/multi_strategy_workflow.ipynb + - ./examples/workflow/parallel_execution.ipynb - ./examples/workflow/rag.ipynb - ./examples/workflow/react_agent.ipynb - ./examples/workflow/reflection.ipynb + - ./examples/workflow/router_query_engine.ipynb + - ./examples/workflow/self_discover_workflow.ipynb + - ./examples/workflow/sub_question_query_engine.ipynb + - ./examples/workflow/workflows_cookbook.ipynb - Component Guides: - ./module_guides/index.md - Models: @@ -804,6 +837,7 @@ nav: - ./api_reference/embeddings/fastembed.md - ./api_reference/embeddings/fireworks.md - ./api_reference/embeddings/gemini.md + - ./api_reference/embeddings/gigachat.md - ./api_reference/embeddings/google.md - ./api_reference/embeddings/gradient.md - ./api_reference/embeddings/huggingface.md @@ -837,6 +871,7 @@ nav: - ./api_reference/embeddings/upstage.md - ./api_reference/embeddings/vertex.md - ./api_reference/embeddings/voyageai.md + - ./api_reference/embeddings/xinference.md - ./api_reference/embeddings/yandexgpt.md - Evaluation: - ./api_reference/evaluation/answer_relevancy.md @@ -855,6 +890,7 @@ nav: - ./api_reference/evaluation/semantic_similarity.md - ./api_reference/evaluation/tonic_validate.md - Indexes: + - ./api_reference/indices/bge_m3.md - ./api_reference/indices/colbert.md - ./api_reference/indices/dashscope.md - ./api_reference/indices/document_summary.md @@ -889,6 +925,7 @@ nav: - ./api_reference/llms/azure_openai.md - ./api_reference/llms/bedrock.md - ./api_reference/llms/bedrock_converse.md + - ./api_reference/llms/cerebras.md - ./api_reference/llms/clarifai.md - ./api_reference/llms/cleanlab.md - ./api_reference/llms/cohere.md @@ -900,6 +937,7 @@ nav: - ./api_reference/llms/fireworks.md - ./api_reference/llms/friendli.md - ./api_reference/llms/gemini.md + - ./api_reference/llms/gigachat.md - ./api_reference/llms/gradient.md - ./api_reference/llms/groq.md - ./api_reference/llms/huggingface.md @@ -945,6 +983,7 @@ nav: - ./api_reference/llms/replicate.md - ./api_reference/llms/rungpt.md - ./api_reference/llms/sagemaker_endpoint.md + - ./api_reference/llms/sambanova.md - ./api_reference/llms/solar.md - ./api_reference/llms/text_generation_inference.md - ./api_reference/llms/together.md @@ -1097,6 +1136,7 @@ nav: - ./api_reference/postprocessor/tei_rerank.md - ./api_reference/postprocessor/time_weighted.md - ./api_reference/postprocessor/voyageai_rerank.md + - ./api_reference/postprocessor/xinference_rerank.md - Object Stores: - ./api_reference/objects/index.md - Output Parsers: @@ -1345,6 +1385,8 @@ nav: - ./api_reference/retrievers/you.md - Schema: - ./api_reference/schema/index.md + - Selectors: + - ./api_reference/selectors/notdiamond.md - Storage: - Chat Store: - ./api_reference/storage/chat_store/azure.md @@ -1463,6 +1505,7 @@ nav: - 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a/llama-index-cli/llama_index/cli/rag/base.py +++ b/llama-index-cli/llama_index/cli/rag/base.py @@ -7,6 +7,7 @@ from typing import Any, Callable, Dict, Optional, Union, cast from llama_index.core import ( + Settings, SimpleDirectoryReader, VectorStoreIndex, ) @@ -16,9 +17,8 @@ StreamingResponse, Response, ) -from llama_index.core.bridge.pydantic import BaseModel, Field, validator +from llama_index.core.bridge.pydantic import BaseModel, Field, field_validator from llama_index.core.chat_engine import CondenseQuestionChatEngine -from llama_index.core.indices.service_context import ServiceContext from llama_index.core.ingestion import IngestionPipeline from llama_index.core.llms import LLM from llama_index.core.query_engine import CustomQueryEngine @@ -100,7 +100,7 @@ class RagCLI(BaseModel): class Config: arbitrary_types_allowed = True - @validator("query_pipeline", always=True) + @field_validator("query_pipeline", mode="before") def query_pipeline_from_ingestion_pipeline( cls, query_pipeline: Any, values: Dict[str, Any] ) -> Optional[QueryPipeline]: @@ -127,15 +127,13 @@ def query_pipeline_from_ingestion_pipeline( embed_model = transformation break - service_context = ServiceContext.from_defaults( - llm=llm, embed_model=embed_model or "default" - ) + Settings.llm = llm + Settings.embed_model = embed_model + retriever = VectorStoreIndex.from_vector_store( - ingestion_pipeline.vector_store, service_context=service_context + ingestion_pipeline.vector_store, ).as_retriever(similarity_top_k=8) - response_synthesizer = CompactAndRefine( - service_context=service_context, streaming=True, verbose=verbose - ) + response_synthesizer = CompactAndRefine(streaming=True, verbose=verbose) # define query pipeline query_pipeline = QueryPipeline(verbose=verbose) @@ -151,7 +149,7 @@ def query_pipeline_from_ingestion_pipeline( query_pipeline.add_link("query", "summarizer", dest_key="query_str") return query_pipeline - @validator("chat_engine", always=True) + @field_validator("chat_engine", mode="before") def chat_engine_from_query_pipeline( cls, chat_engine: Any, values: Dict[str, Any] ) -> Optional[CondenseQuestionChatEngine]: diff --git a/llama-index-cli/llama_index/cli/upgrade/mappings.json b/llama-index-cli/llama_index/cli/upgrade/mappings.json index ffedbfa1da5b0..5713b22e50cac 100644 --- a/llama-index-cli/llama_index/cli/upgrade/mappings.json +++ b/llama-index-cli/llama_index/cli/upgrade/mappings.json @@ -1,6 +1,5 @@ { "StorageContext": "llama_index.core", - "ServiceContext": "llama_index.core", "ComposableGraph": "llama_index.core", "# indicesSummaryIndex": "llama_index.core", "VectorStoreIndex": "llama_index.core", @@ -397,7 +396,6 @@ "generate_qa_embedding_pairs": "llama_index.finetuning", "SentenceTransformersFinetuneEngine": "llama_index.finetuning", "EmbeddingAdapterFinetuneEngine": "llama_index.finetuning", - "GradientFinetuneEngine": "llama_index.finetuning", "generate_cohere_reranker_finetuning_dataset": "llama_index.finetuning", "CohereRerankerFinetuneEngine": "llama_index.finetuning", "MistralAIFinetuneEngine": "llama_index.finetuning", diff --git a/llama-index-cli/pyproject.toml b/llama-index-cli/pyproject.toml index 0a850a056b906..4bc41f2ba7257 100644 --- a/llama-index-cli/pyproject.toml +++ b/llama-index-cli/pyproject.toml @@ -32,13 +32,13 @@ maintainers = [ name = "llama-index-cli" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.13" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-embeddings-openai = "^0.1.1" -llama-index-llms-openai = "^0.1.1" +llama-index-core = "^0.11.0" +llama-index-embeddings-openai = "^0.2.0" +llama-index-llms-openai = "^0.2.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-core/llama_index/core/__init__.py b/llama-index-core/llama_index/core/__init__.py index 454f6eadc52cf..93ef48649d39e 100644 --- a/llama-index-core/llama_index/core/__init__.py +++ b/llama-index-core/llama_index/core/__init__.py @@ -1,6 +1,6 @@ """Init file of LlamaIndex.""" -__version__ = "0.10.61" +__version__ = "0.11.3" import logging from logging import NullHandler @@ -152,8 +152,5 @@ # NOTE: keep for backwards compatibility SQLContextBuilder = SQLDocumentContextBuilder -# global service context for ServiceContext.from_defaults() -global_service_context: Optional[ServiceContext] = None - # global tokenizer global_tokenizer: Optional[Callable[[str], list]] = None diff --git a/llama-index-core/llama_index/core/agent/custom/pipeline_worker.py b/llama-index-core/llama_index/core/agent/custom/pipeline_worker.py index 3bbfaeb0b7324..d7feca5d340eb 100644 --- a/llama-index-core/llama_index/core/agent/custom/pipeline_worker.py +++ b/llama-index-core/llama_index/core/agent/custom/pipeline_worker.py @@ -15,7 +15,7 @@ TaskStepOutput, ) from llama_index.core.base.query_pipeline.query import QueryComponent -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from llama_index.core.callbacks import ( CallbackManager, trace_method, @@ -72,14 +72,12 @@ class QueryPipelineAgentWorker(BaseModel, BaseAgentWorker): """ + model_config = ConfigDict(arbitrary_types_allowed=True) pipeline: QueryPipeline = Field(..., description="Query pipeline") callback_manager: CallbackManager = Field(..., exclude=True) task_key: str = Field("task", description="Key to store task in state") step_state_key: str = Field("step_state", description="Key to store step in state") - class Config: - arbitrary_types_allowed = True - def __init__( self, pipeline: QueryPipeline, diff --git a/llama-index-core/llama_index/core/agent/custom/simple.py b/llama-index-core/llama_index/core/agent/custom/simple.py index 06dc1e542c536..f7a164b0e2868 100644 --- a/llama-index-core/llama_index/core/agent/custom/simple.py +++ b/llama-index-core/llama_index/core/agent/custom/simple.py @@ -19,7 +19,7 @@ TaskStep, TaskStepOutput, ) -from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr +from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr, ConfigDict from llama_index.core.callbacks import ( CallbackManager, trace_method, @@ -55,6 +55,7 @@ class CustomSimpleAgentWorker(BaseModel, BaseAgentWorker): """ + model_config = ConfigDict(arbitrary_types_allowed=True) tools: Sequence[BaseTool] = Field(..., description="Tools to use for reasoning") llm: LLM = Field(..., description="LLM to use") callback_manager: CallbackManager = Field( @@ -67,9 +68,6 @@ class CustomSimpleAgentWorker(BaseModel, BaseAgentWorker): _get_tools: Callable[[str], Sequence[BaseTool]] = PrivateAttr() - class Config: - arbitrary_types_allowed = True - def __init__( self, tools: Sequence[BaseTool], @@ -79,18 +77,7 @@ def __init__( tool_retriever: Optional[ObjectRetriever[BaseTool]] = None, **kwargs: Any, ) -> None: - if len(tools) > 0 and tool_retriever is not None: - raise ValueError("Cannot specify both tools and tool_retriever") - elif len(tools) > 0: - self._get_tools = lambda _: tools - elif tool_retriever is not None: - tool_retriever_c = cast(ObjectRetriever[BaseTool], tool_retriever) - self._get_tools = lambda message: tool_retriever_c.retrieve(message) - else: - self._get_tools = lambda _: [] - callback_manager = callback_manager or CallbackManager([]) - super().__init__( tools=tools, llm=llm, @@ -100,6 +87,16 @@ def __init__( **kwargs, ) + if len(tools) > 0 and tool_retriever is not None: + raise ValueError("Cannot specify both tools and tool_retriever") + elif len(tools) > 0: + self._get_tools = lambda _: tools + elif tool_retriever is not None: + tool_retriever_c = cast(ObjectRetriever[BaseTool], tool_retriever) + self._get_tools = lambda message: tool_retriever_c.retrieve(message) + else: + self._get_tools = lambda _: [] + @classmethod def from_tools( cls, diff --git a/llama-index-core/llama_index/core/agent/custom/simple_function.py b/llama-index-core/llama_index/core/agent/custom/simple_function.py index 562690c911c16..8d704a91554f2 100644 --- a/llama-index-core/llama_index/core/agent/custom/simple_function.py +++ b/llama-index-core/llama_index/core/agent/custom/simple_function.py @@ -16,7 +16,7 @@ TaskStep, TaskStepOutput, ) -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from llama_index.core.callbacks import ( CallbackManager, trace_method, @@ -44,6 +44,7 @@ class FnAgentWorker(BaseModel, BaseAgentWorker): """ + model_config = ConfigDict(arbitrary_types_allowed=True) fn: Callable = Field(..., description="Function to run.") async_fn: Optional[Callable] = Field( None, description="Async function to run. If not provided, will run `fn`." @@ -56,9 +57,6 @@ class FnAgentWorker(BaseModel, BaseAgentWorker): verbose: bool = Field(False, description="Verbose mode.") - class Config: - arbitrary_types_allowed = True - def __init__( self, fn: Callable, diff --git a/llama-index-core/llama_index/core/agent/react/formatter.py b/llama-index-core/llama_index/core/agent/react/formatter.py index 808299c4e3afd..1de4fa744e05b 100644 --- a/llama-index-core/llama_index/core/agent/react/formatter.py +++ b/llama-index-core/llama_index/core/agent/react/formatter.py @@ -13,7 +13,7 @@ ObservationReasoningStep, ) from llama_index.core.base.llms.types import ChatMessage, MessageRole -from llama_index.core.bridge.pydantic import BaseModel +from llama_index.core.bridge.pydantic import BaseModel, ConfigDict from llama_index.core.tools import BaseTool logger = logging.getLogger(__name__) @@ -36,8 +36,7 @@ def get_react_tool_descriptions(tools: Sequence[BaseTool]) -> List[str]: class BaseAgentChatFormatter(BaseModel): """Base chat formatter.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict(arbitrary_types_allowed=True) @abstractmethod def format( diff --git a/llama-index-core/llama_index/core/base/agent/types.py b/llama-index-core/llama_index/core/base/agent/types.py index 38c5921ba1b2a..91973a15ffd04 100644 --- a/llama-index-core/llama_index/core/base/agent/types.py +++ b/llama-index-core/llama_index/core/base/agent/types.py @@ -7,7 +7,12 @@ from llama_index.core.base.base_query_engine import BaseQueryEngine from llama_index.core.base.llms.types import ChatMessage from llama_index.core.base.response.schema import RESPONSE_TYPE, Response -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import ( + BaseModel, + Field, + SerializeAsAny, + ConfigDict, +) from llama_index.core.callbacks import CallbackManager, trace_method from llama_index.core.chat_engine.types import ( BaseChatEngine, @@ -80,11 +85,11 @@ class TaskStep(BaseModel): """ - task_id: str = Field(..., diescription="Task ID") + task_id: str = Field(..., description="Task ID") step_id: str = Field(..., description="Step ID") input: Optional[str] = Field(default=None, description="User input") # memory: BaseMemory = Field( - # ..., type=BaseMemory, description="Conversational Memory" + # ..., description="Conversational Memory" # ) step_state: Dict[str, Any] = Field( default_factory=dict, description="Additional state for a given step." @@ -155,25 +160,22 @@ class Task(BaseModel): """ - class Config: - arbitrary_types_allowed = True - + model_config = ConfigDict(arbitrary_types_allowed=True) task_id: str = Field( - default_factory=lambda: str(uuid.uuid4()), type=str, description="Task ID" + default_factory=lambda: str(uuid.uuid4()), description="Task ID" ) - input: str = Field(..., type=str, description="User input") + input: str = Field(..., description="User input") # NOTE: this is state that may be modified throughout the course of execution of the task - memory: BaseMemory = Field( + memory: SerializeAsAny[BaseMemory] = Field( ..., - type=BaseMemory, description=( "Conversational Memory. Maintains state before execution of this task." ), ) callback_manager: CallbackManager = Field( - default_factory=CallbackManager, + default_factory=lambda: CallbackManager([]), exclude=True, description="Callback manager for the task.", ) @@ -190,8 +192,7 @@ class Config: class BaseAgentWorker(PromptMixin, DispatcherSpanMixin): """Base agent worker.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict(arbitrary_types_allowed=True) def _get_prompts(self) -> PromptDictType: """Get prompts.""" diff --git a/llama-index-core/llama_index/core/base/base_query_engine.py b/llama-index-core/llama_index/core/base/base_query_engine.py index a52ea55a76a0c..0f9f204e4c992 100644 --- a/llama-index-core/llama_index/core/base/base_query_engine.py +++ b/llama-index-core/llama_index/core/base/base_query_engine.py @@ -12,7 +12,7 @@ validate_and_convert_stringable, ) from llama_index.core.base.response.schema import RESPONSE_TYPE -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, ConfigDict, SerializeAsAny from llama_index.core.callbacks.base import CallbackManager from llama_index.core.prompts.mixin import PromptDictType, PromptMixin from llama_index.core.schema import NodeWithScore, QueryBundle, QueryType @@ -108,10 +108,10 @@ def _as_query_component(self, **kwargs: Any) -> QueryComponent: class QueryEngineComponent(QueryComponent): """Query engine component.""" - query_engine: BaseQueryEngine = Field(..., description="Query engine") - - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict(arbitrary_types_allowed=True) + query_engine: SerializeAsAny[BaseQueryEngine] = Field( + ..., description="Query engine" + ) def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" diff --git a/llama-index-core/llama_index/core/base/base_retriever.py b/llama-index-core/llama_index/core/base/base_retriever.py index 5b802fd11ef69..ab891a0fd0ab6 100644 --- a/llama-index-core/llama_index/core/base/base_retriever.py +++ b/llama-index-core/llama_index/core/base/base_retriever.py @@ -11,7 +11,7 @@ QueryComponent, validate_and_convert_stringable, ) -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, ConfigDict from llama_index.core.callbacks.base import CallbackManager from llama_index.core.callbacks.schema import CBEventType, EventPayload from llama_index.core.prompts.mixin import ( @@ -27,7 +27,6 @@ QueryType, TextNode, ) -from llama_index.core.service_context import ServiceContext from llama_index.core.settings import Settings from llama_index.core.utils import print_text from llama_index.core.instrumentation import DispatcherSpanMixin @@ -213,7 +212,10 @@ async def _ahandle_recursive_retrieval( return [ n for n in retrieved_nodes - if not ((n.node.hash, n.node.ref_doc_id) in seen or seen.add((n.node.hash, n.node.ref_doc_id))) # type: ignore[func-returns-value] + if not ( + (n.node.hash, n.node.ref_doc_id) in seen + or seen.add((n.node.hash, n.node.ref_doc_id)) + ) # type: ignore[func-returns-value] ] @dispatcher.span @@ -304,19 +306,6 @@ async def _aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: """ return self._retrieve(query_bundle) - def get_service_context(self) -> Optional[ServiceContext]: - """Attempts to resolve a service context. - Short-circuits at self.service_context, self._service_context, - or self._index.service_context. - """ - if hasattr(self, "service_context"): - return self.service_context - if hasattr(self, "_service_context"): - return self._service_context - elif hasattr(self, "_index") and hasattr(self._index, "service_context"): - return self._index.service_context - return None - def _as_query_component(self, **kwargs: Any) -> QueryComponent: """Return a query component.""" return RetrieverComponent(retriever=self) @@ -325,11 +314,9 @@ def _as_query_component(self, **kwargs: Any) -> QueryComponent: class RetrieverComponent(QueryComponent): """Retriever component.""" + model_config = ConfigDict(arbitrary_types_allowed=True) retriever: BaseRetriever = Field(..., description="Retriever") - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" self.retriever.callback_manager = callback_manager diff --git a/llama-index-core/llama_index/core/base/embeddings/base.py b/llama-index-core/llama_index/core/base/embeddings/base.py index 7a206a1ed28a0..240fc33c81e09 100644 --- a/llama-index-core/llama_index/core/base/embeddings/base.py +++ b/llama-index-core/llama_index/core/base/embeddings/base.py @@ -6,7 +6,11 @@ from typing import Any, Callable, Coroutine, List, Optional, Tuple import numpy as np -from llama_index.core.bridge.pydantic import Field, validator +from llama_index.core.bridge.pydantic import ( + Field, + ConfigDict, + field_validator, +) from llama_index.core.callbacks.base import CallbackManager from llama_index.core.callbacks.schema import CBEventType, EventPayload from llama_index.core.constants import ( @@ -63,6 +67,9 @@ def similarity( class BaseEmbedding(TransformComponent, DispatcherSpanMixin): """Base class for embeddings.""" + model_config = ConfigDict( + protected_namespaces=("pydantic_model_",), arbitrary_types_allowed=True + ) model_name: str = Field( default="unknown", description="The name of the embedding model." ) @@ -70,7 +77,7 @@ class BaseEmbedding(TransformComponent, DispatcherSpanMixin): default=DEFAULT_EMBED_BATCH_SIZE, description="The batch size for embedding calls.", gt=0, - lte=2048, + le=2048, ) callback_manager: CallbackManager = Field( default_factory=lambda: CallbackManager([]), exclude=True @@ -80,13 +87,9 @@ class BaseEmbedding(TransformComponent, DispatcherSpanMixin): description="The number of workers to use for async embedding calls.", ) - class Config: - arbitrary_types_allowed = True - - @validator("callback_manager", pre=True) - def _validate_callback_manager( - cls, v: Optional[CallbackManager] - ) -> CallbackManager: + @field_validator("callback_manager") + @classmethod + def check_callback_manager(cls, v: CallbackManager) -> CallbackManager: if v is None: return CallbackManager([]) return v diff --git a/llama-index-core/llama_index/core/base/llms/base.py b/llama-index-core/llama_index/core/base/llms/base.py index cf4b6dea8669b..cca8a8d78065e 100644 --- a/llama-index-core/llama_index/core/base/llms/base.py +++ b/llama-index-core/llama_index/core/base/llms/base.py @@ -17,7 +17,7 @@ from llama_index.core.base.query_pipeline.query import ( ChainableMixin, ) -from llama_index.core.bridge.pydantic import Field, validator +from llama_index.core.bridge.pydantic import Field, model_validator, ConfigDict from llama_index.core.callbacks import CallbackManager from llama_index.core.instrumentation import DispatcherSpanMixin from llama_index.core.schema import BaseComponent @@ -26,18 +26,16 @@ class BaseLLM(ChainableMixin, BaseComponent, DispatcherSpanMixin): """BaseLLM interface.""" + model_config = ConfigDict(arbitrary_types_allowed=True) callback_manager: CallbackManager = Field( - default_factory=CallbackManager, exclude=True + default_factory=lambda: CallbackManager([]), exclude=True ) - class Config: - arbitrary_types_allowed = True - - @validator("callback_manager", pre=True) - def _validate_callback_manager(cls, v: CallbackManager) -> CallbackManager: - if v is None: - return CallbackManager([]) - return v + @model_validator(mode="after") + def check_callback_manager(self) -> "BaseLLM": + if self.callback_manager is None: + self.callback_manager = CallbackManager([]) + return self @property @abstractmethod diff --git a/llama-index-core/llama_index/core/base/llms/types.py b/llama-index-core/llama_index/core/base/llms/types.py index 3195c2bab5338..ca6f58b5439a1 100644 --- a/llama-index-core/llama_index/core/base/llms/types.py +++ b/llama-index-core/llama_index/core/base/llms/types.py @@ -1,7 +1,7 @@ from enum import Enum from typing import Any, AsyncGenerator, Generator, Optional, Union, List, Any -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from llama_index.core.constants import DEFAULT_CONTEXT_WINDOW, DEFAULT_NUM_OUTPUTS try: @@ -49,7 +49,7 @@ def from_str( def _recursive_serialization(self, value: Any) -> Any: if isinstance(value, (V1BaseModel, V2BaseModel)): - return value.dict() + return value.model_dump() if isinstance(value, dict): return { key: self._recursive_serialization(value) @@ -60,8 +60,11 @@ def _recursive_serialization(self, value: Any) -> Any: return value def dict(self, **kwargs: Any) -> dict: + return self.model_dump(**kwargs) + + def model_dump(self, **kwargs: Any) -> dict: # ensure all additional_kwargs are serializable - msg = super().dict(**kwargs) + msg = super().model_dump(**kwargs) for key, value in msg.get("additional_kwargs", {}).items(): value = self._recursive_serialization(value) @@ -129,6 +132,9 @@ def __str__(self) -> str: class LLMMetadata(BaseModel): + model_config = ConfigDict( + protected_namespaces=("pydantic_model_",), arbitrary_types_allowed=True + ) context_window: int = Field( default=DEFAULT_CONTEXT_WINDOW, description=( diff --git a/llama-index-core/llama_index/core/base/query_pipeline/query.py b/llama-index-core/llama_index/core/base/query_pipeline/query.py index fd5de0bd0e00e..19bc95e55f6a5 100644 --- a/llama-index-core/llama_index/core/base/query_pipeline/query.py +++ b/llama-index-core/llama_index/core/base/query_pipeline/query.py @@ -20,7 +20,7 @@ CompletionResponse, ) from llama_index.core.base.response.schema import Response -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from llama_index.core.callbacks.base import CallbackManager from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode @@ -243,13 +243,11 @@ def sub_query_components(self) -> List["QueryComponent"]: class CustomQueryComponent(QueryComponent): """Custom query component.""" + model_config = ConfigDict(arbitrary_types_allowed=True) callback_manager: CallbackManager = Field( default_factory=CallbackManager, description="Callback manager" ) - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" self.callback_manager = callback_manager diff --git a/llama-index-core/llama_index/core/base/response/schema.py b/llama-index-core/llama_index/core/base/response/schema.py index 84e071e6f67fc..ed8cc936a5183 100644 --- a/llama-index-core/llama_index/core/base/response/schema.py +++ b/llama-index-core/llama_index/core/base/response/schema.py @@ -58,11 +58,11 @@ class PydanticResponse: def __str__(self) -> str: """Convert to string representation.""" - return self.response.json() if self.response else "None" + return self.response.model_dump_json() if self.response else "None" def __getattr__(self, name: str) -> Any: """Get attribute, but prioritize the pydantic response object.""" - if self.response is not None and name in self.response.dict(): + if self.response is not None and name in self.response.model_dump(): return getattr(self.response, name) else: return None @@ -97,7 +97,7 @@ def get_formatted_sources(self, length: int = 100) -> str: def get_response(self) -> Response: """Get a standard response object.""" - response_txt = self.response.json() if self.response else "None" + response_txt = self.response.model_dump_json() if self.response else "None" return Response(response_txt, self.source_nodes, self.metadata) diff --git a/llama-index-core/llama_index/core/bridge/langchain.py b/llama-index-core/llama_index/core/bridge/langchain.py index 66c04c78e5d5d..a54c117b6460f 100644 --- a/llama-index-core/llama_index/core/bridge/langchain.py +++ b/llama-index-core/llama_index/core/bridge/langchain.py @@ -20,10 +20,7 @@ ) # pants: no-infer-dep from langchain.chat_models.base import BaseChatModel # pants: no-infer-dep from langchain.docstore.document import Document # pants: no-infer-dep -from langchain.memory import ( - ChatMessageHistory, - ConversationBufferMemory, -) # pants: no-infer-dep +from langchain.memory import ConversationBufferMemory # pants: no-infer-dep # chat and memory from langchain.memory.chat_memory import BaseChatMemory # pants: no-infer-dep @@ -63,6 +60,9 @@ TextSplitter, ) # pants: no-infer-dep from langchain.tools import BaseTool, StructuredTool, Tool # pants: no-infer-dep +from langchain_community.chat_message_histories import ( + ChatMessageHistory, +) # pants: no-infer-dep from langchain_community.chat_models import ( ChatAnyscale, ChatOpenAI, diff --git a/llama-index-core/llama_index/core/bridge/pydantic.py b/llama-index-core/llama_index/core/bridge/pydantic.py index 6bd61da8b415d..b0c4078e3f0ce 100644 --- a/llama-index-core/llama_index/core/bridge/pydantic.py +++ b/llama-index-core/llama_index/core/bridge/pydantic.py @@ -1,54 +1,61 @@ -try: - import pydantic.v1 as pydantic - from pydantic.v1 import ( - BaseConfig, - BaseModel, - Field, - PrivateAttr, - StrictFloat, - StrictInt, - StrictStr, - create_model, - root_validator, - validator, - parse_obj_as, - ) - from pydantic.v1.error_wrappers import ValidationError - from pydantic.v1.fields import FieldInfo - from pydantic.v1.generics import GenericModel -except ImportError: - import pydantic # type: ignore - from pydantic import ( - BaseConfig, - BaseModel, - Field, - PrivateAttr, - StrictFloat, - StrictInt, - StrictStr, - create_model, - root_validator, - validator, - parse_obj_as, - ) - from pydantic.error_wrappers import ValidationError - from pydantic.fields import FieldInfo - from pydantic.generics import GenericModel +import pydantic +from pydantic import ( + ConfigDict, + BaseModel, + GetJsonSchemaHandler, + GetCoreSchemaHandler, + Field, + PlainSerializer, + PrivateAttr, + StrictFloat, + StrictInt, + StrictStr, + create_model, + model_validator, + field_validator, + ValidationInfo, + ValidationError, + TypeAdapter, + WithJsonSchema, + BeforeValidator, + SerializeAsAny, + WrapSerializer, + field_serializer, + Secret, + SecretStr, + model_serializer, +) +from pydantic.fields import FieldInfo +from pydantic.json_schema import JsonSchemaValue __all__ = [ "pydantic", "BaseModel", + "ConfigDict", + "GetJsonSchemaHandler", + "GetCoreSchemaHandler", "Field", + "PlainSerializer", "PrivateAttr", - "root_validator", - "validator", + "model_validator", + "field_validator", "create_model", "StrictFloat", "StrictInt", "StrictStr", "FieldInfo", + "ValidationInfo", + "TypeAdapter", "ValidationError", - "GenericModel", + "WithJsonSchema", "BaseConfig", "parse_obj_as", + "BeforeValidator", + "JsonSchemaValue", + "SerializeAsAny", + "WrapSerializer", + "field_serializer", + "Secret", + "SecretStr", + "model_serializer", ] diff --git a/llama-index-core/llama_index/core/bridge/pydantic_core.py b/llama-index-core/llama_index/core/bridge/pydantic_core.py new file mode 100644 index 0000000000000..454f89de44a1b --- /dev/null +++ b/llama-index-core/llama_index/core/bridge/pydantic_core.py @@ -0,0 +1,5 @@ +import pydantic_core + +from pydantic_core import CoreSchema, core_schema + +__all__ = ["pydantic_core", "CoreSchema", "core_schema"] diff --git a/llama-index-core/llama_index/core/bridge/pydantic_settings.py b/llama-index-core/llama_index/core/bridge/pydantic_settings.py new file mode 100644 index 0000000000000..82da3f4c3763f --- /dev/null +++ b/llama-index-core/llama_index/core/bridge/pydantic_settings.py @@ -0,0 +1,5 @@ +import pydantic_settings + +from pydantic_settings import BaseSettings, SettingsConfigDict + +__all__ = ["pydantic_settings", "BaseSettings", "SettingsConfigDict"] diff --git a/llama-index-core/llama_index/core/callbacks/base.py b/llama-index-core/llama_index/core/callbacks/base.py index fcf27287474b5..97139ef45dce6 100644 --- a/llama-index-core/llama_index/core/callbacks/base.py +++ b/llama-index-core/llama_index/core/callbacks/base.py @@ -4,7 +4,7 @@ from collections import defaultdict from contextlib import contextmanager from contextvars import ContextVar -from typing import Any, Dict, Generator, List, Optional, cast +from typing import Any, Dict, Generator, List, Optional, cast, Type from llama_index.core.callbacks.base_handler import BaseCallbackHandler from llama_index.core.callbacks.schema import ( @@ -13,6 +13,11 @@ CBEventType, EventPayload, ) +from llama_index.core.bridge.pydantic import ( + GetCoreSchemaHandler, + GetJsonSchemaHandler, +) +from llama_index.core.bridge.pydantic_core import CoreSchema, core_schema logger = logging.getLogger(__name__) global_stack_trace = ContextVar("trace", default=[BASE_TRACE_EVENT]) @@ -148,11 +153,6 @@ def set_handlers(self, handlers: List[BaseCallbackHandler]) -> None: """Set handlers as the only handlers on the callback manager.""" self.handlers = handlers - @classmethod - def __modify_schema__(cls, schema: Dict[str, Any]) -> None: - """Avoids serialization errors.""" - schema.update(type="object", default={}) - @contextmanager def event( self, @@ -250,6 +250,19 @@ def _reset_trace_events(self) -> None: def trace_map(self) -> Dict[str, List[str]]: return self._trace_map + @classmethod + def __get_pydantic_core_schema__( + cls, source: Type[Any], handler: GetCoreSchemaHandler + ) -> CoreSchema: + return core_schema.any_schema() + + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler + ) -> Dict[str, Any]: + json_schema = handler(core_schema) + return handler.resolve_ref_schema(json_schema) + class EventContext: """ diff --git a/llama-index-core/llama_index/core/callbacks/token_counting.py b/llama-index-core/llama_index/core/callbacks/token_counting.py index a0d6e0fe3563e..8f9f1766fa91c 100644 --- a/llama-index-core/llama_index/core/callbacks/token_counting.py +++ b/llama-index-core/llama_index/core/callbacks/token_counting.py @@ -1,5 +1,15 @@ from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Optional, cast +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Dict, + List, + Optional, + Tuple, + Union, + cast, +) from llama_index.core.callbacks.pythonically_printing_base_handler import ( PythonicallyPrintingBaseHandler, @@ -9,6 +19,9 @@ from llama_index.core.utils import get_tokenizer import logging +if TYPE_CHECKING: + from llama_index.core.llms import ChatResponse, CompletionResponse + @dataclass class TokenCountingEvent: @@ -23,21 +36,69 @@ def __post_init__(self) -> None: self.total_token_count = self.prompt_token_count + self.completion_token_count +def get_tokens_from_response( + response: Union["CompletionResponse", "ChatResponse"] +) -> Tuple[int, int]: + """Get the token counts from a raw response.""" + raw_response = response.raw + if not isinstance(raw_response, dict): + raw_response = dict(raw_response) + + usage = raw_response.get("usage", {}) + if usage is None: + usage = response.additional_kwargs + + if not usage: + return 0, 0 + + if not isinstance(usage, dict): + usage = usage.model_dump() + + possible_input_keys = ("prompt_tokens", "input_tokens") + possible_output_keys = ("completion_tokens", "output_tokens") + + prompt_tokens = 0 + for input_key in possible_input_keys: + if input_key in usage: + prompt_tokens = usage[input_key] + break + + completion_tokens = 0 + for output_key in possible_output_keys: + if output_key in usage: + completion_tokens = usage[output_key] + break + + return prompt_tokens, completion_tokens + + def get_llm_token_counts( token_counter: TokenCounter, payload: Dict[str, Any], event_id: str = "" ) -> TokenCountingEvent: from llama_index.core.llms import ChatMessage if EventPayload.PROMPT in payload: - prompt = str(payload.get(EventPayload.PROMPT)) - completion = str(payload.get(EventPayload.COMPLETION)) + prompt = payload.get(EventPayload.PROMPT) + completion = payload.get(EventPayload.COMPLETION) + + if completion: + # get from raw or additional_kwargs + prompt_tokens, completion_tokens = get_tokens_from_response(completion) + else: + prompt_tokens, completion_tokens = 0, 0 + + if prompt_tokens == 0: + prompt_tokens = token_counter.get_string_tokens(str(prompt)) + + if completion_tokens == 0: + completion_tokens = token_counter.get_string_tokens(str(completion)) return TokenCountingEvent( event_id=event_id, - prompt=prompt, - prompt_token_count=token_counter.get_string_tokens(prompt), - completion=completion, - completion_token_count=token_counter.get_string_tokens(completion), + prompt=str(prompt), + prompt_token_count=prompt_tokens, + completion=str(completion), + completion_token_count=completion_tokens, ) elif EventPayload.MESSAGES in payload: @@ -47,52 +108,31 @@ def get_llm_token_counts( response = payload.get(EventPayload.RESPONSE) response_str = str(response) - # try getting attached token counts first - try: - messages_tokens = 0 - response_tokens = 0 - - if response is not None and response.raw is not None: - if isinstance(response.raw, dict): - raw_dict = response.raw - else: - raw_dict = response.raw.model_dump() + if response: + prompt_tokens, completion_tokens = get_tokens_from_response(response) + else: + prompt_tokens, completion_tokens = 0, 0 - usage = raw_dict.get("usage", None) + if prompt_tokens == 0: + prompt_tokens = token_counter.estimate_tokens_in_messages(messages) - if usage is not None: - messages_tokens = usage.get("prompt_tokens", 0) - response_tokens = usage.get("completion_tokens", 0) - - if messages_tokens == 0 or response_tokens == 0: - raise ValueError("Invalid token counts!") - - return TokenCountingEvent( - event_id=event_id, - prompt=messages_str, - prompt_token_count=messages_tokens, - completion=response_str, - completion_token_count=response_tokens, - ) - - except (ValueError, KeyError): - # Invalid token counts, or no token counts attached - pass - - # Should count tokens ourselves - messages_tokens = token_counter.estimate_tokens_in_messages(messages) - response_tokens = token_counter.get_string_tokens(response_str) + if completion_tokens == 0: + completion_tokens = token_counter.get_string_tokens(response_str) return TokenCountingEvent( event_id=event_id, prompt=messages_str, - prompt_token_count=messages_tokens, + prompt_token_count=prompt_tokens, completion=response_str, - completion_token_count=response_tokens, + completion_token_count=completion_tokens, ) else: - raise ValueError( - "Invalid payload! Need prompt and completion or messages and response." + return TokenCountingEvent( + event_id=event_id, + prompt="", + prompt_token_count=0, + completion="", + completion_token_count=0, ) diff --git a/llama-index-core/llama_index/core/chat_engine/condense_plus_context.py b/llama-index-core/llama_index/core/chat_engine/condense_plus_context.py index 12017102a7d83..720ab0c9926c6 100644 --- a/llama-index-core/llama_index/core/chat_engine/condense_plus_context.py +++ b/llama-index-core/llama_index/core/chat_engine/condense_plus_context.py @@ -12,18 +12,13 @@ ) from llama_index.core.indices.base_retriever import BaseRetriever from llama_index.core.indices.query.schema import QueryBundle -from llama_index.core.indices.service_context import ServiceContext from llama_index.core.base.llms.generic_utils import messages_to_history_str from llama_index.core.llms.llm import LLM from llama_index.core.memory import BaseMemory, ChatMemoryBuffer from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.prompts.base import PromptTemplate from llama_index.core.schema import MetadataMode, NodeWithScore -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.types import Thread from llama_index.core.utilities.token_counting import TokenCounter @@ -98,7 +93,6 @@ def from_defaults( cls, retriever: BaseRetriever, llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, chat_history: Optional[List[ChatMessage]] = None, memory: Optional[BaseMemory] = None, system_prompt: Optional[str] = None, @@ -110,7 +104,7 @@ def from_defaults( **kwargs: Any, ) -> "CondensePlusContextChatEngine": """Initialize a CondensePlusContextChatEngine from default parameters.""" - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm chat_history = chat_history or [] memory = memory or ChatMemoryBuffer.from_defaults( @@ -124,9 +118,7 @@ def from_defaults( context_prompt=context_prompt, condense_prompt=condense_prompt, skip_condense=skip_condense, - callback_manager=callback_manager_from_settings_or_context( - Settings, service_context - ), + callback_manager=Settings.callback_manager, node_postprocessors=node_postprocessors, system_prompt=system_prompt, verbose=verbose, diff --git a/llama-index-core/llama_index/core/chat_engine/condense_question.py b/llama-index-core/llama_index/core/chat_engine/condense_question.py index b618b301477a9..81f92de61c396 100644 --- a/llama-index-core/llama_index/core/chat_engine/condense_question.py +++ b/llama-index-core/llama_index/core/chat_engine/condense_question.py @@ -23,13 +23,8 @@ from llama_index.core.llms.llm import LLM from llama_index.core.memory import BaseMemory, ChatMemoryBuffer from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import LLMPredictorType -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings + from llama_index.core.tools import ToolOutput from llama_index.core.types import Thread @@ -66,7 +61,7 @@ def __init__( query_engine: BaseQueryEngine, condense_question_prompt: BasePromptTemplate, memory: BaseMemory, - llm: LLMPredictorType, + llm: LLM, verbose: bool = False, callback_manager: Optional[CallbackManager] = None, ) -> None: @@ -85,7 +80,6 @@ def from_defaults( chat_history: Optional[List[ChatMessage]] = None, memory: Optional[BaseMemory] = None, memory_cls: Type[BaseMemory] = ChatMemoryBuffer, - service_context: Optional[ServiceContext] = None, verbose: bool = False, system_prompt: Optional[str] = None, prefix_messages: Optional[List[ChatMessage]] = None, @@ -95,7 +89,7 @@ def from_defaults( """Initialize a CondenseQuestionChatEngine from default parameters.""" condense_question_prompt = condense_question_prompt or DEFAULT_PROMPT - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm chat_history = chat_history or [] memory = memory or memory_cls.from_defaults(chat_history=chat_history, llm=llm) @@ -115,9 +109,7 @@ def from_defaults( memory, llm, verbose=verbose, - callback_manager=callback_manager_from_settings_or_context( - Settings, service_context - ), + callback_manager=Settings.callback_manager, ) def _condense_question( diff --git a/llama-index-core/llama_index/core/chat_engine/context.py b/llama-index-core/llama_index/core/chat_engine/context.py index 21338cb4f4858..6197381a0a985 100644 --- a/llama-index-core/llama_index/core/chat_engine/context.py +++ b/llama-index-core/llama_index/core/chat_engine/context.py @@ -14,11 +14,8 @@ from llama_index.core.memory import BaseMemory, ChatMemoryBuffer from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle -from llama_index.core.service_context import ServiceContext from llama_index.core.settings import ( Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, ) from llama_index.core.types import Thread @@ -63,7 +60,6 @@ def __init__( def from_defaults( cls, retriever: BaseRetriever, - service_context: Optional[ServiceContext] = None, chat_history: Optional[List[ChatMessage]] = None, memory: Optional[BaseMemory] = None, system_prompt: Optional[str] = None, @@ -74,7 +70,7 @@ def from_defaults( **kwargs: Any, ) -> "ContextChatEngine": """Initialize a ContextChatEngine from default parameters.""" - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm chat_history = chat_history or [] memory = memory or ChatMemoryBuffer.from_defaults( @@ -99,9 +95,7 @@ def from_defaults( memory=memory, prefix_messages=prefix_messages, node_postprocessors=node_postprocessors, - callback_manager=callback_manager_from_settings_or_context( - Settings, service_context - ), + callback_manager=Settings.callback_manager, context_template=context_template, ) diff --git a/llama-index-core/llama_index/core/chat_engine/simple.py b/llama-index-core/llama_index/core/chat_engine/simple.py index 9ebfc90e1e664..9b717e7ab4cac 100644 --- a/llama-index-core/llama_index/core/chat_engine/simple.py +++ b/llama-index-core/llama_index/core/chat_engine/simple.py @@ -10,12 +10,7 @@ ) from llama_index.core.llms.llm import LLM from llama_index.core.memory import BaseMemory, ChatMemoryBuffer -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.types import Thread @@ -48,12 +43,10 @@ def from_defaults( system_prompt: Optional[str] = None, prefix_messages: Optional[List[ChatMessage]] = None, llm: Optional[LLM] = None, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> "SimpleChatEngine": """Initialize a SimpleChatEngine from default parameters.""" - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm chat_history = chat_history or [] memory = memory or memory_cls.from_defaults(chat_history=chat_history, llm=llm) @@ -73,9 +66,7 @@ def from_defaults( llm=llm, memory=memory, prefix_messages=prefix_messages, - callback_manager=callback_manager_from_settings_or_context( - Settings, service_context - ), + callback_manager=Settings.callback_manager, ) @trace_method("chat") diff --git a/llama-index-core/llama_index/core/command_line/mappings.json b/llama-index-core/llama_index/core/command_line/mappings.json index ffedbfa1da5b0..fe732aa3728f1 100644 --- a/llama-index-core/llama_index/core/command_line/mappings.json +++ b/llama-index-core/llama_index/core/command_line/mappings.json @@ -1,6 +1,5 @@ { "StorageContext": "llama_index.core", - "ServiceContext": "llama_index.core", "ComposableGraph": "llama_index.core", "# indicesSummaryIndex": "llama_index.core", "VectorStoreIndex": "llama_index.core", @@ -50,7 +49,6 @@ "load_indices_from_storage": "llama_index.core", "QueryBundle": "llama_index.core", "get_response_synthesizer": "llama_index.core", - "set_global_service_context": "llama_index.core", "set_global_handler": "llama_index.core", "set_global_tokenizer": "llama_index.core", "get_tokenizer": "llama_index.core", @@ -397,7 +395,6 @@ "generate_qa_embedding_pairs": "llama_index.finetuning", "SentenceTransformersFinetuneEngine": "llama_index.finetuning", "EmbeddingAdapterFinetuneEngine": "llama_index.finetuning", - "GradientFinetuneEngine": "llama_index.finetuning", "generate_cohere_reranker_finetuning_dataset": "llama_index.finetuning", "CohereRerankerFinetuneEngine": "llama_index.finetuning", "MistralAIFinetuneEngine": "llama_index.finetuning", diff --git a/llama-index-core/llama_index/core/composability/joint_qa_summary.py b/llama-index-core/llama_index/core/composability/joint_qa_summary.py index c3ac03922b3ab..ab117aa58ae51 100644 --- a/llama-index-core/llama_index/core/composability/joint_qa_summary.py +++ b/llama-index-core/llama_index/core/composability/joint_qa_summary.py @@ -1,6 +1,5 @@ """Joint QA Summary graph.""" - from typing import List, Optional, Sequence from llama_index.core.base.embeddings.base import BaseEmbedding @@ -11,14 +10,7 @@ from llama_index.core.llms.llm import LLM from llama_index.core.query_engine.router_query_engine import RouterQueryEngine from llama_index.core.schema import Document, TransformComponent -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - embed_model_from_settings_or_context, - llm_from_settings_or_context, - transformations_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.storage.storage_context import StorageContext from llama_index.core.tools.query_engine import QueryEngineTool @@ -39,8 +31,6 @@ class QASummaryQueryEngineBuilder: Args: docstore (BaseDocumentStore): A BaseDocumentStore to use for storing nodes. - service_context (ServiceContext): A ServiceContext to use for - building indices. summary_text (str): Text to use for the summary index. qa_text (str): Text to use for the QA index. node_parser (NodeParser): A NodeParser to use for parsing. @@ -56,25 +46,14 @@ def __init__( storage_context: Optional[StorageContext] = None, summary_text: str = DEFAULT_SUMMARY_TEXT, qa_text: str = DEFAULT_QA_TEXT, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: """Init params.""" - self._llm = llm or llm_from_settings_or_context(Settings, service_context) - self._callback_manager = ( - callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) - self._embed_model = embed_model or embed_model_from_settings_or_context( - Settings, service_context - ) - self._transformations = ( - transformations - or transformations_from_settings_or_context(Settings, service_context) - ) + self._llm = llm or Settings.llm + self._callback_manager = callback_manager or Settings.callback_manager + self._embed_model = embed_model or Settings.embed_model + self._transformations = transformations or Settings.transformations self._storage_context = storage_context or StorageContext.from_defaults() - self._service_context = service_context self._summary_text = summary_text self._qa_text = qa_text @@ -94,22 +73,13 @@ def build_from_documents( nodes=nodes, transformations=self._transformations, embed_model=self._embed_model, - service_context=self._service_context, - storage_context=self._storage_context, - ) - summary_index = SummaryIndex( - nodes, - service_context=self._service_context, storage_context=self._storage_context, ) + summary_index = SummaryIndex(nodes, storage_context=self._storage_context) - vector_query_engine = vector_index.as_query_engine( - llm=self._llm, service_context=self._service_context - ) + vector_query_engine = vector_index.as_query_engine(llm=self._llm) list_query_engine = summary_index.as_query_engine( - llm=self._llm, - service_context=self._service_context, - response_mode="tree_summarize", + llm=self._llm, response_mode="tree_summarize" ) # build query engine @@ -123,6 +93,5 @@ def build_from_documents( list_query_engine, description=self._summary_text ), ], - service_context=self._service_context, select_multi=False, ) diff --git a/llama-index-core/llama_index/core/evaluation/answer_relevancy.py b/llama-index-core/llama_index/core/evaluation/answer_relevancy.py index 4240d3821057c..775da73296cbb 100644 --- a/llama-index-core/llama_index/core/evaluation/answer_relevancy.py +++ b/llama-index-core/llama_index/core/evaluation/answer_relevancy.py @@ -1,16 +1,16 @@ """Relevancy evaluation.""" + from __future__ import annotations import asyncio import re from typing import Any, Callable, Optional, Sequence, Tuple -from llama_index.core.indices.service_context import ServiceContext from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult from llama_index.core.llms.llm import LLM from llama_index.core.prompts import BasePromptTemplate, PromptTemplate from llama_index.core.prompts.mixin import PromptDictType -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings DEFAULT_EVAL_TEMPLATE = PromptTemplate( "Your task is to evaluate if the response is relevant to the query.\n" @@ -53,8 +53,6 @@ class AnswerRelevancyEvaluator(BaseEvaluator): This evaluator considers the query string and response string. Args: - service_context(Optional[ServiceContext]): - The service context to use for evaluation. raise_error(Optional[bool]): Whether to raise an error if the response is invalid. Defaults to False. @@ -73,11 +71,9 @@ def __init__( parser_function: Callable[ [str], Tuple[Optional[float], Optional[str]] ] = _default_parser_function, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: """Init params.""" - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._raise_error = raise_error self._eval_template: BasePromptTemplate diff --git a/llama-index-core/llama_index/core/evaluation/context_relevancy.py b/llama-index-core/llama_index/core/evaluation/context_relevancy.py index cc55b76728f3c..3c4ef6e9b15b2 100644 --- a/llama-index-core/llama_index/core/evaluation/context_relevancy.py +++ b/llama-index-core/llama_index/core/evaluation/context_relevancy.py @@ -1,4 +1,5 @@ """Relevancy evaluation.""" + from __future__ import annotations import asyncio @@ -11,8 +12,7 @@ from llama_index.core.prompts import BasePromptTemplate, PromptTemplate from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context + DEFAULT_EVAL_TEMPLATE = PromptTemplate( "Your task is to evaluate if the retrieved context from the document sources are relevant to the query.\n" @@ -70,8 +70,6 @@ class ContextRelevancyEvaluator(BaseEvaluator): This evaluator considers the query string and retrieved contexts. Args: - service_context(Optional[ServiceContext]): - The service context to use for evaluation. raise_error(Optional[bool]): Whether to raise an error if the response is invalid. Defaults to False. @@ -91,11 +89,11 @@ def __init__( parser_function: Callable[ [str], Tuple[Optional[float], Optional[str]] ] = _default_parser_function, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: """Init params.""" - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + from llama_index.core import Settings + + self._llm = llm or Settings.llm self._raise_error = raise_error self._eval_template: BasePromptTemplate diff --git a/llama-index-core/llama_index/core/evaluation/correctness.py b/llama-index-core/llama_index/core/evaluation/correctness.py index bc6a693420d69..90db1dfff40c6 100644 --- a/llama-index-core/llama_index/core/evaluation/correctness.py +++ b/llama-index-core/llama_index/core/evaluation/correctness.py @@ -1,4 +1,5 @@ """Correctness evaluation.""" + import asyncio from typing import Any, Callable, Optional, Sequence, Tuple, Union @@ -13,8 +14,7 @@ PromptTemplate, ) from llama_index.core.prompts.mixin import PromptDictType -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings DEFAULT_SYSTEM_TEMPLATE = """ You are an expert evaluation system for a question answering chatbot. @@ -78,7 +78,6 @@ class CorrectnessEvaluator(BaseEvaluator): Passing is defined as a score greater than or equal to the given threshold. Args: - service_context (Optional[ServiceContext]): Service context. eval_template (Optional[Union[BasePromptTemplate, str]]): Template for the evaluation prompt. score_threshold (float): Numerical threshold for passing the evaluation, @@ -90,13 +89,11 @@ def __init__( llm: Optional[LLM] = None, eval_template: Optional[Union[BasePromptTemplate, str]] = None, score_threshold: float = 4.0, - # deprecated - service_context: Optional[ServiceContext] = None, parser_function: Callable[ [str], Tuple[Optional[float], Optional[str]] ] = default_parser, ) -> None: - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._eval_template: BasePromptTemplate if isinstance(eval_template, str): diff --git a/llama-index-core/llama_index/core/evaluation/dataset_generation.py b/llama-index-core/llama_index/core/evaluation/dataset_generation.py index 117adc6e8aa7b..5925c81e0897b 100644 --- a/llama-index-core/llama_index/core/evaluation/dataset_generation.py +++ b/llama-index-core/llama_index/core/evaluation/dataset_generation.py @@ -1,4 +1,5 @@ """Dataset generation from documents.""" + from __future__ import annotations import asyncio @@ -29,12 +30,8 @@ NodeWithScore, TransformComponent, ) -from llama_index.core.service_context import ServiceContext from llama_index.core.settings import ( Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, - transformations_from_settings_or_context, ) DEFAULT_QUESTION_GENERATION_PROMPT = """\ @@ -102,7 +99,7 @@ def questions(self) -> List[str]: def save_json(self, path: str) -> None: """Save json.""" with open(path, "w") as f: - json.dump(self.dict(), f, indent=4) + json.dump(self.model_dump(), f, indent=4) @classmethod def from_json(cls, path: str) -> QueryResponseDataset: @@ -144,15 +141,10 @@ def __init__( question_gen_query: str | None = None, metadata_mode: MetadataMode = MetadataMode.NONE, show_progress: bool = False, - # deprecated - service_context: ServiceContext | None = None, ) -> None: """Init params.""" - self.llm = llm or llm_from_settings_or_context(Settings, service_context) - self.callback_manager = ( - callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) + self.llm = llm or Settings.llm + self.callback_manager = callback_manager or Settings.callback_manager self.text_question_template = text_question_template or PromptTemplate( DEFAULT_QUESTION_GENERATION_PROMPT ) @@ -183,18 +175,11 @@ def from_documents( required_keywords: List[str] | None = None, exclude_keywords: List[str] | None = None, show_progress: bool = False, - # deprecated - service_context: ServiceContext | None = None, ) -> DatasetGenerator: """Generate dataset from documents.""" - llm = llm or llm_from_settings_or_context(Settings, service_context) - transformations = transformations or transformations_from_settings_or_context( - Settings, service_context - ) - callback_manager = ( - callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) + llm = llm or Settings.llm + transformations = transformations or Settings.transformations + callback_manager = callback_manager or Settings.callback_manager nodes = run_transformations( documents, transformations, show_progress=show_progress @@ -221,7 +206,6 @@ def from_documents( text_qa_template=text_qa_template, question_gen_query=question_gen_query, show_progress=show_progress, - service_context=service_context, ) async def _agenerate_dataset( diff --git a/llama-index-core/llama_index/core/evaluation/eval_utils.py b/llama-index-core/llama_index/core/evaluation/eval_utils.py index 8d6df1f611d69..e2601efc681ed 100644 --- a/llama-index-core/llama_index/core/evaluation/eval_utils.py +++ b/llama-index-core/llama_index/core/evaluation/eval_utils.py @@ -10,7 +10,6 @@ from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING import numpy as np -import pandas as pd from llama_index.core.async_utils import asyncio_module, asyncio_run from llama_index.core.base.base_query_engine import BaseQueryEngine @@ -47,7 +46,7 @@ def get_responses( def get_results_df( eval_results_list: List[EvaluationResult], names: List[str], metric_keys: List[str] -) -> pd.DataFrame: +) -> Any: """Get results df. Args: @@ -59,6 +58,13 @@ def get_results_df( List of metric keys to get. """ + try: + import pandas as pd + except ImportError: + raise ImportError( + "Pandas is required to get results dataframes. Please install it with `pip install pandas`." + ) + metric_dict = defaultdict(list) metric_dict["names"] = names for metric_key in metric_keys: diff --git a/llama-index-core/llama_index/core/evaluation/faithfulness.py b/llama-index-core/llama_index/core/evaluation/faithfulness.py index 9805c6b259e48..51a732681d690 100644 --- a/llama-index-core/llama_index/core/evaluation/faithfulness.py +++ b/llama-index-core/llama_index/core/evaluation/faithfulness.py @@ -11,8 +11,7 @@ from llama_index.core.prompts import BasePromptTemplate, PromptTemplate from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings DEFAULT_EVAL_TEMPLATE = PromptTemplate( "Please tell if a given piece of information " @@ -106,8 +105,6 @@ class FaithfulnessEvaluator(BaseEvaluator): This evaluator only considers the response string and the list of context strings. Args: - service_context(Optional[ServiceContext]): - The service context to use for evaluation. raise_error(bool): Whether to raise an error when the response is invalid. Defaults to False. eval_template(Optional[Union[str, BasePromptTemplate]]): @@ -122,11 +119,9 @@ def __init__( raise_error: bool = False, eval_template: Optional[Union[str, BasePromptTemplate]] = None, refine_template: Optional[Union[str, BasePromptTemplate]] = None, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: """Init params.""" - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._raise_error = raise_error self._eval_template: BasePromptTemplate diff --git a/llama-index-core/llama_index/core/evaluation/guideline.py b/llama-index-core/llama_index/core/evaluation/guideline.py index 13f3f6cc9443d..7216b7b9fcac2 100644 --- a/llama-index-core/llama_index/core/evaluation/guideline.py +++ b/llama-index-core/llama_index/core/evaluation/guideline.py @@ -1,4 +1,5 @@ """Guideline evaluation.""" + import asyncio import logging from typing import Any, Optional, Sequence, Union, cast @@ -9,8 +10,7 @@ from llama_index.core.output_parsers import PydanticOutputParser from llama_index.core.prompts import BasePromptTemplate, PromptTemplate from llama_index.core.prompts.mixin import PromptDictType -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings logger = logging.getLogger(__name__) @@ -46,8 +46,6 @@ class GuidelineEvaluator(BaseEvaluator): This evaluator only considers the query string and the response string. Args: - service_context(Optional[ServiceContext]): - The service context to use for evaluation. guidelines(Optional[str]): User-added guidelines to use for evaluation. Defaults to None, which uses the default guidelines. eval_template(Optional[Union[str, BasePromptTemplate]] ): @@ -60,10 +58,8 @@ def __init__( guidelines: Optional[str] = None, eval_template: Optional[Union[str, BasePromptTemplate]] = None, output_parser: Optional[PydanticOutputParser] = None, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._guidelines = guidelines or DEFAULT_GUIDELINES self._eval_template: BasePromptTemplate diff --git a/llama-index-core/llama_index/core/evaluation/notebook_utils.py b/llama-index-core/llama_index/core/evaluation/notebook_utils.py index 9377d4efcbc8a..06c86bf7a9482 100644 --- a/llama-index-core/llama_index/core/evaluation/notebook_utils.py +++ b/llama-index-core/llama_index/core/evaluation/notebook_utils.py @@ -1,9 +1,8 @@ """Notebook utils.""" from collections import defaultdict -from typing import List, Optional, Tuple +from typing import Any, List, Optional, Tuple -import pandas as pd from llama_index.core.evaluation import EvaluationResult from llama_index.core.evaluation.retrieval.base import RetrievalEvalResult @@ -14,8 +13,15 @@ def get_retrieval_results_df( names: List[str], results_arr: List[List[RetrievalEvalResult]], metric_keys: Optional[List[str]] = None, -) -> pd.DataFrame: +) -> Any: """Display retrieval results.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + metric_keys = metric_keys or DEFAULT_METRIC_KEYS avg_metrics_dict = defaultdict(list) @@ -36,7 +42,7 @@ def get_retrieval_results_df( def get_eval_results_df( names: List[str], results_arr: List[EvaluationResult], metric: Optional[str] = None -) -> Tuple[pd.DataFrame, pd.DataFrame]: +) -> Tuple[Any, Any]: """Organizes EvaluationResults into a deep dataframe and computes the mean score. @@ -44,6 +50,13 @@ def get_eval_results_df( result_df: pd.DataFrame representing all the evaluation results mean_df: pd.DataFrame of average scores groupby names """ + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + if len(names) != len(results_arr): raise ValueError("names and results_arr must have same length.") diff --git a/llama-index-core/llama_index/core/evaluation/pairwise.py b/llama-index-core/llama_index/core/evaluation/pairwise.py index 049258700e724..cd78221e6465b 100644 --- a/llama-index-core/llama_index/core/evaluation/pairwise.py +++ b/llama-index-core/llama_index/core/evaluation/pairwise.py @@ -17,8 +17,7 @@ PromptTemplate, ) from llama_index.core.prompts.mixin import PromptDictType -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings DEFAULT_SYSTEM_TEMPLATE = ( "Please act as an impartial judge and evaluate the quality of the responses provided by two " @@ -99,8 +98,6 @@ class PairwiseComparisonEvaluator(BaseEvaluator): Outputs whether the `response` given is better than the `reference` response. Args: - service_context (Optional[ServiceContext]): - The service context to use for evaluation. eval_template (Optional[Union[str, BasePromptTemplate]]): The template to use for evaluation. enforce_consensus (bool): Whether to enforce consensus (consistency if we @@ -116,10 +113,8 @@ def __init__( [str], Tuple[Optional[bool], Optional[float], Optional[str]] ] = _default_parser_function, enforce_consensus: bool = True, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._eval_template: BasePromptTemplate if isinstance(eval_template, str): diff --git a/llama-index-core/llama_index/core/evaluation/relevancy.py b/llama-index-core/llama_index/core/evaluation/relevancy.py index b7ef4cc06713e..f8ca088eb53b2 100644 --- a/llama-index-core/llama_index/core/evaluation/relevancy.py +++ b/llama-index-core/llama_index/core/evaluation/relevancy.py @@ -1,4 +1,5 @@ """Relevancy evaluation.""" + from __future__ import annotations import asyncio @@ -10,8 +11,7 @@ from llama_index.core.prompts import BasePromptTemplate, PromptTemplate from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings DEFAULT_EVAL_TEMPLATE = PromptTemplate( "Your task is to evaluate if the response for the query \ @@ -46,8 +46,6 @@ class RelevancyEvaluator(BaseEvaluator): This evaluator considers the query string, retrieved contexts, and response string. Args: - service_context(Optional[ServiceContext]): - The service context to use for evaluation. raise_error(Optional[bool]): Whether to raise an error if the response is invalid. Defaults to False. @@ -63,11 +61,9 @@ def __init__( raise_error: bool = False, eval_template: Optional[Union[str, BasePromptTemplate]] = None, refine_template: Optional[Union[str, BasePromptTemplate]] = None, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: """Init params.""" - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._raise_error = raise_error self._eval_template: BasePromptTemplate diff --git a/llama-index-core/llama_index/core/evaluation/retrieval/base.py b/llama-index-core/llama_index/core/evaluation/retrieval/base.py index 8175838750886..66d1ce139bc7e 100644 --- a/llama-index-core/llama_index/core/evaluation/retrieval/base.py +++ b/llama-index-core/llama_index/core/evaluation/retrieval/base.py @@ -6,7 +6,7 @@ from typing import Any, Dict, List, Optional, Tuple from llama_index.core.async_utils import asyncio_run -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from llama_index.core.evaluation.retrieval.metrics import resolve_metrics from llama_index.core.evaluation.retrieval.metrics_base import ( BaseRetrievalMetric, @@ -47,9 +47,7 @@ class RetrievalEvalResult(BaseModel): """ - class Config: - arbitrary_types_allowed = True - + model_config = ConfigDict(arbitrary_types_allowed=True) query: str = Field(..., description="Query string") expected_ids: List[str] = Field(..., description="Expected ids") expected_texts: Optional[List[str]] = Field( @@ -78,13 +76,11 @@ def __str__(self) -> str: class BaseRetrievalEvaluator(BaseModel): """Base Retrieval Evaluator class.""" + model_config = ConfigDict(arbitrary_types_allowed=True) metrics: List[BaseRetrievalMetric] = Field( ..., description="List of metrics to evaluate" ) - class Config: - arbitrary_types_allowed = True - @classmethod def from_metric_names( cls, metric_names: List[str], **kwargs: Any diff --git a/llama-index-core/llama_index/core/evaluation/retrieval/evaluator.py b/llama-index-core/llama_index/core/evaluation/retrieval/evaluator.py index 95b7e12b8d4cb..c4f75222feb0e 100644 --- a/llama-index-core/llama_index/core/evaluation/retrieval/evaluator.py +++ b/llama-index-core/llama_index/core/evaluation/retrieval/evaluator.py @@ -3,7 +3,7 @@ from typing import Any, List, Optional, Sequence, Tuple from llama_index.core.base.base_retriever import BaseRetriever -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, SerializeAsAny from llama_index.core.evaluation.retrieval.base import ( BaseRetrievalEvaluator, RetrievalEvalMode, @@ -30,7 +30,7 @@ class RetrieverEvaluator(BaseRetrievalEvaluator): """ retriever: BaseRetriever = Field(..., description="Retriever to evaluate") - node_postprocessors: Optional[List[BaseNodePostprocessor]] = Field( + node_postprocessors: Optional[List[SerializeAsAny[BaseNodePostprocessor]]] = Field( default=None, description="Optional post-processor" ) @@ -80,7 +80,7 @@ class MultiModalRetrieverEvaluator(BaseRetrievalEvaluator): """ retriever: BaseRetriever = Field(..., description="Retriever to evaluate") - node_postprocessors: Optional[List[BaseNodePostprocessor]] = Field( + node_postprocessors: Optional[List[SerializeAsAny[BaseNodePostprocessor]]] = Field( default=None, description="Optional post-processor" ) diff --git a/llama-index-core/llama_index/core/evaluation/retrieval/metrics.py b/llama-index-core/llama_index/core/evaluation/retrieval/metrics.py index 9b7d24a5f19da..c03a440cd0684 100644 --- a/llama-index-core/llama_index/core/evaluation/retrieval/metrics.py +++ b/llama-index-core/llama_index/core/evaluation/retrieval/metrics.py @@ -406,8 +406,8 @@ def __init__( "Cannot import cohere package, please `pip install cohere`." ) - self._client = Client(api_key=api_key) super().__init__(model=model) + self._client = Client(api_key=api_key) def _get_agg_func(self, agg: Literal["max", "median", "mean"]) -> Callable: """Get agg func.""" diff --git a/llama-index-core/llama_index/core/evaluation/retrieval/metrics_base.py b/llama-index-core/llama_index/core/evaluation/retrieval/metrics_base.py index d51761aa96200..0131e2218030f 100644 --- a/llama-index-core/llama_index/core/evaluation/retrieval/metrics_base.py +++ b/llama-index-core/llama_index/core/evaluation/retrieval/metrics_base.py @@ -1,7 +1,7 @@ from abc import ABC, abstractmethod from typing import Any, ClassVar, Dict, List, Optional -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict class RetrievalMetricResult(BaseModel): @@ -30,6 +30,7 @@ def __float__(self) -> float: class BaseRetrievalMetric(BaseModel, ABC): """Base class for retrieval metrics.""" + model_config = ConfigDict(arbitrary_types_allowed=True) metric_name: ClassVar[str] @abstractmethod @@ -51,6 +52,3 @@ def compute( **kwargs: Additional keyword arguments """ - - class Config: - arbitrary_types_allowed = True diff --git a/llama-index-core/llama_index/core/evaluation/semantic_similarity.py b/llama-index-core/llama_index/core/evaluation/semantic_similarity.py index d66d68830ca86..47c3c431a87a8 100644 --- a/llama-index-core/llama_index/core/evaluation/semantic_similarity.py +++ b/llama-index-core/llama_index/core/evaluation/semantic_similarity.py @@ -7,8 +7,7 @@ ) from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult from llama_index.core.prompts.mixin import PromptDictType -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, embed_model_from_settings_or_context +from llama_index.core.settings import Settings class SemanticSimilarityEvaluator(BaseEvaluator): @@ -23,7 +22,6 @@ class SemanticSimilarityEvaluator(BaseEvaluator): https://arxiv.org/pdf/2108.06130.pdf Args: - service_context (Optional[ServiceContext]): Service context. similarity_threshold (float): Embedding similarity threshold for "passing". Defaults to 0.8. """ @@ -34,12 +32,9 @@ def __init__( similarity_fn: Optional[Callable[..., float]] = None, similarity_mode: Optional[SimilarityMode] = None, similarity_threshold: float = 0.8, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: - self._embed_model = embed_model or embed_model_from_settings_or_context( - Settings, service_context - ) + self._embed_model = embed_model or Settings.embed_model + if similarity_fn is None: similarity_mode = similarity_mode or SimilarityMode.DEFAULT self._similarity_fn = lambda x, y: similarity(x, y, mode=similarity_mode) diff --git a/llama-index-core/llama_index/core/extractors/interface.py b/llama-index-core/llama_index/core/extractors/interface.py index 3302899f9d6ed..f0ae27617d744 100644 --- a/llama-index-core/llama_index/core/extractors/interface.py +++ b/llama-index-core/llama_index/core/extractors/interface.py @@ -1,4 +1,5 @@ """Node parser interface.""" + from abc import abstractmethod from copy import deepcopy from typing import Any, Dict, List, Optional, Sequence, cast diff --git a/llama-index-core/llama_index/core/extractors/metadata_extractors.py b/llama-index-core/llama_index/core/extractors/metadata_extractors.py index 1c0ba070490ae..62aa04b799ac9 100644 --- a/llama-index-core/llama_index/core/extractors/metadata_extractors.py +++ b/llama-index-core/llama_index/core/extractors/metadata_extractors.py @@ -19,17 +19,19 @@ disambiguate the document or subsection from other similar documents or subsections. (similar with contrastive learning) """ + from typing import Any, Dict, List, Optional, Sequence, cast from llama_index.core.async_utils import DEFAULT_NUM_WORKERS, run_jobs -from llama_index.core.bridge.pydantic import Field, PrivateAttr +from llama_index.core.bridge.pydantic import ( + Field, + PrivateAttr, + SerializeAsAny, +) from llama_index.core.extractors.interface import BaseExtractor from llama_index.core.llms.llm import LLM from llama_index.core.prompts import PromptTemplate from llama_index.core.schema import BaseNode, TextNode -from llama_index.core.service_context_elements.llm_predictor import ( - LLMPredictorType, -) from llama_index.core.settings import Settings from llama_index.core.types import BasePydanticProgram @@ -43,6 +45,13 @@ what is the comprehensive title for this document? Title: """ +def add_class_name(value: Any, handler, info) -> Dict[str, Any]: + partial_result = handler(value, info) + if hasattr(value, "class_name"): + partial_result.update({"class_name": value.class_name()}) + return partial_result + + class TitleExtractor(BaseExtractor): """Title extractor. Useful for long documents. Extracts `document_title` metadata field. @@ -56,7 +65,7 @@ class TitleExtractor(BaseExtractor): """ is_text_node_only: bool = False # can work for mixture of text and non-text nodes - llm: LLMPredictorType = Field(description="The LLM to use for generation.") + llm: SerializeAsAny[LLM] = Field(description="The LLM to use for generation.") nodes: int = Field( default=5, description="The number of nodes to extract titles from.", @@ -75,7 +84,7 @@ def __init__( self, llm: Optional[LLM] = None, # TODO: llm_predictor arg is deprecated - llm_predictor: Optional[LLMPredictorType] = None, + llm_predictor: Optional[LLM] = None, nodes: int = 5, node_template: str = DEFAULT_TITLE_NODE_TEMPLATE, combine_template: str = DEFAULT_TITLE_COMBINE_TEMPLATE, @@ -164,7 +173,7 @@ class KeywordExtractor(BaseExtractor): prompt_template (str): template for keyword extraction """ - llm: LLMPredictorType = Field(description="The LLM to use for generation.") + llm: SerializeAsAny[LLM] = Field(description="The LLM to use for generation.") keywords: int = Field( default=5, description="The number of keywords to extract.", gt=0 ) @@ -178,7 +187,7 @@ def __init__( self, llm: Optional[LLM] = None, # TODO: llm_predictor arg is deprecated - llm_predictor: Optional[LLMPredictorType] = None, + llm_predictor: Optional[LLM] = None, keywords: int = 5, prompt_template: str = DEFAULT_KEYWORD_EXTRACT_TEMPLATE, num_workers: int = DEFAULT_NUM_WORKERS, @@ -253,7 +262,7 @@ class QuestionsAnsweredExtractor(BaseExtractor): embedding_only (bool): whether to use embedding only """ - llm: LLMPredictorType = Field(description="The LLM to use for generation.") + llm: SerializeAsAny[LLM] = Field(description="The LLM to use for generation.") questions: int = Field( default=5, description="The number of questions to generate.", @@ -271,7 +280,7 @@ def __init__( self, llm: Optional[LLM] = None, # TODO: llm_predictor arg is deprecated - llm_predictor: Optional[LLMPredictorType] = None, + llm_predictor: Optional[LLM] = None, questions: int = 5, prompt_template: str = DEFAULT_QUESTION_GEN_TMPL, embedding_only: bool = True, @@ -341,7 +350,7 @@ class SummaryExtractor(BaseExtractor): prompt_template (str): template for summary extraction """ - llm: LLMPredictorType = Field(description="The LLM to use for generation.") + llm: SerializeAsAny[LLM] = Field(description="The LLM to use for generation.") summaries: List[str] = Field( description="List of summaries to extract: 'self', 'prev', 'next'" ) @@ -358,7 +367,7 @@ def __init__( self, llm: Optional[LLM] = None, # TODO: llm_predictor arg is deprecated - llm_predictor: Optional[LLMPredictorType] = None, + llm_predictor: Optional[LLM] = None, summaries: List[str] = ["self"], prompt_template: str = DEFAULT_SUMMARY_EXTRACT_TEMPLATE, num_workers: int = DEFAULT_NUM_WORKERS, @@ -367,9 +376,6 @@ def __init__( # validation if not all(s in ["self", "prev", "next"] for s in summaries): raise ValueError("summaries must be one of ['self', 'prev', 'next']") - self._self_summary = "self" in summaries - self._prev_summary = "prev" in summaries - self._next_summary = "next" in summaries super().__init__( llm=llm or llm_predictor or Settings.llm, @@ -379,6 +385,10 @@ def __init__( **kwargs, ) + self._self_summary = "self" in summaries + self._prev_summary = "prev" in summaries + self._next_summary = "next" in summaries + @classmethod def class_name(cls) -> str: return "SummaryExtractor" @@ -460,7 +470,7 @@ class PydanticProgramExtractor(BaseExtractor): """ - program: BasePydanticProgram = Field( + program: SerializeAsAny[BasePydanticProgram] = Field( ..., description="Pydantic program to extract." ) input_key: str = Field( diff --git a/llama-index-core/llama_index/core/graph_stores/simple_labelled.py b/llama-index-core/llama_index/core/graph_stores/simple_labelled.py index 6595b8ba2ad7f..de4bd0f8f2deb 100644 --- a/llama-index-core/llama_index/core/graph_stores/simple_labelled.py +++ b/llama-index-core/llama_index/core/graph_stores/simple_labelled.py @@ -166,7 +166,7 @@ def persist( if fs is None: fs = fsspec.filesystem("file") with fs.open(persist_path, "w") as f: - f.write(self.graph.json()) + f.write(self.graph.model_dump_json()) @classmethod def from_persist_path( @@ -205,9 +205,9 @@ def from_dict( kg_nodes = {} for id, node_dict in node_dicts.items(): if "name" in node_dict: - kg_nodes[id] = EntityNode.parse_obj(node_dict) + kg_nodes[id] = EntityNode.model_validate(node_dict) elif "text" in node_dict: - kg_nodes[id] = ChunkNode.parse_obj(node_dict) + kg_nodes[id] = ChunkNode.model_validate(node_dict) else: raise ValueError(f"Could not infer node type for data: {node_dict!s}") @@ -215,7 +215,7 @@ def from_dict( data["nodes"] = {} # load the graph - graph = LabelledPropertyGraph.parse_obj(data) + graph = LabelledPropertyGraph.model_validate(data) # add the node back graph.nodes = kg_nodes @@ -224,7 +224,7 @@ def from_dict( def to_dict(self) -> dict: """Convert to dict.""" - return self.graph.dict() + return self.graph.model_dump() # NOTE: Unimplemented methods for SimplePropertyGraphStore diff --git a/llama-index-core/llama_index/core/indices/base.py b/llama-index-core/llama_index/core/indices/base.py index 5d45c1948b996..0a8cfe24ce948 100644 --- a/llama-index-core/llama_index/core/indices/base.py +++ b/llama-index-core/llama_index/core/indices/base.py @@ -12,13 +12,7 @@ from llama_index.core.ingestion import run_transformations from llama_index.core.llms.utils import LLMType, resolve_llm from llama_index.core.schema import BaseNode, Document, IndexNode, TransformComponent -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, - transformations_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.storage.docstore.types import BaseDocumentStore, RefDocInfo from llama_index.core.storage.storage_context import StorageContext @@ -34,9 +28,6 @@ class BaseIndex(Generic[IS], ABC): Args: nodes (List[Node]): List of nodes to index show_progress (bool): Whether to show tqdm progress bars. Defaults to False. - service_context (ServiceContext): Service context container (contains - components like LLM, Embeddings, etc.). - """ index_struct_cls: Type[IS] @@ -50,8 +41,6 @@ def __init__( callback_manager: Optional[CallbackManager] = None, transformations: Optional[List[TransformComponent]] = None, show_progress: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize with parameters.""" @@ -71,17 +60,11 @@ def __init__( raise ValueError("nodes must be a list of Node objects.") self._storage_context = storage_context or StorageContext.from_defaults() - # deprecated - self._service_context = service_context - self._docstore = self._storage_context.docstore self._show_progress = show_progress self._vector_store = self._storage_context.vector_store self._graph_store = self._storage_context.graph_store - self._callback_manager = ( - callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) + self._callback_manager = callback_manager or Settings.callback_manager objects = objects or [] self._object_map = {obj.index_id: obj.obj for obj in objects} @@ -92,15 +75,13 @@ def __init__( if index_struct is None: nodes = nodes or [] index_struct = self.build_index_from_nodes( - nodes + objects, **kwargs # type: ignore + nodes + objects, + **kwargs, # type: ignore ) self._index_struct = index_struct self._storage_context.index_store.add_index_struct(self._index_struct) - self._transformations = ( - transformations - or transformations_from_settings_or_context(Settings, service_context) - ) + self._transformations = transformations or Settings.transformations @classmethod def from_documents( @@ -110,8 +91,6 @@ def from_documents( show_progress: bool = False, callback_manager: Optional[CallbackManager] = None, transformations: Optional[List[TransformComponent]] = None, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> IndexType: """Create index from documents. @@ -123,13 +102,8 @@ def from_documents( """ storage_context = storage_context or StorageContext.from_defaults() docstore = storage_context.docstore - callback_manager = ( - callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) - transformations = transformations or transformations_from_settings_or_context( - Settings, service_context - ) + callback_manager = callback_manager or Settings.callback_manager + transformations = transformations or Settings.transformations with callback_manager.as_trace("index_construction"): for doc in documents: @@ -148,7 +122,6 @@ def from_documents( callback_manager=callback_manager, show_progress=show_progress, transformations=transformations, - service_context=service_context, **kwargs, ) @@ -185,10 +158,6 @@ def docstore(self) -> BaseDocumentStore: """Get the docstore corresponding to the index.""" return self._docstore - @property - def service_context(self) -> Optional[ServiceContext]: - return self._service_context - @property def storage_context(self) -> StorageContext: return self._storage_context @@ -405,7 +374,7 @@ def as_query_engine( llm = ( resolve_llm(llm, callback_manager=self._callback_manager) if llm - else llm_from_settings_or_context(Settings, self.service_context) + else Settings.llm ) return RetrieverQueryEngine.from_args( @@ -434,20 +403,11 @@ def as_chat_engine( - `ChatMode.REACT`: Chat engine that uses a react agent with a query engine tool - `ChatMode.OPENAI`: Chat engine that uses an openai agent with a query engine tool """ - service_context = kwargs.get("service_context", self.service_context) - - if service_context is not None: - llm = ( - resolve_llm(llm, callback_manager=self._callback_manager) - if llm - else service_context.llm - ) - else: - llm = ( - resolve_llm(llm, callback_manager=self._callback_manager) - if llm - else Settings.llm - ) + llm = ( + resolve_llm(llm, callback_manager=self._callback_manager) + if llm + else Settings.llm + ) query_engine = self.as_query_engine(llm=llm, **kwargs) diff --git a/llama-index-core/llama_index/core/indices/common/struct_store/base.py b/llama-index-core/llama_index/core/indices/common/struct_store/base.py index d4c4753a9901d..6577ab0b5ca95 100644 --- a/llama-index-core/llama_index/core/indices/common/struct_store/base.py +++ b/llama-index-core/llama_index/core/indices/common/struct_store/base.py @@ -8,6 +8,7 @@ from llama_index.core.data_structs.table import StructDatapoint from llama_index.core.indices.prompt_helper import PromptHelper from llama_index.core.node_parser.interface import TextSplitter +from llama_index.core.llms import LLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.prompts.default_prompt_selectors import ( DEFAULT_REFINE_TABLE_CONTEXT_PROMPT_SEL, @@ -19,15 +20,7 @@ from llama_index.core.prompts.prompt_type import PromptType from llama_index.core.response_synthesizers import get_response_synthesizer from llama_index.core.schema import BaseNode, MetadataMode -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import ( - LLMPredictorType, -) -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.utilities.sql_wrapper import SQLDatabase from llama_index.core.utils import truncate_text @@ -39,7 +32,6 @@ class SQLDocumentContextBuilder: Args: sql_database (Optional[SQLDatabase]): SQL database to use, - service_context (Optional[ServiceContext]): Service Context to use. text_splitter (Optional[TextSplitter]): Text Splitter to use. table_context_prompt (Optional[BasePromptTemplate]): A Table Context Prompt (see :ref:`Prompt-Templates`). @@ -53,8 +45,7 @@ class SQLDocumentContextBuilder: def __init__( self, sql_database: SQLDatabase, - llm: Optional[LLMPredictorType] = None, - service_context: Optional[ServiceContext] = None, + llm: Optional[LLM] = None, text_splitter: Optional[TextSplitter] = None, table_context_prompt: Optional[BasePromptTemplate] = None, refine_table_context_prompt: Optional[BasePromptTemplate] = None, @@ -66,13 +57,11 @@ def __init__( raise ValueError("sql_database must be provided.") self._sql_database = sql_database self._text_splitter = text_splitter - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._prompt_helper = Settings._prompt_helper or PromptHelper.from_llm_metadata( self._llm.metadata, ) - self._callback_manager = callback_manager_from_settings_or_context( - Settings, service_context - ) + self._callback_manager = Settings.callback_manager self._table_context_prompt = ( table_context_prompt or DEFAULT_TABLE_CONTEXT_PROMPT ) @@ -147,7 +136,7 @@ class BaseStructDatapointExtractor: def __init__( self, - llm: LLMPredictorType, + llm: LLM, schema_extract_prompt: BasePromptTemplate, output_parser: OUTPUT_PARSER_TYPE, ) -> None: diff --git a/llama-index-core/llama_index/core/indices/common/struct_store/sql.py b/llama-index-core/llama_index/core/indices/common/struct_store/sql.py index 30e31c7929057..6ee3f02a431b2 100644 --- a/llama-index-core/llama_index/core/indices/common/struct_store/sql.py +++ b/llama-index-core/llama_index/core/indices/common/struct_store/sql.py @@ -7,10 +7,8 @@ OUTPUT_PARSER_TYPE, BaseStructDatapointExtractor, ) +from llama_index.core.llms import LLM from llama_index.core.prompts import BasePromptTemplate -from llama_index.core.service_context_elements.llm_predictor import ( - LLMPredictorType, -) from llama_index.core.utilities.sql_wrapper import SQLDatabase from sqlalchemy import Table @@ -20,7 +18,7 @@ class SQLStructDatapointExtractor(BaseStructDatapointExtractor): def __init__( self, - llm: LLMPredictorType, + llm: LLM, schema_extract_prompt: BasePromptTemplate, output_parser: OUTPUT_PARSER_TYPE, sql_database: SQLDatabase, diff --git a/llama-index-core/llama_index/core/indices/common_tree/base.py b/llama-index-core/llama_index/core/indices/common_tree/base.py index ba10592256817..f8613b798b66b 100644 --- a/llama-index-core/llama_index/core/indices/common_tree/base.py +++ b/llama-index-core/llama_index/core/indices/common_tree/base.py @@ -12,12 +12,7 @@ from llama_index.core.llms.llm import LLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.schema import BaseNode, MetadataMode, TextNode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.storage.docstore import BaseDocumentStore from llama_index.core.storage.docstore.registry import get_default_docstore from llama_index.core.utils import get_tqdm_iterable @@ -37,7 +32,6 @@ def __init__( self, num_children: int, summary_prompt: BasePromptTemplate, - service_context: Optional[ServiceContext] = None, llm: Optional[LLM] = None, docstore: Optional[BaseDocumentStore] = None, show_progress: bool = False, @@ -48,13 +42,11 @@ def __init__( raise ValueError("Invalid number of children.") self.num_children = num_children self.summary_prompt = summary_prompt - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._prompt_helper = Settings._prompt_helper or PromptHelper.from_llm_metadata( self._llm.metadata, ) - self._callback_manager = callback_manager_from_settings_or_context( - Settings, service_context - ) + self._callback_manager = Settings.callback_manager self._use_async = use_async self._show_progress = show_progress self._docstore = docstore or get_default_docstore() diff --git a/llama-index-core/llama_index/core/indices/composability/graph.py b/llama-index-core/llama_index/core/indices/composability/graph.py index c6a6cf502f3a0..1ced5ff645641 100644 --- a/llama-index-core/llama_index/core/indices/composability/graph.py +++ b/llama-index-core/llama_index/core/indices/composability/graph.py @@ -11,7 +11,6 @@ ObjectType, RelatedNodeInfo, ) -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.storage_context import StorageContext @@ -45,23 +44,19 @@ def root_index(self) -> BaseIndex: def index_struct(self) -> IndexStruct: return self._all_indices[self._root_id].index_struct - @property - def service_context(self) -> Optional[ServiceContext]: - return self._all_indices[self._root_id].service_context - @classmethod def from_indices( cls, root_index_cls: Type[BaseIndex], children_indices: Sequence[BaseIndex], index_summaries: Optional[Sequence[str]] = None, - service_context: Optional[ServiceContext] = None, storage_context: Optional[StorageContext] = None, **kwargs: Any, ) -> "ComposableGraph": # type: ignore """Create composable graph using this index class as the root.""" - service_context = service_context or ServiceContext.from_defaults() - with service_context.callback_manager.as_trace("graph_construction"): + from llama_index.core import Settings + + with Settings.callback_manager.as_trace("graph_construction"): if index_summaries is None: for index in children_indices: if index.index_struct.summary is None: @@ -102,7 +97,6 @@ def from_indices( # construct root index root_index = root_index_cls( nodes=index_nodes, - service_context=service_context, storage_context=storage_context, **kwargs, ) diff --git a/llama-index-core/llama_index/core/indices/document_summary/base.py b/llama-index-core/llama_index/core/indices/document_summary/base.py index f1895c53c245d..92389f3ecefdc 100644 --- a/llama-index-core/llama_index/core/indices/document_summary/base.py +++ b/llama-index-core/llama_index/core/indices/document_summary/base.py @@ -31,12 +31,7 @@ RelatedNodeInfo, TextNode, ) -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - embed_model_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.storage.docstore.types import RefDocInfo from llama_index.core.storage.storage_context import StorageContext from llama_index.core.utils import get_tqdm_iterable @@ -88,16 +83,11 @@ def __init__( summary_query: str = DEFAULT_SUMMARY_QUERY, show_progress: bool = False, embed_summaries: bool = True, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" - self._llm = llm or llm_from_settings_or_context(Settings, service_context) - self._embed_model = embed_model or embed_model_from_settings_or_context( - Settings, service_context - ) - + self._llm = llm or Settings.llm + self._embed_model = embed_model or Settings.embed_model self._response_synthesizer = response_synthesizer or get_response_synthesizer( llm=self._llm, response_mode=ResponseMode.TREE_SUMMARIZE ) @@ -107,7 +97,6 @@ def __init__( super().__init__( nodes=nodes, index_struct=index_struct, - service_context=service_context, storage_context=storage_context, show_progress=show_progress, objects=objects, @@ -226,7 +215,7 @@ def _add_nodes_to_index( summary_nodes_with_embedding = [] for node in summary_nodes: - node_with_embedding = node.copy() + node_with_embedding = node.model_copy() node_with_embedding.embedding = id_to_embed_map[node.node_id] summary_nodes_with_embedding.append(node_with_embedding) self._vector_store.add(summary_nodes_with_embedding) diff --git a/llama-index-core/llama_index/core/indices/empty/base.py b/llama-index-core/llama_index/core/indices/empty/base.py index 6d3b2a21a82b7..32d9d719eb2c9 100644 --- a/llama-index-core/llama_index/core/indices/empty/base.py +++ b/llama-index-core/llama_index/core/indices/empty/base.py @@ -13,7 +13,6 @@ from llama_index.core.indices.base import BaseIndex from llama_index.core.llms.utils import LLMType from llama_index.core.schema import BaseNode -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.docstore.types import RefDocInfo @@ -33,15 +32,12 @@ class EmptyIndex(BaseIndex[EmptyIndexStruct]): def __init__( self, index_struct: Optional[EmptyIndexStruct] = None, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" super().__init__( nodes=None, index_struct=index_struct or EmptyIndexStruct(), - service_context=service_context, **kwargs, ) diff --git a/llama-index-core/llama_index/core/indices/keyword_table/base.py b/llama-index-core/llama_index/core/indices/keyword_table/base.py index df465308369d9..91bde3abb0010 100644 --- a/llama-index-core/llama_index/core/indices/keyword_table/base.py +++ b/llama-index-core/llama_index/core/indices/keyword_table/base.py @@ -26,8 +26,7 @@ DEFAULT_QUERY_KEYWORD_EXTRACT_TEMPLATE, ) from llama_index.core.schema import BaseNode, IndexNode, MetadataMode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings from llama_index.core.storage.docstore.types import RefDocInfo from llama_index.core.utils import get_tqdm_iterable @@ -69,7 +68,6 @@ def __init__( objects: Optional[Sequence[IndexNode]] = None, index_struct: Optional[KeywordTable] = None, llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, keyword_extract_template: Optional[BasePromptTemplate] = None, max_keywords_per_chunk: int = 10, use_async: bool = False, @@ -78,7 +76,7 @@ def __init__( ) -> None: """Initialize params.""" # need to set parameters before building index in base class. - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self.max_keywords_per_chunk = max_keywords_per_chunk self.keyword_extract_template = ( @@ -92,7 +90,6 @@ def __init__( super().__init__( nodes=nodes, index_struct=index_struct, - service_context=service_context, show_progress=show_progress, objects=objects, **kwargs, diff --git a/llama-index-core/llama_index/core/indices/keyword_table/utils.py b/llama-index-core/llama_index/core/indices/keyword_table/utils.py index 45b3a8bc9cd87..41a3f3c15bf64 100644 --- a/llama-index-core/llama_index/core/indices/keyword_table/utils.py +++ b/llama-index-core/llama_index/core/indices/keyword_table/utils.py @@ -1,9 +1,9 @@ """Utils for keyword table.""" import re +from collections import Counter from typing import Optional, Set -import pandas as pd from llama_index.core.indices.utils import expand_tokens_with_subtokens from llama_index.core.utils import globals_helper @@ -15,8 +15,9 @@ def simple_extract_keywords( tokens = [t.strip().lower() for t in re.findall(r"\w+", text_chunk)] if filter_stopwords: tokens = [t for t in tokens if t not in globals_helper.stopwords] - value_counts = pd.Series(tokens).value_counts() - keywords = value_counts.index.tolist()[:max_keywords] + + token_counts = Counter(tokens) + keywords = [keyword for keyword, count in token_counts.most_common(max_keywords)] return set(keywords) diff --git a/llama-index-core/llama_index/core/indices/knowledge_graph/base.py b/llama-index-core/llama_index/core/indices/knowledge_graph/base.py index daa434c497982..a6ad5b9138703 100644 --- a/llama-index-core/llama_index/core/indices/knowledge_graph/base.py +++ b/llama-index-core/llama_index/core/indices/knowledge_graph/base.py @@ -21,12 +21,7 @@ DEFAULT_KG_TRIPLET_EXTRACT_PROMPT, ) from llama_index.core.schema import BaseNode, IndexNode, MetadataMode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - embed_model_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.storage.docstore.types import RefDocInfo from llama_index.core.storage.storage_context import StorageContext from llama_index.core.utils import get_tqdm_iterable @@ -52,7 +47,6 @@ class KnowledgeGraphIndex(BaseIndex[KG]): kg_triplet_extract_template (BasePromptTemplate): The prompt to use for extracting triplets. max_triplets_per_chunk (int): The maximum number of triplets to extract. - service_context (Optional[ServiceContext]): The service context to use. storage_context (Optional[StorageContext]): The storage context to use. graph_store (Optional[GraphStore]): The graph store to use. show_progress (bool): Whether to show tqdm progress bars. Defaults to False. @@ -81,8 +75,6 @@ def __init__( show_progress: bool = False, max_object_length: int = 128, kg_triplet_extract_fn: Optional[Callable] = None, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" @@ -101,15 +93,12 @@ def __init__( self._max_object_length = max_object_length self._kg_triplet_extract_fn = kg_triplet_extract_fn - self._llm = llm or llm_from_settings_or_context(Settings, service_context) - self._embed_model = embed_model or embed_model_from_settings_or_context( - Settings, service_context - ) + self._llm = llm or Settings.llm + self._embed_model = embed_model or Settings.embed_model super().__init__( nodes=nodes, index_struct=index_struct, - service_context=service_context, storage_context=storage_context, show_progress=show_progress, objects=objects, diff --git a/llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py b/llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py index 548a6065d443e..a2019c7bab57c 100644 --- a/llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py +++ b/llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py @@ -26,13 +26,7 @@ QueryBundle, TextNode, ) -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - embed_model_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.storage.storage_context import StorageContext from llama_index.core.utils import print_text, truncate_text @@ -135,11 +129,8 @@ def __init__( else KGRetrieverMode.KEYWORD ) - self._llm = llm or llm_from_settings_or_context(Settings, index.service_context) - self._embed_model = embed_model or embed_model_from_settings_or_context( - Settings, index.service_context - ) - + self._llm = llm or Settings.llm + self._embed_model = embed_model or Settings.embed_model self._graph_store = index.graph_store self.graph_store_query_depth = graph_store_query_depth self.use_global_node_triplets = use_global_node_triplets @@ -154,10 +145,7 @@ def __init__( logger.warning(f"Failed to get graph schema: {e}") self._graph_schema = "" super().__init__( - callback_manager=callback_manager - or callback_manager_from_settings_or_context( - Settings, index.service_context - ), + callback_manager=callback_manager or Settings.callback_manager, object_map=object_map, verbose=verbose, ) @@ -429,7 +417,6 @@ class KnowledgeGraphRAGRetriever(BaseRetriever): Retriever that perform SubGraph RAG towards knowledge graph. Args: - service_context (Optional[ServiceContext]): A service context to use. storage_context (Optional[StorageContext]): A storage context to use. entity_extract_fn (Optional[Callable]): A function to extract entities. entity_extract_template Optional[BasePromptTemplate]): A Query Key Entity @@ -477,8 +464,6 @@ def __init__( max_knowledge_sequence: int = REL_TEXT_LIMIT, verbose: bool = False, callback_manager: Optional[CallbackManager] = None, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize the retriever.""" @@ -490,7 +475,7 @@ def __init__( self._storage_context = storage_context self._graph_store = storage_context.graph_store - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._entity_extract_fn = entity_extract_fn self._entity_extract_template = ( @@ -537,7 +522,6 @@ def __init__( refresh_schema=refresh_schema, verbose=verbose, response_synthesizer=response_synthesizer, - service_context=service_context, **kwargs, ) @@ -553,10 +537,7 @@ def __init__( logger.warning(f"Failed to get graph schema: {e}") self._graph_schema = "" - super().__init__( - callback_manager=callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) + super().__init__(callback_manager=callback_manager or Settings.callback_manager) def _process_entities( self, diff --git a/llama-index-core/llama_index/core/indices/list/base.py b/llama-index-core/llama_index/core/indices/list/base.py index f4ca7e9973d10..b5c595d060271 100644 --- a/llama-index-core/llama_index/core/indices/list/base.py +++ b/llama-index-core/llama_index/core/indices/list/base.py @@ -14,12 +14,7 @@ from llama_index.core.indices.base import BaseIndex from llama_index.core.llms.llm import LLM from llama_index.core.schema import BaseNode, IndexNode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - embed_model_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.storage.docstore.types import RefDocInfo from llama_index.core.utils import get_tqdm_iterable @@ -57,15 +52,12 @@ def __init__( objects: Optional[Sequence[IndexNode]] = None, index_struct: Optional[IndexList] = None, show_progress: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" super().__init__( nodes=nodes, index_struct=index_struct, - service_context=service_context, show_progress=show_progress, objects=objects, **kwargs, @@ -87,14 +79,12 @@ def as_retriever( if retriever_mode == ListRetrieverMode.DEFAULT: return SummaryIndexRetriever(self, object_map=self._object_map, **kwargs) elif retriever_mode == ListRetrieverMode.EMBEDDING: - embed_model = embed_model or embed_model_from_settings_or_context( - Settings, self.service_context - ) + embed_model = embed_model or Settings.embed_model return SummaryIndexEmbeddingRetriever( self, object_map=self._object_map, embed_model=embed_model, **kwargs ) elif retriever_mode == ListRetrieverMode.LLM: - llm = llm or llm_from_settings_or_context(Settings, self.service_context) + llm = llm or Settings.llm return SummaryIndexLLMRetriever( self, object_map=self._object_map, llm=llm, **kwargs ) diff --git a/llama-index-core/llama_index/core/indices/list/retrievers.py b/llama-index-core/llama_index/core/indices/list/retrievers.py index 36f658203bcda..67c05bc40b23c 100644 --- a/llama-index-core/llama_index/core/indices/list/retrievers.py +++ b/llama-index-core/llama_index/core/indices/list/retrievers.py @@ -1,4 +1,5 @@ """Retrievers for SummaryIndex.""" + import logging from typing import Any, Callable, List, Optional, Tuple @@ -22,12 +23,7 @@ NodeWithScore, QueryBundle, ) -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - embed_model_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings logger = logging.getLogger(__name__) @@ -89,9 +85,8 @@ def __init__( ) -> None: self._index = index self._similarity_top_k = similarity_top_k - self._embed_model = embed_model or embed_model_from_settings_or_context( - Settings, index.service_context - ) + self._embed_model = embed_model or Settings.embed_model + super().__init__( callback_manager=callback_manager, object_map=object_map, verbose=verbose ) @@ -121,7 +116,7 @@ def _retrieve( logger.debug(f"> Top {len(top_idxs)} nodes:\n") nl = "\n" - logger.debug(f"{ nl.join([n.get_content() for n in top_k_nodes]) }") + logger.debug(f"{nl.join([n.get_content() for n in top_k_nodes])}") return node_with_scores def _get_embeddings( @@ -158,8 +153,6 @@ class SummaryIndexLLMRetriever(BaseRetriever): batch of nodes. parse_choice_select_answer_fn (Optional[Callable]): A function that parses the choice select answer. - service_context (Optional[ServiceContext]): A service context. - """ def __init__( @@ -170,7 +163,6 @@ def __init__( choice_batch_size: int = 10, format_node_batch_fn: Optional[Callable] = None, parse_choice_select_answer_fn: Optional[Callable] = None, - service_context: Optional[ServiceContext] = None, callback_manager: Optional[CallbackManager] = None, object_map: Optional[dict] = None, verbose: bool = False, @@ -187,7 +179,7 @@ def __init__( self._parse_choice_select_answer_fn = ( parse_choice_select_answer_fn or default_parse_choice_select_answer_fn ) - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm super().__init__( callback_manager=callback_manager, object_map=object_map, verbose=verbose ) diff --git a/llama-index-core/llama_index/core/indices/managed/base.py b/llama-index-core/llama_index/core/indices/managed/base.py index 35d375b729545..8a7f403f61511 100644 --- a/llama-index-core/llama_index/core/indices/managed/base.py +++ b/llama-index-core/llama_index/core/indices/managed/base.py @@ -3,6 +3,7 @@ An index that is built on top of a managed service. """ + from abc import ABC, abstractmethod from typing import Any, Dict, List, Optional, Sequence, Type @@ -11,7 +12,6 @@ from llama_index.core.data_structs.data_structs import IndexDict from llama_index.core.indices.base import BaseIndex, IndexType from llama_index.core.schema import BaseNode, Document, TransformComponent -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.docstore.types import RefDocInfo from llama_index.core.storage.storage_context import StorageContext @@ -33,15 +33,12 @@ def __init__( index_struct: Optional[IndexDict] = None, storage_context: Optional[StorageContext] = None, show_progress: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" super().__init__( nodes=nodes, index_struct=index_struct, - service_context=service_context, storage_context=storage_context, show_progress=show_progress, **kwargs, @@ -88,8 +85,6 @@ def from_documents( show_progress: bool = False, callback_manager: Optional[CallbackManager] = None, transformations: Optional[List[TransformComponent]] = None, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> IndexType: """Build an index from a sequence of documents.""" diff --git a/llama-index-core/llama_index/core/indices/multi_modal/base.py b/llama-index-core/llama_index/core/indices/multi_modal/base.py index ceb117330e53f..45c291f71f2ca 100644 --- a/llama-index-core/llama_index/core/indices/multi_modal/base.py +++ b/llama-index-core/llama_index/core/indices/multi_modal/base.py @@ -28,8 +28,7 @@ from llama_index.core.multi_modal_llms import MultiModalLLM from llama_index.core.query_engine.multi_modal import SimpleMultiModalQueryEngine from llama_index.core.schema import BaseNode, ImageNode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings from llama_index.core.storage.storage_context import StorageContext from llama_index.core.vector_stores.simple import ( DEFAULT_VECTOR_STORE, @@ -72,8 +71,6 @@ def __init__( # those flags are used for cases when only one vector store is used is_image_vector_store_empty: bool = False, is_text_vector_store_empty: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" @@ -105,7 +102,6 @@ def __init__( nodes=nodes, index_struct=index_struct, embed_model=embed_model, - service_context=service_context, storage_context=storage_context, show_progress=show_progress, use_async=use_async, @@ -143,7 +139,7 @@ def as_query_engine( ) -> SimpleMultiModalQueryEngine: retriever = cast(MultiModalVectorIndexRetriever, self.as_retriever(**kwargs)) - llm = llm or llm_from_settings_or_context(Settings, self._service_context) + llm = llm or Settings.llm assert isinstance(llm, MultiModalLLM) return SimpleMultiModalQueryEngine( @@ -157,8 +153,6 @@ def from_vector_store( cls, vector_store: BasePydanticVectorStore, embed_model: Optional[EmbedType] = None, - # deprecated - service_context: Optional[ServiceContext] = None, # Image-related kwargs image_vector_store: Optional[BasePydanticVectorStore] = None, image_embed_model: EmbedType = "clip", @@ -172,7 +166,6 @@ def from_vector_store( storage_context = StorageContext.from_defaults(vector_store=vector_store) return cls( nodes=[], - service_context=service_context, storage_context=storage_context, image_vector_store=image_vector_store, image_embed_model=image_embed_model, @@ -227,7 +220,7 @@ def _get_node_with_embedding( results = [] for node in nodes: embedding = id_to_embed_map[node.node_id] - result = node.copy() + result = node.model_copy() result.embedding = embedding if is_image and id_to_text_embed_map: text_embedding = id_to_text_embed_map[node.node_id] @@ -278,7 +271,7 @@ async def _aget_node_with_embedding( results = [] for node in nodes: embedding = id_to_embed_map[node.node_id] - result = node.copy() + result = node.model_copy() result.embedding = embedding if is_image and id_to_text_embed_map: text_embedding = id_to_text_embed_map[node.node_id] @@ -342,7 +335,7 @@ async def _async_add_nodes_to_index( if not self._vector_store.stores_text or self._store_nodes_override: for node, new_id in zip(all_nodes, all_new_ids): # NOTE: remove embedding from node to avoid duplication - node_without_embedding = node.copy() + node_without_embedding = node.model_copy() node_without_embedding.embedding = None index_struct.add_node(node_without_embedding, text_id=new_id) @@ -404,7 +397,7 @@ def _add_nodes_to_index( if not self._vector_store.stores_text or self._store_nodes_override: for node, new_id in zip(all_nodes, all_new_ids): # NOTE: remove embedding from node to avoid duplication - node_without_embedding = node.copy() + node_without_embedding = node.model_copy() node_without_embedding.embedding = None index_struct.add_node(node_without_embedding, text_id=new_id) diff --git a/llama-index-core/llama_index/core/indices/multi_modal/retriever.py b/llama-index-core/llama_index/core/indices/multi_modal/retriever.py index 4941dc7be17f0..983c10dc21508 100644 --- a/llama-index-core/llama_index/core/indices/multi_modal/retriever.py +++ b/llama-index-core/llama_index/core/indices/multi_modal/retriever.py @@ -18,10 +18,7 @@ QueryBundle, QueryType, ) -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.vector_stores.types import ( MetadataFilters, BasePydanticVectorStore, @@ -74,8 +71,6 @@ def __init__( assert isinstance(self._index.image_embed_model, BaseEmbedding) self._image_embed_model = index._image_embed_model self._embed_model = index._embed_model - - self._service_context = self._index.service_context self._docstore = self._index.docstore self._similarity_top_k = similarity_top_k @@ -88,12 +83,7 @@ def __init__( self._sparse_top_k = sparse_top_k self._kwargs: Dict[str, Any] = kwargs.get("vector_store_kwargs", {}) - self.callback_manager = ( - callback_manager - or callback_manager_from_settings_or_context( - Settings, self._service_context - ) - ) + self.callback_manager = callback_manager or Settings.callback_manager @property def similarity_top_k(self) -> int: @@ -262,9 +252,7 @@ def _build_node_list_from_query_result( ): node_id = query_result.nodes[i].node_id if self._docstore.document_exists(node_id): - query_result.nodes[ - i - ] = self._docstore.get_node( # type: ignore[index] + query_result.nodes[i] = self._docstore.get_node( # type: ignore[index] node_id ) diff --git a/llama-index-core/llama_index/core/indices/prompt_helper.py b/llama-index-core/llama_index/core/indices/prompt_helper.py index 6b7f141ed61cd..1c560f10f3f53 100644 --- a/llama-index-core/llama_index/core/indices/prompt_helper.py +++ b/llama-index-core/llama_index/core/indices/prompt_helper.py @@ -88,10 +88,6 @@ def __init__( """Init params.""" if chunk_overlap_ratio > 1.0 or chunk_overlap_ratio < 0.0: raise ValueError("chunk_overlap_ratio must be a float between 0. and 1.") - - # TODO: make configurable - self._token_counter = TokenCounter(tokenizer=tokenizer) - super().__init__( context_window=context_window, num_output=num_output, @@ -100,6 +96,9 @@ def __init__( separator=separator, ) + # TODO: make configurable + self._token_counter = TokenCounter(tokenizer=tokenizer) + @classmethod def from_llm_metadata( cls, diff --git a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/cypher_template.py b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/cypher_template.py index 992eac6b3ab1b..2f575164079db 100644 --- a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/cypher_template.py +++ b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/cypher_template.py @@ -52,7 +52,7 @@ def retrieve_from_graph(self, query_bundle: QueryBundle) -> List[NodeWithScore]: cypher_response = self._graph_store.structured_query( self.cypher_query, - param_map=response.dict(), + param_map=response.model_dump(), ) return [ @@ -75,7 +75,7 @@ async def aretrieve_from_graph( cypher_response = await self._graph_store.astructured_query( self.cypher_query, - param_map=response.dict(), + param_map=response.model_dump(), ) return [ diff --git a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py index 885abe4699184..894a67bd1991a 100644 --- a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py +++ b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py @@ -61,7 +61,7 @@ def __init__( self.allowed_output_fields = allowed_output_fields super().__init__(graph_store=graph_store, include_text=False) - def _parse_generated_cyher(self, cypher_query: str) -> str: + def _parse_generated_cypher(self, cypher_query: str) -> str: if self.cypher_validator is not None: return self.cypher_validator(cypher_query) return cypher_query @@ -101,9 +101,7 @@ def retrieve_from_graph(self, query_bundle: QueryBundle) -> List[NodeWithScore]: question=question, ) - parsed_cypher_query = response - if self.allowed_output_fields is not None: - parsed_cypher_query = self._parse_generated_cyher(response) + parsed_cypher_query = self._parse_generated_cypher(response) query_output = self._graph_store.structured_query(parsed_cypher_query) @@ -135,9 +133,7 @@ async def aretrieve_from_graph( question=question, ) - parsed_cypher_query = response - if self.allowed_output_fields is not None: - parsed_cypher_query = self._parse_generated_cyher(response) + parsed_cypher_query = self._parse_generated_cypher(response) query_output = await self._graph_store.astructured_query(parsed_cypher_query) diff --git a/llama-index-core/llama_index/core/indices/property_graph/transformations/schema_llm.py b/llama-index-core/llama_index/core/indices/property_graph/transformations/schema_llm.py index 50c80b677f942..65ffbf6b64254 100644 --- a/llama-index-core/llama_index/core/indices/property_graph/transformations/schema_llm.py +++ b/llama-index-core/llama_index/core/indices/property_graph/transformations/schema_llm.py @@ -8,7 +8,7 @@ TypeAlias = Any from llama_index.core.async_utils import run_jobs -from llama_index.core.bridge.pydantic import create_model, validator +from llama_index.core.bridge.pydantic import create_model, field_validator from llama_index.core.graph_stores.types import ( EntityNode, Relation, @@ -187,7 +187,7 @@ def __init__( object=(entity_cls, ...), ) - def validate(v: Any, values: Any) -> Any: + def validate(v: Any) -> Any: """Validate triplets.""" passing_triplets = [] for i, triplet in enumerate(v): @@ -207,7 +207,7 @@ def validate(v: Any, values: Any) -> Any: return passing_triplets - root = validator("triplets", pre=True)(validate) + root = field_validator("triplets", mode="before")(validate) kg_schema_cls = create_model( "KGSchema", __validators__={"validator1": root}, diff --git a/llama-index-core/llama_index/core/indices/query/query_transform/base.py b/llama-index-core/llama_index/core/indices/query/query_transform/base.py index 18437fb48df63..3d734a5dec7cb 100644 --- a/llama-index-core/llama_index/core/indices/query/query_transform/base.py +++ b/llama-index-core/llama_index/core/indices/query/query_transform/base.py @@ -12,7 +12,7 @@ validate_and_convert_stringable, ) from llama_index.core.base.response.schema import Response -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, ConfigDict from llama_index.core.indices.query.query_transform.prompts import ( DEFAULT_DECOMPOSE_QUERY_TRANSFORM_PROMPT, DEFAULT_IMAGE_OUTPUT_PROMPT, @@ -22,6 +22,7 @@ StepDecomposeQueryTransformPrompt, ) from llama_index.core.instrumentation import DispatcherSpanMixin +from llama_index.core.llms import LLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.prompts.default_prompts import DEFAULT_HYDE_PROMPT from llama_index.core.prompts.mixin import ( @@ -30,9 +31,6 @@ PromptMixinType, ) from llama_index.core.schema import QueryBundle, QueryType -from llama_index.core.service_context_elements.llm_predictor import ( - LLMPredictorType, -) from llama_index.core.settings import Settings from llama_index.core.utils import print_text @@ -120,7 +118,7 @@ class HyDEQueryTransform(BaseQueryTransform): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, hyde_prompt: Optional[BasePromptTemplate] = None, include_original: bool = True, ) -> None: @@ -178,7 +176,7 @@ class DecomposeQueryTransform(BaseQueryTransform): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, decompose_query_prompt: Optional[DecomposeQueryTransformPrompt] = None, verbose: bool = False, ) -> None: @@ -283,7 +281,7 @@ class StepDecomposeQueryTransform(BaseQueryTransform): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, step_decompose_query_prompt: Optional[StepDecomposeQueryTransformPrompt] = None, verbose: bool = False, ) -> None: @@ -334,11 +332,9 @@ def _run(self, query_bundle: QueryBundle, metadata: Dict) -> QueryBundle: class QueryTransformComponent(QueryComponent): """Query transform component.""" + model_config = ConfigDict(arbitrary_types_allowed=True) query_transform: BaseQueryTransform = Field(..., description="Query transform.") - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: Any) -> None: """Set callback manager.""" # TODO: not implemented yet diff --git a/llama-index-core/llama_index/core/indices/query/query_transform/feedback_transform.py b/llama-index-core/llama_index/core/indices/query/query_transform/feedback_transform.py index 7e60e04576592..cb25e293b9cd2 100644 --- a/llama-index-core/llama_index/core/indices/query/query_transform/feedback_transform.py +++ b/llama-index-core/llama_index/core/indices/query/query_transform/feedback_transform.py @@ -3,12 +3,10 @@ from llama_index.core.evaluation.base import Evaluation from llama_index.core.indices.query.query_transform.base import BaseQueryTransform +from llama_index.core.llms import LLM from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.schema import QueryBundle -from llama_index.core.service_context_elements.llm_predictor import ( - LLMPredictorType, -) from llama_index.core.settings import Settings logger = logging.getLogger(__name__) @@ -40,7 +38,7 @@ class FeedbackQueryTransformation(BaseQueryTransform): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, resynthesize_query: bool = False, resynthesis_prompt: Optional[BasePromptTemplate] = None, ) -> None: diff --git a/llama-index-core/llama_index/core/indices/service_context.py b/llama-index-core/llama_index/core/indices/service_context.py deleted file mode 100644 index 23cdf6423cdd1..0000000000000 --- a/llama-index-core/llama_index/core/indices/service_context.py +++ /dev/null @@ -1,6 +0,0 @@ -# for backwards compatibility -from llama_index.core.service_context import ServiceContext - -__all__ = [ - "ServiceContext", -] diff --git a/llama-index-core/llama_index/core/indices/struct_store/base.py b/llama-index-core/llama_index/core/indices/struct_store/base.py index 7a043ada4a467..7a41de47a317a 100644 --- a/llama-index-core/llama_index/core/indices/struct_store/base.py +++ b/llama-index-core/llama_index/core/indices/struct_store/base.py @@ -8,7 +8,6 @@ from llama_index.core.prompts import BasePromptTemplate from llama_index.core.prompts.default_prompts import DEFAULT_SCHEMA_EXTRACT_PROMPT from llama_index.core.schema import BaseNode -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.docstore.types import RefDocInfo BST = TypeVar("BST", bound=BaseStructTable) @@ -43,7 +42,6 @@ def __init__( self, nodes: Optional[Sequence[BaseNode]] = None, index_struct: Optional[BST] = None, - service_context: Optional[ServiceContext] = None, schema_extract_prompt: Optional[BasePromptTemplate] = None, output_parser: Optional[OUTPUT_PARSER_TYPE] = None, **kwargs: Any, @@ -56,7 +54,6 @@ def __init__( super().__init__( nodes=nodes, index_struct=index_struct, - service_context=service_context, **kwargs, ) diff --git a/llama-index-core/llama_index/core/indices/struct_store/json_query.py b/llama-index-core/llama_index/core/indices/struct_store/json_query.py index 6b6909154f55c..26ede4714eb53 100644 --- a/llama-index-core/llama_index/core/indices/struct_store/json_query.py +++ b/llama-index-core/llama_index/core/indices/struct_store/json_query.py @@ -11,12 +11,7 @@ from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType from llama_index.core.prompts.prompt_type import PromptType from llama_index.core.schema import QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.utils import print_text logger = logging.getLogger(__name__) @@ -100,7 +95,6 @@ class JSONQueryEngine(BaseQueryEngine): Args: json_value (JSONType): JSON value json_schema (JSONType): JSON schema - service_context (ServiceContext): ServiceContext json_path_prompt (BasePromptTemplate): The JSON Path prompt to use. output_processor (Callable): The output processor that executes the JSON Path query. @@ -113,7 +107,6 @@ def __init__( self, json_value: JSONType, json_schema: JSONType, - service_context: Optional[ServiceContext] = None, llm: Optional[LLM] = None, json_path_prompt: Optional[BasePromptTemplate] = None, output_processor: Optional[Callable] = None, @@ -126,7 +119,7 @@ def __init__( """Initialize params.""" self._json_value = json_value self._json_schema = json_schema - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._json_path_prompt = json_path_prompt or DEFAULT_JSON_PATH_PROMPT self._output_processor = output_processor or default_output_processor self._output_kwargs = output_kwargs or {} @@ -136,11 +129,7 @@ def __init__( response_synthesis_prompt or DEFAULT_RESPONSE_SYNTHESIS_PROMPT ) - super().__init__( - callback_manager=callback_manager_from_settings_or_context( - Settings, service_context - ) - ) + super().__init__(callback_manager=Settings.callback_manager) def _get_prompts(self) -> Dict[str, Any]: """Get prompts.""" diff --git a/llama-index-core/llama_index/core/indices/struct_store/sql.py b/llama-index-core/llama_index/core/indices/struct_store/sql.py index 61c6937422d40..6aae76c0f7597 100644 --- a/llama-index-core/llama_index/core/indices/struct_store/sql.py +++ b/llama-index-core/llama_index/core/indices/struct_store/sql.py @@ -1,4 +1,5 @@ """SQL Structured Store.""" + from collections import defaultdict from enum import Enum from typing import Any, Optional, Sequence, Union @@ -16,8 +17,7 @@ ) from llama_index.core.llms.utils import LLMType from llama_index.core.schema import BaseNode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings from llama_index.core.utilities.sql_wrapper import SQLDatabase from sqlalchemy import Table @@ -65,7 +65,6 @@ def __init__( self, nodes: Optional[Sequence[BaseNode]] = None, index_struct: Optional[SQLStructTable] = None, - service_context: Optional[ServiceContext] = None, sql_database: Optional[SQLDatabase] = None, table_name: Optional[str] = None, table: Optional[Table] = None, @@ -89,7 +88,6 @@ def __init__( super().__init__( nodes=nodes, index_struct=index_struct, - service_context=service_context, **kwargs, ) @@ -111,7 +109,7 @@ def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> SQLStructTable: return index_struct else: data_extractor = SQLStructDatapointExtractor( - llm_from_settings_or_context(Settings, self.service_context), + Settings.llm, self.schema_extract_prompt, self.output_parser, self.sql_database, @@ -131,7 +129,7 @@ def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> SQLStructTable: def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None: """Insert a document.""" data_extractor = SQLStructDatapointExtractor( - llm_from_settings_or_context(Settings, self._service_context), + Settings.llm, self.schema_extract_prompt, self.output_parser, self.sql_database, diff --git a/llama-index-core/llama_index/core/indices/struct_store/sql_query.py b/llama-index-core/llama_index/core/indices/struct_store/sql_query.py index 7b44db27ed445..be92c9ae2a647 100644 --- a/llama-index-core/llama_index/core/indices/struct_store/sql_query.py +++ b/llama-index-core/llama_index/core/indices/struct_store/sql_query.py @@ -36,12 +36,7 @@ get_response_synthesizer, ) from llama_index.core.schema import QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.utilities.sql_wrapper import SQLDatabase from sqlalchemy import Table @@ -104,11 +99,7 @@ def __init__( sql_context_container or index.sql_context_container ) self._sql_only = sql_only - super().__init__( - callback_manager=callback_manager_from_settings_or_context( - Settings, index.service_context - ) - ) + super().__init__(callback_manager=Settings.callback_manager) def _get_prompt_modules(self) -> PromptMixinType: """Get prompt modules.""" @@ -183,10 +174,9 @@ def __init__( ) -> None: """Initialize params.""" self._index = index - self._llm = llm_from_settings_or_context(Settings, index.service_context) + self._llm = Settings.llm self._sql_database = index.sql_database self._sql_context_container = index.sql_context_container - self._service_context = index.service_context self._ref_doc_id_column = index.ref_doc_id_column self._text_to_sql_prompt = text_to_sql_prompt or DEFAULT_TEXT_TO_SQL_PROMPT @@ -196,16 +186,7 @@ def __init__( self._context_query_kwargs = context_query_kwargs or {} self._synthesize_response = synthesize_response self._sql_only = sql_only - super().__init__( - callback_manager=callback_manager_from_settings_or_context( - Settings, index.service_context - ) - ) - - @property - def service_context(self) -> Optional[ServiceContext]: - """Get service context.""" - return self._service_context + super().__init__(callback_manager=Settings.callback_manager) def _get_prompt_modules(self) -> PromptMixinType: """Get prompt modules.""" @@ -352,13 +333,10 @@ def __init__( refine_synthesis_prompt: Optional[BasePromptTemplate] = None, verbose: bool = False, streaming: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" - self._service_context = service_context - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm if callback_manager is not None: self._llm.callback_manager = callback_manager @@ -376,10 +354,7 @@ def __init__( self._synthesize_response = synthesize_response self._verbose = verbose self._streaming = streaming - super().__init__( - callback_manager=callback_manager - or callback_manager_from_settings_or_context(Settings, service_context), - ) + super().__init__(callback_manager=callback_manager or Settings.callback_manager) def _get_prompts(self) -> Dict[str, Any]: """Get prompts.""" @@ -399,11 +374,6 @@ def _get_prompt_modules(self) -> PromptMixinType: def sql_retriever(self) -> NLSQLRetriever: """Get SQL retriever.""" - @property - def service_context(self) -> Optional[ServiceContext]: - """Get service context.""" - return self._service_context - def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE: """Answer a query.""" retrieved_nodes, metadata = self.sql_retriever.retrieve_with_metadata( @@ -490,7 +460,6 @@ def __init__( response_synthesis_prompt: Optional[BasePromptTemplate] = None, refine_synthesis_prompt: Optional[BasePromptTemplate] = None, tables: Optional[Union[List[str], List[Table]]] = None, - service_context: Optional[ServiceContext] = None, context_str_prefix: Optional[str] = None, embed_model: Optional[BaseEmbedding] = None, sql_only: bool = False, @@ -507,7 +476,6 @@ def __init__( context_query_kwargs=context_query_kwargs, tables=tables, context_str_prefix=context_str_prefix, - service_context=service_context, embed_model=embed_model, sql_only=sql_only, callback_manager=callback_manager, @@ -518,7 +486,6 @@ def __init__( response_synthesis_prompt=response_synthesis_prompt, refine_synthesis_prompt=refine_synthesis_prompt, llm=llm, - service_context=service_context, callback_manager=callback_manager, verbose=verbose, **kwargs, @@ -555,7 +522,6 @@ def __init__( response_synthesis_prompt: Optional[BasePromptTemplate] = None, refine_synthesis_prompt: Optional[BasePromptTemplate] = None, tables: Optional[Union[List[str], List[Table]]] = None, - service_context: Optional[ServiceContext] = None, context_str_prefix: Optional[str] = None, sql_only: bool = False, callback_manager: Optional[CallbackManager] = None, @@ -571,7 +537,6 @@ def __init__( tables=tables, sql_parser_mode=SQLParserMode.PGVECTOR, context_str_prefix=context_str_prefix, - service_context=service_context, sql_only=sql_only, callback_manager=callback_manager, ) @@ -580,7 +545,6 @@ def __init__( response_synthesis_prompt=response_synthesis_prompt, refine_synthesis_prompt=refine_synthesis_prompt, llm=llm, - service_context=service_context, callback_manager=callback_manager, **kwargs, ) @@ -604,7 +568,6 @@ def __init__( synthesize_response: bool = True, response_synthesis_prompt: Optional[BasePromptTemplate] = None, refine_synthesis_prompt: Optional[BasePromptTemplate] = None, - service_context: Optional[ServiceContext] = None, context_str_prefix: Optional[str] = None, sql_only: bool = False, callback_manager: Optional[CallbackManager] = None, @@ -618,7 +581,6 @@ def __init__( context_query_kwargs=context_query_kwargs, table_retriever=table_retriever, context_str_prefix=context_str_prefix, - service_context=service_context, sql_only=sql_only, callback_manager=callback_manager, ) @@ -627,7 +589,6 @@ def __init__( response_synthesis_prompt=response_synthesis_prompt, refine_synthesis_prompt=refine_synthesis_prompt, llm=llm, - service_context=service_context, callback_manager=callback_manager, **kwargs, ) diff --git a/llama-index-core/llama_index/core/indices/struct_store/sql_retriever.py b/llama-index-core/llama_index/core/indices/struct_store/sql_retriever.py index 33dab4e233355..45c3d2e1c3514 100644 --- a/llama-index-core/llama_index/core/indices/struct_store/sql_retriever.py +++ b/llama-index-core/llama_index/core/indices/struct_store/sql_retriever.py @@ -22,13 +22,7 @@ PromptMixinType, ) from llama_index.core.schema import NodeWithScore, QueryBundle, QueryType, TextNode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - embed_model_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.utilities.sql_wrapper import SQLDatabase from sqlalchemy import Table @@ -149,7 +143,7 @@ def parse_response_to_sql(self, response: str, query_bundle: QueryBundle) -> str sql_result_start = response.find("SQLResult:") if sql_result_start != -1: response = response[:sql_result_start] - return response.strip().strip("```").strip() + return response.strip().strip("```sql").strip("```").strip() class PGVectorSQLParser(BaseSQLParser): @@ -175,7 +169,7 @@ def parse_response_to_sql(self, response: str, query_bundle: QueryBundle) -> str response = response[:sql_result_start] # this gets you the sql string with [query_vector] placeholders - raw_sql_str = response.strip().strip("```").strip() + raw_sql_str = response.strip().strip("```sql").strip("```").strip() query_embedding = self._embed_model.get_query_embedding(query_bundle.query_str) query_embedding_str = str(query_embedding) return raw_sql_str.replace("[query_vector]", query_embedding_str) @@ -196,7 +190,6 @@ class NLSQLRetriever(BaseRetriever, PromptMixin): table_retriever (ObjectRetriever[SQLTableSchema]): Object retriever for SQLTableSchema objects. Defaults to None. context_str_prefix (str): Prefix for context string. Defaults to None. - service_context (ServiceContext): Service context. Defaults to None. return_raw (bool): Whether to return plain-text dump of SQL results, or parsed into Nodes. handle_sql_errors (bool): Whether to handle SQL errors. Defaults to True. sql_only (bool) : Whether to get only sql and not the sql query result. @@ -216,7 +209,6 @@ def __init__( sql_parser_mode: SQLParserMode = SQLParserMode.DEFAULT, llm: Optional[LLM] = None, embed_model: Optional[BaseEmbedding] = None, - service_context: Optional[ServiceContext] = None, return_raw: bool = True, handle_sql_errors: bool = True, sql_only: bool = False, @@ -231,21 +223,16 @@ def __init__( sql_database, tables, context_query_kwargs, table_retriever ) self._context_str_prefix = context_str_prefix - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._text_to_sql_prompt = text_to_sql_prompt or DEFAULT_TEXT_TO_SQL_PROMPT self._sql_parser_mode = sql_parser_mode - embed_model = embed_model or embed_model_from_settings_or_context( - Settings, service_context - ) + embed_model = embed_model or Settings.embed_model self._sql_parser = self._load_sql_parser(sql_parser_mode, embed_model) self._handle_sql_errors = handle_sql_errors self._sql_only = sql_only self._verbose = verbose - super().__init__( - callback_manager=callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) + super().__init__(callback_manager=callback_manager or Settings.callback_manager) def _get_prompts(self) -> Dict[str, Any]: """Get prompts.""" diff --git a/llama-index-core/llama_index/core/indices/tree/base.py b/llama-index-core/llama_index/core/indices/tree/base.py index d97d0b71866bd..fa5025d4d2e24 100644 --- a/llama-index-core/llama_index/core/indices/tree/base.py +++ b/llama-index-core/llama_index/core/indices/tree/base.py @@ -18,12 +18,7 @@ DEFAULT_SUMMARY_PROMPT, ) from llama_index.core.schema import BaseNode, IndexNode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - embed_model_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.storage.docstore.types import RefDocInfo @@ -77,8 +72,6 @@ def __init__( build_tree: bool = True, use_async: bool = False, show_progress: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" @@ -88,11 +81,10 @@ def __init__( self.insert_prompt: BasePromptTemplate = insert_prompt or DEFAULT_INSERT_PROMPT self.build_tree = build_tree self._use_async = use_async - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm super().__init__( nodes=nodes, index_struct=index_struct, - service_context=service_context, show_progress=show_progress, objects=objects, **kwargs, @@ -123,9 +115,7 @@ def as_retriever( if retriever_mode == TreeRetrieverMode.SELECT_LEAF: return TreeSelectLeafRetriever(self, object_map=self._object_map, **kwargs) elif retriever_mode == TreeRetrieverMode.SELECT_LEAF_EMBEDDING: - embed_model = embed_model or embed_model_from_settings_or_context( - Settings, self._service_context - ) + embed_model = embed_model or Settings.embed_model return TreeSelectLeafEmbeddingRetriever( self, embed_model=embed_model, object_map=self._object_map, **kwargs ) @@ -149,7 +139,6 @@ def _build_index_from_nodes(self, nodes: Sequence[BaseNode]) -> IndexGraph: index_builder = GPTTreeIndexBuilder( self.num_children, self.summary_template, - service_context=self.service_context, llm=self._llm, use_async=self._use_async, show_progress=self._show_progress, @@ -162,7 +151,6 @@ def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None: # TODO: allow to customize insert prompt inserter = TreeIndexInserter( self.index_struct, - service_context=self.service_context, llm=self._llm, num_children=self.num_children, insert_prompt=self.insert_prompt, diff --git a/llama-index-core/llama_index/core/indices/tree/inserter.py b/llama-index-core/llama_index/core/indices/tree/inserter.py index 0bb0b54cd492f..a147965f24a6d 100644 --- a/llama-index-core/llama_index/core/indices/tree/inserter.py +++ b/llama-index-core/llama_index/core/indices/tree/inserter.py @@ -16,11 +16,7 @@ DEFAULT_SUMMARY_PROMPT, ) from llama_index.core.schema import BaseNode, MetadataMode, TextNode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.storage.docstore import BaseDocumentStore from llama_index.core.storage.docstore.registry import get_default_docstore @@ -31,7 +27,6 @@ class TreeIndexInserter: def __init__( self, index_graph: IndexGraph, - service_context: Optional[ServiceContext] = None, llm: Optional[LLM] = None, num_children: int = 10, insert_prompt: BasePromptTemplate = DEFAULT_INSERT_PROMPT, @@ -45,7 +40,7 @@ def __init__( self.summary_prompt = summary_prompt self.insert_prompt = insert_prompt self.index_graph = index_graph - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._prompt_helper = Settings._prompt_helper or PromptHelper.from_llm_metadata( self._llm.metadata, ) diff --git a/llama-index-core/llama_index/core/indices/tree/select_leaf_embedding_retriever.py b/llama-index-core/llama_index/core/indices/tree/select_leaf_embedding_retriever.py index eb90d28478304..67669e2197b4f 100644 --- a/llama-index-core/llama_index/core/indices/tree/select_leaf_embedding_retriever.py +++ b/llama-index-core/llama_index/core/indices/tree/select_leaf_embedding_retriever.py @@ -12,7 +12,7 @@ from llama_index.core.indices.utils import get_sorted_node_list from llama_index.core.prompts import BasePromptTemplate from llama_index.core.schema import BaseNode, MetadataMode, QueryBundle -from llama_index.core.settings import Settings, embed_model_from_settings_or_context +from llama_index.core.settings import Settings logger = logging.getLogger(__name__) @@ -68,9 +68,7 @@ def __init__( object_map=object_map, **kwargs, ) - self._embed_model = embed_model or embed_model_from_settings_or_context( - Settings, index.service_context - ) + self._embed_model = embed_model or Settings.embed_model def _query_level( self, @@ -93,7 +91,7 @@ def _query_level( result_response = None for node, index in zip(selected_nodes, selected_indices): logger.debug( - f">[Level {level}] Node [{index+1}] Summary text: " + f">[Level {level}] Node [{index + 1}] Summary text: " f"{' '.join(node.get_content().splitlines())}" ) diff --git a/llama-index-core/llama_index/core/indices/tree/select_leaf_retriever.py b/llama-index-core/llama_index/core/indices/tree/select_leaf_retriever.py index b8dc73d14e972..1aa59c01d1f89 100644 --- a/llama-index-core/llama_index/core/indices/tree/select_leaf_retriever.py +++ b/llama-index-core/llama_index/core/indices/tree/select_leaf_retriever.py @@ -30,10 +30,7 @@ NodeWithScore, QueryBundle, ) -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.utils import print_text, truncate_text logger = logging.getLogger(__name__) @@ -92,7 +89,6 @@ def __init__( self._llm = index._llm self._index_struct = index.index_struct self._docstore = index.docstore - self._service_context = index.service_context self._prompt_helper = Settings._prompt_helper or PromptHelper.from_llm_metadata( self._llm.metadata, ) @@ -105,10 +101,7 @@ def __init__( ) self.child_branch_factor = child_branch_factor super().__init__( - callback_manager=callback_manager - or callback_manager_from_settings_or_context( - Settings, index.service_context - ), + callback_manager=callback_manager or Settings.callback_manager, object_map=object_map, verbose=verbose, ) @@ -131,7 +124,6 @@ def _query_with_selected_node( if len(self._index_struct.get_children(selected_node)) == 0: response_builder = get_response_synthesizer( llm=self._llm, - service_context=self._service_context, text_qa_template=self._text_qa_template, refine_template=self._refine_template, callback_manager=self.callback_manager, @@ -260,7 +252,7 @@ def _query_level( full_debug_str = ( f">[Level {level}] Node " f"[{number}] Summary text: " - f"{ selected_node.get_content(metadata_mode=MetadataMode.LLM) }" + f"{selected_node.get_content(metadata_mode=MetadataMode.LLM)}" ) logger.debug(full_debug_str) if self._verbose: @@ -375,7 +367,7 @@ def _select_nodes( full_debug_str = ( f">[Level {level}] Node " f"[{number}] Summary text: " - f"{ selected_node.get_content(metadata_mode=MetadataMode.LLM) }" + f"{selected_node.get_content(metadata_mode=MetadataMode.LLM)}" ) logger.debug(full_debug_str) if self._verbose: diff --git a/llama-index-core/llama_index/core/indices/vector_store/base.py b/llama-index-core/llama_index/core/indices/vector_store/base.py index 4810e9e6dd63c..8accc27fe4919 100644 --- a/llama-index-core/llama_index/core/indices/vector_store/base.py +++ b/llama-index-core/llama_index/core/indices/vector_store/base.py @@ -23,8 +23,7 @@ MetadataMode, TransformComponent, ) -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, embed_model_from_settings_or_context +from llama_index.core.settings import Settings from llama_index.core.storage.docstore.types import RefDocInfo from llama_index.core.storage.storage_context import StorageContext from llama_index.core.utils import iter_batch @@ -61,8 +60,6 @@ def __init__( callback_manager: Optional[CallbackManager] = None, transformations: Optional[List[TransformComponent]] = None, show_progress: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" @@ -71,14 +68,13 @@ def __init__( self._embed_model = ( resolve_embed_model(embed_model, callback_manager=callback_manager) if embed_model - else embed_model_from_settings_or_context(Settings, service_context) + else Settings.embed_model ) self._insert_batch_size = insert_batch_size super().__init__( nodes=nodes, index_struct=index_struct, - service_context=service_context, storage_context=storage_context, show_progress=show_progress, objects=objects, @@ -92,8 +88,6 @@ def from_vector_store( cls, vector_store: BasePydanticVectorStore, embed_model: Optional[EmbedType] = None, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> "VectorStoreIndex": if not vector_store.stores_text: @@ -107,7 +101,6 @@ def from_vector_store( return cls( nodes=[], embed_model=embed_model, - service_context=service_context, storage_context=storage_context, **kwargs, ) @@ -149,7 +142,7 @@ def _get_node_with_embedding( results = [] for node in nodes: embedding = id_to_embed_map[node.node_id] - result = node.copy() + result = node.model_copy() result.embedding = embedding results.append(result) return results @@ -175,7 +168,7 @@ async def _aget_node_with_embedding( results = [] for node in nodes: embedding = id_to_embed_map[node.node_id] - result = node.copy() + result = node.model_copy() result.embedding = embedding results.append(result) return results @@ -202,7 +195,7 @@ async def _async_add_nodes_to_index( if not self._vector_store.stores_text or self._store_nodes_override: for node, new_id in zip(nodes_batch, new_ids): # NOTE: remove embedding from node to avoid duplication - node_without_embedding = node.copy() + node_without_embedding = node.model_copy() node_without_embedding.embedding = None index_struct.add_node(node_without_embedding, text_id=new_id) @@ -215,7 +208,7 @@ async def _async_add_nodes_to_index( for node, new_id in zip(nodes_batch, new_ids): if isinstance(node, (ImageNode, IndexNode)): # NOTE: remove embedding from node to avoid duplication - node_without_embedding = node.copy() + node_without_embedding = node.model_copy() node_without_embedding.embedding = None index_struct.add_node(node_without_embedding, text_id=new_id) @@ -243,7 +236,7 @@ def _add_nodes_to_index( # we need to add the nodes to the index struct and document store for node, new_id in zip(nodes_batch, new_ids): # NOTE: remove embedding from node to avoid duplication - node_without_embedding = node.copy() + node_without_embedding = node.model_copy() node_without_embedding.embedding = None index_struct.add_node(node_without_embedding, text_id=new_id) @@ -256,7 +249,7 @@ def _add_nodes_to_index( for node, new_id in zip(nodes_batch, new_ids): if isinstance(node, (ImageNode, IndexNode)): # NOTE: remove embedding from node to avoid duplication - node_without_embedding = node.copy() + node_without_embedding = node.model_copy() node_without_embedding.embedding = None index_struct.add_node(node_without_embedding, text_id=new_id) diff --git a/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/auto_retriever.py b/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/auto_retriever.py index 269ba5349b7d9..7af51bd93e110 100644 --- a/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/auto_retriever.py +++ b/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/auto_retriever.py @@ -22,12 +22,7 @@ from llama_index.core.prompts.base import PromptTemplate from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.schema import IndexNode, QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.vector_stores.types import ( FilterCondition, MetadataFilters, @@ -54,8 +49,6 @@ class VectorIndexAutoRetriever(BaseAutoRetriever): parameters. prompt_template_str: custom prompt template string for LLM. Uses default template string if None. - service_context: service context containing reference to an LLM. - Uses service context from index be default if None. similarity_top_k (int): number of top k results to return. empty_query_top_k (Optional[int]): number of top k results to return if the inferred query string is blank (uses metadata filters only). @@ -89,20 +82,13 @@ def __init__( extra_filters: Optional[MetadataFilters] = None, object_map: Optional[dict] = None, objects: Optional[List[IndexNode]] = None, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: self._index = index self._vector_store_info = vector_store_info self._default_empty_query_vector = default_empty_query_vector - - service_context = service_context or self._index.service_context - self._llm = llm or llm_from_settings_or_context(Settings, service_context) - callback_manager = ( - callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) + self._llm = llm or Settings.llm + callback_manager = callback_manager or Settings.callback_manager # prompt prompt_template_str = ( @@ -172,8 +158,8 @@ def generate_retrieval_spec( self, query_bundle: QueryBundle, **kwargs: Any ) -> BaseModel: # prepare input - info_str = self._vector_store_info.json(indent=4) - schema_str = VectorStoreQuerySpec.schema_json(indent=4) + info_str = self._vector_store_info.model_dump_json(indent=4) + schema_str = VectorStoreQuerySpec.model_json_schema() # call LLM output = self._llm.predict( @@ -190,8 +176,8 @@ async def agenerate_retrieval_spec( self, query_bundle: QueryBundle, **kwargs: Any ) -> BaseModel: # prepare input - info_str = self._vector_store_info.json(indent=4) - schema_str = VectorStoreQuerySpec.schema_json(indent=4) + info_str = self._vector_store_info.model_dump_json(indent=4) + schema_str = VectorStoreQuerySpec.model_json_schema() # call LLM output = await self._llm.apredict( diff --git a/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/output_parser.py b/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/output_parser.py index 0f1640570cb63..3198089074d9b 100644 --- a/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/output_parser.py +++ b/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/output_parser.py @@ -9,7 +9,7 @@ class VectorStoreQueryOutputParser(BaseOutputParser): def parse(self, output: str) -> Any: json_dict = parse_json_markdown(output) - query_and_filters = VectorStoreQuerySpec.parse_obj(json_dict) + query_and_filters = VectorStoreQuerySpec.model_validate(json_dict) return StructuredOutput(raw_output=output, parsed_output=query_and_filters) diff --git a/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/prompts.py b/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/prompts.py index e195b43649ddc..063071c3b3e6b 100644 --- a/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/prompts.py +++ b/llama-index-core/llama_index/core/indices/vector_store/retrievers/auto_retriever/prompts.py @@ -102,7 +102,7 @@ << Example 1. >> Data Source: ```json -{example_info.json(indent=4)} +{example_info.model_dump_json(indent=4)} ``` User Query: @@ -110,13 +110,13 @@ Structured Request: ```json -{example_output.json()} +{example_output.model_dump_json()} << Example 2. >> Data Source: ```json -{example_info_2.json(indent=4)} +{example_info_2.model_dump_json(indent=4)} ``` User Query: @@ -124,7 +124,7 @@ Structured Request: ```json -{example_output_2.json()} +{example_output_2.model_dump_json()} ``` """.replace( diff --git a/llama-index-core/llama_index/core/ingestion/cache.py b/llama-index-core/llama_index/core/ingestion/cache.py index 1ec754ef023bf..3ba238c8d0528 100644 --- a/llama-index-core/llama_index/core/ingestion/cache.py +++ b/llama-index-core/llama_index/core/ingestion/cache.py @@ -1,7 +1,7 @@ from typing import List, Optional import fsspec -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from llama_index.core.schema import BaseNode from llama_index.core.storage.docstore.utils import doc_to_json, json_to_doc from llama_index.core.storage.kvstore import ( @@ -15,10 +15,8 @@ class IngestionCache(BaseModel): - class Config: - arbitrary_types_allowed = True - - nodes_key = "nodes" + model_config = ConfigDict(arbitrary_types_allowed=True) + nodes_key: str = "nodes" collection: str = Field( default=DEFAULT_CACHE_NAME, description="Collection name of the cache." diff --git a/llama-index-core/llama_index/core/ingestion/data_sinks.py b/llama-index-core/llama_index/core/ingestion/data_sinks.py index 814d6038b48c0..50dd9bb757325 100644 --- a/llama-index-core/llama_index/core/ingestion/data_sinks.py +++ b/llama-index-core/llama_index/core/ingestion/data_sinks.py @@ -4,7 +4,6 @@ from llama_index.core.bridge.pydantic import ( BaseModel, Field, - GenericModel, ValidationError, ) from llama_index.core.vector_stores.types import BasePydanticVectorStore @@ -151,7 +150,7 @@ def build_configured_data_sink( T = TypeVar("T", bound=BasePydanticVectorStore) -class ConfiguredDataSink(GenericModel, Generic[T]): +class ConfiguredDataSink(BaseModel, Generic[T]): """ A class containing metadata & implementation for a data sink in a pipeline. """ diff --git a/llama-index-core/llama_index/core/ingestion/data_sources.py b/llama-index-core/llama_index/core/ingestion/data_sources.py index 2f6f216546e05..76e1283a617c3 100644 --- a/llama-index-core/llama_index/core/ingestion/data_sources.py +++ b/llama-index-core/llama_index/core/ingestion/data_sources.py @@ -6,7 +6,6 @@ from llama_index.core.bridge.pydantic import ( BaseModel, Field, - GenericModel, ValidationError, ) from llama_index.core.readers.base import BasePydanticReader, ReaderConfig @@ -443,7 +442,7 @@ def build_configured_data_source( T = TypeVar("T", bound=BaseComponent) -class ConfiguredDataSource(GenericModel, Generic[T]): +class ConfiguredDataSource(BaseModel, Generic[T]): """ A class containing metadata & implementation for a data source in a pipeline. """ diff --git a/llama-index-core/llama_index/core/ingestion/pipeline.py b/llama-index-core/llama_index/core/ingestion/pipeline.py index 9e28fbac74b3c..c1dbe5dc2e42c 100644 --- a/llama-index-core/llama_index/core/ingestion/pipeline.py +++ b/llama-index-core/llama_index/core/ingestion/pipeline.py @@ -17,7 +17,7 @@ DEFAULT_PIPELINE_NAME, DEFAULT_PROJECT_NAME, ) -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from llama_index.core.ingestion.cache import DEFAULT_CACHE_NAME, IngestionCache from llama_index.core.instrumentation import get_dispatcher from llama_index.core.node_parser import SentenceSplitter @@ -234,6 +234,7 @@ class IngestionPipeline(BaseModel): ``` """ + model_config = ConfigDict(arbitrary_types_allowed=True) name: str = Field( default=DEFAULT_PIPELINE_NAME, description="Unique name of the ingestion pipeline", @@ -266,9 +267,6 @@ class IngestionPipeline(BaseModel): ) disable_cache: bool = Field(default=False, description="Disable the cache") - class Config: - arbitrary_types_allowed = True - def __init__( self, name: str = DEFAULT_PIPELINE_NAME, diff --git a/llama-index-core/llama_index/core/ingestion/transformations.py b/llama-index-core/llama_index/core/ingestion/transformations.py index 49e62df3715d1..92da0d27da36a 100644 --- a/llama-index-core/llama_index/core/ingestion/transformations.py +++ b/llama-index-core/llama_index/core/ingestion/transformations.py @@ -8,8 +8,8 @@ from llama_index.core.bridge.pydantic import ( BaseModel, Field, - GenericModel, ValidationError, + SerializeAsAny, ) from llama_index.core.node_parser import ( CodeSplitter, @@ -346,13 +346,15 @@ def build_configured_transformation( T = TypeVar("T", bound=BaseComponent) -class ConfiguredTransformation(GenericModel, Generic[T]): +class ConfiguredTransformation(BaseModel, Generic[T]): """ A class containing metadata & implementation for a transformation in a pipeline. """ name: str - component: T = Field(description="Component that implements the transformation") + component: SerializeAsAny[T] = Field( + description="Component that implements the transformation" + ) @classmethod def from_component(cls, component: BaseComponent) -> "ConfiguredTransformation": diff --git a/llama-index-core/llama_index/core/instrumentation/dispatcher.py b/llama-index-core/llama_index/core/instrumentation/dispatcher.py index 52e1691bc1ba7..caf8477830060 100644 --- a/llama-index-core/llama_index/core/instrumentation/dispatcher.py +++ b/llama-index-core/llama_index/core/instrumentation/dispatcher.py @@ -4,7 +4,7 @@ import inspect import uuid from deprecated import deprecated -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from llama_index.core.instrumentation.event_handlers import BaseEventHandler from llama_index.core.instrumentation.span import active_span_id from llama_index.core.instrumentation.span_handlers import ( @@ -52,6 +52,7 @@ class Dispatcher(BaseModel): hierarchy. """ + model_config = ConfigDict(arbitrary_types_allowed=True) name: str = Field(default_factory=str, description="Name of dispatcher") event_handlers: List[BaseEventHandler] = Field( default=[], description="List of attached handlers" @@ -314,9 +315,6 @@ def log_name(self) -> str: else: return self.name - class Config: - arbitrary_types_allowed = True - class Manager: def __init__(self, root: Dispatcher) -> None: @@ -329,4 +327,4 @@ def add_dispatcher(self, d: Dispatcher) -> None: self.dispatchers[d.name] = d -Dispatcher.update_forward_refs() +Dispatcher.model_rebuild() diff --git a/llama-index-core/llama_index/core/instrumentation/event_handlers/base.py b/llama-index-core/llama_index/core/instrumentation/event_handlers/base.py index 9f33f5b3dc841..8377ff24fd896 100644 --- a/llama-index-core/llama_index/core/instrumentation/event_handlers/base.py +++ b/llama-index-core/llama_index/core/instrumentation/event_handlers/base.py @@ -1,12 +1,14 @@ from typing import Any from abc import abstractmethod -from llama_index.core.bridge.pydantic import BaseModel +from llama_index.core.bridge.pydantic import BaseModel, ConfigDict from llama_index.core.instrumentation.events.base import BaseEvent class BaseEventHandler(BaseModel): """Base callback handler that can be used to track event starts and ends.""" + model_config = ConfigDict(arbitrary_types_allowed=True) + @classmethod def class_name(cls) -> str: """Class name.""" @@ -15,6 +17,3 @@ def class_name(cls) -> str: @abstractmethod def handle(self, event: BaseEvent, **kwargs) -> Any: """Logic for handling event.""" - - class Config: - arbitrary_types_allowed = True diff --git a/llama-index-core/llama_index/core/instrumentation/events/agent.py b/llama-index-core/llama_index/core/instrumentation/events/agent.py index 6fd0337c4c6a0..73e1c30a608c8 100644 --- a/llama-index-core/llama_index/core/instrumentation/events/agent.py +++ b/llama-index-core/llama_index/core/instrumentation/events/agent.py @@ -1,7 +1,7 @@ from typing import Any, Optional from llama_index.core.base.agent.types import TaskStepOutput, TaskStep -from llama_index.core.bridge.pydantic import root_validator, validator +from llama_index.core.bridge.pydantic import model_validator, field_validator from llama_index.core.instrumentation.events.base import BaseEvent from llama_index.core.chat_engine.types import ( AGENT_CHAT_RESPONSE_TYPE, @@ -69,7 +69,8 @@ class AgentChatWithStepEndEvent(BaseEvent): response: Optional[AGENT_CHAT_RESPONSE_TYPE] - @root_validator(pre=True) + @model_validator(mode="before") + @classmethod def validate_response(cls: Any, values: Any) -> Any: """Validate response.""" response = values.get("response") @@ -84,7 +85,8 @@ def validate_response(cls: Any, values: Any) -> Any: return values - @validator("response", pre=True) + @field_validator("response", mode="before") + @classmethod def validate_response_type(cls: Any, response: Any) -> Any: """Validate response type.""" if response is None: diff --git a/llama-index-core/llama_index/core/instrumentation/events/base.py b/llama-index-core/llama_index/core/instrumentation/events/base.py index 45acac4c0e1db..add0bac2a1aa3 100644 --- a/llama-index-core/llama_index/core/instrumentation/events/base.py +++ b/llama-index-core/llama_index/core/instrumentation/events/base.py @@ -1,5 +1,5 @@ from typing import Any, Dict, Optional -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from uuid import uuid4 from datetime import datetime @@ -7,6 +7,10 @@ class BaseEvent(BaseModel): + model_config = ConfigDict( + arbitrary_types_allowed=True, + # copy_on_model_validation = "deep" # not supported in Pydantic V2... + ) timestamp: datetime = Field(default_factory=lambda: datetime.now()) id_: str = Field(default_factory=lambda: uuid4()) span_id: Optional[str] = Field(default_factory=active_span_id.get) @@ -17,11 +21,11 @@ def class_name(cls): """Return class name.""" return "BaseEvent" - class Config: - arbitrary_types_allowed = True - copy_on_model_validation = "deep" - def dict(self, **kwargs: Any) -> Dict[str, Any]: - data = super().dict(**kwargs) + """Keep for backwards compatibility.""" + return self.model_dump(**kwargs) + + def model_dump(self, **kwargs: Any) -> Dict[str, Any]: + data = super().model_dump(**kwargs) data["class_name"] = self.class_name() return data diff --git a/llama-index-core/llama_index/core/instrumentation/events/embedding.py b/llama-index-core/llama_index/core/instrumentation/events/embedding.py index 452c3a95f05b3..84429a87765a3 100644 --- a/llama-index-core/llama_index/core/instrumentation/events/embedding.py +++ b/llama-index-core/llama_index/core/instrumentation/events/embedding.py @@ -1,6 +1,7 @@ from typing import List from llama_index.core.instrumentation.events.base import BaseEvent +from llama_index.core.bridge.pydantic import ConfigDict class EmbeddingStartEvent(BaseEvent): @@ -10,6 +11,7 @@ class EmbeddingStartEvent(BaseEvent): model_dict (dict): Model dictionary containing details about the embedding model. """ + model_config = ConfigDict(protected_namespaces=("pydantic_model_",)) model_dict: dict @classmethod diff --git a/llama-index-core/llama_index/core/instrumentation/events/llm.py b/llama-index-core/llama_index/core/instrumentation/events/llm.py index 94955b0ebee8a..9c7749f79d805 100644 --- a/llama-index-core/llama_index/core/instrumentation/events/llm.py +++ b/llama-index-core/llama_index/core/instrumentation/events/llm.py @@ -1,5 +1,5 @@ from typing import Any, List, Optional -from llama_index.core.bridge.pydantic import BaseModel +from llama_index.core.bridge.pydantic import BaseModel, SerializeAsAny, ConfigDict from llama_index.core.base.llms.types import ( ChatMessage, ChatResponse, @@ -17,7 +17,7 @@ class LLMPredictStartEvent(BaseEvent): template_args (Optional[dict]): Prompt template arguments. """ - template: BasePromptTemplate + template: SerializeAsAny[BasePromptTemplate] template_args: Optional[dict] @classmethod @@ -53,7 +53,7 @@ class LLMStructuredPredictStartEvent(BaseEvent): """ output_cls: Any - template: BasePromptTemplate + template: SerializeAsAny[BasePromptTemplate] template_args: Optional[dict] @classmethod @@ -69,7 +69,7 @@ class LLMStructuredPredictEndEvent(BaseEvent): output (BaseModel): Predicted output class. """ - output: BaseModel + output: SerializeAsAny[BaseModel] @classmethod def class_name(cls): @@ -84,7 +84,7 @@ class LLMStructuredPredictInProgressEvent(BaseEvent): output (BaseModel): Predicted output class. """ - output: BaseModel + output: SerializeAsAny[BaseModel] @classmethod def class_name(cls): @@ -101,6 +101,7 @@ class LLMCompletionStartEvent(BaseEvent): model_dict (dict): Model dictionary. """ + model_config = ConfigDict(protected_namespaces=("pydantic_model_",)) prompt: str additional_kwargs: dict model_dict: dict @@ -154,6 +155,7 @@ class LLMChatStartEvent(BaseEvent): model_dict (dict): Model dictionary. """ + model_config = ConfigDict(protected_namespaces=("pydantic_model_",)) messages: List[ChatMessage] additional_kwargs: dict model_dict: dict diff --git a/llama-index-core/llama_index/core/instrumentation/events/rerank.py b/llama-index-core/llama_index/core/instrumentation/events/rerank.py index 326bea5a8f708..91cf491b6baa1 100644 --- a/llama-index-core/llama_index/core/instrumentation/events/rerank.py +++ b/llama-index-core/llama_index/core/instrumentation/events/rerank.py @@ -2,6 +2,7 @@ from llama_index.core.instrumentation.events.base import BaseEvent from llama_index.core.schema import NodeWithScore, QueryType +from llama_index.core.bridge.pydantic import ConfigDict class ReRankStartEvent(BaseEvent): @@ -14,6 +15,7 @@ class ReRankStartEvent(BaseEvent): model_name (str): Name of the model used for reranking. """ + model_config = ConfigDict(protected_namespaces=("pydantic_model_",)) query: Optional[QueryType] nodes: List[NodeWithScore] top_n: int diff --git a/llama-index-core/llama_index/core/instrumentation/span/base.py b/llama-index-core/llama_index/core/instrumentation/span/base.py index 5cd1f65c540ca..27d2ecf5e118c 100644 --- a/llama-index-core/llama_index/core/instrumentation/span/base.py +++ b/llama-index-core/llama_index/core/instrumentation/span/base.py @@ -1,13 +1,11 @@ from typing import Any, Dict, Optional -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict class BaseSpan(BaseModel): """Base data class representing a span.""" + model_config = ConfigDict(arbitrary_types_allowed=True) id_: str = Field(default_factory=str, description="Id of span.") parent_id: Optional[str] = Field(default=None, description="Id of parent span.") tags: Dict[str, Any] = Field(default={}) - - class Config: - arbitrary_types_allowed = True diff --git a/llama-index-core/llama_index/core/instrumentation/span_handlers/base.py b/llama-index-core/llama_index/core/instrumentation/span_handlers/base.py index 8b3bf6908336a..679beed2f6c4f 100644 --- a/llama-index-core/llama_index/core/instrumentation/span_handlers/base.py +++ b/llama-index-core/llama_index/core/instrumentation/span_handlers/base.py @@ -3,13 +3,14 @@ from abc import abstractmethod from typing import Any, Dict, List, Generic, Optional, TypeVar -from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr +from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr, ConfigDict from llama_index.core.instrumentation.span.base import BaseSpan T = TypeVar("T", bound=BaseSpan) class BaseSpanHandler(BaseModel, Generic[T]): + model_config = ConfigDict(arbitrary_types_allowed=True) open_spans: Dict[str, T] = Field( default_factory=dict, description="Dictionary of open spans." ) @@ -24,9 +25,6 @@ class BaseSpanHandler(BaseModel, Generic[T]): ) _lock: Optional[threading.Lock] = PrivateAttr() - class Config: - arbitrary_types_allowed = True - def __init__( self, open_spans: Dict[str, T] = {}, @@ -34,13 +32,13 @@ def __init__( dropped_spans: List[T] = [], current_span_ids: Dict[Any, str] = {}, ): - self._lock = None super().__init__( open_spans=open_spans, completed_spans=completed_spans, dropped_spans=dropped_spans, current_span_ids=current_span_ids, ) + self._lock = None def class_name(cls) -> str: """Class name.""" diff --git a/llama-index-core/llama_index/core/langchain_helpers/agents/toolkits.py b/llama-index-core/llama_index/core/langchain_helpers/agents/toolkits.py index e9333179c02a5..85317fcc78c93 100644 --- a/llama-index-core/llama_index/core/langchain_helpers/agents/toolkits.py +++ b/llama-index-core/llama_index/core/langchain_helpers/agents/toolkits.py @@ -13,13 +13,9 @@ class LlamaToolkit(BaseToolkit): """Toolkit for interacting with Llama indices.""" + model_config = ConfigDict(arbitrary_types_allowed=True) index_configs: List[IndexToolConfig] = Field(default_factory=list) - class Config: - """Configuration for this pydantic object.""" - - arbitrary_types_allowed = True - def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" index_tools: List[BaseTool] = [ diff --git a/llama-index-core/llama_index/core/langchain_helpers/agents/tools.py b/llama-index-core/llama_index/core/langchain_helpers/agents/tools.py index 3dd76210025bc..c0a4fa60be38c 100644 --- a/llama-index-core/llama_index/core/langchain_helpers/agents/tools.py +++ b/llama-index-core/llama_index/core/langchain_helpers/agents/tools.py @@ -29,16 +29,12 @@ def _get_response_with_sources(response: RESPONSE_TYPE) -> str: class IndexToolConfig(BaseModel): """Configuration for LlamaIndex index tool.""" + model_config = ConfigDict(arbitrary_types_allowed=True) query_engine: BaseQueryEngine name: str description: str tool_kwargs: Dict = Field(default_factory=dict) - class Config: - """Configuration for this pydantic object.""" - - arbitrary_types_allowed = True - class LlamaIndexTool(BaseTool): """Tool for querying a LlamaIndex.""" diff --git a/llama-index-core/llama_index/core/llama_dataset/base.py b/llama-index-core/llama_index/core/llama_dataset/base.py index ed3873b6e23a2..2ba08803c5834 100644 --- a/llama-index-core/llama_index/core/llama_dataset/base.py +++ b/llama-index-core/llama_index/core/llama_dataset/base.py @@ -3,16 +3,25 @@ import json from abc import abstractmethod from enum import Enum -from typing import Generator, Generic, List, Optional, Type, TypeVar, Union +from typing import ( + Any, + ClassVar, + Generator, + Generic, + List, + Optional, + Type, + TypeVar, + Union, +) import tqdm from llama_index.core.async_utils import asyncio_module from llama_index.core.base.base_query_engine import BaseQueryEngine from llama_index.core.llms import LLM -from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr +from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr, ConfigDict from llama_index.core.evaluation import BaseEvaluator -from openai import RateLimitError -from pandas import DataFrame as PandasDataFrame + PredictorType = Union[BaseQueryEngine, BaseEvaluator, LLM] P = TypeVar("P", bound=PredictorType) @@ -29,6 +38,7 @@ def __str__(self) -> str: class CreatedBy(BaseModel): + model_config = ConfigDict(protected_namespaces=("pydantic_model_",)) model_name: Optional[str] = Field( default_factory=str, description="When CreatedByType.AI, specify model name." ) @@ -62,7 +72,7 @@ def class_name(self) -> str: class BaseLlamaPredictionDataset(BaseModel): - _prediction_type: Type[BaseLlamaExamplePrediction] = BaseLlamaExamplePrediction # type: ignore[misc] + _prediction_type: ClassVar[Type[BaseLlamaExamplePrediction]] predictions: List[BaseLlamaExamplePrediction] = Field( default=list, description="Predictions on train_examples." ) @@ -75,7 +85,7 @@ def __getitem__(self, val: Union[slice, int]) -> List[BaseLlamaExamplePrediction return self.predictions[val] @abstractmethod - def to_pandas(self) -> PandasDataFrame: + def to_pandas(self) -> Any: """Create pandas dataframe.""" def save_json(self, path: str) -> None: @@ -84,7 +94,7 @@ def save_json(self, path: str) -> None: predictions = None if self.predictions: predictions = [ - self._prediction_type.dict(el) for el in self.predictions + self._prediction_type.model_dump(el) for el in self.predictions ] data = { "predictions": predictions, @@ -98,7 +108,9 @@ def from_json(cls, path: str) -> "BaseLlamaPredictionDataset": with open(path) as f: data = json.load(f) - predictions = [cls._prediction_type.parse_obj(el) for el in data["predictions"]] + predictions = [ + cls._prediction_type.model_validate(el) for el in data["predictions"] + ] return cls( predictions=predictions, @@ -112,7 +124,7 @@ def class_name(self) -> str: class BaseLlamaDataset(BaseModel, Generic[P]): - _example_type: Type[BaseLlamaDataExample] = BaseLlamaDataExample # type: ignore[misc] + _example_type: ClassVar[Type[BaseLlamaDataExample]] examples: List[BaseLlamaDataExample] = Field( default=[], description="Data examples of this dataset." ) @@ -128,13 +140,13 @@ def __getitem__(self, val: Union[slice, int]) -> List[BaseLlamaDataExample]: return self.examples[val] @abstractmethod - def to_pandas(self) -> PandasDataFrame: + def to_pandas(self) -> Any: """Create pandas dataframe.""" def save_json(self, path: str) -> None: """Save json.""" with open(path, "w") as f: - examples = [self._example_type.dict(el) for el in self.examples] + examples = [self._example_type.model_dump(el) for el in self.examples] data = { "examples": examples, } @@ -147,7 +159,7 @@ def from_json(cls, path: str) -> "BaseLlamaDataset": with open(path) as f: data = json.load(f) - examples = [cls._example_type.parse_obj(el) for el in data["examples"]] + examples = [cls._example_type.model_validate(el) for el in data["examples"]] return cls( examples=examples, @@ -296,16 +308,21 @@ async def amake_predictions_with( ) else: batch_predictions = await asyncio_mod.gather(*tasks) - except RateLimitError as err: + except Exception as err: if show_progress: asyncio_mod.close() - raise ValueError( - "You've hit rate limits on your OpenAI subscription. This" - " class caches previous predictions after each successful" - " batch execution. Based off this cache, when executing this" - " command again it will attempt to predict on only the examples " - "that have not yet been predicted. Try reducing your batch_size." - ) from err + + if "RateLimitError" in str(err): + raise ValueError( + "You've hit rate limits on your OpenAI subscription. This" + " class caches previous predictions after each successful" + " batch execution. Based off this cache, when executing this" + " command again it will attempt to predict on only the examples " + "that have not yet been predicted. Try reducing your batch_size." + ) from err + else: + raise err # noqa: TRY201 + self._predictions_cache += batch_predictions # time.sleep(sleep_time_in_seconds) diff --git a/llama-index-core/llama_index/core/llama_dataset/evaluator_evaluation.py b/llama-index-core/llama_index/core/llama_dataset/evaluator_evaluation.py index 7f808740fd976..6ef540efeed9f 100644 --- a/llama-index-core/llama_index/core/llama_dataset/evaluator_evaluation.py +++ b/llama-index-core/llama_index/core/llama_dataset/evaluator_evaluation.py @@ -2,7 +2,7 @@ import asyncio import time -from typing import List, Optional +from typing import Any, List, Optional from llama_index.core.bridge.pydantic import Field from llama_index.core.evaluation import ( @@ -17,7 +17,6 @@ BaseLlamaPredictionDataset, CreatedBy, ) -from pandas import DataFrame as PandasDataFrame class EvaluatorExamplePrediction(BaseLlamaExamplePrediction): @@ -115,8 +114,15 @@ class EvaluatorPredictionDataset(BaseLlamaPredictionDataset): _prediction_type = EvaluatorExamplePrediction - def to_pandas(self) -> PandasDataFrame: + def to_pandas(self) -> Any: """Create pandas dataframe.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + data = {} if self.predictions: data = { @@ -124,7 +130,7 @@ def to_pandas(self) -> PandasDataFrame: "score": [t.score for t in self.predictions], } - return PandasDataFrame(data) + return pd.DataFrame(data) @property def class_name(self) -> str: @@ -137,8 +143,15 @@ class LabelledEvaluatorDataset(BaseLlamaDataset[BaseEvaluator]): _example_type = LabelledEvaluatorDataExample - def to_pandas(self) -> PandasDataFrame: + def to_pandas(self) -> Any: """Create pandas dataframe.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + data = { "query": [t.query for t in self.examples], "answer": [t.answer for t in self.examples], @@ -156,7 +169,7 @@ def to_pandas(self) -> PandasDataFrame: ], } - return PandasDataFrame(data) + return pd.DataFrame(data) async def _apredict_example( self, @@ -273,8 +286,15 @@ class PairwiseEvaluatorPredictionDataset(BaseLlamaPredictionDataset): _prediction_type = PairwiseEvaluatorExamplePrediction - def to_pandas(self) -> PandasDataFrame: + def to_pandas(self) -> Any: """Create pandas dataframe.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + data = {} if self.predictions: data = { @@ -283,7 +303,7 @@ def to_pandas(self) -> PandasDataFrame: "ordering": [t.evaluation_source.value for t in self.predictions], } - return PandasDataFrame(data) + return pd.DataFrame(data) @property def class_name(self) -> str: @@ -318,8 +338,15 @@ class LabelledPairwiseEvaluatorDataset(BaseLlamaDataset[BaseEvaluator]): _example_type = LabelledPairwiseEvaluatorDataExample - def to_pandas(self) -> PandasDataFrame: + def to_pandas(self) -> Any: """Create pandas dataframe.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + data = { "query": [t.query for t in self.examples], "answer": [t.answer for t in self.examples], @@ -339,7 +366,7 @@ def to_pandas(self) -> PandasDataFrame: ], } - return PandasDataFrame(data) + return pd.DataFrame(data) async def _apredict_example( self, diff --git a/llama-index-core/llama_index/core/llama_dataset/generator.py b/llama-index-core/llama_index/core/llama_dataset/generator.py index 808ed7eb00a4f..3b81373848ce8 100644 --- a/llama-index-core/llama_index/core/llama_dataset/generator.py +++ b/llama-index-core/llama_index/core/llama_dataset/generator.py @@ -1,11 +1,12 @@ """Dataset generation from documents.""" + from __future__ import annotations import re import warnings from typing import List, Optional -from llama_index.core import Document, ServiceContext, SummaryIndex +from llama_index.core import Document, SummaryIndex from llama_index.core.async_utils import DEFAULT_NUM_WORKERS, run_jobs, asyncio_run from llama_index.core.base.response.schema import RESPONSE_TYPE from llama_index.core.ingestion import run_transformations @@ -30,11 +31,8 @@ NodeWithScore, TransformComponent, ) -from llama_index.core.settings import ( - Settings, - llm_from_settings_or_context, - transformations_from_settings_or_context, -) +from llama_index.core.settings import Settings + DEFAULT_QUESTION_GENERATION_PROMPT = """\ Context information is below. @@ -55,7 +53,6 @@ class RagDatasetGenerator(PromptMixin): Args: nodes (List[Node]): List of nodes. (Optional) - service_context (ServiceContext): Service Context. num_questions_per_chunk: number of question to be \ generated per chunk. Each document is chunked of size 512 words. text_question_template: Question generation template. @@ -74,11 +71,9 @@ def __init__( metadata_mode: MetadataMode = MetadataMode.NONE, show_progress: bool = False, workers: int = DEFAULT_NUM_WORKERS, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: """Init params.""" - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self.num_questions_per_chunk = num_questions_per_chunk self.text_question_template = text_question_template or PromptTemplate( DEFAULT_QUESTION_GENERATION_PROMPT @@ -107,14 +102,10 @@ def from_documents( exclude_keywords: Optional[List[str]] = None, show_progress: bool = False, workers: int = DEFAULT_NUM_WORKERS, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> RagDatasetGenerator: """Generate dataset from documents.""" - llm = llm or llm_from_settings_or_context(Settings, service_context) - transformations = transformations or transformations_from_settings_or_context( - Settings, service_context - ) + llm = llm or Settings.llm + transformations = transformations or Settings.transformations nodes = run_transformations( documents, transformations, show_progress=show_progress @@ -125,7 +116,6 @@ def from_documents( exclude_keywords = exclude_keywords or [] node_postprocessor = KeywordNodePostprocessor( llm=llm, - service_context=service_context, required_keywords=required_keywords, exclude_keywords=exclude_keywords, ) @@ -136,7 +126,6 @@ def from_documents( return cls( nodes=nodes, llm=llm, - service_context=service_context, num_questions_per_chunk=num_questions_per_chunk, text_question_template=text_question_template, text_qa_template=text_qa_template, diff --git a/llama-index-core/llama_index/core/llama_dataset/legacy/embedding.py b/llama-index-core/llama_index/core/llama_dataset/legacy/embedding.py index ad3922471a404..e202f6cec15f5 100644 --- a/llama-index-core/llama_index/core/llama_dataset/legacy/embedding.py +++ b/llama-index-core/llama_index/core/llama_dataset/legacy/embedding.py @@ -1,4 +1,5 @@ """Common utils for embeddings.""" + import json import re import uuid @@ -37,7 +38,7 @@ def query_docid_pairs(self) -> List[Tuple[str, List[str]]]: def save_json(self, path: str) -> None: """Save json.""" with open(path, "w") as f: - json.dump(self.dict(), f, indent=4) + json.dump(self.model_dump(), f, indent=4) @classmethod def from_json(cls, path: str) -> "EmbeddingQAFinetuneDataset": diff --git a/llama-index-core/llama_index/core/llama_dataset/rag.py b/llama-index-core/llama_index/core/llama_dataset/rag.py index 09750e22b081c..6d1c2dc915526 100644 --- a/llama-index-core/llama_index/core/llama_dataset/rag.py +++ b/llama-index-core/llama_index/core/llama_dataset/rag.py @@ -2,7 +2,7 @@ import asyncio import time -from typing import List, Optional +from typing import Any, List, Optional from llama_index.core.base.base_query_engine import BaseQueryEngine from llama_index.core.bridge.pydantic import Field @@ -13,7 +13,6 @@ BaseLlamaPredictionDataset, CreatedBy, ) -from pandas import DataFrame as PandasDataFrame class RagExamplePrediction(BaseLlamaExamplePrediction): @@ -83,8 +82,15 @@ class RagPredictionDataset(BaseLlamaPredictionDataset): _prediction_type = RagExamplePrediction - def to_pandas(self) -> PandasDataFrame: + def to_pandas(self) -> Any: """Create pandas dataframe.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + data = {} if self.predictions: data = { @@ -92,7 +98,7 @@ def to_pandas(self) -> PandasDataFrame: "contexts": [t.contexts for t in self.predictions], } - return PandasDataFrame(data) + return pd.DataFrame(data) @property def class_name(self) -> str: @@ -105,8 +111,15 @@ class LabelledRagDataset(BaseLlamaDataset[BaseQueryEngine]): _example_type = LabelledRagDataExample - def to_pandas(self) -> PandasDataFrame: + def to_pandas(self) -> Any: """Create pandas dataframe.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + data = { "query": [t.query for t in self.examples], "reference_contexts": [t.reference_contexts for t in self.examples], @@ -115,7 +128,7 @@ def to_pandas(self) -> PandasDataFrame: "query_by": [str(t.query_by) for t in self.examples], } - return PandasDataFrame(data) + return pd.DataFrame(data) async def _apredict_example( self, diff --git a/llama-index-core/llama_index/core/llama_dataset/simple.py b/llama-index-core/llama_index/core/llama_dataset/simple.py index a1712e6248e33..4393ab8c64a5f 100644 --- a/llama-index-core/llama_index/core/llama_dataset/simple.py +++ b/llama-index-core/llama_index/core/llama_dataset/simple.py @@ -1,4 +1,4 @@ -from typing import Optional, List +from typing import Any, Optional, List from llama_index.core.llama_dataset.base import ( BaseLlamaDataExample, BaseLlamaDataset, @@ -8,7 +8,6 @@ ) from llama_index.core.llms import LLM from llama_index.core.bridge.pydantic import Field -from pandas import DataFrame as PandasDataFrame class SimpleExamplePrediction(BaseLlamaExamplePrediction): @@ -36,15 +35,22 @@ class SimplePredictionDataset(BaseLlamaPredictionDataset): _prediction_type = SimpleExamplePrediction - def to_pandas(self) -> PandasDataFrame: + def to_pandas(self) -> Any: """Create pandas dataframe.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + data = {} if self.predictions: data = { "label": [t.label for t in self.predictions], } - return PandasDataFrame(data) + return pd.DataFrame(data) @property def class_name(self) -> str: @@ -81,15 +87,22 @@ def _construct_prediction_dataset( """ return SimplePredictionDataset(predictions=predictions) - def to_pandas(self) -> PandasDataFrame: + def to_pandas(self) -> Any: """Create pandas dataframe.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + data = { "reference_label": [t.reference_label for t in self.examples], "text": [t.text for t in self.examples], "text_by": [str(t.text_by) for t in self.examples], } - return PandasDataFrame(data) + return pd.DataFrame(data) async def _apredict_example( self, diff --git a/llama-index-core/llama_index/core/llms/callbacks.py b/llama-index-core/llama_index/core/llms/callbacks.py index 4706d38b2e13c..1153c2f1d049e 100644 --- a/llama-index-core/llama_index/core/llms/callbacks.py +++ b/llama-index-core/llama_index/core/llms/callbacks.py @@ -42,17 +42,16 @@ def wrap(f: Callable) -> Callable: def wrapper_logic(_self: Any) -> Generator[CallbackManager, None, None]: callback_manager = getattr(_self, "callback_manager", None) if not isinstance(callback_manager, CallbackManager): - raise ValueError( - "Cannot use llm_chat_callback on an instance " - "without a callback_manager attribute." - ) + _self.callback_manager = CallbackManager() yield callback_manager async def wrapped_async_llm_chat( _self: Any, messages: Sequence[ChatMessage], **kwargs: Any ) -> Any: - with wrapper_logic(_self) as callback_manager: + with wrapper_logic(_self) as callback_manager, callback_manager.as_trace( + "chat" + ): span_id = active_span_id.get() model_dict = _self.to_dict() model_dict.pop("api_key", None) @@ -148,7 +147,9 @@ async def wrapped_gen() -> ChatResponseAsyncGen: def wrapped_llm_chat( _self: Any, messages: Sequence[ChatMessage], **kwargs: Any ) -> Any: - with wrapper_logic(_self) as callback_manager: + with wrapper_logic(_self) as callback_manager, callback_manager.as_trace( + "chat" + ): span_id = active_span_id.get() model_dict = _self.to_dict() model_dict.pop("api_key", None) @@ -288,12 +289,9 @@ def wrap(f: Callable) -> Callable: def wrapper_logic(_self: Any) -> Generator[CallbackManager, None, None]: callback_manager = getattr(_self, "callback_manager", None) if not isinstance(callback_manager, CallbackManager): - raise ValueError( - "Cannot use llm_completion_callback on an instance " - "without a callback_manager attribute." - ) + _self.callback_manager = CallbackManager() - yield callback_manager + yield _self.callback_manager def extract_prompt(*args: Any, **kwargs: Any) -> str: if len(args) > 0: @@ -309,7 +307,9 @@ async def wrapped_async_llm_predict( _self: Any, *args: Any, **kwargs: Any ) -> Any: prompt = extract_prompt(*args, **kwargs) - with wrapper_logic(_self) as callback_manager: + with wrapper_logic(_self) as callback_manager, callback_manager.as_trace( + "completion" + ): span_id = active_span_id.get() model_dict = _self.to_dict() model_dict.pop("api_key", None) @@ -405,7 +405,9 @@ async def wrapped_gen() -> CompletionResponseAsyncGen: def wrapped_llm_predict(_self: Any, *args: Any, **kwargs: Any) -> Any: prompt = extract_prompt(*args, **kwargs) - with wrapper_logic(_self) as callback_manager: + with wrapper_logic(_self) as callback_manager, callback_manager.as_trace( + "completion" + ): span_id = active_span_id.get() model_dict = _self.to_dict() model_dict.pop("api_key", None) diff --git a/llama-index-core/llama_index/core/llms/llm.py b/llama-index-core/llama_index/core/llms/llm.py index 7b7c88f9b57c3..501750e0fd5c5 100644 --- a/llama-index-core/llama_index/core/llms/llm.py +++ b/llama-index-core/llama_index/core/llms/llm.py @@ -1,7 +1,6 @@ from collections import ChainMap from typing import ( Any, - Callable, Dict, List, Generator, @@ -14,6 +13,7 @@ runtime_checkable, TYPE_CHECKING, ) +from typing_extensions import Annotated from llama_index.core.base.llms.types import ( ChatMessage, @@ -32,9 +32,11 @@ ) from llama_index.core.bridge.pydantic import ( BaseModel, + WithJsonSchema, Field, - root_validator, - validator, + field_validator, + model_validator, + ConfigDict, ) from llama_index.core.callbacks import CBEventType, EventPayload from llama_index.core.base.llms.base import BaseLLM @@ -147,6 +149,18 @@ def default_completion_to_prompt(prompt: str) -> str: return prompt +MessagesToPromptCallable = Annotated[ + Optional[MessagesToPromptType], + WithJsonSchema({"type": "string"}), +] + + +CompletionToPromptCallable = Annotated[ + Optional[CompletionToPromptType], + WithJsonSchema({"type": "string"}), +] + + class LLM(BaseLLM): """ The LLM class is the main class for interacting with language models. @@ -167,12 +181,12 @@ class LLM(BaseLLM): system_prompt: Optional[str] = Field( default=None, description="System prompt for LLM calls." ) - messages_to_prompt: Callable = Field( + messages_to_prompt: MessagesToPromptCallable = Field( description="Function to convert a list of messages to an LLM prompt.", default=None, exclude=True, ) - completion_to_prompt: Callable = Field( + completion_to_prompt: CompletionToPromptCallable = Field( description="Function to convert a completion to an LLM prompt.", default=None, exclude=True, @@ -193,25 +207,27 @@ class LLM(BaseLLM): # -- Pydantic Configs -- - @validator("messages_to_prompt", pre=True) + @field_validator("messages_to_prompt") + @classmethod def set_messages_to_prompt( cls, messages_to_prompt: Optional[MessagesToPromptType] ) -> MessagesToPromptType: return messages_to_prompt or generic_messages_to_prompt - @validator("completion_to_prompt", pre=True) + @field_validator("completion_to_prompt") + @classmethod def set_completion_to_prompt( cls, completion_to_prompt: Optional[CompletionToPromptType] ) -> CompletionToPromptType: return completion_to_prompt or default_completion_to_prompt - @root_validator - def check_prompts(cls, values: Dict[str, Any]) -> Dict[str, Any]: - if values.get("completion_to_prompt") is None: - values["completion_to_prompt"] = default_completion_to_prompt - if values.get("messages_to_prompt") is None: - values["messages_to_prompt"] = generic_messages_to_prompt - return values + @model_validator(mode="after") + def check_prompts(self) -> "LLM": + if self.completion_to_prompt is None: + self.completion_to_prompt = default_completion_to_prompt + if self.messages_to_prompt is None: + self.messages_to_prompt = generic_messages_to_prompt + return self # -- Utils -- @@ -839,12 +855,10 @@ def as_structured_llm( class BaseLLMComponent(QueryComponent): """Base LLM component.""" + model_config = ConfigDict(arbitrary_types_allowed=True) llm: LLM = Field(..., description="LLM") streaming: bool = Field(default=False, description="Streaming mode") - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: Any) -> None: """Set callback manager.""" self.llm.callback_manager = callback_manager diff --git a/llama-index-core/llama_index/core/llms/mock.py b/llama-index-core/llama_index/core/llms/mock.py index 1a8e12ec46dbf..0cc2225552919 100644 --- a/llama-index-core/llama_index/core/llms/mock.py +++ b/llama-index-core/llama_index/core/llms/mock.py @@ -1,4 +1,4 @@ -from typing import Any, Callable, Optional, Sequence +from typing import Any, Optional, Sequence from llama_index.core.base.llms.types import ( ChatMessage, ChatResponseGen, @@ -9,6 +9,7 @@ from llama_index.core.callbacks import CallbackManager from llama_index.core.llms.callbacks import llm_chat_callback, llm_completion_callback from llama_index.core.llms.custom import CustomLLM +from llama_index.core.llms.llm import MessagesToPromptType, CompletionToPromptType from llama_index.core.types import PydanticProgramMode @@ -20,13 +21,13 @@ def __init__( max_tokens: Optional[int] = None, callback_manager: Optional[CallbackManager] = None, system_prompt: Optional[str] = None, - messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None, - completion_to_prompt: Optional[Callable[[str], str]] = None, + messages_to_prompt: Optional[MessagesToPromptType] = None, + completion_to_prompt: Optional[CompletionToPromptType] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, ) -> None: super().__init__( max_tokens=max_tokens, - callback_manager=callback_manager, + callback_manager=callback_manager or CallbackManager([]), system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, diff --git a/llama-index-core/llama_index/core/llms/structured_llm.py b/llama-index-core/llama_index/core/llms/structured_llm.py index bf2dbcbce33d9..26c58ef076e71 100644 --- a/llama-index-core/llama_index/core/llms/structured_llm.py +++ b/llama-index-core/llama_index/core/llms/structured_llm.py @@ -18,7 +18,12 @@ LLMMetadata, MessageRole, ) -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import ( + BaseModel, + Field, + SerializeAsAny, + ConfigDict, +) from llama_index.core.base.llms.types import LLMMetadata from llama_index.core.llms.callbacks import ( llm_chat_callback, @@ -34,6 +39,40 @@ OutputKeys, QueryComponent, ) +import re + + +def _escape_braces(text: str) -> str: + """ + Escape braces in text. + Only captures template variables, skips already escaped braces. + """ + + def replace(match): + if match.group(0).startswith("{{") and match.group(0).endswith("}}"): + return match.group(0) # Already escaped, return as is + return "{{" + match.group(1) + "}}" + + pattern = r"(? Sequence[ChatMessage]: + """Escape JSON in messages.""" + new_messages = [] + for message in messages: + if isinstance(message.content, str): + escaped_msg = _escape_braces(message.content) + new_messages.append( + ChatMessage( + role=message.role, + content=escaped_msg, + additional_kwargs=message.additional_kwargs, + ) + ) + else: + new_messages.append(message) + return new_messages class StructuredLLM(LLM): @@ -43,7 +82,7 @@ class StructuredLLM(LLM): """ - llm: LLM + llm: SerializeAsAny[LLM] output_cls: Type[BaseModel] = Field( ..., description="Output class for the structured LLM.", exclude=True ) @@ -65,13 +104,15 @@ def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: # make this work with our FunctionCallingProgram, even though # the messages don't technically have any variables (they are already formatted) - chat_prompt = ChatPromptTemplate(message_templates=messages) + chat_prompt = ChatPromptTemplate(message_templates=_escape_json(messages)) output = self.llm.structured_predict( output_cls=self.output_cls, prompt=chat_prompt ) return ChatResponse( - message=ChatMessage(role=MessageRole.ASSISTANT, content=output.json()), + message=ChatMessage( + role=MessageRole.ASSISTANT, content=output.model_dump_json() + ), raw=output, ) @@ -79,7 +120,7 @@ def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: - chat_prompt = ChatPromptTemplate(message_templates=messages) + chat_prompt = ChatPromptTemplate(message_templates=_escape_json(messages)) stream_output = self.llm.stream_structured_predict( output_cls=self.output_cls, prompt=chat_prompt, **kwargs @@ -117,13 +158,15 @@ async def achat( # make this work with our FunctionCallingProgram, even though # the messages don't technically have any variables (they are already formatted) - chat_prompt = ChatPromptTemplate(message_templates=messages) + chat_prompt = ChatPromptTemplate(message_templates=_escape_json(messages)) output = await self.llm.astructured_predict( output_cls=self.output_cls, prompt=chat_prompt ) return ChatResponse( - message=ChatMessage(role=MessageRole.ASSISTANT, content=output.json()), + message=ChatMessage( + role=MessageRole.ASSISTANT, content=output.model_dump_json() + ), raw=output, ) @@ -136,7 +179,7 @@ async def astream_chat( """Async stream chat endpoint for LLM.""" async def gen() -> ChatResponseAsyncGen: - chat_prompt = ChatPromptTemplate(message_templates=messages) + chat_prompt = ChatPromptTemplate(message_templates=_escape_json(messages)) stream_output = await self.llm.astream_structured_predict( output_cls=self.output_cls, prompt=chat_prompt, **kwargs @@ -182,10 +225,8 @@ class StructuredLLMComponent(QueryComponent): """ - llm_component: BaseLLMComponent - - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict(arbitrary_types_allowed=True) + llm_component: SerializeAsAny[BaseLLMComponent] def set_callback_manager(self, callback_manager: Any) -> None: """Set callback manager.""" diff --git a/llama-index-core/llama_index/core/memory/chat_memory_buffer.py b/llama-index-core/llama_index/core/memory/chat_memory_buffer.py index 9e07f0d21426b..fe316c8f63ddb 100644 --- a/llama-index-core/llama_index/core/memory/chat_memory_buffer.py +++ b/llama-index-core/llama_index/core/memory/chat_memory_buffer.py @@ -2,7 +2,7 @@ from typing import Any, Callable, Dict, List, Optional from llama_index.core.base.llms.types import ChatMessage, MessageRole -from llama_index.core.bridge.pydantic import Field, root_validator +from llama_index.core.bridge.pydantic import Field, model_validator from llama_index.core.llms.llm import LLM from llama_index.core.memory.types import ( DEFAULT_CHAT_STORE_KEY, @@ -24,15 +24,14 @@ class ChatMemoryBuffer(BaseChatStoreMemory): default_factory=get_tokenizer, exclude=True, ) - chat_store: BaseChatStore = Field(default_factory=SimpleChatStore) - chat_store_key: str = Field(default=DEFAULT_CHAT_STORE_KEY) @classmethod def class_name(cls) -> str: """Get class name.""" return "ChatMemoryBuffer" - @root_validator(pre=True) + @model_validator(mode="before") + @classmethod def validate_memory(cls, values: dict) -> dict: # Validate token limit token_limit = values.get("token_limit", -1) @@ -82,6 +81,7 @@ def to_string(self) -> str: def from_string(cls, json_str: str) -> "ChatMemoryBuffer": """Create a chat memory buffer from a string.""" dict_obj = json.loads(json_str) + print(f"dict_obj: {dict_obj}", flush=True) return cls.from_dict(dict_obj) def to_dict(self, **kwargs: Any) -> dict: diff --git a/llama-index-core/llama_index/core/memory/chat_summary_memory_buffer.py b/llama-index-core/llama_index/core/memory/chat_summary_memory_buffer.py index ad049202143f0..3f0bc4c21f9aa 100644 --- a/llama-index-core/llama_index/core/memory/chat_summary_memory_buffer.py +++ b/llama-index-core/llama_index/core/memory/chat_summary_memory_buffer.py @@ -3,7 +3,13 @@ from typing import Any, Callable, Dict, List, Tuple, Optional from llama_index.core.base.llms.types import ChatMessage, MessageRole -from llama_index.core.bridge.pydantic import Field, PrivateAttr, root_validator +from llama_index.core.bridge.pydantic import ( + Field, + PrivateAttr, + model_validator, + field_serializer, + SerializeAsAny, +) from llama_index.core.llms.llm import LLM from llama_index.core.memory.types import DEFAULT_CHAT_STORE_KEY, BaseMemory from llama_index.core.storage.chat_store import BaseChatStore, SimpleChatStore @@ -35,7 +41,7 @@ class ChatSummaryMemoryBuffer(BaseMemory): token_limit: int count_initial_tokens: bool = False - llm: Optional[LLM] = None + llm: Optional[SerializeAsAny[LLM]] = None summarize_prompt: Optional[str] = None tokenizer_fn: Callable[[str], List] = Field( # NOTE: mypy does not handle the typing here well, hence the cast @@ -43,12 +49,19 @@ class ChatSummaryMemoryBuffer(BaseMemory): exclude=True, ) - chat_store: BaseChatStore = Field(default_factory=SimpleChatStore) + chat_store: SerializeAsAny[BaseChatStore] = Field(default_factory=SimpleChatStore) chat_store_key: str = Field(default=DEFAULT_CHAT_STORE_KEY) _token_count: int = PrivateAttr(default=0) - @root_validator(pre=True) + @field_serializer("chat_store") + def serialize_courses_in_order(chat_store: BaseChatStore): + res = chat_store.model_dump() + res.update({"class_name": chat_store.class_name()}) + return res + + @model_validator(mode="before") + @classmethod def validate_memory(cls, values: dict) -> dict: """Validate the memory.""" # Validate token limits diff --git a/llama-index-core/llama_index/core/memory/simple_composable_memory.py b/llama-index-core/llama_index/core/memory/simple_composable_memory.py index b536205178bc9..c11c86ad9670f 100644 --- a/llama-index-core/llama_index/core/memory/simple_composable_memory.py +++ b/llama-index-core/llama_index/core/memory/simple_composable_memory.py @@ -1,7 +1,7 @@ from typing import Any, List, Optional from llama_index.core.base.llms.types import ChatMessage, MessageRole -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, SerializeAsAny from llama_index.core.memory.types import ( BaseMemory, ) @@ -25,10 +25,10 @@ class SimpleComposableMemory(BaseMemory): Retrieved messages from these sources get added to the system prompt message. """ - primary_memory: BaseMemory = Field( + primary_memory: SerializeAsAny[BaseMemory] = Field( description="Primary memory source for chat agent.", ) - secondary_memory_sources: List[BaseMemory] = Field( + secondary_memory_sources: List[SerializeAsAny[BaseMemory]] = Field( default_factory=list, description="Secondary memory sources." ) diff --git a/llama-index-core/llama_index/core/memory/types.py b/llama-index-core/llama_index/core/memory/types.py index 3dce71e47eff3..8f7d318e4f491 100644 --- a/llama-index-core/llama_index/core/memory/types.py +++ b/llama-index-core/llama_index/core/memory/types.py @@ -5,7 +5,7 @@ from llama_index.core.llms.llm import LLM from llama_index.core.schema import BaseComponent from llama_index.core.storage.chat_store import BaseChatStore, SimpleChatStore -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, field_serializer, SerializeAsAny DEFAULT_CHAT_STORE_KEY = "chat_history" @@ -62,9 +62,15 @@ class BaseChatStoreMemory(BaseMemory): NOTE: The interface for memory is not yet finalized and is subject to change. """ - chat_store: BaseChatStore = Field(default_factory=SimpleChatStore) + chat_store: SerializeAsAny[BaseChatStore] = Field(default_factory=SimpleChatStore) chat_store_key: str = Field(default=DEFAULT_CHAT_STORE_KEY) + @field_serializer("chat_store") + def serialize_courses_in_order(chat_store: BaseChatStore): + res = chat_store.model_dump() + res.update({"class_name": chat_store.class_name()}) + return res + @classmethod def class_name(cls) -> str: """Get class name.""" diff --git a/llama-index-core/llama_index/core/memory/vector_memory.py b/llama-index-core/llama_index/core/memory/vector_memory.py index c927dcdd5bfcd..58353a765865a 100644 --- a/llama-index-core/llama_index/core/memory/vector_memory.py +++ b/llama-index-core/llama_index/core/memory/vector_memory.py @@ -6,7 +6,7 @@ import uuid from typing import Any, Dict, List, Optional -from llama_index.core.bridge.pydantic import validator +from llama_index.core.bridge.pydantic import field_validator from llama_index.core.schema import TextNode from llama_index.core.vector_stores.types import VectorStore @@ -68,7 +68,8 @@ class VectorMemory(BaseMemory): description="The super node for the current active user-message batch.", ) - @validator("vector_index") + @field_validator("vector_index") + @classmethod def validate_vector_index(cls, value: Any) -> Any: """Validate vector index.""" # NOTE: we can't import VectorStoreIndex directly due to circular imports, @@ -135,7 +136,7 @@ def get( # retrieve underlying messages return [ - ChatMessage.parse_obj(sub_dict) + ChatMessage.model_validate(sub_dict) for node in nodes for sub_dict in node.metadata["sub_dicts"] ] @@ -193,4 +194,4 @@ def reset(self) -> None: self.vector_index.vector_store.clear() -VectorMemory.update_forward_refs() +VectorMemory.model_rebuild() diff --git a/llama-index-core/llama_index/core/multi_modal_llms/base.py b/llama-index-core/llama_index/core/multi_modal_llms/base.py index eed62f4c06540..fe0b61f5f2be8 100644 --- a/llama-index-core/llama_index/core/multi_modal_llms/base.py +++ b/llama-index-core/llama_index/core/multi_modal_llms/base.py @@ -17,7 +17,11 @@ QueryComponent, validate_and_convert_stringable, ) -from llama_index.core.bridge.pydantic import BaseModel, Field, validator +from llama_index.core.bridge.pydantic import ( + BaseModel, + Field, + ConfigDict, +) from llama_index.core.callbacks import CallbackManager from llama_index.core.constants import ( DEFAULT_CONTEXT_WINDOW, @@ -30,6 +34,7 @@ class MultiModalLLMMetadata(BaseModel): + model_config = ConfigDict(protected_namespaces=("pydantic_model_",)) context_window: Optional[int] = Field( default=DEFAULT_CONTEXT_WINDOW, description=( @@ -76,19 +81,11 @@ class MultiModalLLMMetadata(BaseModel): class MultiModalLLM(ChainableMixin, BaseComponent, DispatcherSpanMixin): """Multi-Modal LLM interface.""" + model_config = ConfigDict(arbitrary_types_allowed=True) callback_manager: CallbackManager = Field( default_factory=CallbackManager, exclude=True ) - class Config: - arbitrary_types_allowed = True - - @validator("callback_manager", pre=True) - def _validate_callback_manager(cls, v: CallbackManager) -> CallbackManager: - if v is None: - return CallbackManager([]) - return v - @property @abstractmethod def metadata(self) -> MultiModalLLMMetadata: @@ -186,12 +183,10 @@ def __init_subclass__(cls, **kwargs) -> None: class BaseMultiModalComponent(QueryComponent): """Base LLM component.""" + model_config = ConfigDict(arbitrary_types_allowed=True) multi_modal_llm: MultiModalLLM = Field(..., description="LLM") streaming: bool = Field(default=False, description="Streaming mode") - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: Any) -> None: """Set callback manager.""" # TODO: make callbacks work with multi-modal diff --git a/llama-index-core/llama_index/core/node_parser/interface.py b/llama-index-core/llama_index/core/node_parser/interface.py index d1f125fa2e76a..2d3fedf98c8fe 100644 --- a/llama-index-core/llama_index/core/node_parser/interface.py +++ b/llama-index-core/llama_index/core/node_parser/interface.py @@ -1,9 +1,16 @@ """Node parser interface.""" from abc import ABC, abstractmethod -from typing import Any, Callable, Dict, List, Sequence - -from llama_index.core.bridge.pydantic import Field, validator +from typing import Any, Callable, Dict, List, Sequence, Optional +from typing_extensions import Annotated + +from llama_index.core.bridge.pydantic import ( + Field, + WithJsonSchema, + BeforeValidator, + ConfigDict, + PlainSerializer, +) from llama_index.core.callbacks import CallbackManager, CBEventType, EventPayload from llama_index.core.node_parser.node_utils import ( build_nodes_from_splits, @@ -19,9 +26,30 @@ from llama_index.core.utils import get_tqdm_iterable +def _validate_id_func(v: Any) -> Any: + if v is None: + return default_id_func + return v + + +def _serialize_id_func(f: Callable) -> Any: + return {"id_func_name": f"{f.__name__}", "title": "id_func"} + + +IdFuncCallable = Annotated[ + Callable, + Field(validate_default=True), + BeforeValidator(_validate_id_func), + WithJsonSchema({"type": "string"}, mode="serialization"), + WithJsonSchema({"type": "string"}, mode="validation"), + PlainSerializer(_serialize_id_func), +] + + class NodeParser(TransformComponent, ABC): """Base interface for node parser.""" + model_config = ConfigDict(arbitrary_types_allowed=True) include_metadata: bool = Field( default=True, description="Whether or not to consider metadata when splitting." ) @@ -29,23 +57,13 @@ class NodeParser(TransformComponent, ABC): default=True, description="Include prev/next node relationships." ) callback_manager: CallbackManager = Field( - default_factory=CallbackManager, exclude=True + default_factory=lambda: CallbackManager([]), exclude=True ) - id_func: Callable = Field( + id_func: Optional[IdFuncCallable] = Field( default=None, description="Function to generate node IDs.", - exclude=True, ) - class Config: - arbitrary_types_allowed = True - - @validator("id_func", pre=True) - def _validate_id_func(cls, v: Any) -> Any: - if v is None: - return default_id_func - return v - @abstractmethod def _parse_nodes( self, @@ -68,8 +86,15 @@ def _postprocess_parsed_nodes( ) -> List[BaseNode]: for i, node in enumerate(nodes): parent_doc = parent_doc_map.get(node.ref_doc_id, None) + parent_node = node.relationships.get(NodeRelationship.SOURCE, None) if parent_doc is not None: + if parent_doc.source_node is not None: + node.relationships.update( + { + NodeRelationship.SOURCE: parent_doc.source_node, + } + ) start_char_idx = parent_doc.text.find( node.get_content(metadata_mode=MetadataMode.NONE) ) @@ -83,7 +108,17 @@ def _postprocess_parsed_nodes( # update metadata if self.include_metadata: - node.metadata.update(parent_doc.metadata) + # Merge parent_doc.metadata into nodes.metadata, giving preference to node's values + node.metadata = {**parent_doc.metadata, **node.metadata} + + if parent_node is not None: + if self.include_metadata: + parent_metadata = parent_node.metadata + + combined_metadata = {**parent_metadata, **node.metadata} + + # Merge parent_node.metadata into nodes.metadata, giving preference to node's values + node.metadata.update(combined_metadata) if self.include_prev_next_rel: # establish prev/next relationships if nodes share the same source_node diff --git a/llama-index-core/llama_index/core/node_parser/relational/base_element.py b/llama-index-core/llama_index/core/node_parser/relational/base_element.py index 55365ccb150fa..bba0e38351d95 100644 --- a/llama-index-core/llama_index/core/node_parser/relational/base_element.py +++ b/llama-index-core/llama_index/core/node_parser/relational/base_element.py @@ -1,13 +1,16 @@ import uuid from abc import abstractmethod from typing import Any, Dict, List, Optional, Sequence, Tuple, cast - -import pandas as pd from tqdm import tqdm from llama_index.core.async_utils import DEFAULT_NUM_WORKERS, run_jobs, asyncio_run from llama_index.core.base.response.schema import PydanticResponse -from llama_index.core.bridge.pydantic import BaseModel, Field, ValidationError +from llama_index.core.bridge.pydantic import ( + BaseModel, + Field, + ValidationError, + ConfigDict, +) from llama_index.core.callbacks.base import CallbackManager from llama_index.core.llms.llm import LLM from llama_index.core.node_parser.interface import NodeParser @@ -48,18 +51,16 @@ class TableOutput(BaseModel): class Element(BaseModel): """Element object.""" + model_config = ConfigDict(arbitrary_types_allowed=True) id: str type: str element: Any title_level: Optional[int] = None table_output: Optional[TableOutput] = None - table: Optional[pd.DataFrame] = None + table: Optional[Any] = None markdown: Optional[str] = None page_number: Optional[int] = None - class Config: - arbitrary_types_allowed = True - class BaseElementNodeParser(NodeParser): """ @@ -69,7 +70,7 @@ class BaseElementNodeParser(NodeParser): """ callback_manager: CallbackManager = Field( - default_factory=CallbackManager, exclude=True + default_factory=lambda: CallbackManager([]), exclude=True ) llm: Optional[LLM] = Field( default=None, description="LLM model to use for summarization." @@ -202,9 +203,7 @@ async def _get_table_output(table_context: str, summary_query_str: str) -> Any: _get_table_output(table_context, self.summary_query_str) for table_context in table_context_list ] - summary_co = run_jobs( - summary_jobs, show_progress=self.show_progress, workers=self.num_workers - ) + summary_co = run_jobs(summary_jobs, workers=self.num_workers) summary_outputs = asyncio_run(summary_co) for element, summary_output in zip(elements, summary_outputs): element.table_output = summary_output @@ -251,9 +250,7 @@ async def _get_table_output(table_context: str, summary_query_str: str) -> Any: _get_table_output(table_context, self.summary_query_str) for table_context in table_context_list ] - summary_outputs = await run_jobs( - summary_jobs, show_progress=self.show_progress, workers=self.num_workers - ) + summary_outputs = await run_jobs(summary_jobs, workers=self.num_workers) for element, summary_output in zip(elements, summary_outputs): element.table_output = summary_output @@ -317,6 +314,13 @@ def get_nodes_from_elements( ref_doc_text: Optional[str] = None, ) -> List[BaseNode]: """Get nodes and mappings.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + from llama_index.core.node_parser import SentenceSplitter node_parser = self.nested_node_parser or SentenceSplitter() diff --git a/llama-index-core/llama_index/core/node_parser/relational/utils.py b/llama-index-core/llama_index/core/node_parser/relational/utils.py index 29f53a854a858..19c7a37321eda 100644 --- a/llama-index-core/llama_index/core/node_parser/relational/utils.py +++ b/llama-index-core/llama_index/core/node_parser/relational/utils.py @@ -1,9 +1,17 @@ -import pandas as pd +from typing import Any + from io import StringIO -def md_to_df(md_str: str) -> pd.DataFrame: +def md_to_df(md_str: str) -> Any: """Convert Markdown to dataframe.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "You must install the `pandas` package to use this node parser." + ) + # Replace " by "" in md_str md_str = md_str.replace('"', '""') @@ -26,7 +34,7 @@ def md_to_df(md_str: str) -> pd.DataFrame: return pd.read_csv(StringIO(md_str)) -def html_to_df(html_str: str) -> pd.DataFrame: +def html_to_df(html_str: str) -> Any: """Convert HTML to dataframe.""" try: from lxml import html @@ -35,6 +43,13 @@ def html_to_df(html_str: str) -> pd.DataFrame: "You must install the `lxml` package to use this node parser." ) + try: + import pandas as pd + except ImportError: + raise ImportError( + "You must install the `pandas` package to use this node parser." + ) + tree = html.fromstring(html_str) table_element = tree.xpath("//table")[0] rows = table_element.xpath(".//tr") diff --git a/llama-index-core/llama_index/core/node_parser/text/code.py b/llama-index-core/llama_index/core/node_parser/text/code.py index 30ede43971e27..d8f7a919bdf8c 100644 --- a/llama-index-core/llama_index/core/node_parser/text/code.py +++ b/llama-index-core/llama_index/core/node_parser/text/code.py @@ -56,6 +56,20 @@ def __init__( """Initialize a CodeSplitter.""" from tree_sitter import Parser # pants: no-infer-dep + callback_manager = callback_manager or CallbackManager([]) + id_func = id_func or default_id_func + + super().__init__( + language=language, + chunk_lines=chunk_lines, + chunk_lines_overlap=chunk_lines_overlap, + max_chars=max_chars, + callback_manager=callback_manager, + include_metadata=include_metadata, + include_prev_next_rel=include_prev_next_rel, + id_func=id_func, + ) + if parser is None: try: import tree_sitter_languages # pants: no-infer-dep @@ -78,20 +92,6 @@ def __init__( self._parser = parser - callback_manager = callback_manager or CallbackManager([]) - id_func = id_func or default_id_func - - super().__init__( - language=language, - chunk_lines=chunk_lines, - chunk_lines_overlap=chunk_lines_overlap, - max_chars=max_chars, - callback_manager=callback_manager, - include_metadata=include_metadata, - include_prev_next_rel=include_prev_next_rel, - id_func=id_func, - ) - @classmethod def from_defaults( cls, @@ -108,6 +108,7 @@ def from_defaults( chunk_lines=chunk_lines, chunk_lines_overlap=chunk_lines_overlap, max_chars=max_chars, + callback_manager=callback_manager, parser=parser, ) diff --git a/llama-index-core/llama_index/core/node_parser/text/semantic_double_merging_splitter.py b/llama-index-core/llama_index/core/node_parser/text/semantic_double_merging_splitter.py index 566bac3919fbd..b96f444900002 100644 --- a/llama-index-core/llama_index/core/node_parser/text/semantic_double_merging_splitter.py +++ b/llama-index-core/llama_index/core/node_parser/text/semantic_double_merging_splitter.py @@ -186,7 +186,6 @@ def _parse_nodes( for node in nodes_with_progress: nodes = self.build_semantic_nodes_from_documents([node]) all_nodes.extend(nodes) - return all_nodes def build_semantic_nodes_from_documents( @@ -199,6 +198,7 @@ def build_semantic_nodes_from_documents( for doc in documents: text = doc.text sentences = self.sentence_splitter(text) + sentences = [s.strip() for s in sentences] initial_chunks = self._create_initial_chunks(sentences) chunks = self._merge_initial_chunks(initial_chunks) @@ -215,7 +215,6 @@ def _create_initial_chunks(self, sentences: List[str]) -> List[str]: initial_chunks: List[str] = [] chunk = sentences[0] # "" new = True - for sentence in sentences[1:]: if new: # check if 2 sentences got anything in common @@ -227,6 +226,7 @@ def _create_initial_chunks(self, sentences: List[str]) -> List[str]: self.language_config.nlp(self._clean_text_advanced(sentence)) ) < self.initial_threshold + and len(chunk) + len(sentence) + 1 <= self.max_chunk_size ): # if not then leave first sentence as separate chunk initial_chunks.append(chunk) @@ -234,12 +234,17 @@ def _create_initial_chunks(self, sentences: List[str]) -> List[str]: continue chunk_sentences = [chunk] - - chunk_sentences.append(sentence) - chunk = " ".join(chunk_sentences) - + if len(chunk) + len(sentence) + 1 <= self.max_chunk_size: + chunk_sentences.append(sentence) + chunk = " ".join(chunk_sentences) + new = False + else: + new = True + initial_chunks.append(chunk) + chunk = sentence + continue last_sentences = " ".join(chunk_sentences[-2:]) - new = False + # new = False elif ( self.language_config.nlp( @@ -248,7 +253,8 @@ def _create_initial_chunks(self, sentences: List[str]) -> List[str]: self.language_config.nlp(self._clean_text_advanced(sentence)) ) > self.appending_threshold - and not len(chunk) > self.max_chunk_size + and len(last_sentences) + len(sentence) + 1 <= self.max_chunk_size + # and not len(chunk) > self.max_chunk_size ): # elif nlp(last_sentences).similarity(nlp(sentence)) > self.threshold: chunk_sentences.append(sentence) @@ -277,7 +283,7 @@ def _merge_initial_chunks(self, initial_chunks: List[str]) -> List[str]: current_nlp = self.language_config.nlp(self._clean_text_advanced(current)) - if len(current) > self.max_chunk_size: + if len(current) >= self.max_chunk_size: chunks.append(current) current = initial_chunks[i] @@ -289,6 +295,7 @@ def _merge_initial_chunks(self, initial_chunks: List[str]) -> List[str]: ) ) > self.merging_threshold + and len(current) + len(initial_chunks[i]) + 1 <= self.max_chunk_size ): current += " " + initial_chunks[i] @@ -301,6 +308,11 @@ def _merge_initial_chunks(self, initial_chunks: List[str]) -> List[str]: ) ) > self.merging_threshold + and len(current) + + len(initial_chunks[i]) + + len(initial_chunks[i + 1]) + + 2 + <= self.max_chunk_size ): current += " " + initial_chunks[i] + " " + initial_chunks[i + 1] skip = 1 @@ -315,6 +327,12 @@ def _merge_initial_chunks(self, initial_chunks: List[str]) -> List[str]: ) > self.merging_threshold and self.merging_range == 2 + and len(current) + + len(initial_chunks[i]) + + len(initial_chunks[i + 1]) + + len(initial_chunks[i + 2]) + + 3 + <= self.max_chunk_size ): current += ( " " diff --git a/llama-index-core/llama_index/core/node_parser/text/semantic_splitter.py b/llama-index-core/llama_index/core/node_parser/text/semantic_splitter.py index 386b59e4b145a..3445d29e2dc70 100644 --- a/llama-index-core/llama_index/core/node_parser/text/semantic_splitter.py +++ b/llama-index-core/llama_index/core/node_parser/text/semantic_splitter.py @@ -1,8 +1,9 @@ from typing import Any, Callable, List, Optional, Sequence, TypedDict +from typing_extensions import Annotated import numpy as np from llama_index.core.base.embeddings.base import BaseEmbedding -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, SerializeAsAny, WithJsonSchema from llama_index.core.callbacks.base import CallbackManager from llama_index.core.node_parser import NodeParser from llama_index.core.node_parser.interface import NodeParser @@ -24,6 +25,13 @@ class SentenceCombination(TypedDict): combined_sentence_embedding: List[float] +SentenceSplitterCallable = Annotated[ + Callable[[str], List[str]], + WithJsonSchema({"type": "string"}, mode="serialization"), + WithJsonSchema({"type": "string"}, mode="validation"), +] + + class SemanticSplitterNodeParser(NodeParser): """Semantic node parser. @@ -37,13 +45,13 @@ class SemanticSplitterNodeParser(NodeParser): include_prev_next_rel (bool): whether to include prev/next relationships """ - sentence_splitter: Callable[[str], List[str]] = Field( + sentence_splitter: SentenceSplitterCallable = Field( default_factory=split_by_sentence_tokenizer, description="The text splitter to use when splitting documents.", exclude=True, ) - embed_model: BaseEmbedding = Field( + embed_model: SerializeAsAny[BaseEmbedding] = Field( description="The embedding model to use to for semantic comparison", ) diff --git a/llama-index-core/llama_index/core/node_parser/text/sentence.py b/llama-index-core/llama_index/core/node_parser/text/sentence.py index 79c5dd5e025e1..45d1cc3c85738 100644 --- a/llama-index-core/llama_index/core/node_parser/text/sentence.py +++ b/llama-index-core/llama_index/core/node_parser/text/sentence.py @@ -1,4 +1,5 @@ """Sentence splitter.""" + from dataclasses import dataclass from typing import Callable, List, Optional, Tuple @@ -6,7 +7,9 @@ from llama_index.core.callbacks.base import CallbackManager from llama_index.core.callbacks.schema import CBEventType, EventPayload from llama_index.core.constants import DEFAULT_CHUNK_SIZE -from llama_index.core.node_parser.interface import MetadataAwareTextSplitter +from llama_index.core.node_parser.interface import ( + MetadataAwareTextSplitter, +) from llama_index.core.node_parser.node_utils import default_id_func from llama_index.core.node_parser.text.utils import ( split_by_char, @@ -14,7 +17,6 @@ split_by_sentence_tokenizer, split_by_sep, ) -from llama_index.core.schema import Document from llama_index.core.utils import get_tokenizer SENTENCE_CHUNK_OVERLAP = 200 @@ -45,7 +47,7 @@ class SentenceSplitter(MetadataAwareTextSplitter): chunk_overlap: int = Field( default=SENTENCE_CHUNK_OVERLAP, description="The token overlap of each chunk when splitting.", - gte=0, + ge=0, ) separator: str = Field( default=" ", description="Default separator for splitting into words" @@ -74,7 +76,7 @@ def __init__( callback_manager: Optional[CallbackManager] = None, include_metadata: bool = True, include_prev_next_rel: bool = True, - id_func: Optional[Callable[[int, Document], str]] = None, + id_func: Optional[Callable] = None, ): """Initialize with parameters.""" if chunk_overlap > chunk_size: @@ -83,8 +85,18 @@ def __init__( f"({chunk_size}), should be smaller." ) id_func = id_func or default_id_func - callback_manager = callback_manager or CallbackManager([]) + super().__init__( + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + secondary_chunking_regex=secondary_chunking_regex, + separator=separator, + paragraph_separator=paragraph_separator, + callback_manager=callback_manager, + include_metadata=include_metadata, + include_prev_next_rel=include_prev_next_rel, + id_func=id_func, + ) self._chunking_tokenizer_fn = ( chunking_tokenizer_fn or split_by_sentence_tokenizer() ) @@ -101,18 +113,6 @@ def __init__( split_by_char(), ] - super().__init__( - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - secondary_chunking_regex=secondary_chunking_regex, - separator=separator, - paragraph_separator=paragraph_separator, - callback_manager=callback_manager, - include_metadata=include_metadata, - include_prev_next_rel=include_prev_next_rel, - id_func=id_func, - ) - @classmethod def from_defaults( cls, diff --git a/llama-index-core/llama_index/core/node_parser/text/token.py b/llama-index-core/llama_index/core/node_parser/text/token.py index a354b5f8a093b..48a2930269c77 100644 --- a/llama-index-core/llama_index/core/node_parser/text/token.py +++ b/llama-index-core/llama_index/core/node_parser/text/token.py @@ -1,4 +1,5 @@ """Token splitter.""" + import logging from typing import Callable, List, Optional @@ -29,7 +30,7 @@ class TokenTextSplitter(MetadataAwareTextSplitter): chunk_overlap: int = Field( default=DEFAULT_CHUNK_OVERLAP, description="The token overlap of each chunk when splitting.", - gte=0, + ge=0, ) separator: str = Field( default=" ", description="Default separator for splitting into words" @@ -61,11 +62,6 @@ def __init__( ) callback_manager = callback_manager or CallbackManager([]) id_func = id_func or default_id_func - self._tokenizer = tokenizer or get_tokenizer() - - all_seps = [separator] + (backup_separators or []) - self._split_fns = [split_by_sep(sep) for sep in all_seps] + [split_by_char()] - super().__init__( chunk_size=chunk_size, chunk_overlap=chunk_overlap, @@ -76,6 +72,9 @@ def __init__( include_prev_next_rel=include_prev_next_rel, id_func=id_func, ) + self._tokenizer = tokenizer or get_tokenizer() + all_seps = [separator] + (backup_separators or []) + self._split_fns = [split_by_sep(sep) for sep in all_seps] + [split_by_char()] @classmethod def from_defaults( diff --git a/llama-index-core/llama_index/core/objects/base.py b/llama-index-core/llama_index/core/objects/base.py index 15f7067173c31..24cbf1f86b791 100644 --- a/llama-index-core/llama_index/core/objects/base.py +++ b/llama-index-core/llama_index/core/objects/base.py @@ -12,7 +12,7 @@ QueryComponent, validate_and_convert_stringable, ) -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, ConfigDict from llama_index.core.callbacks.base import CallbackManager from llama_index.core.indices.base import BaseIndex from llama_index.core.indices.vector_store.base import VectorStoreIndex @@ -95,11 +95,9 @@ def _as_query_component(self, **kwargs: Any) -> QueryComponent: class ObjectRetrieverComponent(QueryComponent): """Object retriever component.""" + model_config = ConfigDict(arbitrary_types_allowed=True) retriever: ObjectRetriever = Field(..., description="Retriever.") - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" self.retriever.retriever.callback_manager = callback_manager diff --git a/llama-index-core/llama_index/core/objects/tool_node_mapping.py b/llama-index-core/llama_index/core/objects/tool_node_mapping.py index 3f41bc3b77a8d..6541562b96660 100644 --- a/llama-index-core/llama_index/core/objects/tool_node_mapping.py +++ b/llama-index-core/llama_index/core/objects/tool_node_mapping.py @@ -19,7 +19,7 @@ def convert_tool_to_node(tool: BaseTool) -> TextNode: f"Tool description: {tool.metadata.description}\n" ) if tool.metadata.fn_schema is not None: - node_text += f"Tool schema: {tool.metadata.fn_schema.schema()}\n" + node_text += f"Tool schema: {tool.metadata.fn_schema.model_json_schema()}\n" tool_identity = ( f"{tool.metadata.name}{tool.metadata.description}{tool.metadata.fn_schema}" diff --git a/llama-index-core/llama_index/core/output_parsers/base.py b/llama-index-core/llama_index/core/output_parsers/base.py index 3dff3d6cde91a..d3aa87935a4ff 100644 --- a/llama-index-core/llama_index/core/output_parsers/base.py +++ b/llama-index-core/llama_index/core/output_parsers/base.py @@ -10,7 +10,7 @@ QueryComponent, validate_and_convert_stringable, ) -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, ConfigDict from llama_index.core.types import BaseOutputParser @@ -39,11 +39,9 @@ def _as_query_component(self, **kwargs: Any) -> QueryComponent: class OutputParserComponent(QueryComponent): """Output parser component.""" + model_config = ConfigDict(arbitrary_types_allowed=True) output_parser: BaseOutputParser = Field(..., description="Output parser.") - class Config: - arbitrary_types_allowed = True - def _run_component(self, **kwargs: Any) -> Dict[str, Any]: """Run component.""" output = self.output_parser.parse(kwargs["input"]) diff --git a/llama-index-core/llama_index/core/output_parsers/pydantic.py b/llama-index-core/llama_index/core/output_parsers/pydantic.py index 9b53275af21aa..7487352e03d4b 100644 --- a/llama-index-core/llama_index/core/output_parsers/pydantic.py +++ b/llama-index-core/llama_index/core/output_parsers/pydantic.py @@ -45,7 +45,7 @@ def format_string(self) -> str: def get_format_string(self, escape_json: bool = True) -> str: """Format string.""" - schema_dict = self._output_cls.schema() + schema_dict = self._output_cls.model_json_schema() for key in self._excluded_schema_keys_from_format: del schema_dict[key] @@ -59,7 +59,7 @@ def get_format_string(self, escape_json: bool = True) -> str: def parse(self, text: str) -> Any: """Parse, validate, and correct errors programmatically.""" json_str = extract_json_str(text) - return self._output_cls.parse_raw(json_str) + return self._output_cls.model_validate_json(json_str) def format(self, query: str) -> str: """Format a query with structured output formatting instructions.""" diff --git a/llama-index-core/llama_index/core/playground/base.py b/llama-index-core/llama_index/core/playground/base.py index 496ab8e642998..6c072f300c159 100644 --- a/llama-index-core/llama_index/core/playground/base.py +++ b/llama-index-core/llama_index/core/playground/base.py @@ -1,10 +1,10 @@ """Experiment with different indices, models, and more.""" + from __future__ import annotations import time from typing import Any, Dict, List, Type -import pandas as pd from llama_index.core.callbacks import CallbackManager, TokenCountingHandler from llama_index.core.indices.base import BaseIndex from llama_index.core.indices.list.base import ListRetrieverMode, SummaryIndex @@ -120,7 +120,7 @@ def retriever_modes(self, retriever_modes: INDEX_SPECIFIC_QUERY_MODES_TYPE) -> N def compare( self, query_text: str, to_pandas: bool | None = True - ) -> pd.DataFrame | List[Dict[str, Any]]: + ) -> Any | List[Dict[str, Any]]: """Compare index outputs on an input query. Args: @@ -145,17 +145,11 @@ def compare( ) # insert token counter into service context - service_context = index.service_context token_counter = TokenCountingHandler() callback_manager = CallbackManager([token_counter]) - if service_context is not None: - service_context.llm.callback_manager = callback_manager - service_context.embed_model.callback_manager = callback_manager try: - query_engine = index.as_query_engine( - retriever_mode=retriever_mode, service_context=service_context - ) + query_engine = index.as_query_engine(retriever_mode=retriever_mode) except ValueError: continue @@ -178,6 +172,13 @@ def compare( print(f"\nRan {len(result)} combinations in total.") if to_pandas: + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + return pd.DataFrame(result) else: return result diff --git a/llama-index-core/llama_index/core/postprocessor/llm_rerank.py b/llama-index-core/llama_index/core/postprocessor/llm_rerank.py index 265b5d76bf9a9..52412aa8088cd 100644 --- a/llama-index-core/llama_index/core/postprocessor/llm_rerank.py +++ b/llama-index-core/llama_index/core/postprocessor/llm_rerank.py @@ -1,7 +1,8 @@ """LLM reranker.""" + from typing import Callable, List, Optional -from llama_index.core.bridge.pydantic import Field, PrivateAttr +from llama_index.core.bridge.pydantic import Field, PrivateAttr, SerializeAsAny from llama_index.core.indices.utils import ( default_format_node_batch_fn, default_parse_choice_select_answer_fn, @@ -12,15 +13,14 @@ from llama_index.core.prompts.default_prompts import DEFAULT_CHOICE_SELECT_PROMPT from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.schema import NodeWithScore, QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings class LLMRerank(BaseNodePostprocessor): """LLM-based reranker.""" top_n: int = Field(description="Top N nodes to return.") - choice_select_prompt: BasePromptTemplate = Field( + choice_select_prompt: SerializeAsAny[BasePromptTemplate] = Field( description="Choice select prompt." ) choice_batch_size: int = Field(description="Batch size for choice select.") @@ -36,27 +36,24 @@ def __init__( choice_batch_size: int = 10, format_node_batch_fn: Optional[Callable] = None, parse_choice_select_answer_fn: Optional[Callable] = None, - service_context: Optional[ServiceContext] = None, top_n: int = 10, ) -> None: choice_select_prompt = choice_select_prompt or DEFAULT_CHOICE_SELECT_PROMPT - llm = llm or llm_from_settings_or_context(Settings, service_context) - - self._format_node_batch_fn = ( - format_node_batch_fn or default_format_node_batch_fn - ) - self._parse_choice_select_answer_fn = ( - parse_choice_select_answer_fn or default_parse_choice_select_answer_fn - ) + llm = llm or Settings.llm super().__init__( llm=llm, choice_select_prompt=choice_select_prompt, choice_batch_size=choice_batch_size, - service_context=service_context, top_n=top_n, ) + self._format_node_batch_fn = ( + format_node_batch_fn or default_format_node_batch_fn + ) + self._parse_choice_select_answer_fn = ( + parse_choice_select_answer_fn or default_parse_choice_select_answer_fn + ) def _get_prompts(self) -> PromptDictType: """Get prompts.""" diff --git a/llama-index-core/llama_index/core/postprocessor/node.py b/llama-index-core/llama_index/core/postprocessor/node.py index 017ef18eaa5c4..41397ba743066 100644 --- a/llama-index-core/llama_index/core/postprocessor/node.py +++ b/llama-index-core/llama_index/core/postprocessor/node.py @@ -3,7 +3,12 @@ import logging from typing import Dict, List, Optional, cast -from llama_index.core.bridge.pydantic import Field, validator +from llama_index.core.bridge.pydantic import ( + Field, + field_validator, + SerializeAsAny, + ConfigDict, +) from llama_index.core.llms import LLM from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.prompts.base import PromptTemplate @@ -12,8 +17,7 @@ get_response_synthesizer, ) from llama_index.core.schema import NodeRelationship, NodeWithScore, QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings from llama_index.core.storage.docstore import BaseDocumentStore logger = logging.getLogger(__name__) @@ -162,7 +166,8 @@ class PrevNextNodePostprocessor(BaseNodePostprocessor): num_nodes: int = Field(default=1) mode: str = Field(default="next") - @validator("mode") + @field_validator("mode") + @classmethod def _validate_mode(cls, v: str) -> str: """Validate mode.""" if v not in ["next", "previous", "both"]: @@ -279,20 +284,15 @@ class AutoPrevNextNodePostprocessor(BaseNodePostprocessor): """ + model_config = ConfigDict(arbitrary_types_allowed=True) docstore: BaseDocumentStore - service_context: Optional[ServiceContext] = None - llm: Optional[LLM] = None + llm: Optional[SerializeAsAny[LLM]] = None num_nodes: int = Field(default=1) infer_prev_next_tmpl: str = Field(default=DEFAULT_INFER_PREV_NEXT_TMPL) refine_prev_next_tmpl: str = Field(default=DEFAULT_REFINE_INFER_PREV_NEXT_TMPL) verbose: bool = Field(default=False) response_mode: ResponseMode = Field(default=ResponseMode.COMPACT) - class Config: - """Configuration for this pydantic object.""" - - arbitrary_types_allowed = True - @classmethod def class_name(cls) -> str: return "AutoPrevNextNodePostprocessor" @@ -314,7 +314,7 @@ def _postprocess_nodes( query_bundle: Optional[QueryBundle] = None, ) -> List[NodeWithScore]: """Postprocess nodes.""" - llm = self.llm or llm_from_settings_or_context(Settings, self.service_context) + llm = self.llm or Settings.llm if query_bundle is None: raise ValueError("Missing query bundle.") diff --git a/llama-index-core/llama_index/core/postprocessor/node_recency.py b/llama-index-core/llama_index/core/postprocessor/node_recency.py index 2dadaf2ef7856..29556a5ec7cdb 100644 --- a/llama-index-core/llama_index/core/postprocessor/node_recency.py +++ b/llama-index-core/llama_index/core/postprocessor/node_recency.py @@ -1,9 +1,9 @@ """Node recency post-processor.""" + from datetime import datetime from typing import List, Optional, Set import numpy as np -import pandas as pd # NOTE: currently not being used # DEFAULT_INFER_RECENCY_TMPL = ( @@ -31,7 +31,7 @@ # else: # raise ValueError(f"Invalid recency prediction: {pred}.") from llama_index.core.base.embeddings.base import BaseEmbedding -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, SerializeAsAny from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle from llama_index.core.settings import Settings @@ -58,6 +58,13 @@ def _postprocess_nodes( query_bundle: Optional[QueryBundle] = None, ) -> List[NodeWithScore]: """Postprocess nodes.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + if query_bundle is None: raise ValueError("Missing query bundle in extra info.") @@ -86,7 +93,9 @@ def _postprocess_nodes( class EmbeddingRecencyPostprocessor(BaseNodePostprocessor): """Embedding Recency post-processor.""" - embed_model: BaseEmbedding = Field(default_factory=lambda: Settings.embed_model) + embed_model: SerializeAsAny[BaseEmbedding] = Field( + default_factory=lambda: Settings.embed_model + ) date_key: str = "date" similarity_cutoff: float = Field(default=0.7) query_embedding_tmpl: str = Field(default=DEFAULT_QUERY_EMBEDDING_TMPL) @@ -101,6 +110,13 @@ def _postprocess_nodes( query_bundle: Optional[QueryBundle] = None, ) -> List[NodeWithScore]: """Postprocess nodes.""" + try: + import pandas as pd + except ImportError: + raise ImportError( + "pandas is required for this function. Please install it with `pip install pandas`." + ) + if query_bundle is None: raise ValueError("Missing query bundle in extra info.") diff --git a/llama-index-core/llama_index/core/postprocessor/optimizer.py b/llama-index-core/llama_index/core/postprocessor/optimizer.py index 39850c43d9746..d0d8913255941 100644 --- a/llama-index-core/llama_index/core/postprocessor/optimizer.py +++ b/llama-index-core/llama_index/core/postprocessor/optimizer.py @@ -64,6 +64,12 @@ def __init__( ) response = query_engine.query("") """ + super().__init__( + percentile_cutoff=percentile_cutoff, + threshold_cutoff=threshold_cutoff, + context_after=context_after, + context_before=context_before, + ) self._embed_model = embed_model or Settings.embed_model if self._embed_model is None: try: @@ -79,19 +85,12 @@ def __init__( ) if tokenizer_fn is None: - import nltk.data + import nltk - tokenizer = nltk.data.load("tokenizers/punkt/english.pickle") + tokenizer = nltk.tokenize.PunktSentenceTokenizer() tokenizer_fn = tokenizer.tokenize self._tokenizer_fn = tokenizer_fn - super().__init__( - percentile_cutoff=percentile_cutoff, - threshold_cutoff=threshold_cutoff, - context_after=context_after, - context_before=context_before, - ) - @classmethod def class_name(cls) -> str: return "SentenceEmbeddingOptimizer" diff --git a/llama-index-core/llama_index/core/postprocessor/rankGPT_rerank.py b/llama-index-core/llama_index/core/postprocessor/rankGPT_rerank.py index 4295c460ade51..5c2ff3a973b2b 100644 --- a/llama-index-core/llama_index/core/postprocessor/rankGPT_rerank.py +++ b/llama-index-core/llama_index/core/postprocessor/rankGPT_rerank.py @@ -1,7 +1,7 @@ import logging from typing import Any, Dict, List, Optional, Sequence -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, SerializeAsAny from llama_index.core.llms import LLM, ChatMessage, ChatResponse from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.prompts import BasePromptTemplate @@ -31,7 +31,7 @@ class RankGPTRerank(BaseNodePostprocessor): verbose: bool = Field( default=False, description="Whether to print intermediate steps." ) - rankgpt_rerank_prompt: BasePromptTemplate = Field( + rankgpt_rerank_prompt: SerializeAsAny[BasePromptTemplate] = Field( description="rankGPT rerank prompt." ) diff --git a/llama-index-core/llama_index/core/postprocessor/sbert_rerank.py b/llama-index-core/llama_index/core/postprocessor/sbert_rerank.py index a3756842ba6cf..f4dfd4173a8fc 100644 --- a/llama-index-core/llama_index/core/postprocessor/sbert_rerank.py +++ b/llama-index-core/llama_index/core/postprocessor/sbert_rerank.py @@ -37,15 +37,15 @@ def __init__( "please `pip install torch sentence-transformers`", ) device = infer_torch_device() if device is None else device - self._model = CrossEncoder( - model, max_length=DEFAULT_SENTENCE_TRANSFORMER_MAX_LENGTH, device=device - ) super().__init__( top_n=top_n, model=model, device=device, keep_retrieval_score=keep_retrieval_score, ) + self._model = CrossEncoder( + model, max_length=DEFAULT_SENTENCE_TRANSFORMER_MAX_LENGTH, device=device + ) @classmethod def class_name(cls) -> str: diff --git a/llama-index-core/llama_index/core/postprocessor/types.py b/llama-index-core/llama_index/core/postprocessor/types.py index d18689f9ced71..abf028940929a 100644 --- a/llama-index-core/llama_index/core/postprocessor/types.py +++ b/llama-index-core/llama_index/core/postprocessor/types.py @@ -8,7 +8,7 @@ QueryComponent, validate_and_convert_stringable, ) -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, SerializeAsAny, ConfigDict from llama_index.core.callbacks import CallbackManager from llama_index.core.instrumentation import DispatcherSpanMixin from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType @@ -16,13 +16,11 @@ class BaseNodePostprocessor(ChainableMixin, BaseComponent, DispatcherSpanMixin, ABC): + model_config = ConfigDict(arbitrary_types_allowed=True) callback_manager: CallbackManager = Field( default_factory=CallbackManager, exclude=True ) - class Config: - arbitrary_types_allowed = True - def _get_prompts(self) -> PromptDictType: """Get prompts.""" # set by default since most postprocessors don't require prompts @@ -71,10 +69,10 @@ def _as_query_component(self, **kwargs: Any) -> QueryComponent: class PostprocessorComponent(QueryComponent): """Postprocessor component.""" - postprocessor: BaseNodePostprocessor = Field(..., description="Postprocessor") - - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict(arbitrary_types_allowed=True) + postprocessor: SerializeAsAny[BaseNodePostprocessor] = Field( + ..., description="Postprocessor" + ) def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" diff --git a/llama-index-core/llama_index/core/program/function_program.py b/llama-index-core/llama_index/core/program/function_program.py index df6964a83dcfd..80af54b07687a 100644 --- a/llama-index-core/llama_index/core/program/function_program.py +++ b/llama-index-core/llama_index/core/program/function_program.py @@ -17,6 +17,7 @@ BaseModel, create_model, ValidationError, + ConfigDict, ) from llama_index.core.llms.llm import LLM from llama_index.core.base.llms.types import ChatResponse @@ -50,7 +51,7 @@ def _parse_tool_outputs( def _get_function_tool(output_cls: Type[Model]) -> FunctionTool: """Get function tool.""" - schema = output_cls.schema() + schema = output_cls.model_json_schema() schema_description = schema.get("description", None) # NOTE: this does not specify the schema in the function signature, @@ -68,8 +69,7 @@ def model_fn(**kwargs: Any) -> BaseModel: class FlexibleModel(BaseModel): - class Config: - extra = "allow" + model_config = ConfigDict(extra="allow") def create_flexible_model(model: Type[BaseModel]) -> Type[FlexibleModel]: @@ -246,7 +246,9 @@ def _process_objects( return output_cls() tool_fn_args = [call.tool_kwargs for call in tool_calls] - objects = [output_cls.parse_obj(tool_fn_arg) for tool_fn_arg in tool_fn_args] + objects = [ + output_cls.model_validate(tool_fn_arg) for tool_fn_arg in tool_fn_args + ] if cur_objects is None or num_valid_fields(objects) > num_valid_fields( cur_objects @@ -258,7 +260,7 @@ def _process_objects( new_cur_objects = [] for obj in cur_objects: try: - new_obj = self._output_cls.parse_obj(obj.dict()) + new_obj = self._output_cls.model_validate(obj.model_dump()) except ValidationError as e: _logger.warning(f"Failed to parse object: {e}") new_obj = obj diff --git a/llama-index-core/llama_index/core/prompts/base.py b/llama-index-core/llama_index/core/prompts/base.py index 8d3a7a6181d3e..b615585b7fb9c 100644 --- a/llama-index-core/llama_index/core/prompts/base.py +++ b/llama-index-core/llama_index/core/prompts/base.py @@ -13,8 +13,14 @@ Tuple, Union, ) +from typing_extensions import Annotated -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import ( + Field, + WithJsonSchema, + PlainSerializer, + SerializeAsAny, +) if TYPE_CHECKING: from llama_index.core.bridge.langchain import ( @@ -33,7 +39,7 @@ QueryComponent, validate_and_convert_stringable, ) -from llama_index.core.bridge.pydantic import BaseModel +from llama_index.core.bridge.pydantic import BaseModel, ConfigDict from llama_index.core.base.llms.base import BaseLLM from llama_index.core.base.llms.generic_utils import ( messages_to_prompt as default_messages_to_prompt, @@ -46,7 +52,16 @@ from llama_index.core.types import BaseOutputParser +AnnotatedCallable = Annotated[ + Callable, + WithJsonSchema({"type": "string"}), + WithJsonSchema({"type": "string"}), + PlainSerializer(lambda x: f"{x.__module__}.{x.__name__}", return_type=str), +] + + class BasePromptTemplate(ChainableMixin, BaseModel, ABC): + model_config = ConfigDict(arbitrary_types_allowed=True) metadata: Dict[str, Any] template_vars: List[str] kwargs: Dict[str, str] @@ -54,7 +69,7 @@ class BasePromptTemplate(ChainableMixin, BaseModel, ABC): template_var_mappings: Optional[Dict[str, Any]] = Field( default_factory=dict, description="Template variable mappings (Optional)." ) - function_mappings: Optional[Dict[str, Callable]] = Field( + function_mappings: Optional[Dict[str, AnnotatedCallable]] = Field( default_factory=dict, description=( "Function mappings (Optional). This is a mapping from template " @@ -106,9 +121,6 @@ def _map_all_vars(self, kwargs: Dict[str, Any]) -> Dict[str, Any]: # map template vars (to point to existing format vars in string template) return self._map_template_vars(new_kwargs) - class Config: - arbitrary_types_allowed = True - @abstractmethod def partial_format(self, **kwargs: Any) -> "BasePromptTemplate": ... @@ -301,7 +313,7 @@ def format_messages( # if there's mappings specified, make sure those are used content = content_template.format(**relevant_kwargs) - message: ChatMessage = message_template.copy() + message: ChatMessage = message_template.model_copy() message.content = content messages.append(message) @@ -321,7 +333,7 @@ def _as_query_component( class SelectorPromptTemplate(BasePromptTemplate): - default_template: BasePromptTemplate + default_template: SerializeAsAny[BasePromptTemplate] conditionals: Optional[ List[Tuple[Callable[[BaseLLM], bool], BasePromptTemplate]] ] = None @@ -541,8 +553,9 @@ def get_template(self, llm: Optional[BaseLLM] = None) -> str: class PromptComponent(QueryComponent): """Prompt component.""" - prompt: BasePromptTemplate = Field(..., description="Prompt") - llm: Optional[BaseLLM] = Field( + model_config = ConfigDict(arbitrary_types_allowed=True) + prompt: SerializeAsAny[BasePromptTemplate] = Field(..., description="Prompt") + llm: Optional[SerializeAsAny[BaseLLM]] = Field( default=None, description="LLM to use for formatting prompt." ) format_messages: bool = Field( @@ -550,9 +563,6 @@ class PromptComponent(QueryComponent): description="Whether to format the prompt into a list of chat messages.", ) - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: Any) -> None: """Set callback manager.""" diff --git a/llama-index-core/llama_index/core/prompts/guidance_utils.py b/llama-index-core/llama_index/core/prompts/guidance_utils.py index b62a61561f625..514874c7a0488 100644 --- a/llama-index-core/llama_index/core/prompts/guidance_utils.py +++ b/llama-index-core/llama_index/core/prompts/guidance_utils.py @@ -38,12 +38,16 @@ def wrap_json_markdown(text: str) -> str: def pydantic_to_guidance_output_template(cls: Type[BaseModel]) -> str: """Convert a pydantic model to guidance output template.""" - return json_schema_to_guidance_output_template(cls.schema(), root=cls.schema()) + return json_schema_to_guidance_output_template( + cls.model_json_schema(), root=cls.model_json_schema() + ) def pydantic_to_guidance_output_template_markdown(cls: Type[BaseModel]) -> str: """Convert a pydantic model to guidance output template wrapped in json markdown.""" - output = json_schema_to_guidance_output_template(cls.schema(), root=cls.schema()) + output = json_schema_to_guidance_output_template( + cls.model_json_schema(), root=cls.model_json_schema() + ) return wrap_json_markdown(output) @@ -68,7 +72,7 @@ def json_schema_to_guidance_output_template( ref = schema["$ref"] model = ref.split("/")[-1] return json_schema_to_guidance_output_template( - root["definitions"][model], key, indent, root + root["$defs"][model], key, indent, root ) if schema["type"] == "object": @@ -143,7 +147,7 @@ def parse_pydantic_from_guidance_program( print("Raw output:") print(output) json_dict = parse_json_markdown(output) - sub_questions = cls.parse_obj(json_dict) + sub_questions = cls.model_validate(json_dict) except Exception as e: raise OutputParserException( "Failed to parse pydantic object from guidance program" diff --git a/llama-index-core/llama_index/core/query_engine/citation_query_engine.py b/llama-index-core/llama_index/core/query_engine/citation_query_engine.py index 51c7d28a24b56..7611b6090f7c0 100644 --- a/llama-index-core/llama_index/core/query_engine/citation_query_engine.py +++ b/llama-index-core/llama_index/core/query_engine/citation_query_engine.py @@ -23,11 +23,7 @@ QueryBundle, TextNode, ) -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings CITATION_QA_TEMPLATE = PromptTemplate( "Please provide an answer based solely on the provided sources. " @@ -120,16 +116,11 @@ def __init__( ) self._retriever = retriever - service_context = retriever.get_service_context() - callback_manager = ( - callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) - llm = llm or llm_from_settings_or_context(Settings, service_context) + callback_manager = callback_manager or Settings.callback_manager + llm = llm or Settings.llm self._response_synthesizer = response_synthesizer or get_response_synthesizer( llm=llm, - service_context=service_context, callback_manager=callback_manager, ) self._node_postprocessors = node_postprocessors or [] @@ -177,7 +168,6 @@ def from_args( citation_refine_template (BasePromptTemplate): Template for citation refinement. retriever (BaseRetriever): A retriever object. - service_context (Optional[ServiceContext]): A ServiceContext object. node_postprocessors (Optional[List[BaseNodePostprocessor]]): A list of node postprocessors. verbose (bool): Whether to print out debug info. @@ -192,7 +182,6 @@ def from_args( response_synthesizer = response_synthesizer or get_response_synthesizer( llm=llm, - service_context=index.service_context, text_qa_template=citation_qa_template, refine_template=citation_refine_template, response_mode=response_mode, @@ -204,9 +193,7 @@ def from_args( retriever=retriever, llm=llm, response_synthesizer=response_synthesizer, - callback_manager=callback_manager_from_settings_or_context( - Settings, index.service_context - ), + callback_manager=Settings.callback_manager, citation_chunk_size=citation_chunk_size, citation_chunk_overlap=citation_chunk_overlap, text_splitter=text_splitter, @@ -227,10 +214,10 @@ def _create_citation_nodes(self, nodes: List[NodeWithScore]) -> List[NodeWithSco ) for text_chunk in text_chunks: - text = f"Source {len(new_nodes)+1}:\n{text_chunk}\n" + text = f"Source {len(new_nodes) + 1}:\n{text_chunk}\n" new_node = NodeWithScore( - node=TextNode.parse_obj(node.node), score=node.score + node=TextNode.model_validate(node.node), score=node.score ) new_node.node.text = text new_nodes.append(new_node) diff --git a/llama-index-core/llama_index/core/query_engine/custom.py b/llama-index-core/llama_index/core/query_engine/custom.py index 4793da5956402..2c8a2c6adc0d3 100644 --- a/llama-index-core/llama_index/core/query_engine/custom.py +++ b/llama-index-core/llama_index/core/query_engine/custom.py @@ -5,7 +5,7 @@ from llama_index.core.base.base_query_engine import BaseQueryEngine from llama_index.core.base.response.schema import RESPONSE_TYPE, Response -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from llama_index.core.callbacks.base import CallbackManager from llama_index.core.prompts.mixin import PromptMixinType from llama_index.core.schema import QueryBundle, QueryType @@ -24,6 +24,7 @@ class CustomQueryEngine(BaseModel, BaseQueryEngine): """ + model_config = ConfigDict(arbitrary_types_allowed=True) callback_manager: CallbackManager = Field( default_factory=lambda: CallbackManager([]), exclude=True ) @@ -32,9 +33,6 @@ def _get_prompt_modules(self) -> PromptMixinType: """Get prompt sub-modules.""" return {} - class Config: - arbitrary_types_allowed = True - def query(self, str_or_query_bundle: QueryType) -> RESPONSE_TYPE: with self.callback_manager.as_trace("query"): # if query bundle, just run the query diff --git a/llama-index-core/llama_index/core/query_engine/flare/answer_inserter.py b/llama-index-core/llama_index/core/query_engine/flare/answer_inserter.py index 14f9cee4cb6db..f3a57914e10a5 100644 --- a/llama-index-core/llama_index/core/query_engine/flare/answer_inserter.py +++ b/llama-index-core/llama_index/core/query_engine/flare/answer_inserter.py @@ -11,8 +11,7 @@ PromptMixinType, ) from llama_index.core.query_engine.flare.schema import QueryTask -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings class BaseLookaheadAnswerInserter(PromptMixin): @@ -142,11 +141,10 @@ class LLMLookaheadAnswerInserter(BaseLookaheadAnswerInserter): def __init__( self, llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, answer_insert_prompt: Optional[BasePromptTemplate] = None, ) -> None: """Init params.""" - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._answer_insert_prompt = ( answer_insert_prompt or DEFAULT_ANSWER_INSERT_PROMPT ) diff --git a/llama-index-core/llama_index/core/query_engine/flare/base.py b/llama-index-core/llama_index/core/query_engine/flare/base.py index e7860e10722d0..2a63c4d597e71 100644 --- a/llama-index-core/llama_index/core/query_engine/flare/base.py +++ b/llama-index-core/llama_index/core/query_engine/flare/base.py @@ -23,8 +23,7 @@ from llama_index.core.query_engine.retriever_query_engine import RetrieverQueryEngine from llama_index.core.schema import QueryBundle, NodeWithScore -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings from llama_index.core.utils import print_text # These prompts are taken from the FLARE repo: @@ -106,8 +105,6 @@ class FLAREInstructQueryEngine(BaseQueryEngine): Args: query_engine (BaseQueryEngine): query engine to use llm (Optional[LLM]): LLM model. Defaults to None. - service_context (Optional[ServiceContext]): service context. - Defaults to None. instruct_prompt (Optional[PromptTemplate]): instruct prompt. Defaults to None. lookahead_answer_inserter (Optional[BaseLookaheadAnswerInserter]): lookahead answer inserter. Defaults to None. @@ -127,7 +124,6 @@ def __init__( self, query_engine: BaseQueryEngine, llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, instruct_prompt: Optional[BasePromptTemplate] = None, lookahead_answer_inserter: Optional[BaseLookaheadAnswerInserter] = None, done_output_parser: Optional[IsDoneOutputParser] = None, @@ -140,7 +136,7 @@ def __init__( """Init params.""" super().__init__(callback_manager=callback_manager) self._query_engine = query_engine - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._instruct_prompt = instruct_prompt or DEFAULT_INSTRUCT_PROMPT self._lookahead_answer_inserter = lookahead_answer_inserter or ( LLMLookaheadAnswerInserter(llm=self._llm) diff --git a/llama-index-core/llama_index/core/query_engine/graph_query_engine.py b/llama-index-core/llama_index/core/query_engine/graph_query_engine.py index eef39085a7b9b..9ce4c7f279de1 100644 --- a/llama-index-core/llama_index/core/query_engine/graph_query_engine.py +++ b/llama-index-core/llama_index/core/query_engine/graph_query_engine.py @@ -5,10 +5,8 @@ from llama_index.core.callbacks.schema import CBEventType, EventPayload from llama_index.core.indices.composability.graph import ComposableGraph from llama_index.core.schema import IndexNode, NodeWithScore, QueryBundle, TextNode -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, -) +from llama_index.core.settings import Settings + import llama_index.core.instrumentation as instrument dispatcher = instrument.get_dispatcher(__name__) @@ -35,7 +33,7 @@ def __init__( graph: ComposableGraph, custom_query_engines: Optional[Dict[str, BaseQueryEngine]] = None, recursive: bool = True, - **kwargs: Any + **kwargs: Any, ) -> None: """Init params.""" self._graph = graph @@ -44,9 +42,7 @@ def __init__( # additional configs self._recursive = recursive - callback_manager = callback_manager_from_settings_or_context( - Settings, self._graph.service_context - ) + callback_manager = Settings.callback_manager super().__init__(callback_manager=callback_manager) def _get_prompt_modules(self) -> Dict[str, Any]: diff --git a/llama-index-core/llama_index/core/query_engine/jsonalyze_query_engine.py b/llama-index-core/llama_index/core/query_engine/jsonalyze_query_engine.py index c2b81afbf9e1e..8514179138ec3 100644 --- a/llama-index-core/llama_index/core/query_engine/jsonalyze_query_engine.py +++ b/llama-index-core/llama_index/core/query_engine/jsonalyze_query_engine.py @@ -15,12 +15,7 @@ from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType from llama_index.core.prompts.prompt_type import PromptType from llama_index.core.schema import QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.utils import print_text logger = logging.getLogger(__name__) @@ -212,7 +207,6 @@ class JSONalyzeQueryEngine(BaseQueryEngine): Converts natural language statasical queries to SQL within in-mem SQLite queries. list_of_dict(List[Dict[str, Any]]): List of dictionaries to query. - service_context (ServiceContext): ServiceContext jsonalyze_prompt (BasePromptTemplate): The JSONalyze prompt to use. use_async (bool): Whether to use async. analyzer (Callable): The analyzer that executes the query. @@ -228,7 +222,6 @@ class JSONalyzeQueryEngine(BaseQueryEngine): def __init__( self, list_of_dict: List[Dict[str, Any]], - service_context: Optional[ServiceContext] = None, llm: Optional[LLM] = None, jsonalyze_prompt: Optional[BasePromptTemplate] = None, use_async: bool = False, @@ -242,7 +235,7 @@ def __init__( ) -> None: """Initialize params.""" self._list_of_dict = list_of_dict - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._jsonalyze_prompt = jsonalyze_prompt or DEFAULT_JSONALYZE_PROMPT self._use_async = use_async self._analyzer = load_jsonalyzer(use_async, analyzer) @@ -254,11 +247,7 @@ def __init__( self._table_name = table_name self._verbose = verbose - super().__init__( - callback_manager=callback_manager_from_settings_or_context( - Settings, service_context - ) - ) + super().__init__(callback_manager=Settings.callback_manager) def _get_prompts(self) -> Dict[str, Any]: """Get prompts.""" diff --git a/llama-index-core/llama_index/core/query_engine/knowledge_graph_query_engine.py b/llama-index-core/llama_index/core/query_engine/knowledge_graph_query_engine.py index a6936d8dd1ef6..c6e61ada67acb 100644 --- a/llama-index-core/llama_index/core/query_engine/knowledge_graph_query_engine.py +++ b/llama-index-core/llama_index/core/query_engine/knowledge_graph_query_engine.py @@ -1,4 +1,4 @@ -""" Knowledge Graph Query Engine.""" +"""Knowledge Graph Query Engine.""" import deprecated import logging @@ -19,12 +19,7 @@ get_response_synthesizer, ) from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.storage.storage_context import StorageContext from llama_index.core.utils import print_text @@ -61,7 +56,6 @@ class KnowledgeGraphQueryEngine(BaseQueryEngine): Query engine to call a knowledge graph. Args: - service_context (Optional[ServiceContext]): A service context to use. storage_context (Optional[StorageContext]): A storage context to use. refresh_schema (bool): Whether to refresh the schema. verbose (bool): Whether to print intermediate results. @@ -80,8 +74,6 @@ def __init__( refresh_schema: bool = False, verbose: bool = False, response_synthesizer: Optional[BaseSynthesizer] = None, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ): # Ensure that we have a graph store @@ -92,7 +84,7 @@ def __init__( self._storage_context = storage_context self.graph_store = storage_context.graph_store - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm # Get Graph schema self._graph_schema = self.graph_store.get_schema(refresh=refresh_schema) @@ -104,13 +96,9 @@ def __init__( graph_response_answer_prompt or DEFAULT_KG_RESPONSE_ANSWER_PROMPT ) self._verbose = verbose - callback_manager = callback_manager_from_settings_or_context( - Settings, service_context - ) + callback_manager = Settings.callback_manager self._response_synthesizer = response_synthesizer or get_response_synthesizer( - llm=self._llm, - callback_manager=callback_manager, - service_context=service_context, + llm=self._llm, callback_manager=callback_manager ) super().__init__(callback_manager=callback_manager) diff --git a/llama-index-core/llama_index/core/query_engine/retriever_query_engine.py b/llama-index-core/llama_index/core/query_engine/retriever_query_engine.py index 5bf89372bd263..06c3f5a2cbc1a 100644 --- a/llama-index-core/llama_index/core/query_engine/retriever_query_engine.py +++ b/llama-index-core/llama_index/core/query_engine/retriever_query_engine.py @@ -16,12 +16,7 @@ get_response_synthesizer, ) from llama_index.core.schema import NodeWithScore, QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings import llama_index.core.instrumentation as instrument dispatcher = instrument.get_dispatcher(__name__) @@ -46,11 +41,8 @@ def __init__( ) -> None: self._retriever = retriever self._response_synthesizer = response_synthesizer or get_response_synthesizer( - llm=llm_from_settings_or_context(Settings, retriever.get_service_context()), - callback_manager=callback_manager - or callback_manager_from_settings_or_context( - Settings, retriever.get_service_context() - ), + llm=Settings.llm, + callback_manager=callback_manager or Settings.callback_manager, ) self._node_postprocessors = node_postprocessors or [] @@ -81,15 +73,12 @@ def from_args( output_cls: Optional[BaseModel] = None, use_async: bool = False, streaming: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> "RetrieverQueryEngine": """Initialize a RetrieverQueryEngine object.". Args: retriever (BaseRetriever): A retriever object. - service_context (Optional[ServiceContext]): A ServiceContext object. node_postprocessors (Optional[List[BaseNodePostprocessor]]): A list of node postprocessors. verbose (bool): Whether to print out debug info. @@ -105,11 +94,10 @@ def from_args( object. """ - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm response_synthesizer = response_synthesizer or get_response_synthesizer( llm=llm, - service_context=service_context, text_qa_template=text_qa_template, refine_template=refine_template, summary_template=summary_template, @@ -120,9 +108,7 @@ def from_args( streaming=streaming, ) - callback_manager = callback_manager_from_settings_or_context( - Settings, service_context - ) + callback_manager = Settings.callback_manager return cls( retriever=retriever, diff --git a/llama-index-core/llama_index/core/query_engine/retry_source_query_engine.py b/llama-index-core/llama_index/core/query_engine/retry_source_query_engine.py index 5329ab076352f..bf29bcdca34cb 100644 --- a/llama-index-core/llama_index/core/query_engine/retry_source_query_engine.py +++ b/llama-index-core/llama_index/core/query_engine/retry_source_query_engine.py @@ -12,12 +12,7 @@ RetrieverQueryEngine, ) from llama_index.core.schema import Document, QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings logger = logging.getLogger(__name__) @@ -32,18 +27,13 @@ def __init__( llm: Optional[LLM] = None, max_retries: int = 3, callback_manager: Optional[CallbackManager] = None, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: """Run a BaseQueryEngine with retries.""" self._query_engine = query_engine self._evaluator = evaluator - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self.max_retries = max_retries - super().__init__( - callback_manager=callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) + super().__init__(callback_manager=callback_manager or Settings.callback_manager) def _get_prompt_modules(self) -> PromptMixinType: """Get prompt sub-modules.""" diff --git a/llama-index-core/llama_index/core/query_engine/router_query_engine.py b/llama-index-core/llama_index/core/query_engine/router_query_engine.py index 194ba29cf2865..ecb68e9996fb1 100644 --- a/llama-index-core/llama_index/core/query_engine/router_query_engine.py +++ b/llama-index-core/llama_index/core/query_engine/router_query_engine.py @@ -23,12 +23,7 @@ from llama_index.core.response_synthesizers import TreeSummarize from llama_index.core.schema import BaseNode, QueryBundle from llama_index.core.selectors.utils import get_selector_from_llm -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.tools.query_engine import QueryEngineTool from llama_index.core.tools.types import ToolMetadata from llama_index.core.utils import print_text @@ -99,7 +94,6 @@ class RouterQueryEngine(BaseQueryEngine): query_engine_tools (Sequence[QueryEngineTool]): A sequence of candidate query engines. They must be wrapped as tools to expose metadata to the selector. - service_context (Optional[ServiceContext]): A service context. summarizer (Optional[TreeSummarize]): Tree summarizer to summarize sub-results. """ @@ -111,25 +105,18 @@ def __init__( llm: Optional[LLM] = None, summarizer: Optional[TreeSummarize] = None, verbose: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: - self._llm = llm or llm_from_settings_or_context(Settings, llm) + self._llm = llm or Settings.llm self._selector = selector self._query_engines = [x.query_engine for x in query_engine_tools] self._metadatas = [x.metadata for x in query_engine_tools] self._summarizer = summarizer or TreeSummarize( llm=self._llm, - service_context=service_context, summary_template=DEFAULT_TREE_SUMMARIZE_PROMPT_SEL, ) self._verbose = verbose - super().__init__( - callback_manager=callback_manager_from_settings_or_context( - Settings, service_context - ) - ) + super().__init__(callback_manager=Settings.callback_manager) def _get_prompt_modules(self) -> PromptMixinType: """Get prompt sub-modules.""" @@ -144,11 +131,9 @@ def from_defaults( selector: Optional[BaseSelector] = None, summarizer: Optional[TreeSummarize] = None, select_multi: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> "RouterQueryEngine": - llm = llm or llm_from_settings_or_context(Settings, llm) + llm = llm or Settings.llm selector = selector or get_selector_from_llm(llm, is_multi=select_multi) @@ -158,7 +143,6 @@ def from_defaults( selector, query_engine_tools, llm=llm, - service_context=service_context, summarizer=summarizer, **kwargs, ) @@ -325,7 +309,6 @@ class ToolRetrieverRouterQueryEngine(BaseQueryEngine): Args: retriever (ObjectRetriever): A retriever that retrieves a set of query engine tools. - service_context (Optional[ServiceContext]): A service context. summarizer (Optional[TreeSummarize]): Tree summarizer to summarize sub-results. """ @@ -334,19 +317,16 @@ def __init__( self, retriever: ObjectRetriever[QueryEngineTool], llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, summarizer: Optional[TreeSummarize] = None, ) -> None: - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm self._summarizer = summarizer or TreeSummarize( llm=llm, summary_template=DEFAULT_TREE_SUMMARIZE_PROMPT_SEL, ) self._retriever = retriever - super().__init__( - callback_manager_from_settings_or_context(Settings, service_context) - ) + super().__init__(Settings.callback_manager) def _get_prompt_modules(self) -> PromptMixinType: """Get prompt sub-modules.""" diff --git a/llama-index-core/llama_index/core/query_engine/sql_join_query_engine.py b/llama-index-core/llama_index/core/query_engine/sql_join_query_engine.py index f8eee3c607f2a..f80959a86bf3a 100644 --- a/llama-index-core/llama_index/core/query_engine/sql_join_query_engine.py +++ b/llama-index-core/llama_index/core/query_engine/sql_join_query_engine.py @@ -15,17 +15,14 @@ BaseSQLTableQueryEngine, NLSQLTableQueryEngine, ) +from llama_index.core.llms import LLM from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType from llama_index.core.schema import QueryBundle from llama_index.core.selectors.llm_selectors import LLMSingleSelector from llama_index.core.selectors.pydantic_selectors import PydanticSingleSelector from llama_index.core.selectors.utils import get_selector_from_llm -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import ( - LLMPredictorType, -) -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings from llama_index.core.tools.query_engine import QueryEngineTool from llama_index.core.utils import print_text @@ -125,7 +122,7 @@ class SQLAugmentQueryTransform(BaseQueryTransform): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, sql_augment_transform_prompt: Optional[BasePromptTemplate] = None, check_stop_parser: Optional[Callable[[QueryBundle], bool]] = None, ) -> None: @@ -180,7 +177,6 @@ class SQLJoinQueryEngine(BaseQueryEngine): other_query_tool (QueryEngineTool): Other query engine tool. selector (Optional[Union[LLMSingleSelector, PydanticSingleSelector]]): Selector to use. - service_context (Optional[ServiceContext]): Service context to use. sql_join_synthesis_prompt (Optional[BasePromptTemplate]): PromptTemplate to use for SQL join synthesis. sql_augment_query_transform (Optional[SQLAugmentQueryTransform]): Query @@ -196,15 +192,13 @@ def __init__( sql_query_tool: QueryEngineTool, other_query_tool: QueryEngineTool, selector: Optional[Union[LLMSingleSelector, PydanticSingleSelector]] = None, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, sql_join_synthesis_prompt: Optional[BasePromptTemplate] = None, sql_augment_query_transform: Optional[SQLAugmentQueryTransform] = None, use_sql_join_synthesis: bool = True, callback_manager: Optional[CallbackManager] = None, verbose: bool = True, streaming: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: """Initialize params.""" super().__init__(callback_manager=callback_manager) @@ -220,7 +214,7 @@ def __init__( self._sql_query_tool = sql_query_tool self._other_query_tool = other_query_tool - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._selector = selector or get_selector_from_llm(self._llm, is_multi=False) assert isinstance(self._selector, (LLMSingleSelector, PydanticSingleSelector)) diff --git a/llama-index-core/llama_index/core/query_engine/sql_vector_query_engine.py b/llama-index-core/llama_index/core/query_engine/sql_vector_query_engine.py index 23cb43b32c541..0703e63d76e5e 100644 --- a/llama-index-core/llama_index/core/query_engine/sql_vector_query_engine.py +++ b/llama-index-core/llama_index/core/query_engine/sql_vector_query_engine.py @@ -23,7 +23,6 @@ ) from llama_index.core.selectors.llm_selectors import LLMSingleSelector from llama_index.core.selectors.pydantic_selectors import PydanticSingleSelector -from llama_index.core.service_context import ServiceContext from llama_index.core.tools.query_engine import QueryEngineTool logger = logging.getLogger(__name__) @@ -66,7 +65,6 @@ class SQLAutoVectorQueryEngine(SQLJoinQueryEngine): vector_query_tool (QueryEngineTool): Query engine tool for vector database. selector (Optional[Union[LLMSingleSelector, PydanticSingleSelector]]): Selector to use. - service_context (Optional[ServiceContext]): Service context to use. sql_vector_synthesis_prompt (Optional[BasePromptTemplate]): Prompt to use for SQL vector synthesis. sql_augment_query_transform (Optional[SQLAugmentQueryTransform]): Query @@ -83,7 +81,6 @@ def __init__( vector_query_tool: QueryEngineTool, selector: Optional[Union[LLMSingleSelector, PydanticSingleSelector]] = None, llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, sql_vector_synthesis_prompt: Optional[BasePromptTemplate] = None, sql_augment_query_transform: Optional[SQLAugmentQueryTransform] = None, use_sql_vector_synthesis: bool = True, @@ -121,7 +118,6 @@ def __init__( vector_query_tool, selector=selector, llm=llm, - service_context=service_context, sql_join_synthesis_prompt=sql_vector_synthesis_prompt, sql_augment_query_transform=sql_augment_query_transform, use_sql_join_synthesis=use_sql_vector_synthesis, diff --git a/llama-index-core/llama_index/core/query_engine/sub_question_query_engine.py b/llama-index-core/llama_index/core/query_engine/sub_question_query_engine.py index c2193eece92da..36f7fa02a32d6 100644 --- a/llama-index-core/llama_index/core/query_engine/sub_question_query_engine.py +++ b/llama-index-core/llama_index/core/query_engine/sub_question_query_engine.py @@ -17,12 +17,7 @@ get_response_synthesizer, ) from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.tools.query_engine import QueryEngineTool from llama_index.core.utils import get_color_mapping, print_text @@ -93,17 +88,14 @@ def from_defaults( llm: Optional[LLM] = None, question_gen: Optional[BaseQuestionGenerator] = None, response_synthesizer: Optional[BaseSynthesizer] = None, - service_context: Optional[ServiceContext] = None, verbose: bool = True, use_async: bool = True, ) -> "SubQuestionQueryEngine": - callback_manager = callback_manager_from_settings_or_context( - Settings, service_context - ) + callback_manager = Settings.callback_manager if len(query_engine_tools) > 0: callback_manager = query_engine_tools[0].query_engine.callback_manager - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm if question_gen is None: try: from llama_index.question_gen.openai import ( @@ -125,7 +117,6 @@ def from_defaults( synth = response_synthesizer or get_response_synthesizer( llm=llm, callback_manager=callback_manager, - service_context=service_context, use_async=use_async, ) diff --git a/llama-index-core/llama_index/core/query_pipeline/components/agent.py b/llama-index-core/llama_index/core/query_pipeline/components/agent.py index 384f5fa69d853..50f771de87f99 100644 --- a/llama-index-core/llama_index/core/query_pipeline/components/agent.py +++ b/llama-index-core/llama_index/core/query_pipeline/components/agent.py @@ -2,16 +2,29 @@ from inspect import signature from typing import Any, Callable, Dict, Optional, Set, Tuple, cast +from typing_extensions import Annotated from llama_index.core.base.query_pipeline.query import ( InputKeys, OutputKeys, QueryComponent, ) -from llama_index.core.bridge.pydantic import Field, PrivateAttr +from llama_index.core.bridge.pydantic import ( + Field, + PrivateAttr, + ConfigDict, + WithJsonSchema, +) from llama_index.core.callbacks.base import CallbackManager +AnnotatedCallable = Annotated[ + Callable, + WithJsonSchema({"type": "string"}, mode="serialization"), + WithJsonSchema({"type": "string"}, mode="validation"), +] + + def get_parameters(fn: Callable) -> Tuple[Set[str], Set[str]]: """Get parameters from function. @@ -48,8 +61,9 @@ class AgentInputComponent(QueryComponent): """ - fn: Callable = Field(..., description="Function to run.") - async_fn: Optional[Callable] = Field( + model_config = ConfigDict(arbitrary_types_allowed=True) + fn: AnnotatedCallable = Field(..., description="Function to run.") + async_fn: Optional[AnnotatedCallable] = Field( None, description="Async function to run. If not provided, will run `fn`." ) @@ -66,6 +80,7 @@ def __init__( ) -> None: """Initialize.""" # determine parameters + super().__init__(fn=fn, async_fn=async_fn, **kwargs) default_req_params, default_opt_params = get_parameters(fn) if req_params is None: req_params = default_req_params @@ -74,10 +89,6 @@ def __init__( self._req_params = req_params self._opt_params = opt_params - super().__init__(fn=fn, async_fn=async_fn, **kwargs) - - class Config: - arbitrary_types_allowed = True def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" @@ -157,6 +168,7 @@ class AgentFnComponent(BaseAgentComponent): """ + model_config = ConfigDict(arbitrary_types_allowed=True) fn: Callable = Field(..., description="Function to run.") async_fn: Optional[Callable] = Field( None, description="Async function to run. If not provided, will run `fn`." @@ -175,6 +187,7 @@ def __init__( ) -> None: """Initialize.""" # determine parameters + super().__init__(fn=fn, async_fn=async_fn, **kwargs) default_req_params, default_opt_params = get_parameters(fn) # make sure task and step are part of the list, and remove them from the list if "task" not in default_req_params or "state" not in default_req_params: @@ -192,10 +205,6 @@ def __init__( self._req_params = req_params self._opt_params = opt_params - super().__init__(fn=fn, async_fn=async_fn, **kwargs) - - class Config: - arbitrary_types_allowed = True def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" @@ -267,13 +276,11 @@ class CustomAgentComponent(BaseAgentComponent): """ + model_config = ConfigDict(arbitrary_types_allowed=True) callback_manager: CallbackManager = Field( default_factory=CallbackManager, description="Callback manager" ) - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" self.callback_manager = callback_manager diff --git a/llama-index-core/llama_index/core/query_pipeline/components/function.py b/llama-index-core/llama_index/core/query_pipeline/components/function.py index f6a180da0666c..c36ae5e087a17 100644 --- a/llama-index-core/llama_index/core/query_pipeline/components/function.py +++ b/llama-index-core/llama_index/core/query_pipeline/components/function.py @@ -2,15 +2,27 @@ from inspect import signature from typing import Any, Callable, Dict, Optional, Set, Tuple +from typing_extensions import Annotated from llama_index.core.base.query_pipeline.query import ( InputKeys, OutputKeys, QueryComponent, ) -from llama_index.core.bridge.pydantic import Field, PrivateAttr +from llama_index.core.bridge.pydantic import ( + Field, + PrivateAttr, + ConfigDict, + WithJsonSchema, +) from llama_index.core.callbacks.base import CallbackManager +AnnotatedCallable = Annotated[ + Callable, + WithJsonSchema({"type": "string"}, mode="serialization"), + WithJsonSchema({"type": "string"}, mode="validation"), +] + def get_parameters(fn: Callable) -> Tuple[Set[str], Set[str]]: """Get parameters from function. @@ -35,8 +47,9 @@ def get_parameters(fn: Callable) -> Tuple[Set[str], Set[str]]: class FnComponent(QueryComponent): """Query component that takes in an arbitrary function.""" - fn: Callable = Field(..., description="Function to run.") - async_fn: Optional[Callable] = Field( + model_config = ConfigDict(arbitrary_types_allowed=True) + fn: AnnotatedCallable = Field(..., description="Function to run.") + async_fn: Optional[AnnotatedCallable] = Field( None, description="Async function to run. If not provided, will run `fn`." ) output_key: str = Field( @@ -57,6 +70,7 @@ def __init__( ) -> None: """Initialize.""" # determine parameters + super().__init__(fn=fn, async_fn=async_fn, output_key=output_key, **kwargs) default_req_params, default_opt_params = get_parameters(fn) if req_params is None: req_params = default_req_params @@ -65,10 +79,6 @@ def __init__( self._req_params = req_params self._opt_params = opt_params - super().__init__(fn=fn, async_fn=async_fn, output_key=output_key, **kwargs) - - class Config: - arbitrary_types_allowed = True def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" diff --git a/llama-index-core/llama_index/core/query_pipeline/components/loop.py b/llama-index-core/llama_index/core/query_pipeline/components/loop.py index 1e8f787372cda..9224fd95562f1 100644 --- a/llama-index-core/llama_index/core/query_pipeline/components/loop.py +++ b/llama-index-core/llama_index/core/query_pipeline/components/loop.py @@ -4,25 +4,32 @@ QueryComponent, ) from llama_index.core.query_pipeline.query import QueryPipeline -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, ConfigDict, WithJsonSchema from llama_index.core.callbacks.base import CallbackManager from typing import Any, Dict, Optional, Callable +from typing_extensions import Annotated + +AnnotatedCallable = Annotated[ + Callable, + WithJsonSchema({"type": "string"}, mode="serialization"), + WithJsonSchema({"type": "string"}, mode="validation"), +] class LoopComponent(QueryComponent): """Loop component.""" + model_config = ConfigDict(arbitrary_types_allowed=True) pipeline: QueryPipeline = Field(..., description="Query pipeline") - should_exit_fn: Optional[Callable] = Field(..., description="Should exit function") - add_output_to_input_fn: Optional[Callable] = Field( + should_exit_fn: Optional[AnnotatedCallable] = Field( + ..., description="Should exit function" + ) + add_output_to_input_fn: Optional[AnnotatedCallable] = Field( ..., description="Add output to input function. If not provided, will reuse the original input for the next iteration. If provided, will call the function to combine the output into the input for the next iteration.", ) max_iterations: Optional[int] = Field(5, description="Max iterations") - class Config: - arbitrary_types_allowed = True - def __init__( self, pipeline: QueryPipeline, diff --git a/llama-index-core/llama_index/core/query_pipeline/components/router.py b/llama-index-core/llama_index/core/query_pipeline/components/router.py index 9ce1b1f9b1060..ce762cf12bdc4 100644 --- a/llama-index-core/llama_index/core/query_pipeline/components/router.py +++ b/llama-index-core/llama_index/core/query_pipeline/components/router.py @@ -1,6 +1,5 @@ """Router components.""" - from typing import Any, Dict, List from llama_index.core.base.base_selector import BaseSelector @@ -12,7 +11,12 @@ QueryComponent, validate_and_convert_stringable, ) -from llama_index.core.bridge.pydantic import Field, PrivateAttr +from llama_index.core.bridge.pydantic import ( + Field, + PrivateAttr, + SerializeAsAny, + ConfigDict, +) from llama_index.core.callbacks.base import CallbackManager from llama_index.core.utils import print_text @@ -20,11 +24,9 @@ class SelectorComponent(QueryComponent): """Selector component.""" + model_config = ConfigDict(arbitrary_types_allowed=True) selector: BaseSelector = Field(..., description="Selector") - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" @@ -76,20 +78,18 @@ class RouterComponent(QueryComponent): """ - selector: BaseSelector = Field(..., description="Selector") + model_config = ConfigDict(arbitrary_types_allowed=True) + selector: SerializeAsAny[BaseSelector] = Field(..., description="Selector") choices: List[str] = Field( ..., description="Choices (must correspond to components)" ) - components: List[QueryComponent] = Field( + components: List[SerializeAsAny[QueryComponent]] = Field( ..., description="Components (must correspond to choices)" ) verbose: bool = Field(default=False, description="Verbose") _query_keys: List[str] = PrivateAttr() - class Config: - arbitrary_types_allowed = True - def __init__( self, selector: BaseSelector, @@ -111,15 +111,13 @@ def __init__( raise ValueError("Expected one required input key") query_keys.append(next(iter(new_component.free_req_input_keys))) new_components.append(new_component) - - self._query_keys = query_keys - super().__init__( selector=selector, choices=choices, components=new_components, verbose=verbose, ) + self._query_keys = query_keys def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" diff --git a/llama-index-core/llama_index/core/query_pipeline/components/tool_runner.py b/llama-index-core/llama_index/core/query_pipeline/components/tool_runner.py index 2c586ea208545..1679372223efe 100644 --- a/llama-index-core/llama_index/core/query_pipeline/components/tool_runner.py +++ b/llama-index-core/llama_index/core/query_pipeline/components/tool_runner.py @@ -8,7 +8,7 @@ QueryComponent, validate_and_convert_stringable, ) -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, ConfigDict from llama_index.core.callbacks import ( CallbackManager, CBEventType, @@ -21,6 +21,7 @@ class ToolRunnerComponent(QueryComponent): """Tool runner component that takes in a set of tools.""" + model_config = ConfigDict(arbitrary_types_allowed=True) tool_dict: Dict[str, AsyncBaseTool] = Field( ..., description="Dictionary of tool names to tools." ) @@ -42,9 +43,6 @@ def __init__( tool_dict=tool_dict, callback_manager=callback_manager, **kwargs ) - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" self.callback_manager = callback_manager diff --git a/llama-index-core/llama_index/core/query_pipeline/query.py b/llama-index-core/llama_index/core/query_pipeline/query.py index 524ad4eb0065c..3e40dc838e444 100644 --- a/llama-index-core/llama_index/core/query_pipeline/query.py +++ b/llama-index-core/llama_index/core/query_pipeline/query.py @@ -19,7 +19,7 @@ import networkx from llama_index.core.async_utils import asyncio_run, run_jobs -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, ConfigDict from llama_index.core.callbacks import CallbackManager from llama_index.core.callbacks.schema import CBEventType, EventPayload from llama_index.core.base.query_pipeline.query import ( @@ -205,6 +205,7 @@ class QueryPipeline(QueryComponent): """ + model_config = ConfigDict(arbitrary_types_allowed=True) callback_manager: CallbackManager = Field( default_factory=lambda: CallbackManager([]), exclude=True ) @@ -229,9 +230,6 @@ class QueryPipeline(QueryComponent): default_factory=dict, description="State of the pipeline." ) - class Config: - arbitrary_types_allowed = True - def __init__( self, callback_manager: Optional[CallbackManager] = None, @@ -281,7 +279,7 @@ def _init_graph( self.add_modules(modules) if links is not None: for link in links: - self.add_link(**link.dict()) + self.add_link(**link.model_dump()) def add_chain(self, chain: Sequence[CHAIN_COMPONENT_TYPE]) -> None: """Add a chain of modules to the pipeline. @@ -318,7 +316,7 @@ def add_links( """Add links to the pipeline.""" for link in links: if isinstance(link, Link): - self.add_link(**link.dict()) + self.add_link(**link.model_dump()) else: raise ValueError("Link must be of type `Link` or `ConditionalLinks`.") diff --git a/llama-index-core/llama_index/core/question_gen/llm_generators.py b/llama-index-core/llama_index/core/question_gen/llm_generators.py index 304b64458bb72..4c14520b1c061 100644 --- a/llama-index-core/llama_index/core/question_gen/llm_generators.py +++ b/llama-index-core/llama_index/core/question_gen/llm_generators.py @@ -12,9 +12,7 @@ ) from llama_index.core.question_gen.types import BaseQuestionGenerator, SubQuestion from llama_index.core.schema import QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import LLMPredictorType -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings from llama_index.core.tools.types import ToolMetadata from llama_index.core.types import BaseOutputParser @@ -34,13 +32,12 @@ def __init__( @classmethod def from_defaults( cls, - llm: Optional[LLMPredictorType] = None, - service_context: Optional[ServiceContext] = None, + llm: Optional[LLM] = None, prompt_template_str: Optional[str] = None, output_parser: Optional[BaseOutputParser] = None, ) -> "LLMQuestionGenerator": # optionally initialize defaults - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm prompt_template_str = prompt_template_str or DEFAULT_SUB_QUESTION_PROMPT_TMPL output_parser = output_parser or SubQuestionOutputParser() diff --git a/llama-index-core/llama_index/core/question_gen/output_parser.py b/llama-index-core/llama_index/core/question_gen/output_parser.py index c307f9c98fed9..562837bd5836f 100644 --- a/llama-index-core/llama_index/core/question_gen/output_parser.py +++ b/llama-index-core/llama_index/core/question_gen/output_parser.py @@ -18,7 +18,7 @@ def parse(self, output: str) -> Any: if "items" in json_dict: json_dict = json_dict["items"] - sub_questions = [SubQuestion.parse_obj(item) for item in json_dict] + sub_questions = [SubQuestion.model_validate(item) for item in json_dict] return StructuredOutput(raw_output=output, parsed_output=sub_questions) def format(self, prompt_template: str) -> str: diff --git a/llama-index-core/llama_index/core/question_gen/prompts.py b/llama-index-core/llama_index/core/question_gen/prompts.py index 1c5f6f26e77a2..71a9f4636d780 100644 --- a/llama-index-core/llama_index/core/question_gen/prompts.py +++ b/llama-index-core/llama_index/core/question_gen/prompts.py @@ -47,7 +47,9 @@ def build_tools_text(tools: Sequence[ToolMetadata]) -> str: ), SubQuestion(sub_question="What is the EBITDA of Lyft", tool_name="lyft_10k"), ] -example_output_str = json.dumps({"items": [x.dict() for x in example_output]}, indent=4) +example_output_str = json.dumps( + {"items": [x.model_dump() for x in example_output]}, indent=4 +) EXAMPLES = f"""\ # Example 1 diff --git a/llama-index-core/llama_index/core/readers/base.py b/llama-index-core/llama_index/core/readers/base.py index 5dc4e0481f7e6..667513b89e328 100644 --- a/llama-index-core/llama_index/core/readers/base.py +++ b/llama-index-core/llama_index/core/readers/base.py @@ -7,12 +7,12 @@ Dict, Iterable, List, - Optional, ) if TYPE_CHECKING: from llama_index.core.bridge.langchain import Document as LCDocument -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, GetJsonSchemaHandler, ConfigDict +from llama_index.core.bridge.pydantic_core import CoreSchema from llama_index.core.schema import BaseComponent, Document @@ -46,8 +46,13 @@ def load_langchain_documents(self, **load_kwargs: Any) -> List["LCDocument"]: return [d.to_langchain_format() for d in docs] @classmethod - def __modify_schema__(cls, field_schema: Dict[str, Any], field: Optional[Any]): - field_schema.update({"title": cls.__name__}) + def __get_pydantic_json_schema__( + cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler + ) -> Dict[str, Any]: + json_schema = super().__get_pydantic_json_schema__(core_schema, handler) + json_schema = handler.resolve_ref_schema(json_schema) + json_schema.update({"title": cls.__name__}) + return json_schema @classmethod def __get_pydantic_json_schema__( @@ -62,14 +67,12 @@ def __get_pydantic_json_schema__( class BasePydanticReader(BaseReader, BaseComponent): """Serialiable Data Loader with Pydantic.""" + model_config = ConfigDict(arbitrary_types_allowed=True) is_remote: bool = Field( default=False, description="Whether the data is loaded from a remote API or a local file.", ) - class Config: - arbitrary_types_allowed = True - class ResourcesReaderMixin(ABC): """ @@ -209,15 +212,13 @@ async def aload_resources( class ReaderConfig(BaseComponent): """Represents a reader and it's input arguments.""" + model_config = ConfigDict(arbitrary_types_allowed=True) reader: BasePydanticReader = Field(..., description="Reader to use.") reader_args: List[Any] = Field(default_factory=list, description="Reader args.") reader_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Reader kwargs." ) - class Config: - arbitrary_types_allowed = True - @classmethod def class_name(cls) -> str: """Get the name identifier of the class.""" diff --git a/llama-index-core/llama_index/core/response_synthesizers/accumulate.py b/llama-index-core/llama_index/core/response_synthesizers/accumulate.py index 36e3b20a75e40..dcc20d6a5b62f 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/accumulate.py +++ b/llama-index-core/llama_index/core/response_synthesizers/accumulate.py @@ -4,14 +4,13 @@ from llama_index.core.async_utils import run_async_tasks from llama_index.core.callbacks.base import CallbackManager from llama_index.core.indices.prompt_helper import PromptHelper +from llama_index.core.llms import LLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.prompts.default_prompt_selectors import ( DEFAULT_TEXT_QA_PROMPT_SEL, ) from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.response_synthesizers.base import BaseSynthesizer -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import LLMPredictorType from llama_index.core.types import RESPONSE_TEXT_TYPE @@ -20,21 +19,18 @@ class Accumulate(BaseSynthesizer): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, callback_manager: Optional[CallbackManager] = None, prompt_helper: Optional[PromptHelper] = None, text_qa_template: Optional[BasePromptTemplate] = None, output_cls: Optional[Any] = None, streaming: bool = False, use_async: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: super().__init__( llm=llm, callback_manager=callback_manager, prompt_helper=prompt_helper, - service_context=service_context, streaming=streaming, ) self._text_qa_template = text_qa_template or DEFAULT_TEXT_QA_PROMPT_SEL diff --git a/llama-index-core/llama_index/core/response_synthesizers/base.py b/llama-index-core/llama_index/core/response_synthesizers/base.py index 17e3e502183c1..38d3fd656240d 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/base.py +++ b/llama-index-core/llama_index/core/response_synthesizers/base.py @@ -26,10 +26,11 @@ StreamingResponse, AsyncStreamingResponse, ) -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict from llama_index.core.callbacks.base import CallbackManager from llama_index.core.callbacks.schema import CBEventType, EventPayload from llama_index.core.indices.prompt_helper import PromptHelper +from llama_index.core.llms import LLM from llama_index.core.prompts.mixin import PromptMixin from llama_index.core.schema import ( BaseNode, @@ -38,13 +39,7 @@ QueryBundle, QueryType, ) -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import LLMPredictorType -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.types import RESPONSE_TEXT_TYPE from llama_index.core.instrumentation import DispatcherSpanMixin from llama_index.core.instrumentation.events.synthesis import ( @@ -74,24 +69,19 @@ class BaseSynthesizer(ChainableMixin, PromptMixin, DispatcherSpanMixin): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, callback_manager: Optional[CallbackManager] = None, prompt_helper: Optional[PromptHelper] = None, streaming: bool = False, - output_cls: BaseModel = None, - # deprecated - service_context: Optional[ServiceContext] = None, + output_cls: Optional[BaseModel] = None, ) -> None: """Init params.""" - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm if callback_manager: self._llm.callback_manager = callback_manager - self._callback_manager = ( - callback_manager - or callback_manager_from_settings_or_context(Settings, service_context) - ) + self._callback_manager = callback_manager or Settings.callback_manager self._prompt_helper = ( prompt_helper @@ -170,7 +160,7 @@ def _prepare_response_output( if isinstance(self._llm, StructuredLLM): # convert string to output_cls - output = self._llm.output_cls.parse_raw(response_str) + output = self._llm.output_cls.model_validate_json(response_str) return PydanticResponse( output, source_nodes=source_nodes, @@ -344,11 +334,9 @@ def _as_query_component(self, **kwargs: Any) -> QueryComponent: class SynthesizerComponent(QueryComponent): """Synthesizer component.""" + model_config = ConfigDict(arbitrary_types_allowed=True) synthesizer: BaseSynthesizer = Field(..., description="Synthesizer") - class Config: - arbitrary_types_allowed = True - def set_callback_manager(self, callback_manager: CallbackManager) -> None: """Set callback manager.""" self.synthesizer.callback_manager = callback_manager diff --git a/llama-index-core/llama_index/core/response_synthesizers/factory.py b/llama-index-core/llama_index/core/response_synthesizers/factory.py index 797a4931836d6..2240c119cf2e3 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/factory.py +++ b/llama-index-core/llama_index/core/response_synthesizers/factory.py @@ -10,6 +10,7 @@ DEFAULT_TREE_SUMMARIZE_PROMPT_SEL, ) from llama_index.core.prompts.default_prompts import DEFAULT_SIMPLE_INPUT_PROMPT +from llama_index.core.llms import LLM from llama_index.core.prompts.prompts import PromptTemplate from llama_index.core.response_synthesizers.accumulate import Accumulate from llama_index.core.response_synthesizers.base import BaseSynthesizer @@ -26,20 +27,13 @@ from llama_index.core.response_synthesizers.simple_summarize import SimpleSummarize from llama_index.core.response_synthesizers.tree_summarize import TreeSummarize from llama_index.core.response_synthesizers.type import ResponseMode -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import LLMPredictorType -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.types import BasePydanticProgram def get_response_synthesizer( - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, prompt_helper: Optional[PromptHelper] = None, - service_context: Optional[ServiceContext] = None, text_qa_template: Optional[BasePromptTemplate] = None, refine_template: Optional[BasePromptTemplate] = None, summary_template: Optional[BasePromptTemplate] = None, @@ -59,21 +53,15 @@ def get_response_synthesizer( simple_template = simple_template or DEFAULT_SIMPLE_INPUT_PROMPT summary_template = summary_template or DEFAULT_TREE_SUMMARIZE_PROMPT_SEL - callback_manager = callback_manager or callback_manager_from_settings_or_context( - Settings, service_context - ) - llm = llm or llm_from_settings_or_context(Settings, service_context) - - if service_context is not None: - prompt_helper = service_context.prompt_helper - else: - prompt_helper = ( - prompt_helper - or Settings._prompt_helper - or PromptHelper.from_llm_metadata( - llm.metadata, - ) + callback_manager = callback_manager or Settings.callback_manager + llm = llm or Settings.llm + prompt_helper = ( + prompt_helper + or Settings._prompt_helper + or PromptHelper.from_llm_metadata( + llm.metadata, ) + ) if response_mode == ResponseMode.REFINE: return Refine( @@ -87,8 +75,6 @@ def get_response_synthesizer( structured_answer_filtering=structured_answer_filtering, program_factory=program_factory, verbose=verbose, - # deprecated - service_context=service_context, ) elif response_mode == ResponseMode.COMPACT: return CompactAndRefine( @@ -102,8 +88,6 @@ def get_response_synthesizer( structured_answer_filtering=structured_answer_filtering, program_factory=program_factory, verbose=verbose, - # deprecated - service_context=service_context, ) elif response_mode == ResponseMode.TREE_SUMMARIZE: return TreeSummarize( @@ -115,8 +99,6 @@ def get_response_synthesizer( streaming=streaming, use_async=use_async, verbose=verbose, - # deprecated - service_context=service_context, ) elif response_mode == ResponseMode.SIMPLE_SUMMARIZE: return SimpleSummarize( @@ -125,8 +107,6 @@ def get_response_synthesizer( prompt_helper=prompt_helper, text_qa_template=text_qa_template, streaming=streaming, - # deprecated - service_context=service_context, ) elif response_mode == ResponseMode.GENERATION: return Generation( @@ -135,8 +115,6 @@ def get_response_synthesizer( prompt_helper=prompt_helper, simple_template=simple_template, streaming=streaming, - # deprecated - service_context=service_context, ) elif response_mode == ResponseMode.ACCUMULATE: return Accumulate( @@ -147,8 +125,6 @@ def get_response_synthesizer( output_cls=output_cls, streaming=streaming, use_async=use_async, - # deprecated - service_context=service_context, ) elif response_mode == ResponseMode.COMPACT_ACCUMULATE: return CompactAndAccumulate( @@ -159,22 +135,16 @@ def get_response_synthesizer( output_cls=output_cls, streaming=streaming, use_async=use_async, - # deprecated - service_context=service_context, ) elif response_mode == ResponseMode.NO_TEXT: return NoText( callback_manager=callback_manager, streaming=streaming, - # deprecated - service_context=service_context, ) elif response_mode == ResponseMode.CONTEXT_ONLY: return ContextOnly( callback_manager=callback_manager, streaming=streaming, - # deprecated - service_context=service_context, ) else: raise ValueError(f"Unknown mode: {response_mode}") diff --git a/llama-index-core/llama_index/core/response_synthesizers/generation.py b/llama-index-core/llama_index/core/response_synthesizers/generation.py index e6f176017ec65..72c51acf02176 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/generation.py +++ b/llama-index-core/llama_index/core/response_synthesizers/generation.py @@ -2,34 +2,27 @@ from llama_index.core.callbacks.base import CallbackManager from llama_index.core.indices.prompt_helper import PromptHelper +from llama_index.core.llms import LLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.prompts.default_prompts import DEFAULT_SIMPLE_INPUT_PROMPT from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.response_synthesizers.base import BaseSynthesizer -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import LLMPredictorType from llama_index.core.types import RESPONSE_TEXT_TYPE class Generation(BaseSynthesizer): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, callback_manager: Optional[CallbackManager] = None, prompt_helper: Optional[PromptHelper] = None, simple_template: Optional[BasePromptTemplate] = None, streaming: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: - if service_context is not None: - prompt_helper = service_context.prompt_helper - super().__init__( llm=llm, callback_manager=callback_manager, prompt_helper=prompt_helper, - service_context=service_context, streaming=streaming, ) self._input_prompt = simple_template or DEFAULT_SIMPLE_INPUT_PROMPT diff --git a/llama-index-core/llama_index/core/response_synthesizers/refine.py b/llama-index-core/llama_index/core/response_synthesizers/refine.py index ce48b153981d6..cff9e9571c88d 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/refine.py +++ b/llama-index-core/llama_index/core/response_synthesizers/refine.py @@ -14,6 +14,7 @@ from llama_index.core.callbacks.base import CallbackManager from llama_index.core.indices.prompt_helper import PromptHelper from llama_index.core.indices.utils import truncate_text +from llama_index.core.llms import LLM from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate from llama_index.core.prompts.default_prompt_selectors import ( DEFAULT_REFINE_PROMPT_SEL, @@ -22,10 +23,6 @@ from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.response.utils import get_response_text, aget_response_text from llama_index.core.response_synthesizers.base import BaseSynthesizer -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import ( - LLMPredictorType, -) from llama_index.core.types import RESPONSE_TEXT_TYPE, BasePydanticProgram from llama_index.core.instrumentation.events.synthesis import ( GetResponseEndEvent, @@ -61,9 +58,7 @@ class DefaultRefineProgram(BasePydanticProgram): query_satisfied=True. In effect, doesn't do any answer filtering. """ - def __init__( - self, prompt: BasePromptTemplate, llm: LLMPredictorType, output_cls: BaseModel - ): + def __init__(self, prompt: BasePromptTemplate, llm: LLM, output_cls: BaseModel): self._prompt = prompt self._llm = llm self._output_cls = output_cls @@ -79,7 +74,7 @@ def __call__(self, *args: Any, **kwds: Any) -> StructuredRefineResponse: self._prompt, **kwds, ) - answer = answer.json() + answer = answer.model_dump_json() else: answer = self._llm.predict( self._prompt, @@ -94,7 +89,7 @@ async def acall(self, *args: Any, **kwds: Any) -> StructuredRefineResponse: self._prompt, **kwds, ) - answer = answer.json() + answer = answer.model_dump_json() else: answer = await self._llm.apredict( self._prompt, @@ -108,7 +103,7 @@ class Refine(BaseSynthesizer): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, callback_manager: Optional[CallbackManager] = None, prompt_helper: Optional[PromptHelper] = None, text_qa_template: Optional[BasePromptTemplate] = None, @@ -120,17 +115,11 @@ def __init__( program_factory: Optional[ Callable[[BasePromptTemplate], BasePydanticProgram] ] = None, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: - if service_context is not None: - prompt_helper = service_context.prompt_helper - super().__init__( llm=llm, callback_manager=callback_manager, prompt_helper=prompt_helper, - service_context=service_context, streaming=streaming, ) self._text_qa_template = text_qa_template or DEFAULT_TEXT_QA_PROMPT_SEL @@ -191,7 +180,7 @@ def get_response( prev_response = response if isinstance(response, str): if self._output_cls is not None: - response = self._output_cls.parse_raw(response) + response = self._output_cls.model_validate_json(response) else: response = response or "Empty Response" else: @@ -372,7 +361,7 @@ async def aget_response( response = "Empty Response" if isinstance(response, str): if self._output_cls is not None: - response = self._output_cls.parse_raw(response) + response = self._output_cls.model_validate_json(response) else: response = response or "Empty Response" else: diff --git a/llama-index-core/llama_index/core/response_synthesizers/simple_summarize.py b/llama-index-core/llama_index/core/response_synthesizers/simple_summarize.py index 159837d6edf96..82a5495778ea2 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/simple_summarize.py +++ b/llama-index-core/llama_index/core/response_synthesizers/simple_summarize.py @@ -2,36 +2,29 @@ from llama_index.core.callbacks.base import CallbackManager from llama_index.core.indices.prompt_helper import PromptHelper +from llama_index.core.llms import LLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.prompts.default_prompt_selectors import ( DEFAULT_TEXT_QA_PROMPT_SEL, ) from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.response_synthesizers.base import BaseSynthesizer -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import LLMPredictorType from llama_index.core.types import RESPONSE_TEXT_TYPE class SimpleSummarize(BaseSynthesizer): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, callback_manager: Optional[CallbackManager] = None, prompt_helper: Optional[PromptHelper] = None, text_qa_template: Optional[BasePromptTemplate] = None, streaming: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: - if service_context is not None: - prompt_helper = service_context.prompt_helper - super().__init__( llm=llm, callback_manager=callback_manager, prompt_helper=prompt_helper, - service_context=service_context, streaming=streaming, ) self._text_qa_template = text_qa_template or DEFAULT_TEXT_QA_PROMPT_SEL diff --git a/llama-index-core/llama_index/core/response_synthesizers/tree_summarize.py b/llama-index-core/llama_index/core/response_synthesizers/tree_summarize.py index f623dc7b17e60..3dfbde1022283 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/tree_summarize.py +++ b/llama-index-core/llama_index/core/response_synthesizers/tree_summarize.py @@ -4,14 +4,13 @@ from llama_index.core.async_utils import run_async_tasks from llama_index.core.callbacks.base import CallbackManager from llama_index.core.indices.prompt_helper import PromptHelper +from llama_index.core.llms import LLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.prompts.default_prompt_selectors import ( DEFAULT_TREE_SUMMARIZE_PROMPT_SEL, ) from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.response_synthesizers.base import BaseSynthesizer -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import LLMPredictorType from llama_index.core.types import RESPONSE_TEXT_TYPE, BaseModel @@ -30,7 +29,7 @@ class TreeSummarize(BaseSynthesizer): def __init__( self, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, callback_manager: Optional[CallbackManager] = None, prompt_helper: Optional[PromptHelper] = None, summary_template: Optional[BasePromptTemplate] = None, @@ -38,17 +37,11 @@ def __init__( streaming: bool = False, use_async: bool = False, verbose: bool = False, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> None: - if service_context is not None: - prompt_helper = service_context.prompt_helper - super().__init__( llm=llm, callback_manager=callback_manager, prompt_helper=prompt_helper, - service_context=service_context, streaming=streaming, output_cls=output_cls, ) @@ -130,7 +123,7 @@ async def aget_response( summary_responses = await asyncio.gather(*tasks) if self._output_cls is not None: - summaries = [summary.json() for summary in summary_responses] + summaries = [summary.model_dump_json() for summary in summary_responses] else: summaries = summary_responses @@ -207,7 +200,9 @@ def get_response( summary_responses = run_async_tasks(tasks) if self._output_cls is not None: - summaries = [summary.json() for summary in summary_responses] + summaries = [ + summary.model_dump_json() for summary in summary_responses + ] else: summaries = summary_responses else: @@ -230,7 +225,7 @@ def get_response( ) for text_chunk in text_chunks ] - summaries = [summary.json() for summary in summaries] + summaries = [summary.model_dump_json() for summary in summaries] # recursively summarize the summaries return self.get_response( diff --git a/llama-index-core/llama_index/core/retrievers/fusion_retriever.py b/llama-index-core/llama_index/core/retrievers/fusion_retriever.py index 5c788ad25c940..e64dee7bbbf08 100644 --- a/llama-index-core/llama_index/core/retrievers/fusion_retriever.py +++ b/llama-index-core/llama_index/core/retrievers/fusion_retriever.py @@ -108,18 +108,18 @@ def _reciprocal_rerank_fusion( """ k = 60.0 # `k` is a parameter used to control the impact of outlier rankings. fused_scores = {} - text_to_node = {} + hash_to_node = {} # compute reciprocal rank scores for nodes_with_scores in results.values(): for rank, node_with_score in enumerate( sorted(nodes_with_scores, key=lambda x: x.score or 0.0, reverse=True) ): - text = node_with_score.node.get_content() - text_to_node[text] = node_with_score - if text not in fused_scores: - fused_scores[text] = 0.0 - fused_scores[text] += 1.0 / (rank + k) + hash = node_with_score.node.hash + hash_to_node[hash] = node_with_score + if hash not in fused_scores: + fused_scores[hash] = 0.0 + fused_scores[hash] += 1.0 / (rank + k) # sort results reranked_results = dict( @@ -128,8 +128,8 @@ def _reciprocal_rerank_fusion( # adjust node scores reranked_nodes: List[NodeWithScore] = [] - for text, score in reranked_results.items(): - reranked_nodes.append(text_to_node[text]) + for hash, score in reranked_results.items(): + reranked_nodes.append(hash_to_node[hash]) reranked_nodes[-1].score = score return reranked_nodes @@ -183,11 +183,11 @@ def _relative_score_fusion( # Sum scores for each node for nodes_with_scores in results.values(): for node_with_score in nodes_with_scores: - text = node_with_score.node.get_content() - if text in all_nodes: - all_nodes[text].score += node_with_score.score + hash = node_with_score.node.hash + if hash in all_nodes: + all_nodes[hash].score += node_with_score.score else: - all_nodes[text] = node_with_score + all_nodes[hash] = node_with_score return sorted(all_nodes.values(), key=lambda x: x.score or 0.0, reverse=True) @@ -199,12 +199,12 @@ def _simple_fusion( all_nodes: Dict[str, NodeWithScore] = {} for nodes_with_scores in results.values(): for node_with_score in nodes_with_scores: - text = node_with_score.node.get_content() - if text in all_nodes: - max_score = max(node_with_score.score, all_nodes[text].score) - all_nodes[text].score = max_score + hash = node_with_score.node.hash + if hash in all_nodes: + max_score = max(node_with_score.score, all_nodes[hash].score) + all_nodes[hash].score = max_score else: - all_nodes[text] = node_with_score + all_nodes[hash] = node_with_score return sorted(all_nodes.values(), key=lambda x: x.score or 0.0, reverse=True) diff --git a/llama-index-core/llama_index/core/retrievers/router_retriever.py b/llama-index-core/llama_index/core/retrievers/router_retriever.py index dbfbd74c0363c..9d73426628d7b 100644 --- a/llama-index-core/llama_index/core/retrievers/router_retriever.py +++ b/llama-index-core/llama_index/core/retrievers/router_retriever.py @@ -11,12 +11,7 @@ from llama_index.core.prompts.mixin import PromptMixinType from llama_index.core.schema import IndexNode, NodeWithScore, QueryBundle from llama_index.core.selectors.utils import get_selector_from_llm -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.tools.retriever_tool import RetrieverTool logger = logging.getLogger(__name__) @@ -41,20 +36,17 @@ def __init__( selector: BaseSelector, retriever_tools: Sequence[RetrieverTool], llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, objects: Optional[List[IndexNode]] = None, object_map: Optional[dict] = None, verbose: bool = False, ) -> None: - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._selector = selector self._retrievers: List[BaseRetriever] = [x.retriever for x in retriever_tools] self._metadatas = [x.metadata for x in retriever_tools] super().__init__( - callback_manager=callback_manager_from_settings_or_context( - Settings, service_context - ), + callback_manager=Settings.callback_manager, object_map=object_map, objects=objects, verbose=verbose, @@ -70,18 +62,16 @@ def from_defaults( cls, retriever_tools: Sequence[RetrieverTool], llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, selector: Optional[BaseSelector] = None, select_multi: bool = False, ) -> "RouterRetriever": - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm selector = selector or get_selector_from_llm(llm, is_multi=select_multi) return cls( selector, retriever_tools, llm=llm, - service_context=service_context, ) def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: diff --git a/llama-index-core/llama_index/core/schema.py b/llama-index-core/llama_index/core/schema.py index 78f6c4bf4f309..dd3984b28bb56 100644 --- a/llama-index-core/llama_index/core/schema.py +++ b/llama-index-core/llama_index/core/schema.py @@ -13,7 +13,16 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union from dataclasses_json import DataClassJsonMixin -from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.bridge.pydantic import ( + BaseModel, + Field, + GetJsonSchemaHandler, + SerializeAsAny, + JsonSchemaValue, + ConfigDict, + model_serializer, +) +from llama_index.core.bridge.pydantic_core import CoreSchema from llama_index.core.instrumentation import DispatcherSpanMixin from llama_index.core.utils import SAMPLE_TEXT, truncate_text from typing_extensions import Self @@ -39,15 +48,18 @@ class BaseComponent(BaseModel): """Base component object to capture class names.""" - class Config: - @staticmethod - def schema_extra(schema: Dict[str, Any], model: "BaseComponent") -> None: - """Add class name to schema.""" - schema["properties"]["class_name"] = { - "title": "Class Name", - "type": "string", - "default": model.class_name(), - } + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler + ) -> JsonSchemaValue: + json_schema = handler(core_schema) + json_schema = handler.resolve_ref_schema(json_schema) + json_schema["properties"]["class_name"] = { + "title": "Class Name", + "type": "string", + "default": cls.class_name(), + } + return json_schema @classmethod def class_name(cls) -> str: @@ -62,11 +74,15 @@ def class_name(cls) -> str: def json(self, **kwargs: Any) -> str: return self.to_json(**kwargs) - def dict(self, **kwargs: Any) -> Dict[str, Any]: - data = super().dict(**kwargs) + @model_serializer(mode="wrap") + def custom_model_dump(self, handler: Any) -> Dict[str, Any]: + data = handler(self) data["class_name"] = self.class_name() return data + def dict(self, **kwargs: Any) -> Dict[str, Any]: + return self.model_dump(**kwargs) + def __getstate__(self) -> Dict[str, Any]: state = super().__getstate__() @@ -84,15 +100,17 @@ def __getstate__(self) -> Dict[str, Any]: # remove private attributes if they aren't pickleable -- kind of dangerous keys_to_remove = [] - for key, val in state["__private_attribute_values__"].items(): - try: - pickle.dumps(val) - except Exception: - keys_to_remove.append(key) - - for key in keys_to_remove: - logging.warning(f"Removing unpickleable private attribute {key}") - del state["__private_attribute_values__"][key] + private_attrs = state.get("__pydantic_private__", None) + if private_attrs: + for key, val in state["__pydantic_private__"].items(): + try: + pickle.dumps(val) + except Exception: + keys_to_remove.append(key) + + for key in keys_to_remove: + logging.warning(f"Removing unpickleable private attribute {key}") + del state["__pydantic_private__"][key] return state @@ -133,8 +151,7 @@ def from_json(cls, data_str: str, **kwargs: Any) -> Self: # type: ignore class TransformComponent(BaseComponent, DispatcherSpanMixin): """Base class for transform components.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict(arbitrary_types_allowed=True) @abstractmethod def __call__(self, nodes: List["BaseNode"], **kwargs: Any) -> List["BaseNode"]: @@ -200,10 +217,8 @@ class BaseNode(BaseComponent): """ - class Config: - allow_population_by_field_name = True - # hash is computed on local field, during the validation process - validate_assignment = True + # hash is computed on local field, during the validation process + model_config = ConfigDict(populate_by_name=True, validate_assignment=True) id_: str = Field( default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the node." @@ -530,7 +545,7 @@ def dict(self, **kwargs: Any) -> Dict[str, Any]: elif isinstance(self.obj, BaseNode): data["obj"] = doc_to_json(self.obj) elif isinstance(self.obj, BaseModel): - data["obj"] = self.obj.dict() + data["obj"] = self.obj.model_dump() else: data["obj"] = json.dumps(self.obj) except Exception: @@ -584,7 +599,7 @@ def class_name(cls) -> str: class NodeWithScore(BaseComponent): - node: BaseNode + node: SerializeAsAny[BaseNode] score: Optional[float] = None def __str__(self) -> str: diff --git a/llama-index-core/llama_index/core/selectors/llm_selectors.py b/llama-index-core/llama_index/core/selectors/llm_selectors.py index e9f32637ea72b..b5b57cd776d80 100644 --- a/llama-index-core/llama_index/core/selectors/llm_selectors.py +++ b/llama-index-core/llama_index/core/selectors/llm_selectors.py @@ -5,6 +5,7 @@ SelectorResult, SingleSelection, ) +from llama_index.core.llms import LLM from llama_index.core.output_parsers.base import StructuredOutput from llama_index.core.output_parsers.selection import Answer, SelectionOutputParser from llama_index.core.prompts.mixin import PromptDictType @@ -16,11 +17,7 @@ MultiSelectPrompt, SingleSelectPrompt, ) -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import ( - LLMPredictorType, -) -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings from llama_index.core.tools.types import ToolMetadata from llama_index.core.types import BaseOutputParser @@ -60,7 +57,7 @@ class LLMSingleSelector(BaseSelector): def __init__( self, - llm: LLMPredictorType, + llm: LLM, prompt: SingleSelectPrompt, ) -> None: self._llm = llm @@ -72,13 +69,12 @@ def __init__( @classmethod def from_defaults( cls, - llm: Optional[LLMPredictorType] = None, - service_context: Optional[ServiceContext] = None, + llm: Optional[LLM] = None, prompt_template_str: Optional[str] = None, output_parser: Optional[BaseOutputParser] = None, ) -> "LLMSingleSelector": # optionally initialize defaults - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm prompt_template_str = prompt_template_str or DEFAULT_SINGLE_SELECT_PROMPT_TMPL output_parser = output_parser or SelectionOutputParser() @@ -151,7 +147,7 @@ class LLMMultiSelector(BaseSelector): def __init__( self, - llm: LLMPredictorType, + llm: LLM, prompt: MultiSelectPrompt, max_outputs: Optional[int] = None, ) -> None: @@ -165,14 +161,12 @@ def __init__( @classmethod def from_defaults( cls, - llm: Optional[LLMPredictorType] = None, + llm: Optional[LLM] = None, prompt_template_str: Optional[str] = None, output_parser: Optional[BaseOutputParser] = None, max_outputs: Optional[int] = None, - # deprecated - service_context: Optional[ServiceContext] = None, ) -> "LLMMultiSelector": - llm = llm or llm_from_settings_or_context(Settings, service_context) + llm = llm or Settings.llm prompt_template_str = prompt_template_str or DEFAULT_MULTI_SELECT_PROMPT_TMPL output_parser = output_parser or SelectionOutputParser() diff --git a/llama-index-core/llama_index/core/service_context.py b/llama-index-core/llama_index/core/service_context.py index 534b9016f35aa..6a657f5567845 100644 --- a/llama-index-core/llama_index/core/service_context.py +++ b/llama-index-core/llama_index/core/service_context.py @@ -1,411 +1,46 @@ -import logging -from dataclasses import dataclass -from typing import Any, List, Optional, cast +from typing import Any, Optional -from deprecated import deprecated -import llama_index.core -from llama_index.core.bridge.pydantic import BaseModel -from llama_index.core.callbacks.base import CallbackManager -from llama_index.core.base.embeddings.base import BaseEmbedding -from llama_index.core.indices.prompt_helper import PromptHelper -from llama_index.core.service_context_elements.llm_predictor import ( - LLMPredictor, - BaseLLMPredictor, -) -from llama_index.core.base.llms.types import LLMMetadata -from llama_index.core.llms.llm import LLM -from llama_index.core.llms.utils import LLMType, resolve_llm -from llama_index.core.service_context_elements.llama_logger import LlamaLogger -from llama_index.core.node_parser.interface import NodeParser, TextSplitter -from llama_index.core.node_parser.text.sentence import ( - DEFAULT_CHUNK_SIZE, - SENTENCE_CHUNK_OVERLAP, - SentenceSplitter, -) -from llama_index.core.prompts.base import BasePromptTemplate -from llama_index.core.schema import TransformComponent -from llama_index.core.types import PydanticProgramMode - -logger = logging.getLogger(__name__) - - -def _get_default_node_parser( - chunk_size: int = DEFAULT_CHUNK_SIZE, - chunk_overlap: int = SENTENCE_CHUNK_OVERLAP, - callback_manager: Optional[CallbackManager] = None, -) -> NodeParser: - """Get default node parser.""" - return SentenceSplitter( - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - callback_manager=callback_manager or CallbackManager(), - ) - - -def _get_default_prompt_helper( - llm_metadata: LLMMetadata, - context_window: Optional[int] = None, - num_output: Optional[int] = None, -) -> PromptHelper: - """Get default prompt helper.""" - if context_window is not None: - llm_metadata.context_window = context_window - if num_output is not None: - llm_metadata.num_output = num_output - return PromptHelper.from_llm_metadata(llm_metadata=llm_metadata) - - -class ServiceContextData(BaseModel): - llm: dict - llm_predictor: dict - prompt_helper: dict - embed_model: dict - transformations: List[dict] - - -@dataclass class ServiceContext: """Service Context container. - The service context container is a utility container for LlamaIndex - index and query classes. It contains the following: - - llm_predictor: BaseLLMPredictor - - prompt_helper: PromptHelper - - embed_model: BaseEmbedding - - node_parser: NodeParser - - llama_logger: LlamaLogger (deprecated) - - callback_manager: CallbackManager + NOTE: Deprecated, use llama_index.settings.Settings instead or pass in + modules to local functions/methods/interfaces. """ - llm_predictor: BaseLLMPredictor - prompt_helper: PromptHelper - embed_model: BaseEmbedding - transformations: List[TransformComponent] - llama_logger: LlamaLogger - callback_manager: CallbackManager + def __init__(self, **kwargs: Any) -> None: + raise ValueError( + "ServiceContext is deprecated. Use llama_index.settings.Settings instead, " + "or pass in modules to local functions/methods/interfaces.\n" + "See the docs for updated usage/migration: \n" + "https://docs.llamaindex.ai/en/stable/module_guides/supporting_modules/service_context_migration/" + ) @classmethod - @deprecated( - version="0.10.0", - reason="ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.", - ) def from_defaults( cls, - llm_predictor: Optional[BaseLLMPredictor] = None, - llm: Optional[LLMType] = "default", - prompt_helper: Optional[PromptHelper] = None, - embed_model: Optional[Any] = "default", - node_parser: Optional[NodeParser] = None, - text_splitter: Optional[TextSplitter] = None, - transformations: Optional[List[TransformComponent]] = None, - llama_logger: Optional[LlamaLogger] = None, - callback_manager: Optional[CallbackManager] = None, - system_prompt: Optional[str] = None, - query_wrapper_prompt: Optional[BasePromptTemplate] = None, - # pydantic program mode (used if output_cls is specified) - pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, - # node parser kwargs - chunk_size: Optional[int] = None, - chunk_overlap: Optional[int] = None, - # prompt helper kwargs - context_window: Optional[int] = None, - num_output: Optional[int] = None, - # deprecated kwargs - chunk_size_limit: Optional[int] = None, + **kwargs: Any, ) -> "ServiceContext": """Create a ServiceContext from defaults. - If an argument is specified, then use the argument value provided for that - parameter. If an argument is not specified, then use the default value. - - You can change the base defaults by setting llama_index.global_service_context - to a ServiceContext object with your desired settings. - Args: - llm_predictor (Optional[BaseLLMPredictor]): LLMPredictor - prompt_helper (Optional[PromptHelper]): PromptHelper - embed_model (Optional[BaseEmbedding]): BaseEmbedding - or "local" (use local model) - node_parser (Optional[NodeParser]): NodeParser - llama_logger (Optional[LlamaLogger]): LlamaLogger (deprecated) - chunk_size (Optional[int]): chunk_size - callback_manager (Optional[CallbackManager]): CallbackManager - system_prompt (Optional[str]): System-wide prompt to be prepended - to all input prompts, used to guide system "decision making" - query_wrapper_prompt (Optional[BasePromptTemplate]): A format to wrap - passed-in input queries. - - Deprecated Args: - chunk_size_limit (Optional[int]): renamed to chunk_size + NOTE: Deprecated, use llama_index.settings.Settings instead or pass in + modules to local functions/methods/interfaces. """ - from llama_index.core.embeddings.utils import EmbedType, resolve_embed_model - - embed_model = cast(EmbedType, embed_model) - - if chunk_size_limit is not None and chunk_size is None: - logger.warning( - "chunk_size_limit is deprecated, please specify chunk_size instead" - ) - chunk_size = chunk_size_limit - - if llama_index.core.global_service_context is not None: - return cls.from_service_context( - llama_index.core.global_service_context, - llm=llm, - llm_predictor=llm_predictor, - prompt_helper=prompt_helper, - embed_model=embed_model, - node_parser=node_parser, - text_splitter=text_splitter, - llama_logger=llama_logger, - callback_manager=callback_manager, - context_window=context_window, - chunk_size=chunk_size, - chunk_size_limit=chunk_size_limit, - chunk_overlap=chunk_overlap, - num_output=num_output, - system_prompt=system_prompt, - query_wrapper_prompt=query_wrapper_prompt, - transformations=transformations, - ) - - callback_manager = callback_manager or CallbackManager([]) - if llm != "default": - if llm_predictor is not None: - raise ValueError("Cannot specify both llm and llm_predictor") - llm = resolve_llm(llm) - llm.system_prompt = llm.system_prompt or system_prompt - llm.query_wrapper_prompt = llm.query_wrapper_prompt or query_wrapper_prompt - llm.pydantic_program_mode = ( - llm.pydantic_program_mode or pydantic_program_mode - ) - - if llm_predictor is not None: - print("LLMPredictor is deprecated, please use LLM instead.") - llm_predictor = llm_predictor or LLMPredictor( - llm=llm, pydantic_program_mode=pydantic_program_mode - ) - if isinstance(llm_predictor, LLMPredictor): - llm_predictor.llm.callback_manager = callback_manager - if system_prompt: - llm_predictor.system_prompt = system_prompt - if query_wrapper_prompt: - llm_predictor.query_wrapper_prompt = query_wrapper_prompt - - # NOTE: the embed_model isn't used in all indices - # NOTE: embed model should be a transformation, but the way the service - # context works, we can't put in there yet. - embed_model = resolve_embed_model(embed_model) - embed_model.callback_manager = callback_manager - - prompt_helper = prompt_helper or _get_default_prompt_helper( - llm_metadata=llm_predictor.metadata, - context_window=context_window, - num_output=num_output, - ) - - if text_splitter is not None and node_parser is not None: - raise ValueError("Cannot specify both text_splitter and node_parser") - - node_parser = ( - text_splitter # text splitter extends node parser - or node_parser - or _get_default_node_parser( - chunk_size=chunk_size or DEFAULT_CHUNK_SIZE, - chunk_overlap=chunk_overlap or SENTENCE_CHUNK_OVERLAP, - callback_manager=callback_manager, - ) - ) - - transformations = transformations or [node_parser] - - llama_logger = llama_logger or LlamaLogger() - - return cls( - llm_predictor=llm_predictor, - embed_model=embed_model, - prompt_helper=prompt_helper, - transformations=transformations, - llama_logger=llama_logger, # deprecated - callback_manager=callback_manager, - ) - - @classmethod - def from_service_context( - cls, - service_context: "ServiceContext", - llm_predictor: Optional[BaseLLMPredictor] = None, - llm: Optional[LLMType] = "default", - prompt_helper: Optional[PromptHelper] = None, - embed_model: Optional[Any] = "default", - node_parser: Optional[NodeParser] = None, - text_splitter: Optional[TextSplitter] = None, - transformations: Optional[List[TransformComponent]] = None, - llama_logger: Optional[LlamaLogger] = None, - callback_manager: Optional[CallbackManager] = None, - system_prompt: Optional[str] = None, - query_wrapper_prompt: Optional[BasePromptTemplate] = None, - # node parser kwargs - chunk_size: Optional[int] = None, - chunk_overlap: Optional[int] = None, - # prompt helper kwargs - context_window: Optional[int] = None, - num_output: Optional[int] = None, - # deprecated kwargs - chunk_size_limit: Optional[int] = None, - ) -> "ServiceContext": - """Instantiate a new service context using a previous as the defaults.""" - from llama_index.core.embeddings.utils import EmbedType, resolve_embed_model - - embed_model = cast(EmbedType, embed_model) - - if chunk_size_limit is not None and chunk_size is None: - logger.warning( - "chunk_size_limit is deprecated, please specify chunk_size", - DeprecationWarning, - ) - chunk_size = chunk_size_limit - - callback_manager = callback_manager or service_context.callback_manager - if llm != "default": - if llm_predictor is not None: - raise ValueError("Cannot specify both llm and llm_predictor") - llm = resolve_llm(llm) - llm_predictor = LLMPredictor(llm=llm) - - llm_predictor = llm_predictor or service_context.llm_predictor - if isinstance(llm_predictor, LLMPredictor): - llm_predictor.llm.callback_manager = callback_manager - if system_prompt: - llm_predictor.system_prompt = system_prompt - if query_wrapper_prompt: - llm_predictor.query_wrapper_prompt = query_wrapper_prompt - - # NOTE: the embed_model isn't used in all indices - # default to using the embed model passed from the service context - if embed_model == "default": - embed_model = service_context.embed_model - embed_model = resolve_embed_model(embed_model) - embed_model.callback_manager = callback_manager - - prompt_helper = prompt_helper or service_context.prompt_helper - if context_window is not None or num_output is not None: - prompt_helper = _get_default_prompt_helper( - llm_metadata=llm_predictor.metadata, - context_window=context_window, - num_output=num_output, - ) - - transformations = transformations or [] - node_parser_found = False - for transform in service_context.transformations: - if isinstance(transform, NodeParser): - node_parser_found = True - node_parser = transform - break - - if text_splitter is not None and node_parser is not None: - raise ValueError("Cannot specify both text_splitter and node_parser") - - if not node_parser_found: - node_parser = ( - text_splitter # text splitter extends node parser - or node_parser - or _get_default_node_parser( - chunk_size=chunk_size or DEFAULT_CHUNK_SIZE, - chunk_overlap=chunk_overlap or SENTENCE_CHUNK_OVERLAP, - callback_manager=callback_manager, - ) - ) - - transformations = transformations or service_context.transformations - - llama_logger = llama_logger or service_context.llama_logger - - return cls( - llm_predictor=llm_predictor, - embed_model=embed_model, - prompt_helper=prompt_helper, - transformations=transformations, - llama_logger=llama_logger, # deprecated - callback_manager=callback_manager, - ) - - @property - def llm(self) -> LLM: - return self.llm_predictor.llm - - @property - def node_parser(self) -> NodeParser: - """Get the node parser.""" - for transform in self.transformations: - if isinstance(transform, NodeParser): - return transform - raise ValueError("No node parser found.") - - def to_dict(self) -> dict: - """Convert service context to dict.""" - llm_dict = self.llm_predictor.llm.to_dict() - llm_predictor_dict = self.llm_predictor.to_dict() - - embed_model_dict = self.embed_model.to_dict() - - prompt_helper_dict = self.prompt_helper.to_dict() - - tranform_list_dict = [x.to_dict() for x in self.transformations] - - return ServiceContextData( - llm=llm_dict, - llm_predictor=llm_predictor_dict, - prompt_helper=prompt_helper_dict, - embed_model=embed_model_dict, - transformations=tranform_list_dict, - ).dict() - - @classmethod - def from_dict(cls, data: dict) -> "ServiceContext": - from llama_index.core.embeddings.loading import load_embed_model - from llama_index.core.extractors.loading import load_extractor - from llama_index.core.node_parser.loading import load_parser - from llama_index.core.service_context_elements.llm_predictor import ( - load_predictor, - ) - - service_context_data = ServiceContextData.parse_obj(data) - - llm_predictor = load_predictor(service_context_data.llm_predictor) - - embed_model = load_embed_model(service_context_data.embed_model) - - prompt_helper = PromptHelper.from_dict(service_context_data.prompt_helper) - - transformations: List[TransformComponent] = [] - for transform in service_context_data.transformations: - try: - transformations.append(load_parser(transform)) - except ValueError: - transformations.append(load_extractor(transform)) - - return cls.from_defaults( - llm_predictor=llm_predictor, - prompt_helper=prompt_helper, - embed_model=embed_model, - transformations=transformations, + raise ValueError( + "ServiceContext is deprecated. Use llama_index.settings.Settings instead, " + "or pass in modules to local functions/methods/interfaces.\n" + "See the docs for updated usage/migration: \n" + "https://docs.llamaindex.ai/en/stable/module_guides/supporting_modules/service_context_migration/" ) def set_global_service_context(service_context: Optional[ServiceContext]) -> None: """Helper function to set the global service context.""" - llama_index.core.global_service_context = service_context - - if service_context is not None: - from llama_index.core.settings import Settings - - Settings.llm = service_context.llm - Settings.embed_model = service_context.embed_model - Settings.prompt_helper = service_context.prompt_helper - Settings.transformations = service_context.transformations - Settings.node_parser = service_context.node_parser - Settings.callback_manager = service_context.callback_manager + raise ValueError( + "ServiceContext is deprecated. Use llama_index.settings.Settings instead, " + "or pass in modules to local functions/methods/interfaces.\n" + "See the docs for updated usage/migration: \n" + "https://docs.llamaindex.ai/en/stable/module_guides/supporting_modules/service_context_migration/" + ) diff --git a/llama-index-core/llama_index/core/service_context_elements/llm_predictor.py b/llama-index-core/llama_index/core/service_context_elements/llm_predictor.py index 40e1ae3a057ea..47a851fce861c 100644 --- a/llama-index-core/llama_index/core/service_context_elements/llm_predictor.py +++ b/llama-index-core/llama_index/core/service_context_elements/llm_predictor.py @@ -2,30 +2,16 @@ import logging from abc import ABC, abstractmethod -from collections import ChainMap -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict -from llama_index.core.base.llms.types import ( - ChatMessage, - LLMMetadata, - MessageRole, -) -from llama_index.core.bridge.pydantic import BaseModel, PrivateAttr +from llama_index.core.base.llms.types import LLMMetadata from llama_index.core.callbacks.base import CallbackManager -from llama_index.core.callbacks.schema import CBEventType, EventPayload from llama_index.core.instrumentation import DispatcherSpanMixin -from llama_index.core.llms.llm import ( - LLM, - astream_chat_response_to_tokens, - astream_completion_response_to_tokens, - stream_chat_response_to_tokens, - stream_completion_response_to_tokens, -) -from llama_index.core.llms.utils import LLMType, resolve_llm -from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate +from llama_index.core.llms.llm import LLM +from llama_index.core.prompts.base import BasePromptTemplate from llama_index.core.schema import BaseComponent -from llama_index.core.types import PydanticProgramMode, TokenAsyncGen, TokenGen -from typing_extensions import Self +from llama_index.core.types import TokenAsyncGen, TokenGen + logger = logging.getLogger(__name__) @@ -33,11 +19,16 @@ class BaseLLMPredictor(BaseComponent, DispatcherSpanMixin, ABC): """Base LLM Predictor.""" - def dict(self, **kwargs: Any) -> Dict[str, Any]: - data = super().dict(**kwargs) + def model_dump(self, **kwargs: Any) -> Dict[str, Any]: + print("here", flush=True) + data = super().model_dump(**kwargs) data["llm"] = self.llm.to_dict() return data + def dict(self, **kwargs: Any) -> Dict[str, Any]: + """Keep for backwards compatibility.""" + return self.model_dump(**kwargs) + def to_dict(self, **kwargs: Any) -> Dict[str, Any]: data = super().to_dict(**kwargs) data["llm"] = self.llm.to_dict() @@ -80,276 +71,12 @@ async def astream( class LLMPredictor(BaseLLMPredictor): """LLM predictor class. - A lightweight wrapper on top of LLMs that handles: - - conversion of prompts to the string input format expected by LLMs - - logging of prompts and responses to a callback manager - - NOTE: Mostly keeping around for legacy reasons. A potential future path is to - deprecate this class and move all functionality into the LLM class. + NOTE: Deprecated. Use any LLM class directly. """ - class Config: - arbitrary_types_allowed = True - - system_prompt: Optional[str] - query_wrapper_prompt: Optional[BasePromptTemplate] - pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT - - _llm: LLM = PrivateAttr() - def __init__( self, - llm: Optional[LLMType] = "default", - callback_manager: Optional[CallbackManager] = None, - system_prompt: Optional[str] = None, - query_wrapper_prompt: Optional[BasePromptTemplate] = None, - pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, + **kwargs: Any, ) -> None: """Initialize params.""" - self._llm = resolve_llm(llm, callback_manager=callback_manager) - - if callback_manager: - self._llm.callback_manager = callback_manager - - super().__init__( - system_prompt=system_prompt, - query_wrapper_prompt=query_wrapper_prompt, - pydantic_program_mode=pydantic_program_mode, - ) - - @classmethod - def from_dict(cls, data: Dict[str, Any], **kwargs: Any) -> Self: # type: ignore - if isinstance(kwargs, dict): - data.update(kwargs) - - data.pop("class_name", None) - - llm = data.get("llm", "default") - if llm != "default": - from llama_index.core.llms.loading import load_llm - - llm = load_llm(llm) - - data["llm"] = llm - return cls(**data) - - @classmethod - def class_name(cls) -> str: - return "LLMPredictor" - - @property - def llm(self) -> LLM: - """Get LLM.""" - return self._llm - - @property - def callback_manager(self) -> CallbackManager: - """Get callback manager.""" - return self._llm.callback_manager - - @callback_manager.setter - def callback_manager(self, callback_manager: CallbackManager) -> None: - """Set callback manager.""" - self._llm.callback_manager = callback_manager - - @property - def metadata(self) -> LLMMetadata: - """Get LLM metadata.""" - return self._llm.metadata - - def _log_template_data( - self, prompt: BasePromptTemplate, **prompt_args: Any - ) -> None: - template_vars = { - k: v - for k, v in ChainMap(prompt.kwargs, prompt_args).items() - if k in prompt.template_vars - } - with self.callback_manager.event( - CBEventType.TEMPLATING, - payload={ - EventPayload.TEMPLATE: prompt.get_template(llm=self._llm), - EventPayload.TEMPLATE_VARS: template_vars, - EventPayload.SYSTEM_PROMPT: self.system_prompt, - EventPayload.QUERY_WRAPPER_PROMPT: self.query_wrapper_prompt, - }, - ): - pass - - def _run_program( - self, - output_cls: BaseModel, - prompt: PromptTemplate, - **prompt_args: Any, - ) -> str: - from llama_index.core.program.utils import get_program_for_llm - - program = get_program_for_llm( - output_cls, - prompt, - self._llm, - pydantic_program_mode=self.pydantic_program_mode, - ) - - chat_response = program(**prompt_args) - return chat_response.json() - - async def _arun_program( - self, - output_cls: BaseModel, - prompt: PromptTemplate, - **prompt_args: Any, - ) -> str: - from llama_index.core.program.utils import get_program_for_llm - - program = get_program_for_llm( - output_cls, - prompt, - self._llm, - pydantic_program_mode=self.pydantic_program_mode, - ) - - chat_response = await program.acall(**prompt_args) - return chat_response.json() - - def predict( - self, - prompt: BasePromptTemplate, - output_cls: Optional[BaseModel] = None, - **prompt_args: Any, - ) -> str: - """Predict.""" - self._log_template_data(prompt, **prompt_args) - - if output_cls is not None: - output = self._run_program(output_cls, prompt, **prompt_args) - elif self._llm.metadata.is_chat_model: - messages = prompt.format_messages(llm=self._llm, **prompt_args) - messages = self._extend_messages(messages) - chat_response = self._llm.chat(messages) - output = chat_response.message.content or "" - else: - formatted_prompt = prompt.format(llm=self._llm, **prompt_args) - formatted_prompt = self._extend_prompt(formatted_prompt) - response = self._llm.complete(formatted_prompt) - output = response.text - - logger.debug(output) - - return output - - def stream( - self, - prompt: BasePromptTemplate, - output_cls: Optional[BaseModel] = None, - **prompt_args: Any, - ) -> TokenGen: - """Stream.""" - if output_cls is not None: - raise NotImplementedError("Streaming with output_cls not supported.") - - self._log_template_data(prompt, **prompt_args) - - if self._llm.metadata.is_chat_model: - messages = prompt.format_messages(llm=self._llm, **prompt_args) - messages = self._extend_messages(messages) - chat_response = self._llm.stream_chat(messages) - stream_tokens = stream_chat_response_to_tokens(chat_response) - else: - formatted_prompt = prompt.format(llm=self._llm, **prompt_args) - formatted_prompt = self._extend_prompt(formatted_prompt) - stream_response = self._llm.stream_complete(formatted_prompt) - stream_tokens = stream_completion_response_to_tokens(stream_response) - return stream_tokens - - async def apredict( - self, - prompt: BasePromptTemplate, - output_cls: Optional[BaseModel] = None, - **prompt_args: Any, - ) -> str: - """Async predict.""" - self._log_template_data(prompt, **prompt_args) - - if output_cls is not None: - output = await self._arun_program(output_cls, prompt, **prompt_args) - elif self._llm.metadata.is_chat_model: - messages = prompt.format_messages(llm=self._llm, **prompt_args) - messages = self._extend_messages(messages) - chat_response = await self._llm.achat(messages) - output = chat_response.message.content or "" - else: - formatted_prompt = prompt.format(llm=self._llm, **prompt_args) - formatted_prompt = self._extend_prompt(formatted_prompt) - response = await self._llm.acomplete(formatted_prompt) - output = response.text - - logger.debug(output) - - return output - - async def astream( - self, - prompt: BasePromptTemplate, - output_cls: Optional[BaseModel] = None, - **prompt_args: Any, - ) -> TokenAsyncGen: - """Async stream.""" - if output_cls is not None: - raise NotImplementedError("Streaming with output_cls not supported.") - - self._log_template_data(prompt, **prompt_args) - - if self._llm.metadata.is_chat_model: - messages = prompt.format_messages(llm=self._llm, **prompt_args) - messages = self._extend_messages(messages) - chat_response = await self._llm.astream_chat(messages) - stream_tokens = await astream_chat_response_to_tokens(chat_response) - else: - formatted_prompt = prompt.format(llm=self._llm, **prompt_args) - formatted_prompt = self._extend_prompt(formatted_prompt) - stream_response = await self._llm.astream_complete(formatted_prompt) - stream_tokens = await astream_completion_response_to_tokens(stream_response) - return stream_tokens - - def _extend_prompt( - self, - formatted_prompt: str, - ) -> str: - """Add system and query wrapper prompts to base prompt.""" - extended_prompt = formatted_prompt - if self.system_prompt: - extended_prompt = self.system_prompt + "\n\n" + extended_prompt - - if self.query_wrapper_prompt: - extended_prompt = self.query_wrapper_prompt.format( - query_str=extended_prompt - ) - - return extended_prompt - - def _extend_messages(self, messages: List[ChatMessage]) -> List[ChatMessage]: - """Add system prompt to chat message list.""" - if self.system_prompt: - messages = [ - ChatMessage(role=MessageRole.SYSTEM, content=self.system_prompt), - *messages, - ] - return messages - - -LLMPredictorType = Union[LLMPredictor, LLM] - - -def load_predictor(data: dict) -> BaseLLMPredictor: - """Load predictor by class name.""" - if isinstance(data, BaseLLMPredictor): - return data - predictor_name = data.get("class_name", None) - if predictor_name is None: - raise ValueError("Predictor loading requires a class_name") - - if predictor_name == LLMPredictor.class_name(): - return LLMPredictor.from_dict(data) - else: - raise ValueError(f"Invalid predictor name: {predictor_name}") + raise ValueError("This class is deprecated. Use any LLM class directly.") diff --git a/llama-index-core/llama_index/core/settings.py b/llama-index-core/llama_index/core/settings.py index c280f8a88effd..ea4a4916c8dab 100644 --- a/llama-index-core/llama_index/core/settings.py +++ b/llama-index-core/llama_index/core/settings.py @@ -1,8 +1,5 @@ from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Callable, List, Optional - -if TYPE_CHECKING: - from llama_index.core.service_context import ServiceContext +from typing import Any, Callable, List, Optional from llama_index.core.base.embeddings.base import BaseEmbedding @@ -249,56 +246,3 @@ def transformations(self, transformations: List[TransformComponent]) -> None: # Singleton Settings = _Settings() - - -# -- Helper functions for deprecation/migration -- - - -def llm_from_settings_or_context( - settings: _Settings, context: Optional["ServiceContext"] -) -> LLM: - """Get settings from either settings or context.""" - if context is not None: - return context.llm - - return settings.llm - - -def embed_model_from_settings_or_context( - settings: _Settings, context: Optional["ServiceContext"] -) -> BaseEmbedding: - """Get settings from either settings or context.""" - if context is not None: - return context.embed_model - - return settings.embed_model - - -def callback_manager_from_settings_or_context( - settings: _Settings, context: Optional["ServiceContext"] -) -> CallbackManager: - """Get settings from either settings or context.""" - if context is not None: - return context.callback_manager - - return settings.callback_manager - - -def node_parser_from_settings_or_context( - settings: _Settings, context: Optional["ServiceContext"] -) -> NodeParser: - """Get settings from either settings or context.""" - if context is not None: - return context.node_parser - - return settings.node_parser - - -def transformations_from_settings_or_context( - settings: _Settings, context: Optional["ServiceContext"] -) -> List[TransformComponent]: - """Get settings from either settings or context.""" - if context is not None: - return context.transformations - - return settings.transformations diff --git a/llama-index-core/llama_index/core/storage/chat_store/simple_chat_store.py b/llama-index-core/llama_index/core/storage/chat_store/simple_chat_store.py index 47c6a3172cbe6..253c3efc6a622 100644 --- a/llama-index-core/llama_index/core/storage/chat_store/simple_chat_store.py +++ b/llama-index-core/llama_index/core/storage/chat_store/simple_chat_store.py @@ -1,17 +1,35 @@ import json import os -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional +from typing_extensions import Annotated import fsspec -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, WrapSerializer from llama_index.core.llms import ChatMessage from llama_index.core.storage.chat_store.base import BaseChatStore +def chat_message_serialization(chat_message: Any, handler, info) -> Dict[str, Any]: + partial_result = handler(chat_message, info) + + for key, value in partial_result.get("additional_kwargs", {}).items(): + value = chat_message._recursive_serialization(value) + if not isinstance(value, (str, int, float, bool, dict, list, type(None))): + raise ValueError(f"Failed to serialize additional_kwargs value: {value}") + partial_result["additional_kwargs"][key] = value + + return partial_result + + +AnnotatedChatMessage = Annotated[ + ChatMessage, WrapSerializer(chat_message_serialization) +] + + class SimpleChatStore(BaseChatStore): """Simple chat store.""" - store: Dict[str, List[ChatMessage]] = Field(default_factory=dict) + store: Dict[str, List[AnnotatedChatMessage]] = Field(default_factory=dict) @classmethod def class_name(cls) -> str: @@ -71,7 +89,7 @@ def persist( fs.makedirs(dirpath) with fs.open(persist_path, "w") as f: - f.write(json.dumps(self.json())) + f.write(self.json()) @classmethod def from_persist_path( @@ -85,4 +103,8 @@ def from_persist_path( return cls() with fs.open(persist_path, "r") as f: data = json.load(f) - return cls.parse_raw(data) + + if isinstance(data, str): + return cls.model_validate_json(data) + else: + return cls.model_validate(data) diff --git a/llama-index-core/llama_index/core/storage/docstore/utils.py b/llama-index-core/llama_index/core/storage/docstore/utils.py index a95e9d44c8744..212e5c6908d3b 100644 --- a/llama-index-core/llama_index/core/storage/docstore/utils.py +++ b/llama-index-core/llama_index/core/storage/docstore/utils.py @@ -54,7 +54,7 @@ def legacy_json_to_doc(doc_dict: dict) -> BaseNode: relationships = data_dict.get("relationships", {}) relationships = { - NodeRelationship(k): RelatedNodeInfo(node_id=v) + NodeRelationship(k): RelatedNodeInfo(node_id=str(v)) for k, v in relationships.items() } diff --git a/llama-index-core/llama_index/core/storage/storage_context.py b/llama-index-core/llama_index/core/storage/storage_context.py index c60cf705c3864..8b43721bbf475 100644 --- a/llama-index-core/llama_index/core/storage/storage_context.py +++ b/llama-index-core/llama_index/core/storage/storage_context.py @@ -42,6 +42,7 @@ from llama_index.core.vector_stores.types import ( BasePydanticVectorStore, ) +from llama_index.core.bridge.pydantic import SerializeAsAny DEFAULT_PERSIST_DIR = "./storage" IMAGE_STORE_FNAME = "image_store.json" @@ -64,7 +65,7 @@ class StorageContext: docstore: BaseDocumentStore index_store: BaseIndexStore - vector_stores: Dict[str, BasePydanticVectorStore] + vector_stores: Dict[str, SerializeAsAny[BasePydanticVectorStore]] graph_store: GraphStore property_graph_store: Optional[PropertyGraphStore] = None @@ -229,9 +230,11 @@ def to_dict(self) -> dict: DOC_STORE_KEY: self.docstore.to_dict(), INDEX_STORE_KEY: self.index_store.to_dict(), GRAPH_STORE_KEY: self.graph_store.to_dict(), - PG_STORE_KEY: self.property_graph_store.to_dict() - if self.property_graph_store - else None, + PG_STORE_KEY: ( + self.property_graph_store.to_dict() + if self.property_graph_store + else None + ), } @classmethod diff --git a/llama-index-core/llama_index/core/tools/function_tool.py b/llama-index-core/llama_index/core/tools/function_tool.py index 3f2800d585f3d..8e1e32ef5f0a0 100644 --- a/llama-index-core/llama_index/core/tools/function_tool.py +++ b/llama-index-core/llama_index/core/tools/function_tool.py @@ -4,6 +4,8 @@ if TYPE_CHECKING: from llama_index.core.bridge.langchain import StructuredTool, Tool + +from llama_index.core.async_utils import asyncio_run from llama_index.core.bridge.pydantic import BaseModel from llama_index.core.tools.types import AsyncBaseTool, ToolMetadata, ToolOutput from llama_index.core.tools.utils import create_schema_from_function @@ -21,6 +23,15 @@ async def _async_wrapped_fn(*args: Any, **kwargs: Any) -> Any: return _async_wrapped_fn +def async_to_sync(func_async: AsyncCallable) -> Callable: + """Async from sync.""" + + def _sync_wrapped_fn(*args: Any, **kwargs: Any) -> Any: + return asyncio_run(func_async(*args, **kwargs)) + + return _sync_wrapped_fn + + class FunctionTool(AsyncBaseTool): """Function Tool. @@ -30,21 +41,30 @@ class FunctionTool(AsyncBaseTool): def __init__( self, - fn: Callable[..., Any], - metadata: ToolMetadata, + fn: Optional[Callable[..., Any]] = None, + metadata: Optional[ToolMetadata] = None, async_fn: Optional[AsyncCallable] = None, ) -> None: - self._fn = fn + if fn is None and async_fn is None: + raise ValueError("Either fn or async_fn must be provided.") + if fn is not None: + self._fn = fn + else: + self._fn = async_to_sync(async_fn) if async_fn is not None: self._async_fn = async_fn else: self._async_fn = sync_to_async(self._fn) + + if metadata is None: + raise ValueError("metadata must be provided.") + self._metadata = metadata @classmethod def from_defaults( cls, - fn: Callable[..., Any], + fn: Optional[Callable[..., Any]] = None, name: Optional[str] = None, description: Optional[str] = None, return_direct: bool = False, @@ -58,7 +78,7 @@ def from_defaults( description = description or f"{name}{signature(fn)}\n{docstring}" if fn_schema is None: fn_schema = create_schema_from_function( - f"{name}", fn, additional_fields=None + f"{name}", fn or async_fn, additional_fields=None ) tool_metadata = ToolMetadata( name=name, diff --git a/llama-index-core/llama_index/core/tools/query_plan.py b/llama-index-core/llama_index/core/tools/query_plan.py index c8d75706df4bd..7cd1fffc26160 100644 --- a/llama-index-core/llama_index/core/tools/query_plan.py +++ b/llama-index-core/llama_index/core/tools/query_plan.py @@ -148,7 +148,7 @@ def _execute_node( self, node: QueryNode, nodes_dict: Dict[int, QueryNode] ) -> ToolOutput: """Execute node.""" - print_text(f"Executing node {node.json()}\n", color="blue") + print_text(f"Executing node {node.model_dump_json()}\n", color="blue") if len(node.dependencies) > 0: print_text( f"Executing {len(node.dependencies)} child nodes\n", color="pink" diff --git a/llama-index-core/llama_index/core/tools/retriever_tool.py b/llama-index-core/llama_index/core/tools/retriever_tool.py index dd35cfdffcd58..45aa51f3ab988 100644 --- a/llama-index-core/llama_index/core/tools/retriever_tool.py +++ b/llama-index-core/llama_index/core/tools/retriever_tool.py @@ -80,7 +80,7 @@ def call(self, *args: Any, **kwargs: Any) -> ToolOutput: docs = self._apply_node_postprocessors(docs, QueryBundle(query_str)) content = "" for doc in docs: - node_copy = doc.node.copy() + node_copy = doc.node.model_copy() node_copy.text_template = "{metadata_str}\n{content}" node_copy.metadata_template = "{key} = {value}" content += node_copy.get_content(MetadataMode.LLM) + "\n\n" @@ -105,7 +105,7 @@ async def acall(self, *args: Any, **kwargs: Any) -> ToolOutput: content = "" docs = self._apply_node_postprocessors(docs, QueryBundle(query_str)) for doc in docs: - node_copy = doc.node.copy() + node_copy = doc.node.model_copy() node_copy.text_template = "{metadata_str}\n{content}" node_copy.metadata_template = "{key} = {value}" content += node_copy.get_content(MetadataMode.LLM) + "\n\n" diff --git a/llama-index-core/llama_index/core/tools/types.py b/llama-index-core/llama_index/core/tools/types.py index 0899b7cafd1d2..2355669858cf1 100644 --- a/llama-index-core/llama_index/core/tools/types.py +++ b/llama-index-core/llama_index/core/tools/types.py @@ -34,11 +34,11 @@ def get_parameters_dict(self) -> dict: "required": ["input"], } else: - parameters = self.fn_schema.schema() + parameters = self.fn_schema.model_json_schema() parameters = { k: v for k, v in parameters.items() - if k in ["type", "properties", "required", "definitions"] + if k in ["type", "properties", "required", "definitions", "$defs"] } return parameters diff --git a/llama-index-core/llama_index/core/types.py b/llama-index-core/llama_index/core/types.py index 0ef86cdb58c00..226444e2b892e 100644 --- a/llama-index-core/llama_index/core/types.py +++ b/llama-index-core/llama_index/core/types.py @@ -16,7 +16,12 @@ ) from llama_index.core.base.llms.types import ChatMessage, MessageRole -from llama_index.core.bridge.pydantic import BaseModel +from llama_index.core.bridge.pydantic import ( + BaseModel, + GetCoreSchemaHandler, + GetJsonSchemaHandler, +) +from llama_index.core.bridge.pydantic_core import CoreSchema, core_schema from llama_index.core.instrumentation import DispatcherSpanMixin Model = TypeVar("Model", bound=BaseModel) @@ -31,11 +36,6 @@ class BaseOutputParser(DispatcherSpanMixin, ABC): """Output parser class.""" - @classmethod - def __modify_schema__(cls, schema: Dict[str, Any]) -> None: - """Avoids serialization issues.""" - schema.update(type="object", default={}) - @abstractmethod def parse(self, output: str) -> Any: """Parse, validate, and correct errors programmatically.""" @@ -56,6 +56,19 @@ def format_messages(self, messages: List[ChatMessage]) -> List[ChatMessage]: return messages + @classmethod + def __get_pydantic_core_schema__( + cls, source: Type[Any], handler: GetCoreSchemaHandler + ) -> CoreSchema: + return core_schema.any_schema() + + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler + ) -> Dict[str, Any]: + json_schema = handler(core_schema) + return handler.resolve_ref_schema(json_schema) + class BasePydanticProgram(DispatcherSpanMixin, ABC, Generic[Model]): """A base class for LLM-powered function that return a pydantic model. diff --git a/llama-index-core/llama_index/core/utilities/gemini_utils.py b/llama-index-core/llama_index/core/utilities/gemini_utils.py index ae72367e6b070..e9d2d929f94b1 100644 --- a/llama-index-core/llama_index/core/utilities/gemini_utils.py +++ b/llama-index-core/llama_index/core/utilities/gemini_utils.py @@ -53,13 +53,6 @@ def merge_neighboring_same_role_messages( content="\n".join([str(msg_content) for msg_content in merged_content]), additional_kwargs=current_message.additional_kwargs, ) - # When making function calling, 'assistant->model' response does not contain text. - # Gemini chat takes 'user', 'model' message alternately. - # https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/getting-started/intro_gemini_chat.ipynb - # It cannot skip empty 'model' content. - # It will cause "empty text parameter" issue, the following code can avoid it. - if merged_message.content == "" and merged_message.role == MessageRole.MODEL: - merged_message.content = "Function Calling" merged_messages.append(merged_message) i += 1 diff --git a/llama-index-core/llama_index/core/utils.py b/llama-index-core/llama_index/core/utils.py index f567ed0bbd02c..a776e59523eba 100644 --- a/llama-index-core/llama_index/core/utils.py +++ b/llama-index-core/llama_index/core/utils.py @@ -62,9 +62,9 @@ def __init__(self) -> None: nltk.download("stopwords", download_dir=self._nltk_data_dir) try: - nltk.data.find("tokenizers/punkt") + nltk.data.find("tokenizers/punkt_tab") except LookupError: - nltk.download("punkt", download_dir=self._nltk_data_dir) + nltk.download("punkt_tab", download_dir=self._nltk_data_dir) @property def stopwords(self) -> List[str]: diff --git a/llama-index-core/llama_index/core/vector_stores/simple.py b/llama-index-core/llama_index/core/vector_stores/simple.py index eaf38572f6265..69ae8cd86e635 100644 --- a/llama-index-core/llama_index/core/vector_stores/simple.py +++ b/llama-index-core/llama_index/core/vector_stores/simple.py @@ -91,12 +91,19 @@ def _process_filter_match( filter_matches_list = [] for filter_ in filter_list: filter_matches = True - - filter_matches = _process_filter_match( - operator=filter_.operator, - value=filter_.value, - metadata_value=metadata.get(filter_.key, None), - ) + metadata_value = metadata.get(filter_.key, None) + if filter_.operator == FilterOperator.IS_EMPTY: + filter_matches = ( + metadata_value is None + or metadata_value == "" + or metadata_value == [] + ) + else: + filter_matches = _process_filter_match( + operator=filter_.operator, + value=filter_.value, + metadata_value=metadata_value, + ) filter_matches_list.append(filter_matches) @@ -156,14 +163,11 @@ def __init__( def from_persist_dir( cls, persist_dir: str = DEFAULT_PERSIST_DIR, - namespace: Optional[str] = None, + namespace: str = DEFAULT_VECTOR_STORE, fs: Optional[fsspec.AbstractFileSystem] = None, ) -> "SimpleVectorStore": """Load from persist dir.""" - if namespace: - persist_fname = f"{namespace}{NAMESPACE_SEP}{DEFAULT_PERSIST_FNAME}" - else: - persist_fname = DEFAULT_PERSIST_FNAME + persist_fname = f"{namespace}{NAMESPACE_SEP}{DEFAULT_PERSIST_FNAME}" if fs is not None: persist_path = concat_dirs(persist_dir, persist_fname) diff --git a/llama-index-core/llama_index/core/vector_stores/types.py b/llama-index-core/llama_index/core/vector_stores/types.py index 03a098f4f9a9a..f6e67f2a8c301 100644 --- a/llama-index-core/llama_index/core/vector_stores/types.py +++ b/llama-index-core/llama_index/core/vector_stores/types.py @@ -1,4 +1,5 @@ """Vector store index types.""" + from abc import ABC, abstractmethod from dataclasses import dataclass from enum import Enum @@ -17,6 +18,7 @@ from deprecated import deprecated from llama_index.core.bridge.pydantic import ( BaseModel, + ConfigDict, StrictFloat, StrictInt, StrictStr, @@ -74,6 +76,7 @@ class FilterOperator(str, Enum): ALL = "all" # Contains all (array of strings) TEXT_MATCH = "text_match" # full text match (allows you to search for a specific substring, token or phrase within the text field) CONTAINS = "contains" # metadata array contains value (string or number) + IS_EMPTY = "is_empty" # the field is not exist or empty (null or empty array) class FilterCondition(str, Enum): @@ -85,7 +88,7 @@ class FilterCondition(str, Enum): class MetadataFilter(BaseModel): - """Comprehensive metadata filter for vector stores to support more operators. + r"""Comprehensive metadata filter for vector stores to support more operators. Value uses Strict* types, as int, float and str are compatible types and were all converted to string before. @@ -94,13 +97,15 @@ class MetadataFilter(BaseModel): """ key: str - value: Union[ - StrictInt, - StrictFloat, - StrictStr, - List[StrictStr], - List[StrictFloat], - List[StrictInt], + value: Optional[ + Union[ + StrictInt, + StrictFloat, + StrictStr, + List[StrictStr], + List[StrictFloat], + List[StrictInt], + ] ] operator: FilterOperator = FilterOperator.EQ @@ -115,7 +120,7 @@ def from_dict( filter_dict: Dict with key, value and operator. """ - return MetadataFilter.parse_obj(filter_dict) + return MetadataFilter.model_validate(filter_dict) # # TODO: Deprecate ExactMatchFilter and use MetadataFilter instead @@ -316,12 +321,10 @@ def persist( class BasePydanticVectorStore(BaseComponent, ABC): """Abstract vector store protocol.""" + model_config = ConfigDict(arbitrary_types_allowed=True) stores_text: bool is_embedding_query: bool = True - class Config: - arbitrary_types_allowed = True - @property @abstractmethod def client(self) -> Any: @@ -347,6 +350,7 @@ async def aget_nodes( def add( self, nodes: List[BaseNode], + **kwargs: Any, ) -> List[str]: """Add nodes to vector store.""" @@ -360,7 +364,7 @@ async def async_add( NOTE: this is not implemented for all vector stores. If not implemented, it will just call add synchronously. """ - return self.add(nodes) + return self.add(nodes, **kwargs) @abstractmethod def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: diff --git a/llama-index-core/llama_index/core/workflow/context.py b/llama-index-core/llama_index/core/workflow/context.py index 865572d568301..96b2f1b0c560d 100644 --- a/llama-index-core/llama_index/core/workflow/context.py +++ b/llama-index-core/llama_index/core/workflow/context.py @@ -1,21 +1,103 @@ +import asyncio +import warnings from collections import defaultdict -from typing import Dict, Any, Optional, List, Type +from typing import Dict, Any, Optional, List, Type, TYPE_CHECKING, Set, Tuple +from .decorators import StepConfig from .events import Event +from .errors import WorkflowRuntimeError + +if TYPE_CHECKING: # pragma: no cover + from .workflow import Workflow class Context: - def __init__(self, parent: Optional["Context"] = None) -> None: - # Global state - if parent: - self.data = parent.data - else: - self.data: Dict[str, Any] = {} + """A global object representing a context for a given workflow run. + + The Context object can be used to store data that needs to be available across iterations during a workflow + execution, and across multiple workflow runs. + Every context instance offers two type of data storage: a global one, that's shared among all the steps within a + workflow, and private one, that's only accessible from a single step. + + Both `set` and `get` operations on global data are governed by a lock, and considered coroutine-safe. + """ + def __init__(self, workflow: "Workflow") -> None: + self._workflow = workflow + # Broker machinery + self._queues: Dict[str, asyncio.Queue] = {} + self._tasks: Set[asyncio.Task] = set() + self._broker_log: List[Event] = [] + self._step_flags: Dict[str, asyncio.Event] = {} + self._accepted_events: List[Tuple[str, str]] = [] + self._retval: Any = None + # Streaming machinery + self._streaming_queue: asyncio.Queue = asyncio.Queue() + # Global data storage + self._lock = asyncio.Lock() + self._globals: Dict[str, Any] = {} # Step-specific instance - self.parent = parent self._events_buffer: Dict[Type[Event], List[Event]] = defaultdict(list) + async def set(self, key: str, value: Any, make_private: bool = False) -> None: + """Store `value` into the Context under `key`. + + Args: + key: A unique string to identify the value stored. + value: The data to be stored. + + Raises: + ValueError: When make_private is True but a key already exists in the global storage. + """ + if make_private: + warnings.warn( + "`make_private` is deprecated and will be ignored", DeprecationWarning + ) + + async with self.lock: + self._globals[key] = value + + async def get(self, key: str, default: Optional[Any] = None) -> Any: + """Get the value corresponding to `key` from the Context. + + Args: + key: A unique string to identify the value stored. + default: The value to return when `key` is missing instead of raising an exception. + + Raises: + ValueError: When there's not value accessible corresponding to `key`. + """ + async with self.lock: + if key in self._globals: + return self._globals[key] + elif default is not None: + return default + + msg = f"Key '{key}' not found in Context" + raise ValueError(msg) + + @property + def data(self): + """This property is provided for backward compatibility. + + Use `get` and `set` instead. + """ + msg = "`data` is deprecated, please use the `get` and `set` method to store data into the Context." + warnings.warn(msg, DeprecationWarning) + return self._globals + + @property + def lock(self) -> asyncio.Lock: + """Returns a mutex to lock the Context.""" + return self._lock + + @property + def session(self) -> "Context": + """This property is provided for backward compatibility.""" + msg = "`session` is deprecated, please use the Context instance directly." + warnings.warn(msg, DeprecationWarning) + return self + def collect_events( self, ev: Event, expected: List[Type[Event]] ) -> Optional[List[Event]]: @@ -35,3 +117,41 @@ def collect_events( self._events_buffer[type(ev)].append(ev) return None + + def send_event(self, message: Event, step: Optional[str] = None) -> None: + """Sends an event to a specific step in the workflow. + + If step is None, the event is sent to all the receivers and we let + them discard events they don't want. + """ + if step is None: + for queue in self._queues.values(): + queue.put_nowait(message) + else: + if step not in self._workflow._get_steps(): + raise WorkflowRuntimeError(f"Step {step} does not exist") + + step_func = self._workflow._get_steps()[step] + step_config: Optional[StepConfig] = getattr( + step_func, "__step_config", None + ) + + if step_config and type(message) in step_config.accepted_events: + self._queues[step].put_nowait(message) + else: + raise WorkflowRuntimeError( + f"Step {step} does not accept event of type {type(message)}" + ) + + self._broker_log.append(message) + + def write_event_to_stream(self, ev: Optional[Event]) -> None: + self._streaming_queue.put_nowait(ev) + + def get_result(self) -> Any: + """Returns the result of the workflow.""" + return self._retval + + @property + def streaming_queue(self) -> asyncio.Queue: + return self._streaming_queue diff --git a/llama-index-core/llama_index/core/workflow/decorators.py b/llama-index-core/llama_index/core/workflow/decorators.py index eea748b063645..ab979b70c7c13 100644 --- a/llama-index-core/llama_index/core/workflow/decorators.py +++ b/llama-index-core/llama_index/core/workflow/decorators.py @@ -1,14 +1,16 @@ -from typing import Any, Callable, List, Optional, TYPE_CHECKING, Type +from typing import TYPE_CHECKING, Any, Callable, List, Optional, Type from llama_index.core.bridge.pydantic import BaseModel + +from .errors import WorkflowValidationError from .utils import ( - validate_step_signature, is_free_function, + validate_step_signature, + inspect_signature, + ServiceDefinition, ) -from .errors import WorkflowValidationError - -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from .workflow import Workflow @@ -16,10 +18,17 @@ class StepConfig(BaseModel): accepted_events: List[Any] event_name: str return_types: List[Any] - pass_context: bool + context_parameter: Optional[str] + num_workers: int + requested_services: List[ServiceDefinition] -def step(workflow: Optional[Type["Workflow"]] = None, pass_context: bool = False): +def step( + *args, + workflow: Optional[Type["Workflow"]] = None, + pass_context: bool = False, + num_workers: int = 1, +): """Decorator used to mark methods and functions as workflow steps. Decorators are evaluated at import time, but we need to wait for @@ -29,24 +38,38 @@ def step(workflow: Optional[Type["Workflow"]] = None, pass_context: bool = False """ def decorator(func: Callable) -> Callable: - # If this is a free function, call add_step() explicitly. - if is_free_function(func.__qualname__): - if workflow is None: - msg = f"To decorate {func.__name__} please pass a workflow instance to the @step() decorator." - raise WorkflowValidationError(msg) - workflow.add_step(func) + if not isinstance(num_workers, int) or num_workers <= 0: + raise WorkflowValidationError( + "num_workers must be an integer greater than 0" + ) # This will raise providing a message with the specific validation failure - event_name, event_types, return_types = validate_step_signature(func) + spec = inspect_signature(func) + validate_step_signature(spec) + event_name, accepted_events = next(iter(spec.accepted_events.items())) # store the configuration in the function object func.__step_config = StepConfig( - accepted_events=event_types, + accepted_events=accepted_events, event_name=event_name, - return_types=return_types, - pass_context=pass_context, + return_types=spec.return_types, + context_parameter=spec.context_parameter, + num_workers=num_workers, + requested_services=spec.requested_services or [], ) + # If this is a free function, call add_step() explicitly. + if is_free_function(func.__qualname__): + if workflow is None: + msg = f"To decorate {func.__name__} please pass a workflow class to the @step decorator." + raise WorkflowValidationError(msg) + workflow.add_step(func) + return func + if len(args): + # The decorator was used without parentheses, like `@step` + func = args[0] + decorator(func) + return func return decorator diff --git a/llama-index-core/llama_index/core/workflow/events.py b/llama-index-core/llama_index/core/workflow/events.py index 72916f24b1c14..b197b1ca41084 100644 --- a/llama-index-core/llama_index/core/workflow/events.py +++ b/llama-index-core/llama_index/core/workflow/events.py @@ -1,37 +1,91 @@ from typing import Any, Dict, Type +from _collections_abc import dict_keys, dict_items, dict_values -from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr +from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr, ConfigDict class Event(BaseModel): - """Base class for event types.""" + """Base class for event types that mimics dict interface. - class Config: - arbitrary_types_allowed = True + PrivateAttr: + _data (Dict[str, Any]): Underlying Python dict. + Examples: + Basic example usage -class StartEvent(Event): - """StartEvent is implicitly sent when a workflow runs. Mimics the interface of a dict.""" + ```python + from llama_index.core.workflows.events import Event + + evt = Event(a=1, b=2) + + # can use dot access to get values of `a` and `b` + print((evt.a, evt.b)) + + # can also set the attrs + evt.a = 2 + ``` + + Custom event with additional Fields/PrivateAttr + + ```python + from llama_index.core.workflows.events import Event + from llama_index.core.bridge.pydantic import Field, PrivateAttr + + class CustomEvent(Event): + field_1: int = Field(description="my custom field") + _private_attr_1: int = PrivateAttr() + evt = CustomEvent(a=1, b=2, field_1=3, _private_attr_1=4) + + # `field_1` and `_private_attr_1` get set as they do with Pydantic BaseModel + print(evt.field_1) + print(evt._private_attr_1) + + # `a` and `b` get set in the underliying dict, namely `evt._data` + print((evt.a, evt.b)) + ``` + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) _data: Dict[str, Any] = PrivateAttr(default_factory=dict) - def __init__(self, **data: Any): - super().__init__() + def __init__(self, **params: Any): + """__init__. + + NOTE: fields and private_attrs are pulled from params by name. + """ + # extract and set fields, private attrs and remaining shove in _data + fields = {} + private_attrs = {} + data = {} + for k, v in params.items(): + if k in self.model_fields: + fields[k] = v + elif k in self.__private_attributes__: + private_attrs[k] = v + else: + data[k] = v + super().__init__(**fields) + for private_attr, value in private_attrs.items(): + super().__setattr__(private_attr, value) self._data = data def __getattr__(self, __name: str) -> Any: - if __name in self._data: - return self._data[__name] + if __name in self.__private_attributes__ or __name in self.model_fields: + return super().__getattr__(__name) # type: ignore else: - raise AttributeError( - f"'{self.__class__.__name__}' object has no attribute '{__name}'" - ) - - def __setattr__(self, __name: str, value: Any) -> None: - if __name in self._data: - self._data[__name] = value + try: + return self._data[__name] + except KeyError: + raise AttributeError( + f"'{self.__class__.__name__}' object has no attribute '{__name}'" + ) + + def __setattr__(self, name, value) -> None: + if name in self.__private_attributes__ or name in self.model_fields: + super().__setattr__(name, value) else: - super().__setattr__(__name, value) + self._data.__setitem__(name, value) def __getitem__(self, key: str) -> Any: return self._data[key] @@ -45,13 +99,13 @@ def get(self, key: str, default: Any = None) -> Any: def __contains__(self, key: str) -> bool: return key in self._data - def keys(self) -> Dict[str, Any].keys: + def keys(self) -> "dict_keys[str, Any]": return self._data.keys() - def values(self) -> Dict[str, Any].values: + def values(self) -> "dict_values[str, Any]": return self._data.values() - def items(self) -> Dict[str, Any].items: + def items(self) -> "dict_items[str, Any]": return self._data.items() def __len__(self) -> int: @@ -64,6 +118,10 @@ def dict(self, *args: Any, **kwargs: Any) -> Dict[str, Any]: return self._data +class StartEvent(Event): + """StartEvent is implicitly sent when a workflow runs.""" + + class StopEvent(Event): """EndEvent signals the workflow to stop.""" diff --git a/llama-index-core/llama_index/core/workflow/service.py b/llama-index-core/llama_index/core/workflow/service.py new file mode 100644 index 0000000000000..823837a829ed3 --- /dev/null +++ b/llama-index-core/llama_index/core/workflow/service.py @@ -0,0 +1,33 @@ +from typing import Dict, TYPE_CHECKING, Optional + + +if TYPE_CHECKING: # pragma: no cover + from .workflow import Workflow + + +class ServiceNotFoundError(Exception): + """An error raised when the service manager couldn't find a certain service name.""" + + +class ServiceManager: + """An helper class to decouple how services are managed from the Workflow class. + + A Service is nothing more than a workflow instance attached to another workflow. + The service is made available to the steps of the main workflow. + """ + + def __init__(self) -> None: + self._services: Dict[str, "Workflow"] = {} + + def get(self, name: str, default: Optional["Workflow"] = None) -> "Workflow": + try: + return self._services[name] + except KeyError as e: + if default: + return default + + msg = f"Service {name} not found" + raise ServiceNotFoundError(msg) + + def add(self, name: str, service: "Workflow") -> None: + self._services[name] = service diff --git a/llama-index-core/llama_index/core/workflow/utils.py b/llama-index-core/llama_index/core/workflow/utils.py index e7fb320df726a..d2eef68f4d016 100644 --- a/llama-index-core/llama_index/core/workflow/utils.py +++ b/llama-index-core/llama_index/core/workflow/utils.py @@ -5,7 +5,6 @@ Any, List, Optional, - Tuple, Union, Callable, Dict, @@ -15,67 +14,94 @@ # handle python version compatibility try: from types import UnionType -except ImportError: +except ImportError: # pragma: no cover UnionType = Union -from .context import Context -from .events import Event +from llama_index.core.bridge.pydantic import BaseModel, ConfigDict + +from .events import Event, EventType from .errors import WorkflowValidationError -def validate_step_signature(fn: Callable) -> Tuple[str, List[object], List[object]]: - """Given a function, ensure the signature is compatible with a workflow step. +class ServiceDefinition(BaseModel): + # Make the service definition hashable + model_config = ConfigDict(frozen=True) - This function returns a tuple with: - - the name of the parameter delivering the event - - the list of event types in input - - the list of event types in output - """ + name: str + service: Any + default_value: Optional[Any] + + +class StepSignatureSpec(BaseModel): + """A Pydantic model representing the signature of a step function or method.""" + + accepted_events: Dict[str, List[EventType]] + return_types: List[Any] + context_parameter: Optional[str] + requested_services: Optional[List[ServiceDefinition]] + + +def inspect_signature(fn: Callable) -> StepSignatureSpec: + """Given a function, ensure the signature is compatible with a workflow step.""" sig = inspect.signature(fn) - # At least one parameter - if len(sig.parameters) == 0: - msg = "Step signature must have at least one parameter" - raise WorkflowValidationError(msg) + accepted_events: Dict[str, List[EventType]] = {} + context_parameter = None + requested_services = [] - event_name = "" - event_types = [] - num_of_possible_events = 0 + # Inspect function parameters for name, t in sig.parameters.items(): + # Ignore self and cls if name in ("self", "cls"): continue - # All parameters must be annotated - if t.annotation == inspect._empty: - msg = "Step signature parameters must be annotated" - raise WorkflowValidationError(msg) - - if t.annotation == Context: + # Get name and type of the Context param + if hasattr(t.annotation, "__name__") and t.annotation.__name__ == "Context": + context_parameter = name continue - event_types = _get_param_types(t) - - all_events = all(et == Event or issubclass(et, Event) for et in event_types) - - if not all_events: - msg = "Events in step signature parameters must be of type Event" - raise WorkflowValidationError(msg) - - # Number of events in the signature must be exactly one - num_of_possible_events += 1 - event_name = name + # Collect name and types of the event param + param_types = _get_param_types(t) + if all( + param_t == Event + or (inspect.isclass(param_t) and issubclass(param_t, Event)) + for param_t in param_types + ): + accepted_events[name] = param_types + continue - if num_of_possible_events != 1: - msg = f"Step signature must contain exactly one parameter of type Event but found {num_of_possible_events}." + # Everything else will be treated as a service request + default_value = t.default + if default_value is inspect.Parameter.empty: + default_value = None + + requested_services.append( + ServiceDefinition( + name=name, service=param_types[0], default_value=default_value + ) + ) + + return StepSignatureSpec( + accepted_events=accepted_events, + return_types=_get_return_types(fn), + context_parameter=context_parameter, + requested_services=requested_services, + ) + + +def validate_step_signature(spec: StepSignatureSpec) -> None: + num_of_events = len(spec.accepted_events) + if num_of_events == 0: + msg = "Step signature must have at least one parameter annotated as type Event" + raise WorkflowValidationError(msg) + elif num_of_events > 1: + msg = f"Step signature must contain exactly one parameter of type Event but found {num_of_events}." raise WorkflowValidationError(msg) - return_types = _get_return_types(fn) - if not return_types: + if not spec.return_types: msg = f"Return types of workflows step functions must be annotated with their type." raise WorkflowValidationError(msg) - return (event_name, event_types, return_types) - def get_steps_from_class(_class: object) -> Dict[str, Callable]: """Given a class, return the list of its methods that were defined as steps.""" @@ -101,7 +127,7 @@ def get_steps_from_instance(workflow: object) -> Dict[str, Callable]: return step_methods -def _get_param_types(param: inspect.Parameter) -> List[object]: +def _get_param_types(param: inspect.Parameter) -> List[Any]: """Extract the types of a parameter. Handles Union and Optional types.""" typ = param.annotation if typ is inspect.Parameter.empty: @@ -111,7 +137,7 @@ def _get_param_types(param: inspect.Parameter) -> List[object]: return [typ] -def _get_return_types(func: Callable) -> List[object]: +def _get_return_types(func: Callable) -> List[Any]: """Extract the return type hints from a function. Handles Union, Optional, and List types. diff --git a/llama-index-core/llama_index/core/workflow/workflow.py b/llama-index-core/llama_index/core/workflow/workflow.py index 42a4d510ac52f..585ab4270b321 100644 --- a/llama-index-core/llama_index/core/workflow/workflow.py +++ b/llama-index-core/llama_index/core/workflow/workflow.py @@ -1,22 +1,21 @@ import asyncio +import functools import warnings -from typing import Any, Callable, Dict, List, Optional, Set, Tuple +from typing import Any, Callable, Dict, Optional, AsyncGenerator, Set from llama_index.core.instrumentation import get_dispatcher -from llama_index.core.workflow.decorators import step, StepConfig -from llama_index.core.workflow.events import StartEvent, StopEvent, Event -from llama_index.core.workflow.utils import ( + +from .decorators import StepConfig, step +from .context import Context +from .events import Event, StartEvent, StopEvent +from .errors import * +from .service import ServiceManager +from .utils import ( get_steps_from_class, get_steps_from_instance, + ServiceDefinition, ) -from .errors import ( - WorkflowRuntimeError, - WorkflowTimeoutError, - WorkflowValidationError, - WorkflowDone, -) -from .context import Context dispatcher = get_dispatcher(__name__) @@ -33,21 +32,40 @@ def __init__( timeout: Optional[float] = 10.0, disable_validation: bool = False, verbose: bool = False, + service_manager: Optional[ServiceManager] = None, ) -> None: # Configuration self._timeout = timeout self._verbose = verbose self._disable_validation = disable_validation # Broker machinery - self._queues: Dict[str, asyncio.Queue] = {} - self._tasks: Set[asyncio.Task] = set() - self._broker_log: List[Event] = [] - self._step_flags: Dict[str, asyncio.Event] = {} - self._accepted_events: List[Tuple[str, str]] = [] - self._retval: Any = None - # Context management - self._root_context: Context = Context() - self._step_to_context: Dict[str, Context] = {} + self._contexts: Set[Context] = set() + self._stepwise_context: Optional[Context] = None + # Services management + self._service_manager = service_manager or ServiceManager() + + async def stream_events(self) -> AsyncGenerator[Event, None]: + # In the typical streaming use case, `run()` is not awaited but wrapped in a asyncio.Task. Since we'll be + # consuming events produced by `run()`, we must give its Task the chance to run before entering the dequeueing + # loop. + await asyncio.sleep(0) + + if len(self._contexts) > 1: + # We can't possibly know from what session we should stream events, raise an error. + msg = ( + "This workflow has multiple concurrent runs in progress and cannot stream events. " + "To be able to stream events, make sure you call `run()` on this workflow only once." + ) + raise WorkflowRuntimeError(msg) + + # Enter the dequeuing loop. + ctx = next(iter(self._contexts)) + while True: + ev = await ctx.streaming_queue.get() + if type(ev) is StopEvent: + break + + yield ev @classmethod def add_step(cls, func: Callable) -> None: @@ -55,40 +73,43 @@ def add_step(cls, func: Callable) -> None: It raises an exception if a step with the same name was already added to the workflow. """ + step_config: Optional[StepConfig] = getattr(func, "__step_config", None) + if not step_config: + msg = f"Step function {func.__name__} is missing the `@step` decorator." + raise WorkflowValidationError(msg) + if func.__name__ in {**get_steps_from_class(cls), **cls._step_functions}: msg = f"A step {func.__name__} is already part of this workflow, please choose another name." raise WorkflowValidationError(msg) cls._step_functions[func.__name__] = func - def get_context(self, step_name: str) -> Context: - """Get the global context for this workflow. + def add_workflows(self, **workflows: "Workflow") -> None: + """Adds one or more nested workflows to this workflow. - The Workflow instance is ultimately responsible for managing the lifecycle - of the global context object and for passing it to the steps functions that - require it. + This method only accepts keyword arguments, and the name of the parameter + will be used as the name of the workflow. """ - if step_name not in self._step_to_context: - self._step_to_context[step_name] = Context(parent=self._root_context) - return self._step_to_context[step_name] + for name, wf in workflows.items(): + self._service_manager.add(name, wf) def _get_steps(self) -> Dict[str, Callable]: """Returns all the steps, whether defined as methods or free functions.""" return {**get_steps_from_instance(self), **self._step_functions} - def _start(self, stepwise: bool = False) -> None: + def _start(self, stepwise: bool = False) -> Context: """Sets up the queues and tasks for each declared step. This method also launches each step as an async task. """ + ctx = Context(self) + self._contexts.add(ctx) + for name, step_func in self._get_steps().items(): - self._queues[name] = asyncio.Queue() - self._step_flags[name] = asyncio.Event() - step_config: Optional[StepConfig] = getattr( - step_func, "__step_config", None - ) - if not step_config: - raise ValueError(f"Step {name} is missing `@step()` decorator.") + ctx._queues[name] = asyncio.Queue() + ctx._step_flags[name] = asyncio.Event() + # At this point, step_func is guaranteed to have the `__step_config` attribute + step_config: StepConfig = getattr(step_func, "__step_config") async def _task( name: str, @@ -103,30 +124,36 @@ async def _task( # do we need to wait for the step flag? if stepwise: - await self._step_flags[name].wait() + await ctx._step_flags[name].wait() # clear all flags so that we only run one step - for flag in self._step_flags.values(): + for flag in ctx._step_flags.values(): flag.clear() if self._verbose and name != "_done": print(f"Running step {name}") # run step - args = [] - if config.pass_context: - args.append(self.get_context(name)) - args.append(ev) + kwargs = {} + if config.context_parameter: + kwargs[config.context_parameter] = ctx + for service_definition in config.requested_services: + service = self._service_manager.get( + service_definition.name, service_definition.default_value + ) + kwargs[service_definition.name] = service + kwargs[config.event_name] = ev # - check if its async or not # - if not async, run it in an executor instrumented_step = dispatcher.span(step) if asyncio.iscoroutinefunction(step): - new_ev = await instrumented_step(*args) + new_ev = await instrumented_step(**kwargs) else: + run_task = functools.partial(instrumented_step, **kwargs) new_ev = await asyncio.get_event_loop().run_in_executor( - None, instrumented_step, *args + None, run_task ) if self._verbose and name != "_done": @@ -140,31 +167,39 @@ async def _task( continue # Store the accepted event for the drawing operations - self._accepted_events.append((name, type(ev).__name__)) + ctx._accepted_events.append((name, type(ev).__name__)) if not isinstance(new_ev, Event): warnings.warn( f"Step function {name} returned {type(new_ev).__name__} instead of an Event instance." ) else: - self.send_event(new_ev) - - self._tasks.add( - asyncio.create_task( - _task(name, self._queues[name], step_func, step_config), name=name + ctx.send_event(new_ev) + + for _ in range(step_config.num_workers): + ctx._tasks.add( + asyncio.create_task( + _task(name, ctx._queues[name], step_func, step_config), + name=name, + ) ) - ) + return ctx - def send_event(self, message: Event) -> None: - """Sends an event to a specific step in the workflow. + def send_event(self, message: Event, step: Optional[str] = None) -> None: + msg = ( + "Use a Context instance to send events from a step. " + "Make sure your step method or function takes a parameter of type Context like `ctx: Context` and " + "replace `self.send_event(...)` with `ctx.send_event(...)` in your code." + ) - Currently we send all the events to all the receivers and we let - them discard events they don't want. This should be optimized so - that we efficiently send events where we know won't be discarded. - """ - for queue in self._queues.values(): - queue.put_nowait(message) - self._broker_log.append(message) + if len(self._contexts) > 1: + # We can't possibly know to what session we should send this event, raise an error. + raise WorkflowRuntimeError(msg) + + # Emit a warning as this won't work for multiple run()s. + warnings.warn(msg) + ctx = next(iter(self._contexts)) + ctx.send_event(message=message, step=step) @dispatcher.span async def run(self, **kwargs: Any) -> str: @@ -176,21 +211,17 @@ async def run(self, **kwargs: Any) -> str: 3. sending a StartEvent to kick things off 4. waiting for all tasks to finish or be cancelled """ - if self._tasks: - msg = "Workflow is already running, wait for it to finish before running again." - raise WorkflowRuntimeError(msg) - - # Reset the events log - self._accepted_events = [] # Validate the workflow if needed self._validate() - # Start the machinery - self._start() + + # Start the machinery in a new Context + ctx = self._start() + # Send the first event - self.send_event(StartEvent(**kwargs)) + ctx.send_event(StartEvent(**kwargs)) done, unfinished = await asyncio.wait( - self._tasks, timeout=self._timeout, return_when=asyncio.FIRST_EXCEPTION + ctx._tasks, timeout=self._timeout, return_when=asyncio.FIRST_EXCEPTION ) we_done = False @@ -214,11 +245,10 @@ async def run(self, **kwargs: Any) -> str: t.cancel() await asyncio.sleep(0) - # Remove any reference to the tasks - self._tasks = set() - # Bubble up the error if any step raised an exception if exception_raised: + # Make sure to stop streaming, in case the workflow terminated abnormally + ctx.write_event_to_stream(StopEvent()) raise exception_raised # Raise WorkflowTimeoutError if the workflow timed out @@ -226,7 +256,7 @@ async def run(self, **kwargs: Any) -> str: msg = f"Operation timed out after {self._timeout} seconds" raise WorkflowTimeoutError(msg) - return self._retval + return ctx._retval @dispatcher.span async def run_step(self, **kwargs: Any) -> Optional[str]: @@ -239,16 +269,15 @@ async def run_step(self, **kwargs: Any) -> Optional[str]: 4. Waiting for the next step(s) to finish 5. Returning the result if the workflow is done """ - # Check if we need to start - if not self._tasks: - self._accepted_events = [] + # Check if we need to start a new session + if self._stepwise_context is None: self._validate() - self._start(stepwise=True) + self._stepwise_context = self._start(stepwise=True) # Run the first step - self.send_event(StartEvent(**kwargs)) + self._stepwise_context.send_event(StartEvent(**kwargs)) # Unblock all pending steps - for flag in self._step_flags.values(): + for flag in self._stepwise_context._step_flags.values(): flag.set() # Yield back control to the event loop to give an unblocked step @@ -258,46 +287,40 @@ async def run_step(self, **kwargs: Any) -> Optional[str]: # See if we're done, or if a step raised any error we_done = False exception_raised = None - for t in self._tasks: + for t in self._stepwise_context._tasks: + # Check if we're done if not t.done(): continue + we_done = True e = t.exception() - if e is None: - continue - - # Check if we're done - if type(e) == WorkflowDone: - we_done = True - continue - - # In any other case, bubble up the exception - exception_raised = e + if type(e) != WorkflowDone: + exception_raised = e + retval = None if we_done: # Remove any reference to the tasks - for t in self._tasks: + for t in self._stepwise_context._tasks: t.cancel() await asyncio.sleep(0) - self._tasks = set() + retval = self._stepwise_context._retval + self._stepwise_context = None if exception_raised: raise exception_raised - return self._retval + return retval def is_done(self) -> bool: """Checks if the workflow is done.""" - return len(self._tasks) == 0 - - def get_result(self) -> Any: - """Returns the result of the workflow.""" - return self._retval + return self._stepwise_context is None - @step() - async def _done(self, ev: StopEvent) -> None: + @step + async def _done(self, ctx: Context, ev: StopEvent) -> None: """Tears down the whole workflow and stop execution.""" - self._retval = ev.result or None + ctx._retval = ev.result or None + ctx.write_event_to_stream(ev) + # Signal we want to stop the workflow raise WorkflowDone @@ -308,13 +331,12 @@ def _validate(self) -> None: produced_events: Set[type] = {StartEvent} consumed_events: Set[type] = set() + requested_services: Set[ServiceDefinition] = set() - for name, step_func in self._get_steps().items(): - step_config: Optional[StepConfig] = getattr( - step_func, "__step_config", None - ) - if not step_config: - raise ValueError(f"Step {name} is missing `@step()` decorator.") + for step_func in self._get_steps().values(): + step_config: Optional[StepConfig] = getattr(step_func, "__step_config") + # At this point we know step config is not None, let's make the checker happy + assert step_config is not None for event_type in step_config.accepted_events: consumed_events.add(event_type) @@ -326,24 +348,31 @@ def _validate(self) -> None: produced_events.add(event_type) + requested_services.update(step_config.requested_services) + # Check if all consumed events are produced unconsumed_events = consumed_events - produced_events if unconsumed_events: + names = ", ".join(ev.__name__ for ev in unconsumed_events) raise WorkflowValidationError( - f"The following events are consumed but never produced: {unconsumed_events}" + f"The following events are consumed but never produced: {names}" ) # Check if there are any unused produced events (except StopEvent) unused_events = produced_events - consumed_events - {StopEvent} if unused_events: + names = ", ".join(ev.__name__ for ev in unused_events) raise WorkflowValidationError( - f"The following events are produced but never consumed: {unused_events}" + f"The following events are produced but never consumed: {names}" ) - # Check if there's at least one step that consumes StartEvent - if StartEvent not in consumed_events: - raise WorkflowValidationError("No step consumes StartEvent") - - # Check if there's at least one step that produces StopEvent - if StopEvent not in produced_events: - raise WorkflowValidationError("No step produces StopEvent") + # Check all the requested services are available + required_service_names = { + sd.name for sd in requested_services if sd.default_value is None + } + if required_service_names: + avail_service_names = set(self._service_manager._services.keys()) + missing = required_service_names - avail_service_names + if missing: + msg = f"The following services are not available: {', '.join(str(m) for m in missing)}" + raise WorkflowValidationError(msg) diff --git a/llama-index-core/poetry.lock b/llama-index-core/poetry.lock index 9be8130904e44..e72fb656a19fc 100644 --- a/llama-index-core/poetry.lock +++ b/llama-index-core/poetry.lock @@ -1,91 +1,118 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.4.0" +description = "Happy Eyeballs for asyncio" 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[package.extras] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "1eacc236ad85eb1284f403dab52a51ffd27b26de969f2d58f45ddde6cf6892a1" +content-hash = "0f036a85cb30ef45b0846ea4d6dcd00d771312d5c6f989c272ee8c4ea579adb5" diff --git a/llama-index-core/pyproject.toml b/llama-index-core/pyproject.toml index ad1c6db960ed3..96fcacf877911 100644 --- a/llama-index-core/pyproject.toml +++ b/llama-index-core/pyproject.toml @@ -28,7 +28,7 @@ description = "Interface between LLMs and your data" documentation = "https://docs.llamaindex.ai/en/stable/" exclude = ["**/BUILD"] homepage = "https://llamaindex.ai" -include = ["llama_index/core/_static/nltk_cache/corpora/stopwords/*", "llama_index/core/_static/nltk_cache/tokenizers/punkt/*", "llama_index/core/_static/nltk_cache/tokenizers/punkt/PY3/*", "llama_index/core/_static/tiktoken_cache/*"] +include = ["llama_index/core/_static/nltk_cache/corpora/stopwords/*", "llama_index/core/_static/nltk_cache/tokenizers/punkt_tab/*", "llama_index/core/_static/tiktoken_cache/*"] keywords = ["LLM", "NLP", "RAG", "data", "devtools", "index", "retrieval"] license = "MIT" maintainers = [ @@ -43,7 +43,7 @@ name = "llama-index-core" packages = [{include = "llama_index"}] readme = "README.md" repository = "https://github.com/run-llama/llama_index" -version = "0.10.61" +version = "0.11.3" [tool.poetry.dependencies] SQLAlchemy = {extras = ["asyncio"], version = ">=1.4.49"} @@ -52,10 +52,8 @@ deprecated = ">=1.2.9.3" fsspec = ">=2023.5.0" httpx = "*" nest-asyncio = "^1.5.8" -nltk = "^3.8.1" +nltk = ">3.8.1" numpy = "<2.0.0" # Pin until we adapt to Numpy v2 -openai = ">=1.1.0" -pandas = "*" python = ">=3.8.1,<4.0" tenacity = ">=8.2.0,!=8.4.0,<9.0.0" # Avoid 8.4.0 which lacks tenacity.asyncio tiktoken = ">=0.3.3" @@ -69,6 +67,7 @@ tqdm = "^4.66.1" pillow = ">=9.0.0" PyYAML = ">=6.0.1" wrapt = "*" +pydantic = ">=2.7.0,<3.0.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = ">=23.7.0,<=24.3.0"} @@ -81,6 +80,8 @@ jupyter = "^1.0.0" llama-cloud = ">=0.0.6" motor = "^3.3.2" mypy = "0.991" +openai = "*" +pandas = "*" pre-commit = "3.2.0" pylint = "2.15.10" pypdf = "*" @@ -99,7 +100,7 @@ types-protobuf = "^4.24.0.4" types-redis = "4.5.5.0" types-requests = ">=2.28.11.8" # TODO: unpin when mypy>0.991 types-setuptools = "67.1.0.0" -vellum-ai = "^0.0.42" +vellum-ai = "^0.7.8" [tool.poetry.group.docs] optional = true diff --git a/llama-index-core/tests/agent/react/test_react_agent.py b/llama-index-core/tests/agent/react/test_react_agent.py index f853847eba71d..53f2aa7ba5055 100644 --- a/llama-index-core/tests/agent/react/test_react_agent.py +++ b/llama-index-core/tests/agent/react/test_react_agent.py @@ -35,11 +35,10 @@ class MockChatLLM(MockLLM): _responses: List[ChatMessage] = PrivateAttr() def __init__(self, responses: List[ChatMessage]) -> None: + super().__init__() self._i = 0 # call counter, determines which response to return self._responses = responses # list of responses to return - super().__init__() - def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: del messages # unused response = ChatResponse( @@ -141,11 +140,10 @@ class MockStreamChatLLM(MockLLM): _responses: List[ChatMessage] = PrivateAttr() def __init__(self, responses: List[ChatMessage]) -> None: + super().__init__() self._i = 0 # call counter, determines which response to return self._responses = responses # list of responses to return - super().__init__() - def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: diff --git a/llama-index-core/tests/agent/runner/test_base.py b/llama-index-core/tests/agent/runner/test_base.py index 2650a2874a948..dfde698f35de8 100644 --- a/llama-index-core/tests/agent/runner/test_base.py +++ b/llama-index-core/tests/agent/runner/test_base.py @@ -1,7 +1,7 @@ """Test agent executor.""" import uuid -from typing import Any, cast +from typing import Any, List, cast import llama_index.core.instrumentation as instrument from llama_index.core.agent.runner.base import AgentRunner from llama_index.core.agent.runner.parallel import ParallelAgentRunner @@ -27,7 +27,7 @@ class _TestEventHandler(BaseEventHandler): - events = [] + events: List[BaseEvent] = [] @classmethod def class_name(cls): diff --git a/llama-index-core/tests/agent/runner/test_planner.py b/llama-index-core/tests/agent/runner/test_planner.py index 185974291ccb9..70221f494bb0c 100644 --- a/llama-index-core/tests/agent/runner/test_planner.py +++ b/llama-index-core/tests/agent/runner/test_planner.py @@ -36,7 +36,7 @@ def complete( dependencies=["one", "two"], ), ] - ).json() + ).model_dump_json() return CompletionResponse(text=text) # dummy response for react diff --git a/llama-index-core/tests/chat_engine/test_condense_plus_context.py b/llama-index-core/tests/chat_engine/test_condense_plus_context.py index a7fe2e8e67c1a..8a64ed4bbb8e0 100644 --- a/llama-index-core/tests/chat_engine/test_condense_plus_context.py +++ b/llama-index-core/tests/chat_engine/test_condense_plus_context.py @@ -5,7 +5,6 @@ CondensePlusContextChatEngine, ) from llama_index.core.indices.base_retriever import BaseRetriever -from llama_index.core.indices.service_context import ServiceContext from llama_index.core.llms.mock import MockLLM from llama_index.core.memory.chat_memory_buffer import ChatMemoryBuffer from llama_index.core.prompts import BasePromptTemplate @@ -21,9 +20,7 @@ def override_predict(self: Any, prompt: BasePromptTemplate, **prompt_args: Any) "predict", override_predict, ) -def test_condense_plus_context_chat_engine( - mock_service_context: ServiceContext, -) -> None: +def test_condense_plus_context_chat_engine(mock_llm) -> None: mock_retriever = Mock(spec=BaseRetriever) def source_url(query: str) -> str: @@ -61,9 +58,7 @@ def override_retrieve(query: str) -> List[NodeWithScore]: engine = CondensePlusContextChatEngine( retriever=mock_retriever, llm=MockLLM(), - memory=ChatMemoryBuffer.from_defaults( - chat_history=[], llm=mock_service_context.llm - ), + memory=ChatMemoryBuffer.from_defaults(chat_history=[], llm=mock_llm), context_prompt=context_prompt, condense_prompt=condense_prompt, ) diff --git a/llama-index-core/tests/chat_engine/test_condense_question.py b/llama-index-core/tests/chat_engine/test_condense_question.py index 15a040b3ccffe..0a40ca975619c 100644 --- a/llama-index-core/tests/chat_engine/test_condense_question.py +++ b/llama-index-core/tests/chat_engine/test_condense_question.py @@ -6,18 +6,12 @@ from llama_index.core.chat_engine.condense_question import ( CondenseQuestionChatEngine, ) -from llama_index.core.service_context import ServiceContext -def test_condense_question_chat_engine( - mock_service_context: ServiceContext, -) -> None: +def test_condense_question_chat_engine(patch_llm_predictor) -> None: query_engine = Mock(spec=BaseQueryEngine) query_engine.query.side_effect = lambda x: Response(response=x) - engine = CondenseQuestionChatEngine.from_defaults( - query_engine=query_engine, - service_context=mock_service_context, - ) + engine = CondenseQuestionChatEngine.from_defaults(query_engine=query_engine) engine.reset() response = engine.chat("Test message 1") @@ -36,22 +30,17 @@ def test_condense_question_chat_engine( assert str(response) == "Test message 3" -def test_condense_question_chat_engine_with_init_history( - mock_service_context: ServiceContext, -) -> None: +def test_condense_question_chat_engine_with_init_history(patch_llm_predictor) -> None: query_engine = Mock(spec=BaseQueryEngine) query_engine.query.side_effect = lambda x: Response(response=x) engine = CondenseQuestionChatEngine.from_defaults( query_engine=query_engine, - service_context=mock_service_context, chat_history=[ ChatMessage(role=MessageRole.USER, content="test human message"), ChatMessage(role=MessageRole.ASSISTANT, content="test ai message"), ], ) - print(engine.chat_history) - response = engine.chat("new human message") assert str(response) == ( "{'question': 'new human message', 'chat_history': 'user: test human " diff --git a/llama-index-core/tests/chat_engine/test_simple.py b/llama-index-core/tests/chat_engine/test_simple.py index 0551a09a01f62..20dd816a52591 100644 --- a/llama-index-core/tests/chat_engine/test_simple.py +++ b/llama-index-core/tests/chat_engine/test_simple.py @@ -1,12 +1,9 @@ from llama_index.core.base.llms.types import ChatMessage, MessageRole from llama_index.core.chat_engine.simple import SimpleChatEngine -from llama_index.core.service_context import ServiceContext -def test_simple_chat_engine( - mock_service_context: ServiceContext, -) -> None: - engine = SimpleChatEngine.from_defaults(service_context=mock_service_context) +def test_simple_chat_engine() -> None: + engine = SimpleChatEngine.from_defaults() engine.reset() response = engine.chat("Test message 1") @@ -24,11 +21,8 @@ def test_simple_chat_engine( assert str(response) == "user: Test message 3\nassistant: " -def test_simple_chat_engine_with_init_history( - mock_service_context: ServiceContext, -) -> None: +def test_simple_chat_engine_with_init_history() -> None: engine = SimpleChatEngine.from_defaults( - service_context=mock_service_context, chat_history=[ ChatMessage(role=MessageRole.USER, content="test human message"), ChatMessage(role=MessageRole.ASSISTANT, content="test ai message"), diff --git a/llama-index-core/tests/conftest.py b/llama-index-core/tests/conftest.py index 0338aae68f452..0b990f66aebae 100644 --- a/llama-index-core/tests/conftest.py +++ b/llama-index-core/tests/conftest.py @@ -1,15 +1,16 @@ import os # import socket -from typing import Any, List, Optional +from typing import List, Optional import openai import pytest from llama_index.core.base.llms.types import LLMMetadata from llama_index.core.llms.mock import MockLLM from llama_index.core.node_parser.text import SentenceSplitter, TokenTextSplitter -from llama_index.core.service_context import ServiceContext from llama_index.core.service_context_elements.llm_predictor import LLMPredictor +from llama_index.core.settings import _Settings + from tests.indices.vector_store.mock_services import MockEmbedding from tests.mock_utils.mock_predict import ( patch_llmpredictor_apredict, @@ -94,16 +95,24 @@ def patch_llm_predictor(monkeypatch: pytest.MonkeyPatch) -> None: @pytest.fixture() -def mock_service_context( - patch_token_text_splitter: Any, - patch_llm_predictor: Any, -) -> ServiceContext: - return ServiceContext.from_defaults(embed_model=MockEmbedding()) +def mock_llm() -> MockLLM: + return MockLLM() @pytest.fixture() -def mock_llm() -> MockLLM: - return MockLLM() +def mock_embed_model(): + return MockEmbedding() + + +@pytest.fixture() +def mock_settings(): + from llama_index.core import Settings + + old = Settings + Settings = _Settings() + Settings.embed_model = MockEmbedding() + yield Settings + Settings = old @pytest.fixture(autouse=True) diff --git a/llama-index-core/tests/evaluation/test_batch_runner.py b/llama-index-core/tests/evaluation/test_batch_runner.py index dad7a99ec80b6..d171092e84f92 100644 --- a/llama-index-core/tests/evaluation/test_batch_runner.py +++ b/llama-index-core/tests/evaluation/test_batch_runner.py @@ -6,7 +6,6 @@ from llama_index.core.prompts.mixin import PromptDictType from llama_index.core.evaluation.batch_runner import BatchEvalRunner -import pytest class MockEvaluator(BaseEvaluator): @@ -56,7 +55,6 @@ def get_eval_results(key, eval_results): return correct / len(results) -@pytest.mark.asyncio() def test_batch_runner() -> None: # single evaluator runner = BatchEvalRunner( diff --git a/llama-index-core/tests/evaluation/test_dataset_generation.py b/llama-index-core/tests/evaluation/test_dataset_generation.py index 1a30249128495..9e3f0317da01b 100644 --- a/llama-index-core/tests/evaluation/test_dataset_generation.py +++ b/llama-index-core/tests/evaluation/test_dataset_generation.py @@ -4,12 +4,9 @@ from llama_index.core.prompts.base import PromptTemplate from llama_index.core.prompts.prompt_type import PromptType from llama_index.core.schema import TextNode -from llama_index.core.service_context import ServiceContext -def test_dataset_generation( - mock_service_context: ServiceContext, -) -> None: +def test_dataset_generation(patch_llm_predictor) -> None: """Test dataset generation.""" test_nodes = [TextNode(text="hello_world"), TextNode(text="foo_bar")] @@ -28,7 +25,6 @@ def test_dataset_generation( dataset_generator = DatasetGenerator( test_nodes, - service_context=mock_service_context, text_question_template=question_gen_prompt, question_gen_query="gen_question", ) diff --git a/llama-index-core/tests/indices/document_summary/conftest.py b/llama-index-core/tests/indices/document_summary/conftest.py index 1bf605d87cdc5..3e5093c1d1fca 100644 --- a/llama-index-core/tests/indices/document_summary/conftest.py +++ b/llama-index-core/tests/indices/document_summary/conftest.py @@ -4,7 +4,6 @@ from llama_index.core.indices.document_summary.base import DocumentSummaryIndex from llama_index.core.response_synthesizers import get_response_synthesizer from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext from tests.mock_utils.mock_prompts import MOCK_REFINE_PROMPT, MOCK_TEXT_QA_PROMPT @@ -20,17 +19,15 @@ def docs() -> List[Document]: @pytest.fixture() def index( - docs: List[Document], mock_service_context: ServiceContext + docs: List[Document], patch_llm_predictor, mock_embed_model ) -> DocumentSummaryIndex: response_synthesizer = get_response_synthesizer( - llm=mock_service_context.llm, text_qa_template=MOCK_TEXT_QA_PROMPT, refine_template=MOCK_REFINE_PROMPT, - callback_manager=mock_service_context.callback_manager, ) return DocumentSummaryIndex.from_documents( docs, - service_context=mock_service_context, response_synthesizer=response_synthesizer, summary_query="summary_query", + embed_model=mock_embed_model, ) diff --git a/llama-index-core/tests/indices/document_summary/test_retrievers.py b/llama-index-core/tests/indices/document_summary/test_retrievers.py index adaa6e4eec346..2c2f0e171ed8d 100644 --- a/llama-index-core/tests/indices/document_summary/test_retrievers.py +++ b/llama-index-core/tests/indices/document_summary/test_retrievers.py @@ -1,4 +1,5 @@ """Test document summary retrievers.""" + from llama_index.core.indices.document_summary.base import ( DocumentSummaryIndex, DocumentSummaryRetrieverMode, @@ -9,9 +10,7 @@ ) -def test_embedding_retriever( - index: DocumentSummaryIndex, -) -> None: +def test_embedding_retriever(index: DocumentSummaryIndex) -> None: retriever = index.as_retriever() assert isinstance(retriever, DocumentSummaryIndexEmbeddingRetriever) results = retriever.retrieve("Test query") diff --git a/llama-index-core/tests/indices/empty/test_base.py b/llama-index-core/tests/indices/empty/test_base.py index 1d58a0937392d..0a53642115bb3 100644 --- a/llama-index-core/tests/indices/empty/test_base.py +++ b/llama-index-core/tests/indices/empty/test_base.py @@ -2,14 +2,11 @@ from llama_index.core.data_structs.data_structs import EmptyIndexStruct from llama_index.core.indices.empty.base import EmptyIndex -from llama_index.core.service_context import ServiceContext -def test_empty( - mock_service_context: ServiceContext, -) -> None: +def test_empty() -> None: """Test build list.""" - empty_index = EmptyIndex(service_context=mock_service_context) + empty_index = EmptyIndex() assert isinstance(empty_index.index_struct, EmptyIndexStruct) retriever = empty_index.as_retriever() diff --git a/llama-index-core/tests/indices/keyword_table/test_base.py b/llama-index-core/tests/indices/keyword_table/test_base.py index d64c1de3a51a5..e2a663cf88e83 100644 --- a/llama-index-core/tests/indices/keyword_table/test_base.py +++ b/llama-index-core/tests/indices/keyword_table/test_base.py @@ -8,7 +8,6 @@ SimpleKeywordTableIndex, ) from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext from tests.mock_utils.mock_utils import mock_extract_keywords @@ -29,16 +28,12 @@ def documents() -> List[Document]: "llama_index.core.indices.keyword_table.simple_base.simple_extract_keywords", mock_extract_keywords, ) -def test_build_table( - documents: List[Document], mock_service_context: ServiceContext -) -> None: +def test_build_table(documents: List[Document], patch_token_text_splitter) -> None: """Test build table.""" # test simple keyword table # NOTE: here the keyword extraction isn't mocked because we're using # the regex-based keyword extractor, not GPT - table = SimpleKeywordTableIndex.from_documents( - documents, service_context=mock_service_context - ) + table = SimpleKeywordTableIndex.from_documents(documents) nodes = table.docstore.get_nodes(list(table.index_struct.node_ids)) table_chunks = {n.get_content() for n in nodes} assert len(table_chunks) == 4 @@ -67,17 +62,13 @@ def test_build_table( mock_extract_keywords, ) def test_build_table_async( - allow_networking: Any, - documents: List[Document], - mock_service_context: ServiceContext, + allow_networking: Any, documents: List[Document], patch_token_text_splitter ) -> None: """Test build table.""" # test simple keyword table # NOTE: here the keyword extraction isn't mocked because we're using # the regex-based keyword extractor, not GPT - table = SimpleKeywordTableIndex.from_documents( - documents, use_async=True, service_context=mock_service_context - ) + table = SimpleKeywordTableIndex.from_documents(documents, use_async=True) nodes = table.docstore.get_nodes(list(table.index_struct.node_ids)) table_chunks = {n.get_content() for n in nodes} assert len(table_chunks) == 4 @@ -105,12 +96,9 @@ def test_build_table_async( "llama_index.core.indices.keyword_table.simple_base.simple_extract_keywords", mock_extract_keywords, ) -def test_insert( - documents: List[Document], - mock_service_context: ServiceContext, -) -> None: +def test_insert(documents: List[Document], patch_token_text_splitter) -> None: """Test insert.""" - table = SimpleKeywordTableIndex([], service_context=mock_service_context) + table = SimpleKeywordTableIndex([]) assert len(table.index_struct.table.keys()) == 0 table.insert(documents[0]) nodes = table.docstore.get_nodes(list(table.index_struct.node_ids)) @@ -144,7 +132,12 @@ def test_insert( chunk_index2_1 = next(iter(table.index_struct.table["test"])) chunk_index2_2 = next(iter(table.index_struct.table["v3"])) nodes = table.docstore.get_nodes( - [chunk_index1_1, chunk_index1_2, chunk_index2_1, chunk_index2_2] + [ + chunk_index1_1, + chunk_index1_2, + chunk_index2_1, + chunk_index2_2, + ] ) assert nodes[0].ref_doc_id == "test_id1" assert nodes[1].ref_doc_id == "test_id1" @@ -156,9 +149,7 @@ def test_insert( "llama_index.core.indices.keyword_table.simple_base.simple_extract_keywords", mock_extract_keywords, ) -def test_delete( - mock_service_context: ServiceContext, -) -> None: +def test_delete(patch_token_text_splitter) -> None: """Test insert.""" new_documents = [ Document(text="Hello world.\nThis is a test.", id_="test_id_1"), @@ -167,9 +158,7 @@ def test_delete( ] # test delete - table = SimpleKeywordTableIndex.from_documents( - new_documents, service_context=mock_service_context - ) + table = SimpleKeywordTableIndex.from_documents(new_documents) # test delete table.delete_ref_doc("test_id_1") assert len(table.index_struct.table.keys()) == 6 @@ -180,9 +169,7 @@ def test_delete( node_texts = {n.get_content() for n in nodes} assert node_texts == {"This is another test.", "This is a test v2."} - table = SimpleKeywordTableIndex.from_documents( - new_documents, service_context=mock_service_context - ) + table = SimpleKeywordTableIndex.from_documents(new_documents) # test ref doc info all_ref_doc_info = table.ref_doc_info diff --git a/llama-index-core/tests/indices/keyword_table/test_retrievers.py b/llama-index-core/tests/indices/keyword_table/test_retrievers.py index 3677dcc984b4b..9b5211e3d9916 100644 --- a/llama-index-core/tests/indices/keyword_table/test_retrievers.py +++ b/llama-index-core/tests/indices/keyword_table/test_retrievers.py @@ -5,7 +5,6 @@ SimpleKeywordTableIndex, ) from llama_index.core.schema import Document, QueryBundle -from llama_index.core.service_context import ServiceContext from tests.mock_utils.mock_utils import mock_extract_keywords @@ -18,17 +17,17 @@ mock_extract_keywords, ) def test_retrieve( - documents: List[Document], mock_service_context: ServiceContext + documents: List[Document], mock_embed_model, patch_token_text_splitter ) -> None: """Test query.""" # test simple keyword table # NOTE: here the keyword extraction isn't mocked because we're using # the regex-based keyword extractor, not GPT - table = SimpleKeywordTableIndex.from_documents( - documents, service_context=mock_service_context - ) + table = SimpleKeywordTableIndex.from_documents(documents) - retriever = table.as_retriever(retriever_mode="simple") + retriever = table.as_retriever( + retriever_mode="simple", embed_model=mock_embed_model + ) nodes = retriever.retrieve(QueryBundle("Hello")) assert len(nodes) == 1 assert nodes[0].node.get_content() == "Hello world." diff --git a/llama-index-core/tests/indices/knowledge_graph/test_base.py b/llama-index-core/tests/indices/knowledge_graph/test_base.py index c8d76276bf49f..e9dde2497f20d 100644 --- a/llama-index-core/tests/indices/knowledge_graph/test_base.py +++ b/llama-index-core/tests/indices/knowledge_graph/test_base.py @@ -7,7 +7,6 @@ from llama_index.core.base.embeddings.base import BaseEmbedding from llama_index.core.indices.knowledge_graph.base import KnowledgeGraphIndex from llama_index.core.schema import Document, TextNode -from llama_index.core.service_context import ServiceContext from tests.mock_utils.mock_prompts import ( MOCK_KG_TRIPLET_EXTRACT_PROMPT, MOCK_QUERY_KEYWORD_EXTRACT_PROMPT, @@ -84,12 +83,9 @@ def mock_extract_triplets(text: str) -> List[Tuple[str, str, str]]: @patch.object( KnowledgeGraphIndex, "_extract_triplets", side_effect=mock_extract_triplets ) -def test_build_kg_manual( - _patch_extract_triplets: Any, - mock_service_context: ServiceContext, -) -> None: +def test_build_kg_manual(_patch_extract_triplets: Any) -> None: """Test build knowledge graph.""" - index = KnowledgeGraphIndex([], service_context=mock_service_context) + index = KnowledgeGraphIndex([]) tuples = [ ("foo", "is", "bar"), ("hello", "is not", "world"), @@ -122,7 +118,7 @@ def test_build_kg_manual( } # test upsert_triplet_and_node - index = KnowledgeGraphIndex([], service_context=mock_service_context) + index = KnowledgeGraphIndex([]) tuples = [ ("foo", "is", "bar"), ("hello", "is not", "world"), @@ -152,7 +148,7 @@ def test_build_kg_manual( } # try inserting same node twice - index = KnowledgeGraphIndex([], service_context=mock_service_context) + index = KnowledgeGraphIndex([]) node = TextNode(text=str(("foo", "is", "bar")), id_="test_node") index.upsert_triplet_and_node(tup, node) index.upsert_triplet_and_node(tup, node) @@ -162,15 +158,11 @@ def test_build_kg_manual( KnowledgeGraphIndex, "_extract_triplets", side_effect=mock_extract_triplets ) def test_build_kg_similarity( - _patch_extract_triplets: Any, - documents: List[Document], - mock_service_context: ServiceContext, + _patch_extract_triplets: Any, documents: List[Document] ) -> None: """Test build knowledge graph.""" - mock_service_context.embed_model = MockEmbedding() - index = KnowledgeGraphIndex.from_documents( - documents, include_embeddings=True, service_context=mock_service_context + documents, include_embeddings=True, embed_model=MockEmbedding() ) # get embedding dict from KG index struct rel_text_embeddings = index.index_struct.embedding_dict @@ -185,14 +177,10 @@ def test_build_kg_similarity( KnowledgeGraphIndex, "_extract_triplets", side_effect=mock_extract_triplets ) def test_build_kg( - _patch_extract_triplets: Any, - documents: List[Document], - mock_service_context: ServiceContext, + _patch_extract_triplets: Any, documents: List[Document], patch_token_text_splitter ) -> None: """Test build knowledge graph.""" - index = KnowledgeGraphIndex.from_documents( - documents, service_context=mock_service_context - ) + index = KnowledgeGraphIndex.from_documents(documents) # NOTE: in these unit tests, document text == triplets nodes = index.docstore.get_nodes(list(index.index_struct.node_ids)) table_chunks = {n.get_content() for n in nodes} @@ -219,10 +207,7 @@ def test_build_kg( assert len(ref_doc_info.node_ids) == 3 -def test__parse_triplet_response( - doc_triplets_with_text_around: List[Document], - mock_service_context: ServiceContext, -) -> None: +def test__parse_triplet_response(doc_triplets_with_text_around: List[Document]) -> None: """Test build knowledge graph with triplet response in other format.""" parsed_triplets = [] for doc_triplet in doc_triplets_with_text_around: diff --git a/llama-index-core/tests/indices/knowledge_graph/test_retrievers.py b/llama-index-core/tests/indices/knowledge_graph/test_retrievers.py index f95e43bf1d419..6d5df08c53ebe 100644 --- a/llama-index-core/tests/indices/knowledge_graph/test_retrievers.py +++ b/llama-index-core/tests/indices/knowledge_graph/test_retrievers.py @@ -6,7 +6,6 @@ from llama_index.core.indices.knowledge_graph.base import KnowledgeGraphIndex from llama_index.core.indices.knowledge_graph.retrievers import KGTableRetriever from llama_index.core.schema import Document, QueryBundle -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.storage_context import StorageContext from tests.mock_utils.mock_prompts import MOCK_QUERY_KEYWORD_EXTRACT_PROMPT @@ -69,16 +68,12 @@ def mock_extract_triplets(text: str) -> List[Tuple[str, str, str]]: @patch.object( KnowledgeGraphIndex, "_extract_triplets", side_effect=mock_extract_triplets ) -def test_as_retriever( - _patch_extract_triplets: Any, - documents: List[Document], - mock_service_context: ServiceContext, -) -> None: +def test_as_retriever(_patch_extract_triplets: Any, documents: List[Document]) -> None: """Test query.""" graph_store = SimpleGraphStore() storage_context = StorageContext.from_defaults(graph_store=graph_store) index = KnowledgeGraphIndex.from_documents( - documents, service_context=mock_service_context, storage_context=storage_context + documents, storage_context=storage_context ) retriever: KGTableRetriever = index.as_retriever() # type: ignore nodes = retriever.retrieve(QueryBundle("foo")) @@ -101,17 +96,13 @@ def test_as_retriever( @patch.object( KnowledgeGraphIndex, "_extract_triplets", side_effect=mock_extract_triplets ) -def test_retrievers( - _patch_extract_triplets: Any, - documents: List[Document], - mock_service_context: ServiceContext, -) -> None: +def test_retrievers(_patch_extract_triplets: Any, documents: List[Document]) -> None: # test specific retriever class graph_store = SimpleGraphStore() storage_context = StorageContext.from_defaults(graph_store=graph_store) index = KnowledgeGraphIndex.from_documents( - documents, service_context=mock_service_context, storage_context=storage_context + documents, storage_context=storage_context ) retriever = KGTableRetriever( index, @@ -134,16 +125,14 @@ def test_retrievers( KnowledgeGraphIndex, "_extract_triplets", side_effect=mock_extract_triplets ) def test_retriever_no_text( - _patch_extract_triplets: Any, - documents: List[Document], - mock_service_context: ServiceContext, + _patch_extract_triplets: Any, documents: List[Document] ) -> None: # test specific retriever class graph_store = SimpleGraphStore() storage_context = StorageContext.from_defaults(graph_store=graph_store) index = KnowledgeGraphIndex.from_documents( - documents, service_context=mock_service_context, storage_context=storage_context + documents, storage_context=storage_context ) retriever = KGTableRetriever( index, @@ -167,20 +156,17 @@ def test_retriever_no_text( KnowledgeGraphIndex, "_extract_triplets", side_effect=mock_extract_triplets ) def test_retrieve_similarity( - _patch_extract_triplets: Any, - documents: List[Document], - mock_service_context: ServiceContext, + _patch_extract_triplets: Any, documents: List[Document] ) -> None: """Test query.""" - mock_service_context.embed_model = MockEmbedding() graph_store = SimpleGraphStore() storage_context = StorageContext.from_defaults(graph_store=graph_store) index = KnowledgeGraphIndex.from_documents( documents, include_embeddings=True, - service_context=mock_service_context, storage_context=storage_context, + embed_model=MockEmbedding(), ) retriever = KGTableRetriever(index, similarity_top_k=2, graph_store=graph_store) diff --git a/llama-index-core/tests/indices/list/test_index.py b/llama-index-core/tests/indices/list/test_index.py index 194fa49db6ccc..38f93e4e9e12b 100644 --- a/llama-index-core/tests/indices/list/test_index.py +++ b/llama-index-core/tests/indices/list/test_index.py @@ -5,16 +5,11 @@ from llama_index.core.base.base_retriever import BaseRetriever from llama_index.core.indices.list.base import ListRetrieverMode, SummaryIndex from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext -def test_build_list( - documents: List[Document], mock_service_context: ServiceContext -) -> None: +def test_build_list(documents: List[Document], patch_token_text_splitter) -> None: """Test build list.""" - summary_index = SummaryIndex.from_documents( - documents, service_context=mock_service_context - ) + summary_index = SummaryIndex.from_documents(documents) assert len(summary_index.index_struct.nodes) == 4 # check contents of nodes node_ids = summary_index.index_struct.nodes @@ -25,10 +20,7 @@ def test_build_list( assert nodes[3].get_content() == "This is a test v2." -def test_refresh_list( - documents: List[Document], - mock_service_context: ServiceContext, -) -> None: +def test_refresh_list(documents: List[Document]) -> None: """Test build list.""" # add extra document more_documents = [*documents, Document(text="Test document 2")] @@ -38,9 +30,7 @@ def test_refresh_list( more_documents[i].doc_id = str(i) # type: ignore[misc] # create index - summary_index = SummaryIndex.from_documents( - more_documents, service_context=mock_service_context - ) + summary_index = SummaryIndex.from_documents(more_documents) # check that no documents are refreshed refreshed_docs = summary_index.refresh_ref_docs(more_documents) @@ -61,15 +51,13 @@ def test_refresh_list( assert test_node.get_content() == "Test document 2, now with changes!" -def test_build_list_multiple(mock_service_context: ServiceContext) -> None: +def test_build_list_multiple(patch_token_text_splitter) -> None: """Test build list multiple.""" documents = [ Document(text="Hello world.\nThis is a test."), Document(text="This is another test.\nThis is a test v2."), ] - summary_index = SummaryIndex.from_documents( - documents, service_context=mock_service_context - ) + summary_index = SummaryIndex.from_documents(documents) assert len(summary_index.index_struct.nodes) == 4 nodes = summary_index.docstore.get_nodes(summary_index.index_struct.nodes) # check contents of nodes @@ -79,12 +67,9 @@ def test_build_list_multiple(mock_service_context: ServiceContext) -> None: assert nodes[3].get_content() == "This is a test v2." -def test_list_insert( - documents: List[Document], - mock_service_context: ServiceContext, -) -> None: +def test_list_insert(documents: List[Document], patch_token_text_splitter) -> None: """Test insert to list.""" - summary_index = SummaryIndex([], service_context=mock_service_context) + summary_index = SummaryIndex([]) assert len(summary_index.index_struct.nodes) == 0 summary_index.insert(documents[0]) nodes = summary_index.docstore.get_nodes(summary_index.index_struct.nodes) @@ -106,10 +91,7 @@ def test_list_insert( assert node.ref_doc_id == "test_id" -def test_list_delete( - documents: List[Document], - mock_service_context: ServiceContext, -) -> None: +def test_list_delete(documents: List[Document], patch_token_text_splitter) -> None: """Test insert to list and then delete.""" new_documents = [ Document(text="Hello world.\nThis is a test.", id_="test_id_1"), @@ -117,9 +99,7 @@ def test_list_delete( Document(text="This is a test v2.", id_="test_id_3"), ] - summary_index = SummaryIndex.from_documents( - new_documents, service_context=mock_service_context - ) + summary_index = SummaryIndex.from_documents(new_documents) # test ref doc info for three docs all_ref_doc_info = summary_index.ref_doc_info @@ -138,9 +118,7 @@ def test_list_delete( source_doc = summary_index.docstore.get_document("test_id_1", raise_error=False) assert source_doc is None - summary_index = SummaryIndex.from_documents( - new_documents, service_context=mock_service_context - ) + summary_index = SummaryIndex.from_documents(new_documents) summary_index.delete_ref_doc("test_id_2") assert len(summary_index.index_struct.nodes) == 3 nodes = summary_index.docstore.get_nodes(summary_index.index_struct.nodes) @@ -152,13 +130,8 @@ def test_list_delete( assert nodes[2].get_content() == "This is a test v2." -def test_as_retriever( - documents: List[Document], - mock_service_context: ServiceContext, -) -> None: - summary_index = SummaryIndex.from_documents( - documents, service_context=mock_service_context - ) +def test_as_retriever(documents: List[Document]) -> None: + summary_index = SummaryIndex.from_documents(documents) default_retriever = summary_index.as_retriever( retriever_mode=ListRetrieverMode.DEFAULT ) diff --git a/llama-index-core/tests/indices/list/test_retrievers.py b/llama-index-core/tests/indices/list/test_retrievers.py index 4eb86f147eed4..8c820aa843285 100644 --- a/llama-index-core/tests/indices/list/test_retrievers.py +++ b/llama-index-core/tests/indices/list/test_retrievers.py @@ -6,7 +6,6 @@ from llama_index.core.llms.mock import MockLLM from llama_index.core.prompts import BasePromptTemplate from llama_index.core.schema import BaseNode, Document -from llama_index.core.service_context import ServiceContext def _get_embeddings( @@ -26,11 +25,9 @@ def _get_embeddings( return [1.0, 0, 0, 0, 0], node_embeddings -def test_retrieve_default( - documents: List[Document], mock_service_context: ServiceContext -) -> None: +def test_retrieve_default(documents: List[Document], patch_token_text_splitter) -> None: """Test list query.""" - index = SummaryIndex.from_documents(documents, service_context=mock_service_context) + index = SummaryIndex.from_documents(documents) query_str = "What is?" retriever = index.as_retriever(retriever_mode="default") @@ -46,12 +43,10 @@ def test_retrieve_default( side_effect=_get_embeddings, ) def test_embedding_query( - _patch_get_embeddings: Any, - documents: List[Document], - mock_service_context: ServiceContext, + _patch_get_embeddings: Any, documents: List[Document], patch_token_text_splitter ) -> None: """Test embedding query.""" - index = SummaryIndex.from_documents(documents, service_context=mock_service_context) + index = SummaryIndex.from_documents(documents) # test embedding query query_str = "What is?" @@ -74,12 +69,9 @@ def mock_llmpredictor_predict( "predict", mock_llmpredictor_predict, ) -def test_llm_query( - documents: List[Document], - mock_service_context: ServiceContext, -) -> None: +def test_llm_query(documents: List[Document], patch_token_text_splitter) -> None: """Test llm query.""" - index = SummaryIndex.from_documents(documents, service_context=mock_service_context) + index = SummaryIndex.from_documents(documents) # test llm query (batch size 10) query_str = "What is?" diff --git a/llama-index-core/tests/indices/query/query_transform/test_base.py b/llama-index-core/tests/indices/query/query_transform/test_base.py index 788863466c3a4..e65d8e908b497 100644 --- a/llama-index-core/tests/indices/query/query_transform/test_base.py +++ b/llama-index-core/tests/indices/query/query_transform/test_base.py @@ -1,18 +1,15 @@ """Test query transform.""" - from llama_index.core.indices.query.query_transform.base import ( DecomposeQueryTransform, ) -from llama_index.core.service_context import ServiceContext from tests.indices.query.query_transform.mock_utils import MOCK_DECOMPOSE_PROMPT -def test_decompose_query_transform(mock_service_context: ServiceContext) -> None: +def test_decompose_query_transform(patch_llm_predictor) -> None: """Test decompose query transform.""" query_transform = DecomposeQueryTransform( - decompose_query_prompt=MOCK_DECOMPOSE_PROMPT, - llm=mock_service_context.llm, + decompose_query_prompt=MOCK_DECOMPOSE_PROMPT ) query_str = "What is?" diff --git a/llama-index-core/tests/indices/query/test_compose.py b/llama-index-core/tests/indices/query/test_compose.py index f898d5c759db4..9ca97fb81c299 100644 --- a/llama-index-core/tests/indices/query/test_compose.py +++ b/llama-index-core/tests/indices/query/test_compose.py @@ -9,30 +9,22 @@ from llama_index.core.indices.list.base import SummaryIndex from llama_index.core.indices.tree.base import TreeIndex from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext def test_recursive_query_list_tree( documents: List[Document], - mock_service_context: ServiceContext, index_kwargs: Dict, + patch_token_text_splitter, + patch_llm_predictor, ) -> None: """Test query.""" list_kwargs = index_kwargs["list"] tree_kwargs = index_kwargs["tree"] # try building a list for every two, then a tree - list1 = SummaryIndex.from_documents( - documents[0:2], service_context=mock_service_context, **list_kwargs - ) - list2 = SummaryIndex.from_documents( - documents[2:4], service_context=mock_service_context, **list_kwargs - ) - list3 = SummaryIndex.from_documents( - documents[4:6], service_context=mock_service_context, **list_kwargs - ) - list4 = SummaryIndex.from_documents( - documents[6:8], service_context=mock_service_context, **list_kwargs - ) + list1 = SummaryIndex.from_documents(documents[0:2], **list_kwargs) + list2 = SummaryIndex.from_documents(documents[2:4], **list_kwargs) + list3 = SummaryIndex.from_documents(documents[4:6], **list_kwargs) + list4 = SummaryIndex.from_documents(documents[6:8], **list_kwargs) summary1 = "summary1" summary2 = "summary2" @@ -51,8 +43,7 @@ def test_recursive_query_list_tree( list4, ], index_summaries=summaries, - service_context=mock_service_context, - **tree_kwargs + **tree_kwargs, ) assert isinstance(graph, ComposableGraph) query_str = "What is?" @@ -67,7 +58,8 @@ def test_recursive_query_list_tree( def test_recursive_query_tree_list( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, index_kwargs: Dict, ) -> None: """Test query.""" @@ -75,14 +67,8 @@ def test_recursive_query_tree_list( tree_kwargs = index_kwargs["tree"] # try building a tree for a group of 4, then a list # use a diff set of documents - tree1 = TreeIndex.from_documents( - documents[2:6], service_context=mock_service_context, **tree_kwargs - ) - tree2 = TreeIndex.from_documents( - documents[:2] + documents[6:], - service_context=mock_service_context, - **tree_kwargs - ) + tree1 = TreeIndex.from_documents(documents[2:6], **tree_kwargs) + tree2 = TreeIndex.from_documents(documents[:2] + documents[6:], **tree_kwargs) summaries = [ "tree_summary1", "tree_summary2", @@ -94,8 +80,7 @@ def test_recursive_query_tree_list( SummaryIndex, [tree1, tree2], index_summaries=summaries, - service_context=mock_service_context, - **list_kwargs + **list_kwargs, ) assert isinstance(graph, ComposableGraph) query_str = "What is?" @@ -110,7 +95,8 @@ def test_recursive_query_tree_list( def test_recursive_query_table_list( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, index_kwargs: Dict, ) -> None: """Test query.""" @@ -118,12 +104,8 @@ def test_recursive_query_table_list( table_kwargs = index_kwargs["table"] # try building a tree for a group of 4, then a list # use a diff set of documents - table1 = SimpleKeywordTableIndex.from_documents( - documents[4:6], service_context=mock_service_context, **table_kwargs - ) - table2 = SimpleKeywordTableIndex.from_documents( - documents[2:3], service_context=mock_service_context, **table_kwargs - ) + table1 = SimpleKeywordTableIndex.from_documents(documents[4:6], **table_kwargs) + table2 = SimpleKeywordTableIndex.from_documents(documents[2:3], **table_kwargs) summaries = [ "table_summary1", "table_summary2", @@ -133,8 +115,7 @@ def test_recursive_query_table_list( SummaryIndex, [table1, table2], index_summaries=summaries, - service_context=mock_service_context, - **list_kwargs + **list_kwargs, ) assert isinstance(graph, ComposableGraph) query_str = "World?" @@ -149,7 +130,8 @@ def test_recursive_query_table_list( def test_recursive_query_list_table( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, index_kwargs: Dict, ) -> None: """Test query.""" @@ -158,18 +140,10 @@ def test_recursive_query_list_table( # try building a tree for a group of 4, then a list # use a diff set of documents # try building a list for every two, then a tree - list1 = SummaryIndex.from_documents( - documents[0:2], service_context=mock_service_context, **list_kwargs - ) - list2 = SummaryIndex.from_documents( - documents[2:4], service_context=mock_service_context, **list_kwargs - ) - list3 = SummaryIndex.from_documents( - documents[4:6], service_context=mock_service_context, **list_kwargs - ) - list4 = SummaryIndex.from_documents( - documents[6:8], service_context=mock_service_context, **list_kwargs - ) + list1 = SummaryIndex.from_documents(documents[0:2], **list_kwargs) + list2 = SummaryIndex.from_documents(documents[2:4], **list_kwargs) + list3 = SummaryIndex.from_documents(documents[4:6], **list_kwargs) + list4 = SummaryIndex.from_documents(documents[6:8], **list_kwargs) summaries = [ "foo bar", "apple orange", @@ -181,8 +155,7 @@ def test_recursive_query_list_table( SimpleKeywordTableIndex, [list1, list2, list3, list4], index_summaries=summaries, - service_context=mock_service_context, - **table_kwargs + **table_kwargs, ) assert isinstance(graph, ComposableGraph) query_str = "Foo?" diff --git a/llama-index-core/tests/indices/query/test_compose_vector.py b/llama-index-core/tests/indices/query/test_compose_vector.py index 6ffa07e7f9bc3..4c468cf31f504 100644 --- a/llama-index-core/tests/indices/query/test_compose_vector.py +++ b/llama-index-core/tests/indices/query/test_compose_vector.py @@ -2,7 +2,6 @@ from typing import Any, Dict, List -import pytest from llama_index.core.async_utils import asyncio_run from llama_index.core.base.embeddings.base import BaseEmbedding from llama_index.core.data_structs.data_structs import IndexStruct @@ -12,7 +11,6 @@ ) from llama_index.core.indices.vector_store.base import VectorStoreIndex from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext from tests.mock_utils.mock_prompts import MOCK_QUERY_KEYWORD_EXTRACT_PROMPT @@ -86,17 +84,11 @@ def _get_text_embedding(self, text: str) -> List[float]: raise ValueError("Invalid text for `mock_get_text_embedding`.") -@pytest.fixture() -def mock_service_context( - patch_token_text_splitter: Any, patch_llm_predictor: Any -) -> ServiceContext: - return ServiceContext.from_defaults(embed_model=MockEmbedding()) - - def test_recursive_query_vector_table( documents: List[Document], - mock_service_context: ServiceContext, index_kwargs: Dict, + patch_token_text_splitter, + patch_llm_predictor, ) -> None: """Test query.""" vector_kwargs = index_kwargs["vector"] @@ -105,16 +97,16 @@ def test_recursive_query_vector_table( # use a diff set of documents # try building a list for every two, then a tree vector1 = VectorStoreIndex.from_documents( - documents[0:2], service_context=mock_service_context, **vector_kwargs + documents[0:2], embed_model=MockEmbedding(), **vector_kwargs ) vector2 = VectorStoreIndex.from_documents( - documents[2:4], service_context=mock_service_context, **vector_kwargs + documents[2:4], embed_model=MockEmbedding(), **vector_kwargs ) list3 = VectorStoreIndex.from_documents( - documents[4:6], service_context=mock_service_context, **vector_kwargs + documents[4:6], embed_model=MockEmbedding(), **vector_kwargs ) list4 = VectorStoreIndex.from_documents( - documents[6:8], service_context=mock_service_context, **vector_kwargs + documents[6:8], embed_model=MockEmbedding(), **vector_kwargs ) indices = [vector1, vector2, list3, list4] @@ -129,8 +121,7 @@ def test_recursive_query_vector_table( SimpleKeywordTableIndex, indices, index_summaries=summaries, - service_context=mock_service_context, - **table_kwargs + **table_kwargs, ) custom_query_engines = { @@ -154,8 +145,9 @@ def test_recursive_query_vector_table( def test_recursive_query_vector_table_query_configs( documents: List[Document], - mock_service_context: ServiceContext, index_kwargs: Dict, + patch_llm_predictor, + patch_token_text_splitter, ) -> None: """Test query. @@ -169,10 +161,10 @@ def test_recursive_query_vector_table_query_configs( # use a diff set of documents # try building a list for every two, then a tree vector1 = VectorStoreIndex.from_documents( - documents[0:2], service_context=mock_service_context, **vector_kwargs + documents[0:2], embed_model=MockEmbedding(), **vector_kwargs ) vector2 = VectorStoreIndex.from_documents( - documents[2:4], service_context=mock_service_context, **vector_kwargs + documents[2:4], embed_model=MockEmbedding(), **vector_kwargs ) assert isinstance(vector1.index_struct, IndexStruct) assert isinstance(vector2.index_struct, IndexStruct) @@ -187,8 +179,7 @@ def test_recursive_query_vector_table_query_configs( SimpleKeywordTableIndex, [vector1, vector2], index_summaries=summaries, - service_context=mock_service_context, - **table_kwargs + **table_kwargs, ) assert isinstance(graph, ComposableGraph) @@ -211,7 +202,8 @@ def test_recursive_query_vector_table_query_configs( def test_recursive_query_vector_table_async( allow_networking: Any, documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, index_kwargs: Dict, ) -> None: """Test async query of table index over vector indices.""" @@ -221,16 +213,16 @@ def test_recursive_query_vector_table_async( # use a diff set of documents # try building a list for every two, then a tree vector1 = VectorStoreIndex.from_documents( - documents[0:2], service_context=mock_service_context, **vector_kwargs + documents[0:2], embed_model=MockEmbedding(), **vector_kwargs ) vector2 = VectorStoreIndex.from_documents( - documents[2:4], service_context=mock_service_context, **vector_kwargs + documents[2:4], embed_model=MockEmbedding(), **vector_kwargs ) list3 = VectorStoreIndex.from_documents( - documents[4:6], service_context=mock_service_context, **vector_kwargs + documents[4:6], embed_model=MockEmbedding(), **vector_kwargs ) list4 = VectorStoreIndex.from_documents( - documents[6:8], service_context=mock_service_context, **vector_kwargs + documents[6:8], embed_model=MockEmbedding(), **vector_kwargs ) indices = [vector1, vector2, list3, list4] @@ -245,8 +237,7 @@ def test_recursive_query_vector_table_async( SimpleKeywordTableIndex, children_indices=indices, index_summaries=summaries, - service_context=mock_service_context, - **table_kwargs + **table_kwargs, ) custom_query_engines = { @@ -264,7 +255,8 @@ def test_recursive_query_vector_table_async( def test_recursive_query_vector_vector( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, index_kwargs: Dict, ) -> None: """Test query.""" @@ -273,16 +265,16 @@ def test_recursive_query_vector_vector( # use a diff set of documents # try building a list for every two, then a tree vector1 = VectorStoreIndex.from_documents( - documents[0:2], service_context=mock_service_context, **vector_kwargs + documents[0:2], embed_model=MockEmbedding(), **vector_kwargs ) vector2 = VectorStoreIndex.from_documents( - documents[2:4], service_context=mock_service_context, **vector_kwargs + documents[2:4], embed_model=MockEmbedding(), **vector_kwargs ) list3 = VectorStoreIndex.from_documents( - documents[4:6], service_context=mock_service_context, **vector_kwargs + documents[4:6], embed_model=MockEmbedding(), **vector_kwargs ) list4 = VectorStoreIndex.from_documents( - documents[6:8], service_context=mock_service_context, **vector_kwargs + documents[6:8], embed_model=MockEmbedding(), **vector_kwargs ) indices = [vector1, vector2, list3, list4] @@ -297,8 +289,8 @@ def test_recursive_query_vector_vector( VectorStoreIndex, children_indices=indices, index_summaries=summaries, - service_context=mock_service_context, - **vector_kwargs + embed_model=MockEmbedding(), + **vector_kwargs, ) custom_query_engines = { index.index_id: index.as_query_engine(similarity_top_k=1) for index in indices diff --git a/llama-index-core/tests/indices/query/test_query_bundle.py b/llama-index-core/tests/indices/query/test_query_bundle.py index 34cb5618f132e..1c80a809d313d 100644 --- a/llama-index-core/tests/indices/query/test_query_bundle.py +++ b/llama-index-core/tests/indices/query/test_query_bundle.py @@ -6,7 +6,6 @@ from llama_index.core.base.embeddings.base import BaseEmbedding from llama_index.core.indices.list.base import SummaryIndex from llama_index.core.schema import Document, QueryBundle -from llama_index.core.service_context import ServiceContext @pytest.fixture() @@ -70,12 +69,10 @@ def _get_query_embedding(self, query: str) -> List[float]: def test_embedding_query( - documents: List[Document], - mock_service_context: ServiceContext, + documents: List[Document], patch_llm_predictor, patch_token_text_splitter ) -> None: """Test embedding query.""" - mock_service_context.embed_model = MockEmbedding() - index = SummaryIndex.from_documents(documents, service_context=mock_service_context) + index = SummaryIndex.from_documents(documents) # test embedding query query_bundle = QueryBundle( @@ -85,7 +82,9 @@ def test_embedding_query( "The meaning of life", ], ) - retriever = index.as_retriever(retriever_mode="embedding", similarity_top_k=1) + retriever = index.as_retriever( + retriever_mode="embedding", similarity_top_k=1, embed_model=MockEmbedding() + ) nodes = retriever.retrieve(query_bundle) assert len(nodes) == 1 assert nodes[0].node.get_content() == "Correct." diff --git a/llama-index-core/tests/indices/response/test_response_builder.py b/llama-index-core/tests/indices/response/test_response_builder.py index 5a199a4c3426c..ba568a6b3e50a 100644 --- a/llama-index-core/tests/indices/response/test_response_builder.py +++ b/llama-index-core/tests/indices/response/test_response_builder.py @@ -12,31 +12,25 @@ get_response_synthesizer, ) from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext -from tests.indices.vector_store.mock_services import MockEmbedding from tests.mock_utils.mock_prompts import MOCK_REFINE_PROMPT, MOCK_TEXT_QA_PROMPT from tests.mock_utils.mock_utils import mock_tokenizer def test_give_response( - mock_service_context: ServiceContext, - documents: List[Document], + documents: List[Document], patch_llm_predictor, patch_token_text_splitter ) -> None: """Test give response.""" prompt_helper = PromptHelper( context_window=DEFAULT_CONTEXT_WINDOW, num_output=DEFAULT_NUM_OUTPUTS ) - - service_context = mock_service_context - service_context.prompt_helper = prompt_helper query_str = "What is?" # test single line builder = get_response_synthesizer( response_mode=ResponseMode.REFINE, - service_context=service_context, text_qa_template=MOCK_TEXT_QA_PROMPT, refine_template=MOCK_REFINE_PROMPT, + prompt_helper=prompt_helper, ) response = builder.get_response( text_chunks=["This is a single line."], query_str=query_str @@ -56,7 +50,7 @@ def test_give_response( assert str(response) == expected_answer -def test_compact_response(mock_service_context: ServiceContext) -> None: +def test_compact_response(patch_llm_predictor, patch_token_text_splitter) -> None: """Test give response.""" # test response with ResponseMode.COMPACT # NOTE: here we want to guarantee that prompts have 0 extra tokens @@ -80,8 +74,6 @@ def test_compact_response(mock_service_context: ServiceContext) -> None: separator="\n\n", chunk_size_limit=4, ) - service_context = mock_service_context - service_context.prompt_helper = prompt_helper cur_chunk_size = prompt_helper._get_available_chunk_size( mock_qa_prompt, 1, padding=1 ) @@ -95,20 +87,17 @@ def test_compact_response(mock_service_context: ServiceContext) -> None: "This\n\nis\n\na\n\ntest", ] builder = get_response_synthesizer( - service_context=service_context, text_qa_template=mock_qa_prompt, refine_template=mock_refine_prompt, response_mode=ResponseMode.COMPACT, + prompt_helper=prompt_helper, ) response = builder.get_response(text_chunks=texts, query_str=query_str) assert str(response) == "What is?:This:is:a:bar:This:is:a:test" -def test_accumulate_response( - mock_service_context: ServiceContext, - documents: List[Document], -) -> None: +def test_accumulate_response(patch_llm_predictor, patch_token_text_splitter) -> None: """Test accumulate response.""" # test response with ResponseMode.ACCUMULATE # NOTE: here we want to guarantee that prompts have 0 extra tokens @@ -127,8 +116,6 @@ def test_accumulate_response( separator="\n\n", chunk_size_limit=4, ) - service_context = mock_service_context - service_context.prompt_helper = prompt_helper cur_chunk_size = prompt_helper._get_available_chunk_size( mock_qa_prompt, 1, padding=1 ) @@ -142,9 +129,9 @@ def test_accumulate_response( "This\nis\nfoo", ] builder = get_response_synthesizer( - service_context=service_context, text_qa_template=mock_qa_prompt, response_mode=ResponseMode.ACCUMULATE, + prompt_helper=prompt_helper, ) response = builder.get_response(text_chunks=texts, query_str=query_str) @@ -165,8 +152,7 @@ def test_accumulate_response( def test_accumulate_response_async( - mock_service_context: ServiceContext, - documents: List[Document], + patch_llm_predictor, patch_token_text_splitter ) -> None: """Test accumulate response.""" # test response with ResponseMode.ACCUMULATE @@ -186,8 +172,6 @@ def test_accumulate_response_async( separator="\n\n", chunk_size_limit=4, ) - service_context = mock_service_context - service_context.prompt_helper = prompt_helper cur_chunk_size = prompt_helper._get_available_chunk_size( mock_qa_prompt, 1, padding=1 ) @@ -201,10 +185,10 @@ def test_accumulate_response_async( "This\nis\nfoo", ] builder = get_response_synthesizer( - service_context=service_context, text_qa_template=mock_qa_prompt, response_mode=ResponseMode.ACCUMULATE, use_async=True, + prompt_helper=prompt_helper, ) response = builder.get_response(text_chunks=texts, query_str=query_str) @@ -225,8 +209,7 @@ def test_accumulate_response_async( def test_accumulate_response_aget( - mock_service_context: ServiceContext, - documents: List[Document], + patch_llm_predictor, patch_token_text_splitter ) -> None: """Test accumulate response.""" # test response with ResponseMode.ACCUMULATE @@ -246,8 +229,6 @@ def test_accumulate_response_aget( separator="\n\n", chunk_size_limit=4, ) - service_context = mock_service_context - service_context.prompt_helper = prompt_helper cur_chunk_size = prompt_helper._get_available_chunk_size( mock_qa_prompt, 1, padding=1 ) @@ -261,9 +242,9 @@ def test_accumulate_response_aget( "This\nis\nfoo", ] builder = get_response_synthesizer( - service_context=service_context, text_qa_template=mock_qa_prompt, response_mode=ResponseMode.ACCUMULATE, + prompt_helper=prompt_helper, ) response = asyncio_run( @@ -289,7 +270,7 @@ def test_accumulate_response_aget( assert str(response) == expected -def test_accumulate_compact_response(patch_llm_predictor: None) -> None: +def test_accumulate_compact_response(patch_llm_predictor): """Test accumulate response.""" # test response with ResponseMode.ACCUMULATE # NOTE: here we want to guarantee that prompts have 0 extra tokens @@ -308,8 +289,6 @@ def test_accumulate_compact_response(patch_llm_predictor: None) -> None: separator="\n\n", chunk_size_limit=4, ) - service_context = ServiceContext.from_defaults(embed_model=MockEmbedding()) - service_context.prompt_helper = prompt_helper cur_chunk_size = prompt_helper._get_available_chunk_size( mock_qa_prompt, 1, padding=1 ) @@ -330,9 +309,9 @@ def test_accumulate_compact_response(patch_llm_predictor: None) -> None: assert compacted_chunks == ["This\n\nis\n\nbar\n\nThis", "is\n\nfoo"] builder = get_response_synthesizer( - service_context=service_context, text_qa_template=mock_qa_prompt, response_mode=ResponseMode.COMPACT_ACCUMULATE, + prompt_helper=prompt_helper, ) response = builder.get_response(text_chunks=texts, query_str=query_str) diff --git a/llama-index-core/tests/indices/response/test_tree_summarize.py b/llama-index-core/tests/indices/response/test_tree_summarize.py index 321c6f84ce422..d6b70540db833 100644 --- a/llama-index-core/tests/indices/response/test_tree_summarize.py +++ b/llama-index-core/tests/indices/response/test_tree_summarize.py @@ -10,14 +10,10 @@ from llama_index.core.prompts.base import PromptTemplate from llama_index.core.prompts.prompt_type import PromptType from llama_index.core.response_synthesizers import TreeSummarize -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import LLMPredictor @pytest.fixture() -def mock_service_context_merge_chunks( - mock_service_context: ServiceContext, -) -> ServiceContext: +def mock_prompt_helper(patch_llm_predictor, patch_token_text_splitter): def mock_repack( prompt_template: PromptTemplate, text_chunks: Sequence[str] ) -> List[str]: @@ -28,11 +24,10 @@ def mock_repack( mock_prompt_helper = Mock(spec=PromptHelper) mock_prompt_helper.repack.side_effect = mock_repack - mock_service_context.prompt_helper = mock_prompt_helper - return mock_service_context + return mock_prompt_helper -def test_tree_summarize(mock_service_context_merge_chunks: ServiceContext) -> None: +def test_tree_summarize(mock_prompt_helper) -> None: mock_summary_prompt_tmpl = "{context_str}{query_str}" mock_summary_prompt = PromptTemplate( mock_summary_prompt_tmpl, prompt_type=PromptType.SUMMARY @@ -48,7 +43,7 @@ def test_tree_summarize(mock_service_context_merge_chunks: ServiceContext) -> No # test sync tree_summarize = TreeSummarize( - service_context=mock_service_context_merge_chunks, + prompt_helper=mock_prompt_helper, summary_template=mock_summary_prompt, ) response = tree_summarize.get_response(text_chunks=texts, query_str=query_str) @@ -64,11 +59,7 @@ def mock_return_class(*args: Any, **kwargs: Any) -> TestModel: @patch.object(MockLLM, "structured_predict", mock_return_class) -def test_tree_summarize_output_cls( - mock_service_context_merge_chunks: ServiceContext, -) -> None: - mock_service_context_merge_chunks.llm_predictor = LLMPredictor(MockLLM()) - +def test_tree_summarize_output_cls(mock_prompt_helper) -> None: mock_summary_prompt_tmpl = "{context_str}{query_str}" mock_summary_prompt = PromptTemplate( mock_summary_prompt_tmpl, prompt_type=PromptType.SUMMARY @@ -85,19 +76,17 @@ def test_tree_summarize_output_cls( # test sync tree_summarize = TreeSummarize( - service_context=mock_service_context_merge_chunks, + prompt_helper=mock_prompt_helper, summary_template=mock_summary_prompt, output_cls=TestModel, ) full_response = "\n".join(texts) response = tree_summarize.get_response(text_chunks=texts, query_str=query_str) assert isinstance(response, TestModel) - assert response.dict() == response_dict + assert response.model_dump() == response_dict -def test_tree_summarize_use_async( - mock_service_context_merge_chunks: ServiceContext, -) -> None: +def test_tree_summarize_use_async(mock_prompt_helper) -> None: mock_summary_prompt_tmpl = "{context_str}{query_str}" mock_summary_prompt = PromptTemplate( mock_summary_prompt_tmpl, prompt_type=PromptType.SUMMARY @@ -113,7 +102,7 @@ def test_tree_summarize_use_async( # test async tree_summarize = TreeSummarize( - service_context=mock_service_context_merge_chunks, + prompt_helper=mock_prompt_helper, summary_template=mock_summary_prompt, use_async=True, ) @@ -122,9 +111,7 @@ def test_tree_summarize_use_async( @pytest.mark.asyncio() -async def test_tree_summarize_async( - mock_service_context_merge_chunks: ServiceContext, -) -> None: +async def test_tree_summarize_async(mock_prompt_helper) -> None: mock_summary_prompt_tmpl = "{context_str}{query_str}" mock_summary_prompt = PromptTemplate( mock_summary_prompt_tmpl, prompt_type=PromptType.SUMMARY @@ -140,7 +127,7 @@ async def test_tree_summarize_async( # test async tree_summarize = TreeSummarize( - service_context=mock_service_context_merge_chunks, + prompt_helper=mock_prompt_helper, summary_template=mock_summary_prompt, ) response = await tree_summarize.aget_response( diff --git a/llama-index-core/tests/indices/struct_store/test_base.py b/llama-index-core/tests/indices/struct_store/test_base.py index 1edf41e3e04f6..3589437b65838 100644 --- a/llama-index-core/tests/indices/struct_store/test_base.py +++ b/llama-index-core/tests/indices/struct_store/test_base.py @@ -18,7 +18,6 @@ RelatedNodeInfo, TextNode, ) -from llama_index.core.service_context import ServiceContext from llama_index.core.utilities.sql_wrapper import SQLDatabase from sqlalchemy import ( Column, @@ -41,8 +40,7 @@ def _delete_table_items(engine: Any, table: Table) -> None: def test_sql_index( - mock_service_context: ServiceContext, - struct_kwargs: Tuple[Dict, Dict], + struct_kwargs: Tuple[Dict, Dict], patch_llm_predictor, patch_token_text_splitter ) -> None: """Test SQLStructStoreIndex.""" engine = create_engine("sqlite:///:memory:") @@ -63,8 +61,7 @@ def test_sql_index( docs, sql_database=sql_database, table_name=table_name, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) assert isinstance(index, SQLStructStoreIndex) @@ -83,8 +80,7 @@ def test_sql_index( docs, sql_database=sql_database, table_name=table_name, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) assert isinstance(index, SQLStructStoreIndex) # test that the document is inserted @@ -96,7 +92,8 @@ def test_sql_index( def test_sql_index_nodes( - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Tuple[Dict, Dict], ) -> None: """Test SQLStructStoreIndex with nodes.""" @@ -129,8 +126,7 @@ def test_sql_index_nodes( nodes, sql_database=sql_database, table_name=table_name, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) assert isinstance(index, SQLStructStoreIndex) @@ -160,8 +156,7 @@ def test_sql_index_nodes( nodes, sql_database=sql_database, table_name=table_name, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) assert isinstance(index, SQLStructStoreIndex) @@ -175,7 +170,8 @@ def test_sql_index_nodes( def test_sql_index_with_context( - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Tuple[Dict, Dict], ) -> None: """Test SQLStructStoreIndex.""" @@ -206,8 +202,7 @@ def test_sql_index_with_context( sql_database=sql_database, table_name=table_name, sql_context_container=sql_context_container, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) assert isinstance(index, SQLStructStoreIndex) assert index.sql_context_container.context_dict == table_context_dict @@ -224,8 +219,7 @@ def test_sql_index_with_context( sql_database=sql_database, table_name=table_name, sql_context_container=sql_context_container, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) assert isinstance(index, SQLStructStoreIndex) for k, v in table_context_dict.items(): @@ -246,7 +240,6 @@ def test_sql_index_with_context( sql_database=sql_database, table_context_prompt=MOCK_TABLE_CONTEXT_PROMPT, table_context_task="extract_test", - service_context=mock_service_context, ) sql_context_container = sql_context_builder.build_context_container( ignore_db_schema=True @@ -256,8 +249,7 @@ def test_sql_index_with_context( sql_database=sql_database, table_name=table_name, sql_context_container=sql_context_container, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) assert isinstance(index, SQLStructStoreIndex) assert index.sql_context_container.context_dict == { @@ -268,7 +260,9 @@ def test_sql_index_with_context( # TODO: -def test_sql_index_with_derive_index(mock_service_context: ServiceContext) -> None: +def test_sql_index_with_derive_index( + patch_llm_predictor, patch_token_text_splitter +) -> None: """Test derive index.""" # test setting table_context_dict engine = create_engine("sqlite:///:memory:") @@ -299,7 +293,8 @@ def test_sql_index_with_derive_index(mock_service_context: ServiceContext) -> No def test_sql_index_with_index_context( - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Tuple[Dict, Dict], ) -> None: """Test SQLStructStoreIndex.""" @@ -324,7 +319,7 @@ def test_sql_index_with_index_context( sql_database, context_dict=table_context_dict ) context_index = context_builder.derive_index_from_context( - SummaryIndex, ignore_db_schema=True, service_context=mock_service_context + SummaryIndex, ignore_db_schema=True ) # NOTE: the response only contains the first line (metadata), since # with the mock patch, newlines are treated as separate calls. @@ -348,8 +343,7 @@ def test_sql_index_with_index_context( sql_database=sql_database, table_name=table_name, sql_context_container=sql_context_container, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) # just assert this runs sql_query_engine = NLStructStoreQueryEngine(index) diff --git a/llama-index-core/tests/indices/struct_store/test_json_query.py b/llama-index-core/tests/indices/struct_store/test_json_query.py index 2761a99dc5626..cdc38aa795ead 100644 --- a/llama-index-core/tests/indices/struct_store/test_json_query.py +++ b/llama-index-core/tests/indices/struct_store/test_json_query.py @@ -14,8 +14,6 @@ from llama_index.core.llms.mock import MockLLM from llama_index.core.prompts.base import BasePromptTemplate from llama_index.core.schema import QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.service_context_elements.llm_predictor import LLMPredictor TEST_PARAMS = [ # synthesize_response, call_apredict @@ -51,11 +49,10 @@ async def amock_predict( def test_json_query_engine( synthesize_response: bool, call_apredict: bool, - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, ) -> None: """Test GPTNLJSONQueryEngine.""" - mock_service_context.llm_predictor = LLMPredictor(MockLLM()) - # Test on some sample data json_val = cast(JSONType, {}) json_schema = cast(JSONType, {}) @@ -71,7 +68,6 @@ def test_output_processor(llm_output: str, json_value: JSONType) -> JSONType: query_engine = JSONQueryEngine( json_value=json_val, json_schema=json_schema, - service_context=mock_service_context, output_processor=test_output_processor, verbose=True, synthesize_response=synthesize_response, diff --git a/llama-index-core/tests/indices/struct_store/test_sql_query.py b/llama-index-core/tests/indices/struct_store/test_sql_query.py index 0ccbb4a68929e..79f8d4bbe6d1f 100644 --- a/llama-index-core/tests/indices/struct_store/test_sql_query.py +++ b/llama-index-core/tests/indices/struct_store/test_sql_query.py @@ -10,14 +10,14 @@ SQLStructStoreQueryEngine, ) from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext from llama_index.core.utilities.sql_wrapper import SQLDatabase from sqlalchemy import Column, Integer, MetaData, String, Table, create_engine from sqlalchemy.exc import OperationalError def test_sql_index_query( - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Tuple[Dict, Dict], ) -> None: """Test SQLStructStoreIndex.""" @@ -40,8 +40,7 @@ def test_sql_index_query( docs, sql_database=sql_database, table_name=table_name, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) # query the index with SQL @@ -55,9 +54,7 @@ def test_sql_index_query( response = nl_query_engine.query("test_table:user_id,foo") assert str(response) == "[(2, 'bar'), (8, 'hello')]" - nl_table_engine = NLSQLTableQueryEngine( - index.sql_database, service_context=mock_service_context - ) + nl_table_engine = NLSQLTableQueryEngine(index.sql_database) response = nl_table_engine.query("test_table:user_id,foo") assert str(response) == "[(2, 'bar'), (8, 'hello')]" @@ -76,16 +73,15 @@ def test_sql_index_query( response = nl_query_engine.query("test_table:user_id,foo") assert str(response) == sql_to_test - nl_table_engine = NLSQLTableQueryEngine( - index.sql_database, service_context=mock_service_context, sql_only=True - ) + nl_table_engine = NLSQLTableQueryEngine(index.sql_database, sql_only=True) response = nl_table_engine.query("test_table:user_id,foo") assert str(response) == sql_to_test def test_sql_index_async_query( allow_networking: Any, - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Tuple[Dict, Dict], ) -> None: """Test SQLStructStoreIndex.""" @@ -108,8 +104,7 @@ def test_sql_index_async_query( docs, sql_database=sql_database, table_name=table_name, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) sql_to_test = "SELECT user_id, foo FROM test_table" @@ -125,9 +120,7 @@ def test_sql_index_async_query( response = asyncio_run(task) assert str(response) == "[(2, 'bar'), (8, 'hello')]" - nl_table_engine = NLSQLTableQueryEngine( - index.sql_database, service_context=mock_service_context - ) + nl_table_engine = NLSQLTableQueryEngine(index.sql_database) task = nl_table_engine.aquery("test_table:user_id,foo") response = asyncio_run(task) assert str(response) == "[(2, 'bar'), (8, 'hello')]" @@ -145,9 +138,7 @@ def test_sql_index_async_query( response = asyncio_run(task) assert str(response) == sql_to_test - nl_table_engine = NLSQLTableQueryEngine( - index.sql_database, service_context=mock_service_context, sql_only=True - ) + nl_table_engine = NLSQLTableQueryEngine(index.sql_database, sql_only=True) task = nl_table_engine.aquery("test_table:user_id,foo") response = asyncio_run(task) assert str(response) == sql_to_test @@ -166,7 +157,8 @@ def test_default_output_parser() -> None: def test_nl_query_engine_parser( - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Tuple[Dict, Dict], ) -> None: """Test the sql response parser.""" @@ -189,8 +181,7 @@ def test_nl_query_engine_parser( docs, sql_database=sql_database, table_name=table_name, - service_context=mock_service_context, - **index_kwargs + **index_kwargs, ) nl_query_engine = NLStructStoreQueryEngine(index) diff --git a/llama-index-core/tests/indices/test_loading.py b/llama-index-core/tests/indices/test_loading.py index 28da6fb7547d2..a136f285c9975 100644 --- a/llama-index-core/tests/indices/test_loading.py +++ b/llama-index-core/tests/indices/test_loading.py @@ -8,16 +8,12 @@ load_indices_from_storage, ) from llama_index.core.indices.vector_store.base import VectorStoreIndex -from llama_index.core.query_engine.retriever_query_engine import ( - RetrieverQueryEngine, -) from llama_index.core.schema import BaseNode, Document -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.storage_context import StorageContext def test_load_index_from_storage_simple( - documents: List[Document], tmp_path: Path, mock_service_context: ServiceContext + documents: List[Document], tmp_path: Path ) -> None: # construct simple (i.e. in memory) storage context storage_context = StorageContext.from_defaults() @@ -26,7 +22,6 @@ def test_load_index_from_storage_simple( index = VectorStoreIndex.from_documents( documents=documents, storage_context=storage_context, - service_context=mock_service_context, ) # persist storage to disk @@ -36,17 +31,13 @@ def test_load_index_from_storage_simple( new_storage_context = StorageContext.from_defaults(persist_dir=str(tmp_path)) # load index - new_index = load_index_from_storage( - storage_context=new_storage_context, service_context=mock_service_context - ) + new_index = load_index_from_storage(storage_context=new_storage_context) assert index.index_id == new_index.index_id def test_load_index_from_storage_multiple( - nodes: List[BaseNode], - tmp_path: Path, - mock_service_context: ServiceContext, + nodes: List[BaseNode], tmp_path: Path ) -> None: # construct simple (i.e. in memory) storage context storage_context = StorageContext.from_defaults() @@ -55,18 +46,10 @@ def test_load_index_from_storage_multiple( storage_context.docstore.add_documents(nodes) # construct multiple indices - vector_index = VectorStoreIndex( - nodes=nodes, - storage_context=storage_context, - service_context=mock_service_context, - ) + vector_index = VectorStoreIndex(nodes=nodes, storage_context=storage_context) vector_id = vector_index.index_id - summary_index = SummaryIndex( - nodes=nodes, - storage_context=storage_context, - service_context=mock_service_context, - ) + summary_index = SummaryIndex(nodes=nodes, storage_context=storage_context) list_id = summary_index.index_id @@ -78,9 +61,7 @@ def test_load_index_from_storage_multiple( # load single index should fail since there are multiple indices in index store with pytest.raises(ValueError): - load_index_from_storage( - new_storage_context, service_context=mock_service_context - ) + load_index_from_storage(new_storage_context) # test load all indices indices = load_indices_from_storage(storage_context) @@ -98,18 +79,14 @@ def test_load_index_from_storage_multiple( def test_load_index_from_storage_retrieval_result_identical( - documents: List[Document], - tmp_path: Path, - mock_service_context: ServiceContext, + documents: List[Document], tmp_path: Path ) -> None: # construct simple (i.e. in memory) storage context storage_context = StorageContext.from_defaults() # construct index index = VectorStoreIndex.from_documents( - documents=documents, - storage_context=storage_context, - service_context=mock_service_context, + documents=documents, storage_context=storage_context ) nodes = index.as_retriever().retrieve("test query str") @@ -121,46 +98,8 @@ def test_load_index_from_storage_retrieval_result_identical( new_storage_context = StorageContext.from_defaults(persist_dir=str(tmp_path)) # load index - new_index = load_index_from_storage( - new_storage_context, service_context=mock_service_context - ) + new_index = load_index_from_storage(new_storage_context) new_nodes = new_index.as_retriever().retrieve("test query str") assert nodes == new_nodes - - -def test_load_index_query_engine_service_context( - documents: List[Document], - tmp_path: Path, - mock_service_context: ServiceContext, -) -> None: - # construct simple (i.e. in memory) storage context - storage_context = StorageContext.from_defaults() - - # construct index - index = VectorStoreIndex.from_documents( - documents=documents, - storage_context=storage_context, - service_context=mock_service_context, - ) - - # persist storage to disk - storage_context.persist(str(tmp_path)) - - # load storage context - new_storage_context = StorageContext.from_defaults(persist_dir=str(tmp_path)) - - # load index - new_index = load_index_from_storage( - storage_context=new_storage_context, service_context=mock_service_context - ) - - query_engine = index.as_query_engine() - new_query_engine = new_index.as_query_engine() - - # make types happy - assert isinstance(query_engine, RetrieverQueryEngine) - assert isinstance(new_query_engine, RetrieverQueryEngine) - # Ensure that the loaded index will end up querying with the same service_context - assert new_query_engine._response_synthesizer._llm == mock_service_context.llm diff --git a/llama-index-core/tests/indices/test_loading_graph.py b/llama-index-core/tests/indices/test_loading_graph.py index d478dd075af42..ce5ac97a47541 100644 --- a/llama-index-core/tests/indices/test_loading_graph.py +++ b/llama-index-core/tests/indices/test_loading_graph.py @@ -6,14 +6,11 @@ from llama_index.core.indices.loading import load_graph_from_storage from llama_index.core.indices.vector_store.base import VectorStoreIndex from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.storage_context import StorageContext def test_load_graph_from_storage_simple( - documents: List[Document], - tmp_path: Path, - mock_service_context: ServiceContext, + documents: List[Document], tmp_path: Path ) -> None: # construct simple (i.e. in memory) storage context storage_context = StorageContext.from_defaults() @@ -22,21 +19,18 @@ def test_load_graph_from_storage_simple( vector_index_1 = VectorStoreIndex.from_documents( documents=documents, storage_context=storage_context, - service_context=mock_service_context, ) # construct second index, testing vector store overlap vector_index_2 = VectorStoreIndex.from_documents( documents=documents, storage_context=storage_context, - service_context=mock_service_context, ) # construct index summary_index = SummaryIndex.from_documents( documents=documents, storage_context=storage_context, - service_context=mock_service_context, ) # construct graph @@ -45,7 +39,6 @@ def test_load_graph_from_storage_simple( children_indices=[vector_index_1, vector_index_2, summary_index], index_summaries=["vector index 1", "vector index 2", "summary index"], storage_context=storage_context, - service_context=mock_service_context, ) query_engine = graph.as_query_engine() @@ -58,9 +51,7 @@ def test_load_graph_from_storage_simple( new_storage_context = StorageContext.from_defaults(persist_dir=str(tmp_path)) # load index - new_graph = load_graph_from_storage( - new_storage_context, root_id=graph.root_id, service_context=mock_service_context - ) + new_graph = load_graph_from_storage(new_storage_context, root_id=graph.root_id) new_query_engine = new_graph.as_query_engine() new_response = new_query_engine.query("test query") diff --git a/llama-index-core/tests/indices/test_service_context.py b/llama-index-core/tests/indices/test_service_context.py deleted file mode 100644 index b7c222682fdae..0000000000000 --- a/llama-index-core/tests/indices/test_service_context.py +++ /dev/null @@ -1,56 +0,0 @@ -from typing import List - -from llama_index.core.embeddings.mock_embed_model import MockEmbedding -from llama_index.core.extractors import ( - QuestionsAnsweredExtractor, - SummaryExtractor, - TitleExtractor, -) -from llama_index.core.indices.prompt_helper import PromptHelper -from llama_index.core.llms.mock import MockLLM -from llama_index.core.node_parser import SentenceSplitter -from llama_index.core.schema import TransformComponent -from llama_index.core.service_context import ServiceContext - - -def test_service_context_serialize() -> None: - extractors: List[TransformComponent] = [ - SummaryExtractor(), - QuestionsAnsweredExtractor(), - TitleExtractor(), - ] - - node_parser = SentenceSplitter(chunk_size=1, chunk_overlap=0) - - transformations: List[TransformComponent] = [node_parser, *extractors] - - llm = MockLLM(max_tokens=1) - embed_model = MockEmbedding(embed_dim=1) - - prompt_helper = PromptHelper(context_window=1) - - service_context = ServiceContext.from_defaults( - llm=llm, - embed_model=embed_model, - transformations=transformations, - prompt_helper=prompt_helper, - ) - - service_context_dict = service_context.to_dict() - - assert service_context_dict["llm"]["max_tokens"] == 1 - assert service_context_dict["embed_model"]["embed_dim"] == 1 - assert service_context_dict["prompt_helper"]["context_window"] == 1 - - loaded_service_context = ServiceContext.from_dict(service_context_dict) - - assert isinstance(loaded_service_context.llm, MockLLM) - assert isinstance(loaded_service_context.embed_model, MockEmbedding) - assert isinstance(loaded_service_context.transformations[0], SentenceSplitter) - assert isinstance(loaded_service_context.prompt_helper, PromptHelper) - - assert len(loaded_service_context.transformations) == 4 - assert loaded_service_context.transformations[0].chunk_size == 1 - assert loaded_service_context.prompt_helper.context_window == 1 - assert loaded_service_context.llm.max_tokens == 1 - assert loaded_service_context.embed_model.embed_dim == 1 diff --git a/llama-index-core/tests/indices/tree/test_embedding_retriever.py b/llama-index-core/tests/indices/tree/test_embedding_retriever.py index 301c1cf1105f8..097ca40511a3f 100644 --- a/llama-index-core/tests/indices/tree/test_embedding_retriever.py +++ b/llama-index-core/tests/indices/tree/test_embedding_retriever.py @@ -10,7 +10,6 @@ TreeSelectLeafEmbeddingRetriever, ) from llama_index.core.schema import BaseNode, Document, QueryBundle -from llama_index.core.service_context import ServiceContext from tests.mock_utils.mock_prompts import ( MOCK_INSERT_PROMPT, MOCK_SUMMARY_PROMPT, @@ -66,20 +65,14 @@ def test_embedding_query( _patch_similarity: Any, index_kwargs: Dict, documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, ) -> None: """Test embedding query.""" - tree = TreeIndex.from_documents( - documents, service_context=mock_service_context, **index_kwargs - ) + tree = TreeIndex.from_documents(documents, **index_kwargs) # test embedding query query_str = "What is?" retriever = tree.as_retriever(retriever_mode="select_leaf_embedding") nodes = retriever.retrieve(QueryBundle(query_str)) assert nodes[0].node.get_content() == "Hello world." - - -def _mock_tokenizer(text: str) -> int: - """Mock tokenizer that splits by spaces.""" - return len(text.split(" ")) diff --git a/llama-index-core/tests/indices/tree/test_index.py b/llama-index-core/tests/indices/tree/test_index.py index 36aaafb8cacd6..bca73105eb886 100644 --- a/llama-index-core/tests/indices/tree/test_index.py +++ b/llama-index-core/tests/indices/tree/test_index.py @@ -6,7 +6,6 @@ from llama_index.core.data_structs.data_structs import IndexGraph from llama_index.core.indices.tree.base import TreeIndex from llama_index.core.schema import BaseNode, Document -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.docstore import BaseDocumentStore @@ -26,14 +25,13 @@ def _get_left_or_right_node( def test_build_tree( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Dict, ) -> None: """Test build tree.""" index_kwargs, _ = struct_kwargs - tree = TreeIndex.from_documents( - documents, service_context=mock_service_context, **index_kwargs - ) + tree = TreeIndex.from_documents(documents, **index_kwargs) assert len(tree.index_struct.all_nodes) == 6 # check contents of nodes @@ -53,8 +51,9 @@ def test_build_tree( def test_build_tree_with_embed( documents: List[Document], - mock_service_context: ServiceContext, struct_kwargs: Dict, + patch_llm_predictor, + patch_token_text_splitter, ) -> None: """Test build tree.""" index_kwargs, _ = struct_kwargs @@ -65,9 +64,7 @@ def test_build_tree_with_embed( "This is a test v2." ) document = Document(text=doc_text, embedding=[0.1, 0.2, 0.3]) - tree = TreeIndex.from_documents( - [document], service_context=mock_service_context, **index_kwargs - ) + tree = TreeIndex.from_documents([document], **index_kwargs) assert len(tree.index_struct.all_nodes) == 6 # check contents of nodes all_nodes = tree.docstore.get_node_dict(tree.index_struct.all_nodes) @@ -95,14 +92,13 @@ def test_build_tree_with_embed( def test_build_tree_async( _mock_run_async_tasks: Any, documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Dict, ) -> None: """Test build tree with use_async.""" index_kwargs, _ = struct_kwargs - tree = TreeIndex.from_documents( - documents, use_async=True, service_context=mock_service_context, **index_kwargs - ) + tree = TreeIndex.from_documents(documents, use_async=True, **index_kwargs) assert len(tree.index_struct.all_nodes) == 6 # check contents of nodes nodes = tree.docstore.get_nodes(list(tree.index_struct.all_nodes.values())) @@ -115,7 +111,8 @@ def test_build_tree_async( def test_build_tree_multiple( - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Dict, ) -> None: """Test build tree.""" @@ -124,9 +121,7 @@ def test_build_tree_multiple( Document(text="This is another test.\nThis is a test v2."), ] index_kwargs, _ = struct_kwargs - tree = TreeIndex.from_documents( - new_docs, service_context=mock_service_context, **index_kwargs - ) + tree = TreeIndex.from_documents(new_docs, **index_kwargs) assert len(tree.index_struct.all_nodes) == 6 # check contents of nodes nodes = tree.docstore.get_nodes(list(tree.index_struct.all_nodes.values())) @@ -138,14 +133,13 @@ def test_build_tree_multiple( def test_insert( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Dict, ) -> None: """Test insert.""" index_kwargs, _ = struct_kwargs - tree = TreeIndex.from_documents( - documents, service_context=mock_service_context, **index_kwargs - ) + tree = TreeIndex.from_documents(documents, **index_kwargs) # test insert new_doc = Document(text="This is a new doc.", id_="new_doc") @@ -176,9 +170,7 @@ def test_insert( assert right_root3.ref_doc_id == "new_doc" # test insert from empty (no_id) - tree = TreeIndex.from_documents( - [], service_context=mock_service_context, **index_kwargs - ) + tree = TreeIndex.from_documents([], **index_kwargs) new_doc = Document(text="This is a new doc.") tree.insert(new_doc) nodes = tree.docstore.get_nodes(list(tree.index_struct.all_nodes.values())) @@ -186,9 +178,7 @@ def test_insert( assert nodes[0].get_content() == "This is a new doc." # test insert from empty (with_id) - tree = TreeIndex.from_documents( - [], service_context=mock_service_context, **index_kwargs - ) + tree = TreeIndex.from_documents([], **index_kwargs) new_doc = Document(text="This is a new doc.", id_="new_doc_test") tree.insert(new_doc) assert len(tree.index_struct.all_nodes) == 1 @@ -197,11 +187,9 @@ def test_insert( assert nodes[0].ref_doc_id == "new_doc_test" -def test_twice_insert_empty( - mock_service_context: ServiceContext, -) -> None: +def test_twice_insert_empty(patch_llm_predictor, patch_token_text_splitter) -> None: """# test twice insert from empty (with_id).""" - tree = TreeIndex.from_documents([], service_context=mock_service_context) + tree = TreeIndex.from_documents([]) # test first insert new_doc = Document(text="This is a new doc.", id_="new_doc") diff --git a/llama-index-core/tests/indices/tree/test_retrievers.py b/llama-index-core/tests/indices/tree/test_retrievers.py index 1d1c5963bf320..cafc5e4b133db 100644 --- a/llama-index-core/tests/indices/tree/test_retrievers.py +++ b/llama-index-core/tests/indices/tree/test_retrievers.py @@ -2,19 +2,17 @@ from llama_index.core.indices.tree.base import TreeIndex from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext def test_query( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Dict, ) -> None: """Test query.""" index_kwargs, query_kwargs = struct_kwargs - tree = TreeIndex.from_documents( - documents, service_context=mock_service_context, **index_kwargs - ) + tree = TreeIndex.from_documents(documents, **index_kwargs) # test default query query_str = "What is?" @@ -25,7 +23,8 @@ def test_query( def test_summarize_query( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, struct_kwargs: Dict, ) -> None: """Test summarize query.""" @@ -33,9 +32,7 @@ def test_summarize_query( index_kwargs, orig_query_kwargs = struct_kwargs index_kwargs = index_kwargs.copy() index_kwargs.update({"build_tree": False}) - tree = TreeIndex.from_documents( - documents, service_context=mock_service_context, **index_kwargs - ) + tree = TreeIndex.from_documents(documents, **index_kwargs) # test retrieve all leaf query_str = "What is?" diff --git a/llama-index-core/tests/indices/vector_store/test_retrievers.py b/llama-index-core/tests/indices/vector_store/test_retrievers.py index 847c38de03947..b29f41383f9d0 100644 --- a/llama-index-core/tests/indices/vector_store/test_retrievers.py +++ b/llama-index-core/tests/indices/vector_store/test_retrievers.py @@ -8,18 +8,17 @@ RelatedNodeInfo, TextNode, ) -from llama_index.core.service_context import ServiceContext from llama_index.core.vector_stores.simple import SimpleVectorStore def test_simple_query( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, + mock_embed_model, ) -> None: """Test embedding query.""" - index = VectorStoreIndex.from_documents( - documents, service_context=mock_service_context - ) + index = VectorStoreIndex.from_documents(documents, embed_model=mock_embed_model) # test embedding query query_str = "What is?" @@ -30,7 +29,8 @@ def test_simple_query( def test_query_and_similarity_scores( - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, ) -> None: """Test that sources nodes have similarity scores.""" doc_text = ( @@ -40,9 +40,7 @@ def test_query_and_similarity_scores( "This is a test v2." ) document = Document(text=doc_text) - index = VectorStoreIndex.from_documents( - [document], service_context=mock_service_context - ) + index = VectorStoreIndex.from_documents([document]) # test embedding query query_str = "What is?" @@ -53,7 +51,8 @@ def test_query_and_similarity_scores( def test_simple_check_ids( - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, ) -> None: """Test build VectorStoreIndex.""" ref_doc_id = "ref_doc_id_test" @@ -64,7 +63,7 @@ def test_simple_check_ids( TextNode(text="This is another test.", id_="node3", relationships=source_rel), TextNode(text="This is a test v2.", id_="node4", relationships=source_rel), ] - index = VectorStoreIndex(all_nodes, service_context=mock_service_context) + index = VectorStoreIndex(all_nodes) # test query query_str = "What is?" @@ -78,7 +77,10 @@ def test_simple_check_ids( assert "node3" in vector_store._data.text_id_to_ref_doc_id -def test_query(mock_service_context: ServiceContext) -> None: +def test_query( + patch_llm_predictor, + patch_token_text_splitter, +) -> None: """Test embedding query.""" doc_text = ( "Hello world.\n" @@ -87,9 +89,7 @@ def test_query(mock_service_context: ServiceContext) -> None: "This is a test v2." ) document = Document(text=doc_text) - index = VectorStoreIndex.from_documents( - [document], service_context=mock_service_context - ) + index = VectorStoreIndex.from_documents([document]) # test embedding query query_str = "What is?" diff --git a/llama-index-core/tests/indices/vector_store/test_simple.py b/llama-index-core/tests/indices/vector_store/test_simple.py index 10957b0e2983e..01e74f63acc76 100644 --- a/llama-index-core/tests/indices/vector_store/test_simple.py +++ b/llama-index-core/tests/indices/vector_store/test_simple.py @@ -1,23 +1,24 @@ """Test vector store indexes.""" + import pickle from typing import Any, List, cast from llama_index.core.indices.loading import load_index_from_storage from llama_index.core.indices.vector_store.base import VectorStoreIndex -from llama_index.core.llms.mock import MockLLM from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.storage_context import StorageContext from llama_index.core.vector_stores.simple import SimpleVectorStore def test_build_simple( - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, + mock_embed_model, documents: List[Document], ) -> None: """Test build VectorStoreIndex.""" index = VectorStoreIndex.from_documents( - documents=documents, service_context=mock_service_context + documents=documents, embed_model=mock_embed_model ) assert isinstance(index, VectorStoreIndex) assert len(index.index_struct.nodes_dict) == 4 @@ -44,11 +45,13 @@ def test_build_simple( def test_simple_insert( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, + mock_embed_model, ) -> None: """Test insert VectorStoreIndex.""" index = VectorStoreIndex.from_documents( - documents=documents, service_context=mock_service_context + documents=documents, embed_model=mock_embed_model ) assert isinstance(index, VectorStoreIndex) # insert into index @@ -72,7 +75,7 @@ def test_simple_insert( def test_simple_delete( - mock_service_context: ServiceContext, + patch_llm_predictor, patch_token_text_splitter, mock_embed_model ) -> None: """Test delete VectorStoreIndex.""" new_documents = [ @@ -82,7 +85,7 @@ def test_simple_delete( Document(text="This is a test v2.", id_="test_id_3"), ] index = VectorStoreIndex.from_documents( - documents=new_documents, service_context=mock_service_context + documents=new_documents, embed_model=mock_embed_model ) assert isinstance(index, VectorStoreIndex) @@ -121,7 +124,7 @@ def test_simple_delete( def test_simple_delete_ref_node_from_docstore( - mock_service_context: ServiceContext, + patch_llm_predictor, patch_token_text_splitter, mock_embed_model ) -> None: """Test delete VectorStoreIndex.""" new_documents = [ @@ -129,7 +132,7 @@ def test_simple_delete_ref_node_from_docstore( Document(text="This is another test.", id_="test_id_2"), ] index = VectorStoreIndex.from_documents( - documents=new_documents, service_context=mock_service_context + documents=new_documents, embed_model=mock_embed_model ) assert isinstance(index, VectorStoreIndex) @@ -148,11 +151,13 @@ def test_simple_delete_ref_node_from_docstore( def test_simple_async( allow_networking: Any, documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, + mock_embed_model, ) -> None: """Test simple vector index with use_async.""" index = VectorStoreIndex.from_documents( - documents=documents, use_async=True, service_context=mock_service_context + documents=documents, use_async=True, embed_model=mock_embed_model ) assert isinstance(index, VectorStoreIndex) assert len(index.index_struct.nodes_dict) == 4 @@ -173,12 +178,14 @@ def test_simple_async( def test_simple_insert_save( documents: List[Document], - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, + mock_embed_model, ) -> None: storage_context = StorageContext.from_defaults() index = VectorStoreIndex.from_documents( documents=documents, - service_context=mock_service_context, + embed_model=mock_embed_model, storage_context=storage_context, ) assert isinstance(index, VectorStoreIndex) @@ -196,17 +203,14 @@ def test_simple_insert_save( def test_simple_pickle( - mock_service_context: ServiceContext, + patch_llm_predictor, + patch_token_text_splitter, + mock_embed_model, documents: List[Document], ) -> None: """Test build VectorStoreIndex.""" - service_context = ServiceContext.from_service_context( - mock_service_context, - llm=MockLLM(), - ) - index = VectorStoreIndex.from_documents( - documents=documents, service_context=service_context + documents=documents, embed_model=mock_embed_model ) data = pickle.dumps(index) diff --git a/llama-index-core/tests/ingestion/test_data_sinks.py b/llama-index-core/tests/ingestion/test_data_sinks.py index 3c6eb252913f1..aa60eaa046775 100644 --- a/llama-index-core/tests/ingestion/test_data_sinks.py +++ b/llama-index-core/tests/ingestion/test_data_sinks.py @@ -17,14 +17,14 @@ def test_can_generate_schema_for_data_sink_component_type( configurable_data_sink_type: ConfigurableDataSinks, ) -> None: - schema = configurable_data_sink_type.value.schema() # type: ignore + schema = configurable_data_sink_type.value.model_json_schema() # type: ignore assert schema is not None assert len(schema) > 0 # also check that we can generate schemas for # ConfiguredDataSink[component_type] component_type = configurable_data_sink_type.value.component_type - configured_schema = ConfiguredDataSink[component_type].schema() # type: ignore + configured_schema = ConfiguredDataSink[component_type].model_json_schema() # type: ignore assert configured_schema is not None assert len(configured_schema) > 0 diff --git a/llama-index-core/tests/ingestion/test_data_sources.py b/llama-index-core/tests/ingestion/test_data_sources.py index e2cbb72e4df29..77b792b883889 100644 --- a/llama-index-core/tests/ingestion/test_data_sources.py +++ b/llama-index-core/tests/ingestion/test_data_sources.py @@ -10,14 +10,14 @@ def test_can_generate_schema_for_data_source_component_type( configurable_data_source_type: ConfigurableDataSources, ) -> None: - schema = configurable_data_source_type.value.schema() # type: ignore + schema = configurable_data_source_type.value.model_json_schema() # type: ignore assert schema is not None assert len(schema) > 0 # also check that we can generate schemas for # ConfiguredDataSource[component_type] component_type = configurable_data_source_type.value.component_type - configured_schema = ConfiguredDataSource[component_type].schema() # type: ignore + configured_schema = ConfiguredDataSource[component_type].model_json_schema() # type: ignore assert configured_schema is not None assert len(configured_schema) > 0 diff --git a/llama-index-core/tests/ingestion/test_pipeline.py b/llama-index-core/tests/ingestion/test_pipeline.py index 79cba797ebf7d..e7e86a2b7877f 100644 --- a/llama-index-core/tests/ingestion/test_pipeline.py +++ b/llama-index-core/tests/ingestion/test_pipeline.py @@ -4,7 +4,7 @@ from llama_index.core.extractors import KeywordExtractor from llama_index.core.ingestion.pipeline import IngestionPipeline from llama_index.core.llms.mock import MockLLM -from llama_index.core.node_parser import SentenceSplitter +from llama_index.core.node_parser import SentenceSplitter, MarkdownElementNodeParser from llama_index.core.readers import ReaderConfig, StringIterableReader from llama_index.core.schema import Document from llama_index.core.storage.docstore import SimpleDocumentStore @@ -57,6 +57,25 @@ def test_run_pipeline() -> None: assert len(nodes[0].metadata) > 0 +def test_run_pipeline_with_ref_doc_id(): + documents = [ + Document(text="one", doc_id="1"), + ] + pipeline = IngestionPipeline( + documents=documents, + transformations=[ + MarkdownElementNodeParser(), + SentenceSplitter(), + MockEmbedding(embed_dim=8), + ], + ) + + nodes = pipeline.run() + + assert len(nodes) == 1 + assert nodes[0].ref_doc_id == "1" + + def test_save_load_pipeline() -> None: documents = [ Document(text="one", doc_id="1"), diff --git a/llama-index-core/tests/ingestion/test_transformations.py b/llama-index-core/tests/ingestion/test_transformations.py index f4e7fa9b17d39..41c15933b9f66 100644 --- a/llama-index-core/tests/ingestion/test_transformations.py +++ b/llama-index-core/tests/ingestion/test_transformations.py @@ -12,7 +12,7 @@ def test_can_generate_schema_for_transformation_component_type( configurable_transformation_type: ConfigurableTransformations, ) -> None: - schema = configurable_transformation_type.value.schema() # type: ignore + schema = configurable_transformation_type.value.model_json_schema() # type: ignore assert schema is not None assert len(schema) > 0 @@ -21,7 +21,7 @@ def test_can_generate_schema_for_transformation_component_type( component_type = configurable_transformation_type.value.component_type configured_schema = ConfiguredTransformation[ component_type # type: ignore - ].schema() + ].model_json_schema() assert configured_schema is not None assert len(configured_schema) > 0 diff --git a/llama-index-core/tests/instrumentation/test_dispatcher.py b/llama-index-core/tests/instrumentation/test_dispatcher.py index 6240fc82d80c2..2607ec4683f5e 100644 --- a/llama-index-core/tests/instrumentation/test_dispatcher.py +++ b/llama-index-core/tests/instrumentation/test_dispatcher.py @@ -49,7 +49,7 @@ def class_name(cls): class _TestEventHandler(BaseEventHandler): - events = [] + events: List[BaseEvent] = [] @classmethod def class_name(cls): diff --git a/llama-index-core/tests/llms/test_structured_llm.py b/llama-index-core/tests/llms/test_structured_llm.py new file mode 100644 index 0000000000000..2216058125659 --- /dev/null +++ b/llama-index-core/tests/llms/test_structured_llm.py @@ -0,0 +1,56 @@ +from llama_index.core.llms.structured_llm import _escape_json +from llama_index.core.base.llms.types import ChatMessage + + +def test_escape_json() -> None: + """Test escape JSON. + + If there's curly brackets, escape it. + + """ + # create dumb test case + test_case_1 = _escape_json([ChatMessage(role="user", content="test message")]) + assert test_case_1 == [ChatMessage(role="user", content="test message")] + + # create test case with two brackets + test_case_2 = _escape_json( + [ChatMessage(role="user", content="test {message} {test}")] + ) + assert test_case_2 == [ + ChatMessage(role="user", content="test {{message}} {{test}}") + ] + + # create test case with a bracket that's already escaped - shouldn't change! + test_case_3 = _escape_json( + [ChatMessage(role="user", content="test {{message}} {test}")] + ) + print(test_case_3[0].content) + assert test_case_3 == [ + ChatMessage(role="user", content="test {{message}} {{test}}") + ] + + # test with additional kwargs + test_case_4 = _escape_json( + [ + ChatMessage( + role="user", + content="test {{message}} {test}", + additional_kwargs={"test": "test"}, + ) + ] + ) + assert test_case_4 == [ + ChatMessage( + role="user", + content="test {{message}} {{test}}", + additional_kwargs={"test": "test"}, + ) + ] + + # shouldn't escape already escaped brackets with 4 brackets + test_case_5 = _escape_json( + [ChatMessage(role="user", content="test {{{{message}}}} {test}")] + ) + assert test_case_5 == [ + ChatMessage(role="user", content="test {{{{message}}}} {{test}}") + ] diff --git a/llama-index-core/tests/memory/test_chat_summary_memory_buffer.py b/llama-index-core/tests/memory/test_chat_summary_memory_buffer.py index 54be2adf76e36..833bad19e85d5 100644 --- a/llama-index-core/tests/memory/test_chat_summary_memory_buffer.py +++ b/llama-index-core/tests/memory/test_chat_summary_memory_buffer.py @@ -6,10 +6,6 @@ from llama_index.core.bridge.pydantic import PrivateAttr from llama_index.core.base.llms.types import ChatResponse from llama_index.core.llms import ChatMessage, MessageRole, MockLLM -from openai.types.chat.chat_completion_message_tool_call import ( - ChatCompletionMessageToolCall, - Function, -) from llama_index.core.memory.chat_summary_memory_buffer import ( ChatSummaryMemoryBuffer, ) @@ -24,34 +20,47 @@ def _get_role_alternating_order(i: int): return MessageRole.ASSISTANT -USER_CHAT_MESSAGE = ChatMessage(role=MessageRole.USER, content="first message") -ASSISTANT_CHAT_MESSAGE = ChatMessage(role=MessageRole.ASSISTANT, content="first answer") -ASSISTANT_TOOL_CALLING_MESSAGE = ChatMessage( - role=MessageRole.ASSISTANT, - content=None, - additional_kwargs={ - "tool_calls": [ - ChatCompletionMessageToolCall( - id="call_Opq33YZVi0usHbNcrvEYN9QO", - function=Function(arguments='{"a":363,"b":42}', name="add"), - type="function", - ) - ] - }, -) -TOOL_CHAT_MESSAGE = ChatMessage(role=MessageRole.TOOL, content="first tool") -USER_CHAT_MESSAGE_TOKENS = len(tokenizer(str(USER_CHAT_MESSAGE.content))) -LONG_USER_CHAT_MESSAGE = ChatMessage( - role=MessageRole.USER, - content="".join( - ["This is a message that is longer than the proposed token length"] * 10 - ), -) -LONG_RUNNING_CONVERSATION = [ - ChatMessage(role=_get_role_alternating_order(i), content=f"Message {i}") - for i in range(6) -] -LONG_USER_CHAT_MESSAGE_TOKENS = len(tokenizer(str(LONG_USER_CHAT_MESSAGE.content))) +try: + from openai.types.chat.chat_completion_message_tool_call import ( + ChatCompletionMessageToolCall, + Function, + ) + + openai_installed = True +except ImportError: + openai_installed = False + +if openai_installed: + USER_CHAT_MESSAGE = ChatMessage(role=MessageRole.USER, content="first message") + ASSISTANT_CHAT_MESSAGE = ChatMessage( + role=MessageRole.ASSISTANT, content="first answer" + ) + ASSISTANT_TOOL_CALLING_MESSAGE = ChatMessage( + role=MessageRole.ASSISTANT, + content=None, + additional_kwargs={ + "tool_calls": [ + ChatCompletionMessageToolCall( + id="call_Opq33YZVi0usHbNcrvEYN9QO", + function=Function(arguments='{"a":363,"b":42}', name="add"), + type="function", + ) + ] + }, + ) + TOOL_CHAT_MESSAGE = ChatMessage(role=MessageRole.TOOL, content="first tool") + USER_CHAT_MESSAGE_TOKENS = len(tokenizer(str(USER_CHAT_MESSAGE.content))) + LONG_USER_CHAT_MESSAGE = ChatMessage( + role=MessageRole.USER, + content="".join( + ["This is a message that is longer than the proposed token length"] * 10 + ), + ) + LONG_RUNNING_CONVERSATION = [ + ChatMessage(role=_get_role_alternating_order(i), content=f"Message {i}") + for i in range(6) + ] + LONG_USER_CHAT_MESSAGE_TOKENS = len(tokenizer(str(LONG_USER_CHAT_MESSAGE.content))) class MockSummarizerLLM(MockLLM): @@ -114,6 +123,7 @@ def summarizer_llm(): ) +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_put_get(summarizer_llm) -> None: # Given one message with fewer tokens than token_limit memory = ChatSummaryMemoryBuffer.from_defaults( @@ -128,6 +138,7 @@ def test_put_get(summarizer_llm) -> None: assert history[0].content == USER_CHAT_MESSAGE.content +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_put_get_summarize_long_message(summarizer_llm) -> None: # Given one message with more tokens than token_limit memory = ChatSummaryMemoryBuffer.from_defaults( @@ -144,6 +155,7 @@ def test_put_get_summarize_long_message(summarizer_llm) -> None: assert history[0].content == FIRST_SUMMARY_RESPONSE +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_put_get_summarize_message_with_tool_call(summarizer_llm) -> None: # Given one message with more tokens than token_limit and tool calls # This case test 2 things: @@ -170,6 +182,7 @@ def test_put_get_summarize_message_with_tool_call(summarizer_llm) -> None: assert history[0].content == FIRST_SUMMARY_RESPONSE +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_put_get_summarize_part_of_conversation(summarizer_llm) -> None: # Given a chat history where only 2 responses fit in the token_limit tokens_most_recent_messages = sum( @@ -208,6 +221,7 @@ def test_put_get_summarize_part_of_conversation(summarizer_llm) -> None: assert history[2].content == "Message 7" +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_get_when_initial_tokens_less_than_limit_returns_history() -> None: # Given some initial tokens much smaller than token_limit and message tokens initial_tokens = 5 @@ -225,6 +239,7 @@ def test_get_when_initial_tokens_less_than_limit_returns_history() -> None: assert history[0] == USER_CHAT_MESSAGE +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_get_when_initial_tokens_exceed_limit_raises_value_error() -> None: # Given some initial tokens exceeding token_limit initial_tokens = 50 @@ -242,6 +257,7 @@ def test_get_when_initial_tokens_exceed_limit_raises_value_error() -> None: assert str(error.value) == "Initial token count exceeds token limit" +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_set() -> None: memory = ChatSummaryMemoryBuffer.from_defaults(chat_history=[USER_CHAT_MESSAGE]) @@ -253,6 +269,7 @@ def test_set() -> None: assert len(memory.get()) == 1 +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_max_tokens_without_summarizer() -> None: memory = ChatSummaryMemoryBuffer.from_defaults( chat_history=[USER_CHAT_MESSAGE], token_limit=5 @@ -275,6 +292,7 @@ def test_max_tokens_without_summarizer() -> None: assert len(memory.get()) == 2 +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_max_tokens_with_summarizer(summarizer_llm) -> None: max_tokens = 1 summarizer_llm.set_max_tokens(max_tokens) @@ -309,6 +327,7 @@ def test_max_tokens_with_summarizer(summarizer_llm) -> None: ) +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_assistant_never_first_message(summarizer_llm) -> None: chat_history = [ USER_CHAT_MESSAGE, @@ -336,6 +355,7 @@ def test_assistant_never_first_message(summarizer_llm) -> None: assert memory_results[2].role == MessageRole.ASSISTANT +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_assistant_tool_pairs(summarizer_llm) -> None: chat_history = [ USER_CHAT_MESSAGE, @@ -364,6 +384,7 @@ def test_assistant_tool_pairs(summarizer_llm) -> None: assert memory_results[2].role == MessageRole.ASSISTANT +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_string_save_load(summarizer_llm) -> None: memory = ChatSummaryMemoryBuffer.from_defaults( llm=summarizer_llm, @@ -386,6 +407,7 @@ def test_string_save_load(summarizer_llm) -> None: new_memory.llm = summarizer_llm +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_dict_save_load(summarizer_llm) -> None: memory = ChatSummaryMemoryBuffer.from_defaults( llm=summarizer_llm, @@ -408,6 +430,7 @@ def test_dict_save_load(summarizer_llm) -> None: new_memory.llm = summarizer_llm +@pytest.mark.skipif(not openai_installed, reason="OpenAI not installed") def test_pickle() -> None: """Unpickleable tiktoken tokenizer should be circumvented when pickling.""" memory = ChatSummaryMemoryBuffer.from_defaults() diff --git a/llama-index-core/tests/node_parser/metadata_extractor.py b/llama-index-core/tests/node_parser/metadata_extractor.py index e7eb7f97f8f45..9a30f3ebe515a 100644 --- a/llama-index-core/tests/node_parser/metadata_extractor.py +++ b/llama-index-core/tests/node_parser/metadata_extractor.py @@ -9,10 +9,9 @@ from llama_index.core.ingestion import run_transformations from llama_index.core.node_parser import SentenceSplitter from llama_index.core.schema import Document, TransformComponent -from llama_index.core.service_context import ServiceContext -def test_metadata_extractor(mock_service_context: ServiceContext) -> None: +def test_metadata_extractor() -> None: extractors: List[TransformComponent] = [ TitleExtractor(nodes=5), QuestionsAnsweredExtractor(questions=3), diff --git a/llama-index-core/tests/node_parser/test_node_parser.py b/llama-index-core/tests/node_parser/test_node_parser.py new file mode 100644 index 0000000000000..6c97b7ce0b5ff --- /dev/null +++ b/llama-index-core/tests/node_parser/test_node_parser.py @@ -0,0 +1,41 @@ +from typing import Any, List, Sequence +from llama_index.core.node_parser import NodeParser +from llama_index.core.schema import BaseNode, TextNode, Document, NodeRelationship + + +class _TestNodeParser(NodeParser): + def _parse_nodes( + self, nodes: Sequence[BaseNode], show_progress: bool = False, **kwargs: Any + ) -> List[BaseNode]: + return super()._parse_nodes(nodes, show_progress, **kwargs) + + +def test__postprocess_parsed_nodes_include_metadata(): + np = _TestNodeParser() + + nodes = [] + for i in range(3): + node = TextNode(text=f"I am Node number {i}") + node.metadata = {"node_number": i} + nodes.append(node) + + ret = np._postprocess_parsed_nodes(nodes, {}) + for i, node in enumerate(ret): + assert node.metadata == {"node_number": i} + + +def test__postprocess_parsed_nodes_include_metadata_parent_doc(): + np = _TestNodeParser() + doc = Document(text="I am root") + doc.metadata = {"document_type": "root"} + + nodes = [] + for i in range(3): + node = TextNode(text=f"I am Node number {i}") + node.metadata = {"node_number": i} + node.relationships = {NodeRelationship.SOURCE: doc.as_related_node_info()} + nodes.append(node) + + ret = np._postprocess_parsed_nodes(nodes, {}) + for i, node in enumerate(ret): + assert node.metadata == {"node_number": i, "document_type": "root"} diff --git a/llama-index-core/tests/node_parser/test_semantic_double_merging_splitter.py b/llama-index-core/tests/node_parser/test_semantic_double_merging_splitter.py index a1f00336acac0..5bb267ff7e378 100644 --- a/llama-index-core/tests/node_parser/test_semantic_double_merging_splitter.py +++ b/llama-index-core/tests/node_parser/test_semantic_double_merging_splitter.py @@ -21,7 +21,10 @@ try: splitter = SemanticDoubleMergingSplitterNodeParser( - initial_threshold=0.7, appending_threshold=0.8, merging_threshold=0.7 + initial_threshold=0.7, + appending_threshold=0.8, + merging_threshold=0.7, + max_chunk_size=1000, ) splitter.language_config.load_model() spacy_available = True @@ -30,7 +33,7 @@ @pytest.mark.skipif(not spacy_available, reason="Spacy model not available") -def test_number_of_returned_nides() -> None: +def test_number_of_returned_nodes() -> None: nodes = splitter.get_nodes_from_documents([doc]) assert len(nodes) == 3 @@ -60,3 +63,41 @@ def test_config_models() -> None: LanguageConfig(language="empty", spacy_model="empty") LanguageConfig(language="english", spacy_model="en_core_web_md") + + +@pytest.mark.skipif(not spacy_available, reason="Spacy model not available") +def test_chunk_size_1() -> None: + splitter.max_chunk_size = 0 + nodes = splitter.get_nodes_from_documents([doc]) + + # length of each sentence + assert len(nodes) == 13 + assert len(nodes[0].get_content()) == 111 + assert len(nodes[1].get_content()) == 72 + assert len(nodes[2].get_content()) == 91 + assert len(nodes[3].get_content()) == 99 + assert len(nodes[4].get_content()) == 100 + assert len(nodes[5].get_content()) == 66 + assert len(nodes[6].get_content()) == 94 + assert len(nodes[7].get_content()) == 100 + assert len(nodes[8].get_content()) == 69 + assert len(nodes[9].get_content()) == 95 + assert len(nodes[10].get_content()) == 77 + assert len(nodes[11].get_content()) == 114 + assert len(nodes[12].get_content()) == 65 + + +@pytest.mark.skipif(not spacy_available, reason="Spacy model not available") +def test_chunk_size_2() -> None: + splitter.max_chunk_size = 200 + nodes = splitter.get_nodes_from_documents([doc]) + assert len(nodes) == 9 + assert len(nodes[0].get_content()) < 200 + assert len(nodes[1].get_content()) < 200 + assert len(nodes[2].get_content()) < 200 + assert len(nodes[3].get_content()) < 200 + assert len(nodes[4].get_content()) < 200 + assert len(nodes[5].get_content()) < 200 + assert len(nodes[6].get_content()) < 200 + assert len(nodes[7].get_content()) < 200 + assert len(nodes[8].get_content()) < 200 diff --git a/llama-index-core/tests/objects/test_base.py b/llama-index-core/tests/objects/test_base.py index ca2f96b3442a9..0d859d14c01b4 100644 --- a/llama-index-core/tests/objects/test_base.py +++ b/llama-index-core/tests/objects/test_base.py @@ -4,12 +4,11 @@ from llama_index.core.objects.base import ObjectIndex from llama_index.core.objects.base_node_mapping import SimpleObjectNodeMapping from llama_index.core.objects.tool_node_mapping import SimpleToolNodeMapping -from llama_index.core.service_context import ServiceContext from llama_index.core.tools.function_tool import FunctionTool from llama_index.core.schema import TextNode -def test_object_index(mock_service_context: ServiceContext) -> None: +def test_object_index() -> None: """Test object index.""" object_mapping = SimpleObjectNodeMapping.from_objects(["a", "b", "c"]) obj_index = ObjectIndex.from_objects( @@ -23,7 +22,7 @@ def test_object_index(mock_service_context: ServiceContext) -> None: assert obj_index.as_retriever().retrieve("test") == ["a", "b", "c", "d"] -def test_object_index_default_mapping(mock_service_context: ServiceContext) -> None: +def test_object_index_default_mapping() -> None: """Test object index.""" obj_index = ObjectIndex.from_objects(["a", "b", "c"], index_cls=SummaryIndex) # should just retrieve everything @@ -34,7 +33,7 @@ def test_object_index_default_mapping(mock_service_context: ServiceContext) -> N assert obj_index.as_retriever().retrieve("test") == ["a", "b", "c", "d"] -def test_object_index_fn_mapping(mock_service_context: ServiceContext) -> None: +def test_object_index_fn_mapping() -> None: """Test object index.""" objects = {obj: obj for obj in ["a", "b", "c", "d"]} print(objects) @@ -60,7 +59,7 @@ def from_node_fn(node: TextNode) -> str: assert obj_index.as_retriever().retrieve("test") == ["a", "b", "c", "d"] -def test_object_index_persist(mock_service_context: ServiceContext) -> None: +def test_object_index_persist() -> None: """Test object index persist/load.""" object_mapping = SimpleObjectNodeMapping.from_objects(["a", "b", "c"]) obj_index = ObjectIndex.from_objects( @@ -88,7 +87,7 @@ def test_object_index_persist(mock_service_context: ServiceContext) -> None: ) -def test_object_index_with_tools(mock_service_context: ServiceContext) -> None: +def test_object_index_with_tools() -> None: """Test object index with tools.""" tool1 = FunctionTool.from_defaults(fn=lambda x: x, name="test_tool") tool2 = FunctionTool.from_defaults(fn=lambda x, y: x + y, name="test_tool2") diff --git a/llama-index-core/tests/objects/test_node_mapping.py b/llama-index-core/tests/objects/test_node_mapping.py index e8d7e163e6ef5..c6e3f9e756489 100644 --- a/llama-index-core/tests/objects/test_node_mapping.py +++ b/llama-index-core/tests/objects/test_node_mapping.py @@ -12,7 +12,7 @@ from pytest_mock import MockerFixture -class TestObject(BaseModel): +class _TestObject(BaseModel): """Test object for node mapping.""" __test__ = False @@ -23,10 +23,10 @@ def __hash__(self) -> int: return hash(self.name) def __str__(self) -> str: - return f"TestObject(name='{self.name}')" + return f"_TestObject(name='{self.name}')" -class TestSQLDatabase(SQLDatabase): +class _TestSQLDatabase(SQLDatabase): """Test object for SQL Table Schema Node Mapping.""" def __init__(self) -> None: @@ -40,9 +40,9 @@ def test_simple_object_node_mapping() -> None: assert node_mapping.to_node("a").text == "a" assert node_mapping.from_node(node_mapping.to_node("a")) == "a" - objects = [TestObject(name="a"), TestObject(name="b"), TestObject(name="c")] + objects = [_TestObject(name="a"), _TestObject(name="b"), _TestObject(name="c")] node_mapping = SimpleObjectNodeMapping.from_objects(objects) - assert node_mapping.to_node(objects[0]).text == "TestObject(name='a')" + assert node_mapping.to_node(objects[0]).text == "_TestObject(name='a')" assert node_mapping.from_node(node_mapping.to_node(objects[0])) == objects[0] @@ -102,7 +102,7 @@ def test_sql_table_node_mapping_to_node(mocker: MockerFixture) -> None: tables = [table1, table2] # Create the mapping - sql_database = TestSQLDatabase() + sql_database = _TestSQLDatabase() mapping = SQLTableNodeMapping(sql_database) # Create the nodes diff --git a/llama-index-core/tests/playground/test_base.py b/llama-index-core/tests/playground/test_base.py index afaf112085fb2..0e9f165839f80 100644 --- a/llama-index-core/tests/playground/test_base.py +++ b/llama-index-core/tests/playground/test_base.py @@ -13,7 +13,6 @@ Playground, ) from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext class MockEmbedding(BaseEmbedding): @@ -80,21 +79,16 @@ def _get_query_embedding(self, query: str) -> List[float]: return [0, 0, 1, 0, 0] -def test_get_set_compare( - mock_service_context: ServiceContext, -) -> None: +def test_get_set_compare(patch_llm_predictor, patch_token_text_splitter) -> None: """Test basic comparison of indices.""" - mock_service_context.embed_model = MockEmbedding() documents = [Document(text="They're taking the Hobbits to Isengard!")] indices = [ VectorStoreIndex.from_documents( - documents=documents, service_context=mock_service_context - ), - SummaryIndex.from_documents(documents, service_context=mock_service_context), - TreeIndex.from_documents( - documents=documents, service_context=mock_service_context + documents=documents, embed_model=MockEmbedding() ), + SummaryIndex.from_documents(documents), + TreeIndex.from_documents(documents=documents), ] playground = Playground(indices=indices) # type: ignore @@ -107,36 +101,27 @@ def test_get_set_compare( playground.indices = [ VectorStoreIndex.from_documents( - documents=documents, service_context=mock_service_context + documents=documents, embed_model=MockEmbedding() ) ] assert len(playground.indices) == 1 -def test_from_docs( - mock_service_context: ServiceContext, -) -> None: +def test_from_docs(patch_llm_predictor, patch_token_text_splitter) -> None: """Test initialization via a list of documents.""" - mock_service_context.embed_model = MockEmbedding() documents = [ Document(text="I can't carry it for you."), Document(text="But I can carry you!"), ] - playground = Playground.from_docs( - documents=documents, service_context=mock_service_context - ) + playground = Playground.from_docs(documents=documents) assert len(playground.indices) == len(DEFAULT_INDEX_CLASSES) assert len(playground.retriever_modes) == len(DEFAULT_MODES) with pytest.raises(ValueError): - playground = Playground.from_docs( - documents=documents, - retriever_modes={}, - service_context=mock_service_context, - ) + playground = Playground.from_docs(documents=documents, retriever_modes={}) def test_validation() -> None: diff --git a/llama-index-core/tests/postprocessor/test_base.py b/llama-index-core/tests/postprocessor/test_base.py index effd40162e7d8..8106398197347 100644 --- a/llama-index-core/tests/postprocessor/test_base.py +++ b/llama-index-core/tests/postprocessor/test_base.py @@ -22,7 +22,6 @@ RelatedNodeInfo, TextNode, ) -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.docstore.simple_docstore import SimpleDocumentStore spacy_installed = bool(find_spec("spacy")) @@ -70,7 +69,10 @@ def test_forward_back_processor(tmp_path: Path) -> None: docstore=docstore, num_nodes=1, mode="next" ) processed_nodes = node_postprocessor.postprocess_nodes( - [nodes_with_scores[1], nodes_with_scores[2]] + [ + nodes_with_scores[1], + nodes_with_scores[2], + ] ) assert len(processed_nodes) == 3 assert processed_nodes[0].node.node_id == "2" @@ -82,7 +84,10 @@ def test_forward_back_processor(tmp_path: Path) -> None: docstore=docstore, num_nodes=1, mode="previous" ) processed_nodes = node_postprocessor.postprocess_nodes( - [nodes_with_scores[1], nodes_with_scores[2]] + [ + nodes_with_scores[1], + nodes_with_scores[2], + ] ) assert len(processed_nodes) == 3 assert processed_nodes[0].node.node_id == "3" @@ -118,7 +123,10 @@ def test_forward_back_processor(tmp_path: Path) -> None: docstore=docstore, num_nodes=1, mode="both" ) processed_nodes = node_postprocessor.postprocess_nodes( - [nodes_with_scores[0], nodes_with_scores[4]] + [ + nodes_with_scores[0], + nodes_with_scores[4], + ] ) assert len(processed_nodes) == 4 # nodes are sorted @@ -132,7 +140,10 @@ def test_forward_back_processor(tmp_path: Path) -> None: docstore=docstore, num_nodes=0, mode="both" ) processed_nodes = node_postprocessor.postprocess_nodes( - [nodes_with_scores[0], nodes_with_scores[4]] + [ + nodes_with_scores[0], + nodes_with_scores[4], + ] ) assert len(processed_nodes) == 2 # nodes are sorted @@ -144,9 +155,7 @@ def test_forward_back_processor(tmp_path: Path) -> None: PrevNextNodePostprocessor(docstore=docstore, num_nodes=4, mode="asdfasdf") -def test_fixed_recency_postprocessor( - mock_service_context: ServiceContext, -) -> None: +def test_fixed_recency_postprocessor() -> None: """Test fixed recency processor.""" # try in metadata nodes = [ @@ -177,9 +186,7 @@ def test_fixed_recency_postprocessor( ] node_with_scores = [NodeWithScore(node=node) for node in nodes] - postprocessor = FixedRecencyPostprocessor( - top_k=1, service_context=mock_service_context - ) + postprocessor = FixedRecencyPostprocessor(top_k=1) query_bundle: QueryBundle = QueryBundle(query_str="What is?") result_nodes = postprocessor.postprocess_nodes( node_with_scores, query_bundle=query_bundle @@ -191,9 +198,7 @@ def test_fixed_recency_postprocessor( ) -def test_embedding_recency_postprocessor( - mock_service_context: ServiceContext, -) -> None: +def test_embedding_recency_postprocessor() -> None: """Test fixed recency processor.""" # try in node info nodes = [ @@ -232,7 +237,6 @@ def test_embedding_recency_postprocessor( postprocessor = EmbeddingRecencyPostprocessor( top_k=1, - embed_model=mock_service_context.embed_model, in_metadata=False, query_embedding_tmpl="{context_str}", ) diff --git a/llama-index-core/tests/postprocessor/test_llm_rerank.py b/llama-index-core/tests/postprocessor/test_llm_rerank.py index be733971912b0..d63c578a9a892 100644 --- a/llama-index-core/tests/postprocessor/test_llm_rerank.py +++ b/llama-index-core/tests/postprocessor/test_llm_rerank.py @@ -7,7 +7,6 @@ from llama_index.core.postprocessor.llm_rerank import LLMRerank from llama_index.core.prompts import BasePromptTemplate from llama_index.core.schema import BaseNode, NodeWithScore, QueryBundle, TextNode -from llama_index.core.service_context import ServiceContext def mock_llmpredictor_predict( @@ -46,7 +45,7 @@ def mock_format_node_batch_fn(nodes: List[BaseNode]) -> str: "predict", mock_llmpredictor_predict, ) -def test_llm_rerank(mock_service_context: ServiceContext) -> None: +def test_llm_rerank() -> None: """Test LLM rerank.""" nodes = [ TextNode(text="Test"), @@ -63,10 +62,7 @@ def test_llm_rerank(mock_service_context: ServiceContext) -> None: # choice batch size 4 (so two batches) # take top-3 across all data llm_rerank = LLMRerank( - format_node_batch_fn=mock_format_node_batch_fn, - choice_batch_size=4, - top_n=3, - service_context=mock_service_context, + format_node_batch_fn=mock_format_node_batch_fn, choice_batch_size=4, top_n=3 ) query_str = "What is?" result_nodes = llm_rerank.postprocess_nodes( diff --git a/llama-index-core/tests/query_engine/test_retriever_query_engine.py b/llama-index-core/tests/query_engine/test_retriever_query_engine.py index 4ded2cf3228d4..39388581612e1 100644 --- a/llama-index-core/tests/query_engine/test_retriever_query_engine.py +++ b/llama-index-core/tests/query_engine/test_retriever_query_engine.py @@ -1,43 +1,20 @@ -import pytest -from llama_index.core import ( - Document, - ServiceContext, - TreeIndex, -) -from llama_index.core.indices.tree.select_leaf_retriever import ( - TreeSelectLeafRetriever, -) -from llama_index.core.query_engine.retriever_query_engine import ( - RetrieverQueryEngine, -) +from llama_index.core import Document, TreeIndex +from llama_index.core.indices.tree.select_leaf_retriever import TreeSelectLeafRetriever +from llama_index.core.query_engine.retriever_query_engine import RetrieverQueryEngine +from llama_index.core import Settings -try: - from llama_index.llms.openai import OpenAI # pants: no-infer-dep -except ImportError: - OpenAI = None # type: ignore - -@pytest.mark.skipif(OpenAI is None, reason="llama-index-llms-openai not installed") -def test_query_engine_falls_back_to_inheriting_retrievers_service_context() -> None: +def test_query_engine_falls_back_to_inheriting_retrievers_service_context( + monkeypatch, mock_llm +) -> None: documents = [Document(text="Hi")] - gpt35turbo_predictor = OpenAI( - temperature=0, - model_name="gpt-3.5-turbo-0613", - streaming=True, - openai_api_key="test-test-test", - ) - gpt35_sc = ServiceContext.from_defaults( - llm=gpt35turbo_predictor, - chunk_size=512, - ) + monkeypatch.setattr(Settings, "llm", mock_llm) - gpt35_tree_index = TreeIndex.from_documents(documents, service_context=gpt35_sc) + gpt35_tree_index = TreeIndex.from_documents(documents) retriever = TreeSelectLeafRetriever(index=gpt35_tree_index, child_branch_factor=2) query_engine = RetrieverQueryEngine(retriever=retriever) - assert ( - retriever._llm.metadata.model_name == gpt35turbo_predictor.metadata.model_name - ) + assert retriever._llm.class_name() == "MockLLM" assert ( query_engine._response_synthesizer._llm.metadata.model_name == retriever._llm.metadata.model_name diff --git a/llama-index-core/tests/question_gen/test_llm_generators.py b/llama-index-core/tests/question_gen/test_llm_generators.py index 6e6355fb63a70..0ad3e57104829 100644 --- a/llama-index-core/tests/question_gen/test_llm_generators.py +++ b/llama-index-core/tests/question_gen/test_llm_generators.py @@ -1,17 +1,11 @@ from llama_index.core.question_gen.llm_generators import LLMQuestionGenerator from llama_index.core.question_gen.types import SubQuestion from llama_index.core.schema import QueryBundle -from llama_index.core.service_context import ServiceContext from llama_index.core.tools.types import ToolMetadata -def test_llm_question_gen( - mock_service_context: ServiceContext, -) -> None: - question_gen = LLMQuestionGenerator.from_defaults( - service_context=mock_service_context - ) - +def test_llm_question_gen(patch_llm_predictor, patch_token_text_splitter) -> None: + question_gen = LLMQuestionGenerator.from_defaults() tools = [ ToolMetadata(description="data source 1", name="source_1"), ToolMetadata(description="data source 2", name="source_2"), diff --git a/llama-index-core/tests/response_synthesizers/test_refine.py b/llama-index-core/tests/response_synthesizers/test_refine.py index 7689efdc5b960..897d27df77322 100644 --- a/llama-index-core/tests/response_synthesizers/test_refine.py +++ b/llama-index-core/tests/response_synthesizers/test_refine.py @@ -3,10 +3,8 @@ import pytest from llama_index.core.bridge.pydantic import BaseModel -from llama_index.core.callbacks import CallbackManager from llama_index.core.response_synthesizers import Refine from llama_index.core.response_synthesizers.refine import StructuredRefineResponse -from llama_index.core.service_context import ServiceContext from llama_index.core.types import BasePydanticProgram @@ -28,7 +26,7 @@ def __call__( *args: Any, context_str: Optional[str] = None, context_msg: Optional[str] = None, - **kwargs: Any + **kwargs: Any, ) -> StructuredRefineResponse: input_str = context_str or context_msg input_str = cast(str, input_str) @@ -42,7 +40,7 @@ async def acall( *args: Any, context_str: Optional[str] = None, context_msg: Optional[str] = None, - **kwargs: Any + **kwargs: Any, ) -> StructuredRefineResponse: input_str = context_str or context_msg input_str = cast(str, input_str) @@ -53,45 +51,31 @@ async def acall( @pytest.fixture() -def mock_refine_service_context(patch_llm_predictor: Any) -> ServiceContext: - cb_manager = CallbackManager([]) - return ServiceContext.from_defaults( - llm_predictor=patch_llm_predictor, - callback_manager=cb_manager, - ) - - -@pytest.fixture() -def refine_instance(mock_refine_service_context: ServiceContext) -> Refine: +def refine_instance() -> Refine: return Refine( - service_context=mock_refine_service_context, streaming=False, verbose=True, structured_answer_filtering=True, ) -def test_constructor_args(mock_refine_service_context: ServiceContext) -> None: +def test_constructor_args() -> None: with pytest.raises(ValueError): # can't construct refine with both streaming and answer filtering Refine( - service_context=mock_refine_service_context, streaming=True, structured_answer_filtering=True, ) with pytest.raises(ValueError): # can't construct refine with a program factory but not answer filtering Refine( - service_context=mock_refine_service_context, program_factory=lambda _: MockRefineProgram({}), structured_answer_filtering=False, ) @pytest.mark.asyncio() -async def test_answer_filtering_one_answer( - mock_refine_service_context: ServiceContext, -) -> None: +async def test_answer_filtering_one_answer() -> None: input_to_query_satisfied = OrderedDict( [ ("input1", False), @@ -104,7 +88,6 @@ def program_factory(*args: Any, **kwargs: Any) -> MockRefineProgram: return MockRefineProgram(input_to_query_satisfied) refine_instance = Refine( - service_context=mock_refine_service_context, structured_answer_filtering=True, program_factory=program_factory, ) @@ -115,9 +98,7 @@ def program_factory(*args: Any, **kwargs: Any) -> MockRefineProgram: @pytest.mark.asyncio() -async def test_answer_filtering_no_answers( - mock_refine_service_context: ServiceContext, -) -> None: +async def test_answer_filtering_no_answers() -> None: input_to_query_satisfied = OrderedDict( [ ("input1", False), @@ -130,7 +111,6 @@ def program_factory(*args: Any, **kwargs: Any) -> MockRefineProgram: return MockRefineProgram(input_to_query_satisfied) refine_instance = Refine( - service_context=mock_refine_service_context, structured_answer_filtering=True, program_factory=program_factory, ) diff --git a/llama-index-core/tests/selectors/test_llm_selectors.py b/llama-index-core/tests/selectors/test_llm_selectors.py index 0e2d3dd1eceff..dcf27f90698d7 100644 --- a/llama-index-core/tests/selectors/test_llm_selectors.py +++ b/llama-index-core/tests/selectors/test_llm_selectors.py @@ -1,20 +1,19 @@ from unittest.mock import patch from llama_index.core.llms import CompletionResponse -from llama_index.core.selectors.llm_selectors import ( - LLMMultiSelector, - LLMSingleSelector, -) -from llama_index.core.service_context import ServiceContext +from llama_index.core.selectors.llm_selectors import LLMMultiSelector, LLMSingleSelector +from llama_index.core import Settings + from tests.mock_utils.mock_predict import _mock_single_select -def test_llm_single_selector() -> None: - service_context = ServiceContext.from_defaults(llm=None, embed_model=None) - selector = LLMSingleSelector.from_defaults(service_context=service_context) +def test_llm_single_selector(mock_llm, monkeypatch) -> None: + selector = LLMSingleSelector.from_defaults() + + monkeypatch.setattr(Settings, "llm", mock_llm) with patch.object( - type(service_context.llm), + type(mock_llm), "complete", return_value=CompletionResponse(text=_mock_single_select()), ) as mock_complete: @@ -26,10 +25,8 @@ def test_llm_single_selector() -> None: assert mock_complete.call_args.args[0].count("Here is an example") <= 1 -def test_llm_multi_selector( - mock_service_context: ServiceContext, -) -> None: - selector = LLMMultiSelector.from_defaults(service_context=mock_service_context) +def test_llm_multi_selector(patch_llm_predictor) -> None: + selector = LLMMultiSelector.from_defaults() choices = [ "apple", @@ -42,12 +39,8 @@ def test_llm_multi_selector( assert result.inds == [0, 1, 2] -def test_llm_multi_selector_max_choices( - mock_service_context: ServiceContext, -) -> None: - selector = LLMMultiSelector.from_defaults( - service_context=mock_service_context, max_outputs=2 - ) +def test_llm_multi_selector_max_choices(patch_llm_predictor) -> None: + selector = LLMMultiSelector.from_defaults(max_outputs=2) choices = [ "apple", diff --git a/llama-index-core/tests/token_predictor/test_base.py b/llama-index-core/tests/token_predictor/test_base.py index 582b1b3d6e019..644e5ed9df2db 100644 --- a/llama-index-core/tests/token_predictor/test_base.py +++ b/llama-index-core/tests/token_predictor/test_base.py @@ -6,10 +6,9 @@ from llama_index.core.indices.keyword_table.base import KeywordTableIndex from llama_index.core.indices.list.base import SummaryIndex from llama_index.core.indices.tree.base import TreeIndex -from llama_index.core.llms.mock import MockLLM from llama_index.core.node_parser import TokenTextSplitter from llama_index.core.schema import Document -from llama_index.core.service_context import ServiceContext + from tests.mock_utils.mock_text_splitter import mock_token_splitter_newline @@ -25,24 +24,18 @@ def test_token_predictor(mock_split: Any) -> None: "This is a test v2." ) document = Document(text=doc_text) - llm = MockLLM(max_tokens=256) - service_context = ServiceContext.from_defaults(llm=llm) # test tree index - index = TreeIndex.from_documents([document], service_context=service_context) + index = TreeIndex.from_documents([document]) query_engine = index.as_query_engine() query_engine.query("What is?") # test keyword table index - index_keyword = KeywordTableIndex.from_documents( - [document], service_context=service_context - ) + index_keyword = KeywordTableIndex.from_documents([document]) query_engine = index_keyword.as_query_engine() query_engine.query("What is?") # test summary index - index_list = SummaryIndex.from_documents( - [document], service_context=service_context - ) + index_list = SummaryIndex.from_documents([document]) query_engine = index_list.as_query_engine() query_engine.query("What is?") diff --git a/llama-index-core/tests/tools/test_base.py b/llama-index-core/tests/tools/test_base.py index fec2b9a09e818..f890eb637c68c 100644 --- a/llama-index-core/tests/tools/test_base.py +++ b/llama-index-core/tests/tools/test_base.py @@ -1,4 +1,5 @@ """Test tools.""" + import json from typing import List, Optional @@ -28,7 +29,7 @@ def test_function_tool() -> None: assert function_tool.metadata.name == "foo" assert function_tool.metadata.description == "bar" assert function_tool.metadata.fn_schema is not None - actual_schema = function_tool.metadata.fn_schema.schema() + actual_schema = function_tool.metadata.fn_schema.model_json_schema() # note: no type assert "x" in actual_schema["properties"] @@ -41,7 +42,7 @@ def test_function_tool() -> None: tmp_function, name="foo", description="bar" ) assert function_tool.metadata.fn_schema is not None - actual_schema = function_tool.metadata.fn_schema.schema() + actual_schema = function_tool.metadata.fn_schema.model_json_schema() assert actual_schema["properties"]["x"]["type"] == "integer" @@ -81,7 +82,7 @@ async def test_function_tool_async() -> None: fn=tmp_function, async_fn=async_tmp_function, name="foo", description="bar" ) assert function_tool.metadata.fn_schema is not None - actual_schema = function_tool.metadata.fn_schema.schema() + actual_schema = function_tool.metadata.fn_schema.model_json_schema() assert actual_schema["properties"]["x"]["type"] == "integer" assert str(function_tool(2)) == "2" @@ -132,7 +133,7 @@ async def test_function_tool_async_defaults() -> None: fn=tmp_function, name="foo", description="bar" ) assert function_tool.metadata.fn_schema is not None - actual_schema = function_tool.metadata.fn_schema.schema() + actual_schema = function_tool.metadata.fn_schema.model_json_schema() assert actual_schema["properties"]["x"]["type"] == "integer" @@ -150,11 +151,7 @@ async def test_function_tool_async_defaults_langchain() -> None: assert result == "1" -from llama_index.core import ( - ServiceContext, - VectorStoreIndex, -) -from llama_index.core.embeddings.mock_embed_model import MockEmbedding +from llama_index.core import VectorStoreIndex from llama_index.core.schema import Document from llama_index.core.tools import RetrieverTool, ToolMetadata @@ -169,12 +166,7 @@ def test_retreiver_tool() -> None: text=("# title2:This is another test.\n" "This is a test v2."), metadata={"file_path": "/data/personal/essay.md"}, ) - service_context = ServiceContext.from_defaults( - llm=None, embed_model=MockEmbedding(embed_dim=1) - ) - vs_index = VectorStoreIndex.from_documents( - [doc1, doc2], service_context=service_context - ) + vs_index = VectorStoreIndex.from_documents([doc1, doc2]) vs_retriever = vs_index.as_retriever() vs_ret_tool = RetrieverTool( retriever=vs_retriever, diff --git a/llama-index-core/tests/tools/test_ondemand_loader.py b/llama-index-core/tests/tools/test_ondemand_loader.py index 30ee946a9eba3..a0abbecb7a8aa 100644 --- a/llama-index-core/tests/tools/test_ondemand_loader.py +++ b/llama-index-core/tests/tools/test_ondemand_loader.py @@ -12,11 +12,10 @@ from llama_index.core.bridge.pydantic import BaseModel from llama_index.core.indices.vector_store.base import VectorStoreIndex from llama_index.core.readers.string_iterable import StringIterableReader -from llama_index.core.service_context import ServiceContext from llama_index.core.tools.ondemand_loader_tool import OnDemandLoaderTool -class TestSchemaSpec(BaseModel): +class _TestSchemaSpec(BaseModel): """Test schema spec.""" texts: List[str] @@ -24,16 +23,15 @@ class TestSchemaSpec(BaseModel): @pytest.fixture() -def tool(mock_service_context: ServiceContext) -> OnDemandLoaderTool: +def tool(patch_llm_predictor) -> OnDemandLoaderTool: # import most basic string reader reader = StringIterableReader() return OnDemandLoaderTool.from_defaults( reader=reader, index_cls=VectorStoreIndex, - index_kwargs={"service_context": mock_service_context}, name="ondemand_loader_tool", description="ondemand_loader_tool_desc", - fn_schema=TestSchemaSpec, + fn_schema=_TestSchemaSpec, ) @@ -51,6 +49,6 @@ def test_ondemand_loader_tool_langchain( ) -> None: # convert tool to structured langchain tool lc_tool = tool.to_langchain_structured_tool() - assert lc_tool.args_schema == TestSchemaSpec + assert lc_tool.args_schema == _TestSchemaSpec response = lc_tool.run({"texts": ["Hello world."], "query_str": "What is?"}) assert str(response) == "What is?:Hello world." diff --git a/llama-index-core/tests/tools/test_query_engine_tool.py b/llama-index-core/tests/tools/test_query_engine_tool.py index 61cec06055b59..26880e5fc4c31 100644 --- a/llama-index-core/tests/tools/test_query_engine_tool.py +++ b/llama-index-core/tests/tools/test_query_engine_tool.py @@ -1,4 +1,5 @@ """Test tools.""" + from typing import Type, cast import pytest @@ -29,7 +30,7 @@ def test_query_engine_tool() -> None: fn_schema_cls = cast(Type[BaseModel], query_tool.metadata.fn_schema) fn_schema_obj = cast(BaseModel, fn_schema_cls(input="bar")) - response = query_tool(**fn_schema_obj.dict()) + response = query_tool(**fn_schema_obj.model_dump()) assert str(response) == "custom_bar" # test resolve input errors diff --git a/llama-index-core/tests/tools/test_types.py b/llama-index-core/tests/tools/test_types.py index 5dd8a99bf68e6..f38831b5a092a 100644 --- a/llama-index-core/tests/tools/test_types.py +++ b/llama-index-core/tests/tools/test_types.py @@ -1,7 +1,18 @@ import pytest + +from llama_index.core.bridge.pydantic import BaseModel +from llama_index.core.program.function_program import _get_function_tool from llama_index.core.tools.types import ToolMetadata +class Inner(BaseModel): + name: str + + +class Outer(BaseModel): + inner: Inner + + def test_toolmetadata_openai_tool_description_max_length() -> None: openai_tool_description_limit = 1024 valid_description = "a" * openai_tool_description_limit @@ -12,3 +23,18 @@ def test_toolmetadata_openai_tool_description_max_length() -> None: with pytest.raises(ValueError): ToolMetadata(invalid_description).to_openai_tool() + + +def test_nested_tool_schema() -> None: + tool = _get_function_tool(Outer) + schema = tool.metadata.get_parameters_dict() + + assert "$defs" in schema + defs = schema["$defs"] + assert "Inner" in defs + inner = defs["Inner"] + assert inner["required"][0] == "name" + assert inner["properties"] == {"name": {"title": "Name", "type": "string"}} + + assert schema["required"][0] == "inner" + assert schema["properties"] == {"inner": {"$ref": "#/$defs/Inner"}} diff --git a/llama-index-core/tests/tools/test_utils.py b/llama-index-core/tests/tools/test_utils.py index 613633fee796e..275eb95dfb702 100644 --- a/llama-index-core/tests/tools/test_utils.py +++ b/llama-index-core/tests/tools/test_utils.py @@ -1,4 +1,5 @@ """Test utils.""" + from typing import List from llama_index.core.bridge.pydantic import Field @@ -12,21 +13,21 @@ def test_fn(x: int, y: int, z: List[str]) -> None: """Test function.""" SchemaCls = create_schema_from_function("test_schema", test_fn) - schema = SchemaCls.schema() + schema = SchemaCls.model_json_schema() assert schema["properties"]["x"]["type"] == "integer" assert schema["properties"]["y"]["type"] == "integer" assert schema["properties"]["z"]["type"] == "array" assert schema["required"] == ["x", "y", "z"] SchemaCls = create_schema_from_function("test_schema", test_fn, [("a", bool, 1)]) - schema = SchemaCls.schema() + schema = SchemaCls.model_json_schema() assert schema["properties"]["a"]["type"] == "boolean" def test_fn2(x: int = 1) -> None: """Optional input.""" SchemaCls = create_schema_from_function("test_schema", test_fn2) - schema = SchemaCls.schema() + schema = SchemaCls.model_json_schema() assert "required" not in schema @@ -37,7 +38,7 @@ def tmp_function(x: int = Field(3, description="An integer")) -> str: return str(x) schema = create_schema_from_function("TestSchema", tmp_function) - actual_schema = schema.schema() + actual_schema = schema.model_json_schema() assert "x" in actual_schema["properties"] assert actual_schema["properties"]["x"]["type"] == "integer" diff --git a/llama-index-core/tests/tools/tool_spec/test_base.py b/llama-index-core/tests/tools/tool_spec/test_base.py index 483cc7b0fe21a..b8ea9c94a05b2 100644 --- a/llama-index-core/tests/tools/tool_spec/test_base.py +++ b/llama-index-core/tests/tools/tool_spec/test_base.py @@ -91,14 +91,14 @@ def test_tool_spec() -> None: assert tools[0].metadata.name == "foo_name" assert tools[0].metadata.description == "foo_description" assert tools[0].metadata.fn_schema is not None - fn_schema = tools[0].metadata.fn_schema.schema() + fn_schema = tools[0].metadata.fn_schema.model_json_schema() print(fn_schema) assert fn_schema["properties"]["arg1"]["type"] == "string" assert fn_schema["properties"]["arg2"]["type"] == "integer" assert tools[1].metadata.name == "bar" assert tools[1].metadata.description == "bar(arg1: bool) -> str\nBar." assert tools[1].metadata.fn_schema is not None - fn_schema = tools[1].metadata.fn_schema.schema() + fn_schema = tools[1].metadata.fn_schema.model_json_schema() assert fn_schema["properties"]["arg1"]["type"] == "boolean" diff --git a/llama-index-core/tests/vector_stores/test_simple.py b/llama-index-core/tests/vector_stores/test_simple.py index d6dbed25f1827..ba62fb7f0a646 100644 --- a/llama-index-core/tests/vector_stores/test_simple.py +++ b/llama-index-core/tests/vector_stores/test_simple.py @@ -1,7 +1,16 @@ import unittest +from pathlib import Path from typing import List -from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode +import pytest + +from llama_index.core import VectorStoreIndex, MockEmbedding +from llama_index.core.schema import ( + NodeRelationship, + RelatedNodeInfo, + TextNode, + Document, +) from llama_index.core.vector_stores import SimpleVectorStore from llama_index.core.vector_stores.types import ( ExactMatchFilter, @@ -17,6 +26,15 @@ _NODE_ID_WEIGHT_3_RANK_C = "452D24AB-F185-414C-A352-590B4B9EE51B" +@pytest.fixture() +def persist_dir(tmp_path: Path): + index = VectorStoreIndex.from_documents( + [Document(id_="1", text="1")], embed_model=MockEmbedding(embed_dim=1) + ) + index.storage_context.persist(str(tmp_path)) + return str(tmp_path) + + def _node_embeddings_for_test() -> List[TextNode]: return [ TextNode( @@ -400,6 +418,23 @@ def test_query_with_all_filter_returns_matches(self) -> None: assert result.ids is not None self.assertEqual(len(result.ids), 2) + def test_query_with_is_empty_filter_returns_matches(self) -> None: + simple_vector_store = SimpleVectorStore() + simple_vector_store.add(_node_embeddings_for_test()) + + filters = MetadataFilters( + filters=[ + MetadataFilter( + key="not_existed_key", operator=FilterOperator.IS_EMPTY, value=None + ) + ] + ) + query = VectorStoreQuery( + query_embedding=[1.0, 1.0], filters=filters, similarity_top_k=3 + ) + result = simple_vector_store.query(query) + self.assertEqual(len(result.ids), len(_node_embeddings_for_test())) + def test_clear(self) -> None: simple_vector_store = SimpleVectorStore() simple_vector_store.add(_node_embeddings_for_test()) @@ -417,3 +452,15 @@ def test_delete_nodes(self) -> None: query = VectorStoreQuery(query_embedding=[1.0, 1.0], similarity_top_k=3) result = simple_vector_store.query(query) self.assertEqual(result.ids, [_NODE_ID_WEIGHT_3_RANK_C]) + + +def test_from_persist_dir(persist_dir: str) -> None: + vector_store = SimpleVectorStore.from_persist_dir(persist_dir=persist_dir) + assert vector_store is not None + + +def test_from_namespaced_persist_dir(persist_dir: str) -> None: + vector_store = SimpleVectorStore.from_namespaced_persist_dir( + persist_dir=persist_dir + ) + assert vector_store is not None diff --git a/llama-index-core/tests/workflow/conftest.py b/llama-index-core/tests/workflow/conftest.py index cc3211f923bc7..e9d6278b2d982 100644 --- a/llama-index-core/tests/workflow/conftest.py +++ b/llama-index-core/tests/workflow/conftest.py @@ -1,8 +1,9 @@ import pytest -from llama_index.core.workflow.workflow import Workflow from llama_index.core.workflow.decorators import step from llama_index.core.workflow.events import StartEvent, StopEvent, Event +from llama_index.core.workflow.context import Context +from llama_index.core.workflow.workflow import Workflow from llama_index.core.bridge.pydantic import Field @@ -19,15 +20,15 @@ class LastEvent(Event): class DummyWorkflow(Workflow): - @step() + @step async def start_step(self, ev: StartEvent) -> OneTestEvent: return OneTestEvent() - @step() + @step async def middle_step(self, ev: OneTestEvent) -> LastEvent: return LastEvent() - @step() + @step async def end_step(self, ev: LastEvent) -> StopEvent: return StopEvent(result="Workflow completed") @@ -40,3 +41,8 @@ def workflow(): @pytest.fixture() def events(): return [OneTestEvent, AnotherTestEvent] + + +@pytest.fixture() +def ctx(): + return Context(workflow=Workflow()) diff --git a/llama-index-core/tests/workflow/test_context.py b/llama-index-core/tests/workflow/test_context.py index 764c4538f3140..fd4b3c6613114 100644 --- a/llama-index-core/tests/workflow/test_context.py +++ b/llama-index-core/tests/workflow/test_context.py @@ -1,12 +1,15 @@ -import pytest +from unittest import mock from typing import Union, Optional +import pytest from llama_index.core.workflow.workflow import ( Workflow, Context, ) from llama_index.core.workflow.decorators import step -from llama_index.core.workflow.events import StartEvent, StopEvent +from llama_index.core.workflow.errors import WorkflowRuntimeError +from llama_index.core.workflow.events import StartEvent, StopEvent, Event +from llama_index.core.workflow.workflow import Workflow from .conftest import OneTestEvent, AnotherTestEvent @@ -17,15 +20,15 @@ async def test_collect_events(): ev2 = AnotherTestEvent() class TestWorkflow(Workflow): - @step() + @step async def step1(self, _: StartEvent) -> OneTestEvent: return ev1 - @step() + @step async def step2(self, _: StartEvent) -> AnotherTestEvent: return ev2 - @step(pass_context=True) + @step async def step3( self, ctx: Context, ev: Union[OneTestEvent, AnotherTestEvent] ) -> Optional[StopEvent]: @@ -37,3 +40,83 @@ async def step3( workflow = TestWorkflow() result = await workflow.run() assert result == [ev1, ev2] + + +@pytest.mark.asyncio() +async def test_get_default(workflow): + c1 = Context(workflow) + assert await c1.get(key="test_key", default=42) == 42 + + +@pytest.mark.asyncio() +async def test_get(ctx): + await ctx.set("foo", 42) + assert await ctx.get("foo") == 42 + + +@pytest.mark.asyncio() +async def test_get_not_found(ctx): + with pytest.raises(ValueError): + await ctx.get("foo") + + +@pytest.mark.asyncio() +async def test_legacy_data(workflow): + c1 = Context(workflow) + await c1.set(key="test_key", value=42) + assert c1.data["test_key"] == 42 + + +def test_send_event_step_is_none(ctx): + ctx._queues = {"step1": mock.MagicMock(), "step2": mock.MagicMock()} + ev = Event(foo="bar") + ctx.send_event(ev) + for q in ctx._queues.values(): + q.put_nowait.assert_called_with(ev) + assert ctx._broker_log == [ev] + + +def test_send_event_to_non_existent_step(ctx): + with pytest.raises( + WorkflowRuntimeError, match="Step does_not_exist does not exist" + ): + ctx.send_event(Event(), "does_not_exist") + + +def test_send_event_to_wrong_step(ctx): + ctx._workflow._get_steps = mock.MagicMock(return_value={"step": mock.MagicMock()}) + + with pytest.raises( + WorkflowRuntimeError, + match="Step step does not accept event of type ", + ): + ctx.send_event(Event(), "step") + + +def test_send_event_to_step(ctx): + step2 = mock.MagicMock() + step2.__step_config.accepted_events = [Event] + + ctx._workflow._get_steps = mock.MagicMock( + return_value={"step1": mock.MagicMock(), "step2": step2} + ) + ctx._queues = {"step1": mock.MagicMock(), "step2": mock.MagicMock()} + + ev = Event(foo="bar") + ctx.send_event(ev, "step2") + + ctx._queues["step1"].put_nowait.assert_not_called() + ctx._queues["step2"].put_nowait.assert_called_with(ev) + + +def test_get_result(ctx): + ctx._retval = 42 + assert ctx.get_result() == 42 + + +@pytest.mark.asyncio() +async def test_deprecated_params(ctx): + with pytest.warns( + DeprecationWarning, match="`make_private` is deprecated and will be ignored" + ): + await ctx.set("foo", 42, make_private=True) diff --git a/llama-index-core/tests/workflow/test_decorator.py b/llama-index-core/tests/workflow/test_decorator.py index 5c458c3e90fe1..48559afcf9ddc 100644 --- a/llama-index-core/tests/workflow/test_decorator.py +++ b/llama-index-core/tests/workflow/test_decorator.py @@ -3,8 +3,8 @@ import pytest from llama_index.core.workflow.decorators import step -from llama_index.core.workflow.events import Event from llama_index.core.workflow.errors import WorkflowValidationError +from llama_index.core.workflow.events import Event from llama_index.core.workflow.workflow import Workflow @@ -19,6 +19,21 @@ def f(self, ev: Event) -> Event: assert config.return_types == [Event] +def test_decorate_method(): + class TestWorkflow(Workflow): + @step + def f1(self, ev: Event) -> Event: + return ev + + @step + def f2(self, ev: Event) -> Event: + return ev + + wf = TestWorkflow() + assert getattr(wf.f1, "__step_config") + assert getattr(wf.f2, "__step_config") + + def test_decorate_wrong_signature(): def f(): pass @@ -42,10 +57,31 @@ def test_decorate_free_function_wrong_decorator(): with pytest.raises( WorkflowValidationError, match=re.escape( - "To decorate f please pass a workflow instance to the @step() decorator." + "To decorate f please pass a workflow class to the @step decorator." ), ): - @step() + @step def f(ev: Event) -> Event: return Event() + + +def test_decorate_free_function_wrong_num_workers(): + class TestWorkflow(Workflow): + pass + + with pytest.raises( + WorkflowValidationError, match="num_workers must be an integer greater than 0" + ): + + @step(workflow=TestWorkflow, num_workers=0) + def f1(ev: Event) -> Event: + return Event() + + with pytest.raises( + WorkflowValidationError, match="num_workers must be an integer greater than 0" + ): + + @step(workflow=TestWorkflow, num_workers=0.5) + def f2(ev: Event) -> Event: + return Event() diff --git a/llama-index-core/tests/workflow/test_event.py b/llama-index-core/tests/workflow/test_event.py new file mode 100644 index 0000000000000..49973fdfef065 --- /dev/null +++ b/llama-index-core/tests/workflow/test_event.py @@ -0,0 +1,89 @@ +import pytest + +from llama_index.core.workflow.events import Event +from llama_index.core.bridge.pydantic import PrivateAttr +from typing import Any + + +class _TestEvent(Event): + param: str + _private_param_1: str = PrivateAttr() + _private_param_2: str = PrivateAttr(default_factory=str) + + +class _TestEvent2(Event): + """Custom Test Event. + + Private Attrs: + _private_param: doesn't get modified during construction + _modified_private_param: gets processed before being set + """ + + _private_param: int = PrivateAttr() + _modified_private_param: int = PrivateAttr() + + def __init__(self, _modified_private_param: int, **params: Any): + super().__init__(**params) + self._modified_private_param = _modified_private_param * 2 + + +def test_event_init_basic(): + evt = Event(a=1, b=2, c="c") + + assert evt.a == 1 + assert evt.b == 2 + assert evt.c == "c" + assert evt["a"] == evt.a + assert evt["b"] == evt.b + assert evt["c"] == evt.c + assert evt.keys() == {"a": 1, "b": 2, "c": "c"}.keys() + + +def test_custom_event_with_fields_and_private_params(): + evt = _TestEvent(a=1, param="test_param", _private_param_1="test_private_param_1") # type: ignore + + assert evt.a == 1 + assert evt["a"] == evt.a + assert evt.param == "test_param" + assert evt._data == {"a": 1} + assert evt._private_param_1 == "test_private_param_1" + assert evt._private_param_2 == "" + + +def test_custom_event_override_init(): + evt = _TestEvent2(a=1, b=2, _private_param=2, _modified_private_param=2) + + assert evt.a == 1 + assert evt.b == 2 + assert evt._data == {"a": 1, "b": 2} + assert evt._private_param == 2 + assert evt._modified_private_param == 4 + + +def test_event_missing_key(): + ev = _TestEvent(param="bar") + with pytest.raises(AttributeError): + ev.wrong_key + + +def test_event_not_a_field(): + ev = _TestEvent(param="foo", not_a_field="bar") # type: ignore + assert ev._data["not_a_field"] == "bar" + ev.not_a_field = "baz" + assert ev._data["not_a_field"] == "baz" + ev["not_a_field"] = "barbaz" + assert ev._data["not_a_field"] == "barbaz" + assert ev.get("not_a_field") == "barbaz" + + +def test_event_dict_api(): + ev = _TestEvent(param="foo") + assert len(ev) == 0 + ev["a_new_key"] = "bar" + assert len(ev) == 1 + assert list(ev.values()) == ["bar"] + k, v = next(iter(ev.items())) + assert k == "a_new_key" + assert v == "bar" + assert next(iter(ev)) == "a_new_key" + assert ev.dict() == {"a_new_key": "bar"} diff --git a/llama-index-core/tests/workflow/test_service.py b/llama-index-core/tests/workflow/test_service.py new file mode 100644 index 0000000000000..147b5fe3ad0cf --- /dev/null +++ b/llama-index-core/tests/workflow/test_service.py @@ -0,0 +1,83 @@ +import pytest + +from llama_index.core.workflow.decorators import step +from llama_index.core.workflow.events import Event, StartEvent, StopEvent +from llama_index.core.workflow.workflow import Workflow +from llama_index.core.workflow.context import Context +from llama_index.core.workflow.service import ServiceManager, ServiceNotFoundError + + +class ServiceWorkflow(Workflow): + """This wokflow is only responsible to generate a number, it knows nothing about the caller.""" + + def __init__(self, *args, **kwargs) -> None: + self._the_answer = kwargs.pop("the_answer", 42) + super().__init__(*args, **kwargs) + + @step + async def generate(self, ev: StartEvent) -> StopEvent: + return StopEvent(result=self._the_answer) + + +class NumGenerated(Event): + """To be used in the dummy workflow below.""" + + num: int + + +class DummyWorkflow(Workflow): + """ + This workflow needs a number, and it calls another workflow to get one. + A service named "service_workflow" must be added to `DummyWorkflow` for + the step to be able to use it (see below). + This step knows nothing about the other workflow, it gets an instance + and it only knows it has to call `run` on that instance. + """ + + @step + async def get_a_number( + self, + ev: StartEvent, + ctx: Context, + service_workflow: ServiceWorkflow = ServiceWorkflow(), + ) -> NumGenerated: + res = await service_workflow.run() + return NumGenerated(num=int(res)) + + @step + async def multiply(self, ev: NumGenerated) -> StopEvent: + return StopEvent(ev.num * 2) + + +@pytest.mark.asyncio() +async def test_e2e(): + wf = DummyWorkflow() + # We are responsible for passing the ServiceWorkflow instances to the dummy workflow + # and give it a name, in this case "service_workflow" + wf.add_workflows(service_workflow=ServiceWorkflow(the_answer=1337)) + res = await wf.run() + assert res == 2674 + + +@pytest.mark.asyncio() +async def test_default_value_for_service(): + wf = DummyWorkflow() + # We don't add any workflow to leverage the default value defined by the user + res = await wf.run() + assert res == 84 + + +def test_service_manager_add(): + s = ServiceManager() + w = Workflow() + s.add("test_id", w) + assert s._services["test_id"] == w + + +def test_service_manager_get(): + s = ServiceManager() + w = Workflow() + s._services["test_id"] = w + assert s.get("test_id") == w + with pytest.raises(ServiceNotFoundError): + s.get("not_found") diff --git a/llama-index-core/tests/workflow/test_streaming.py b/llama-index-core/tests/workflow/test_streaming.py new file mode 100644 index 0000000000000..74572647b7de7 --- /dev/null +++ b/llama-index-core/tests/workflow/test_streaming.py @@ -0,0 +1,69 @@ +import asyncio + +import pytest + +from llama_index.core.workflow.context import Context +from llama_index.core.workflow.decorators import step +from llama_index.core.workflow.events import Event, StartEvent, StopEvent +from llama_index.core.workflow.workflow import Workflow +from llama_index.core.workflow.errors import WorkflowRuntimeError + +from .conftest import OneTestEvent + + +class StreamingWorkflow(Workflow): + @step + async def chat(self, ctx: Context, ev: StartEvent) -> StopEvent: + async def stream_messages(): + resp = "Paul Graham is a British-American computer scientist, entrepreneur, vc, and writer." + for word in resp.split(): + yield word + + async for w in stream_messages(): + ctx.session.write_event_to_stream(Event(msg=w)) + + return StopEvent(result=None) + + +@pytest.mark.asyncio() +async def test_e2e(): + wf = StreamingWorkflow() + r = asyncio.create_task(wf.run()) + + async for ev in wf.stream_events(): + assert "msg" in ev + + await r + + +@pytest.mark.asyncio() +async def test_too_many_runs(): + wf = StreamingWorkflow() + r = asyncio.gather(wf.run(), wf.run()) + with pytest.raises( + WorkflowRuntimeError, + match="This workflow has multiple concurrent runs in progress and cannot stream events", + ): + async for ev in wf.stream_events(): + pass + await r + + +@pytest.mark.asyncio() +async def test_task_raised(): + class DummyWorkflow(Workflow): + @step + async def step(self, ctx: Context, ev: StartEvent) -> StopEvent: + ctx.write_event_to_stream(OneTestEvent(test_param="foo")) + raise ValueError("The step raised an error!") + + wf = DummyWorkflow() + r = asyncio.create_task(wf.run()) + + # Make sure we don't block indefinitely here because the step raised + async for ev in wf.stream_events(): + assert ev.test_param == "foo" + + # Make sure the await actually caught the exception + with pytest.raises(ValueError, match="The step raised an error!"): + await r diff --git a/llama-index-core/tests/workflow/test_utils.py b/llama-index-core/tests/workflow/test_utils.py index 411716aca8d5f..f785381671e23 100644 --- a/llama-index-core/tests/workflow/test_utils.py +++ b/llama-index-core/tests/workflow/test_utils.py @@ -8,6 +8,7 @@ from llama_index.core.workflow.events import StartEvent, StopEvent from llama_index.core.workflow.utils import ( validate_step_signature, + inspect_signature, get_steps_from_class, get_steps_from_instance, _get_param_types, @@ -23,28 +24,28 @@ def test_validate_step_signature_of_method(): def f(self, ev: OneTestEvent) -> OneTestEvent: return OneTestEvent() - validate_step_signature(f) + validate_step_signature(inspect_signature(f)) def test_validate_step_signature_of_free_function(): def f(ev: OneTestEvent) -> OneTestEvent: return OneTestEvent() - validate_step_signature(f) + validate_step_signature(inspect_signature(f)) def test_validate_step_signature_union(): def f(ev: Union[OneTestEvent, AnotherTestEvent]) -> OneTestEvent: return OneTestEvent() - validate_step_signature(f) + validate_step_signature(inspect_signature(f)) def test_validate_step_signature_of_free_function_with_context(): def f(ctx: Context, ev: OneTestEvent) -> OneTestEvent: return OneTestEvent() - validate_step_signature(f) + validate_step_signature(inspect_signature(f)) def test_validate_step_signature_union_invalid(): @@ -53,9 +54,9 @@ def f(ev: Union[OneTestEvent, str]) -> None: with pytest.raises( WorkflowValidationError, - match="Events in step signature parameters must be of type Event", + match="Step signature must have at least one parameter annotated as type Event", ): - validate_step_signature(f) + validate_step_signature(inspect_signature(f)) def test_validate_step_signature_no_params(): @@ -65,7 +66,7 @@ def f() -> None: with pytest.raises( WorkflowValidationError, match="Step signature must have at least one parameter" ): - validate_step_signature(f) + validate_step_signature(inspect_signature(f)) def test_validate_step_signature_no_annotations(): @@ -74,9 +75,9 @@ def f(self, ev) -> None: with pytest.raises( WorkflowValidationError, - match="Step signature parameters must be annotated", + match="Step signature must have at least one parameter annotated as type Event", ): - validate_step_signature(f) + validate_step_signature(inspect_signature(f)) def test_validate_step_signature_wrong_annotations(): @@ -85,9 +86,9 @@ def f(self, ev: str) -> None: with pytest.raises( WorkflowValidationError, - match="Events in step signature parameters must be of type Event", + match="Step signature must have at least one parameter annotated as type Event", ): - validate_step_signature(f) + validate_step_signature(inspect_signature(f)) def test_validate_step_signature_no_return_annotations(): @@ -98,7 +99,7 @@ def f(self, ev: OneTestEvent): WorkflowValidationError, match="Return types of workflows step functions must be annotated with their type", ): - validate_step_signature(f) + validate_step_signature(inspect_signature(f)) def test_validate_step_signature_no_events(): @@ -107,9 +108,9 @@ def f(self, ctx: Context) -> None: with pytest.raises( WorkflowValidationError, - match="Step signature must contain exactly one parameter of type Event but found 0.", + match="Step signature must have at least one parameter annotated as type Event", ): - validate_step_signature(f) + validate_step_signature(inspect_signature(f)) def test_validate_step_signature_too_many_params(): @@ -123,22 +124,22 @@ def f2(ev: OneTestEvent, foo: OneTestEvent): WorkflowValidationError, match="Step signature must contain exactly one parameter of type Event but found 2.", ): - validate_step_signature(f1) + validate_step_signature(inspect_signature(f1)) with pytest.raises( WorkflowValidationError, match="Step signature must contain exactly one parameter of type Event but found 2.", ): - validate_step_signature(f2) + validate_step_signature(inspect_signature(f2)) def test_get_steps_from(): class Test: - @step() + @step def start(self, start: StartEvent) -> OneTestEvent: return OneTestEvent() - @step() + @step def my_method(self, event: OneTestEvent) -> StopEvent: return StopEvent() @@ -217,3 +218,5 @@ def test_is_free_function(): assert is_free_function("MyClass.my_method") is False assert is_free_function("some_function..my_function") is True assert is_free_function("some_function..MyClass.my_function") is False + with pytest.raises(ValueError): + is_free_function("") diff --git a/llama-index-core/tests/workflow/test_workflow.py b/llama-index-core/tests/workflow/test_workflow.py index 2d694b8bf60b0..d96006fc68d14 100644 --- a/llama-index-core/tests/workflow/test_workflow.py +++ b/llama-index-core/tests/workflow/test_workflow.py @@ -1,16 +1,20 @@ -import pytest import asyncio +import time +from unittest import mock + +import pytest +from llama_index.core.workflow.decorators import step +from llama_index.core.workflow.events import StartEvent, StopEvent from llama_index.core.workflow.workflow import ( + Context, Workflow, - WorkflowValidationError, WorkflowTimeoutError, + WorkflowValidationError, + WorkflowRuntimeError, ) -from llama_index.core.workflow.decorators import step -from llama_index.core.workflow.events import StartEvent, StopEvent - -from .conftest import OneTestEvent +from .conftest import AnotherTestEvent, LastEvent, OneTestEvent @pytest.mark.asyncio() @@ -54,7 +58,7 @@ async def test_workflow_run_step(workflow): @pytest.mark.asyncio() async def test_workflow_timeout(): class SlowWorkflow(Workflow): - @step() + @step async def slow_step(self, ev: StartEvent) -> StopEvent: await asyncio.sleep(5.0) return StopEvent(result="Done") @@ -65,14 +69,55 @@ async def slow_step(self, ev: StartEvent) -> StopEvent: @pytest.mark.asyncio() -async def test_workflow_validation(): +async def test_workflow_validation_unproduced_events(): class InvalidWorkflow(Workflow): - @step() + @step async def invalid_step(self, ev: StartEvent) -> None: pass workflow = InvalidWorkflow() - with pytest.raises(WorkflowValidationError): + with pytest.raises( + WorkflowValidationError, + match="The following events are consumed but never produced: StopEvent", + ): + await workflow.run() + + +@pytest.mark.asyncio() +async def test_workflow_validation_unconsumed_events(): + class InvalidWorkflow(Workflow): + @step + async def invalid_step(self, ev: StartEvent) -> OneTestEvent: + return OneTestEvent() + + @step + async def a_step(self, ev: StartEvent) -> StopEvent: + return StopEvent() + + workflow = InvalidWorkflow() + with pytest.raises( + WorkflowValidationError, + match="The following events are produced but never consumed: OneTestEvent", + ): + await workflow.run() + + +@pytest.mark.asyncio() +async def test_workflow_validation_start_event_not_consumed(): + class InvalidWorkflow(Workflow): + @step + async def a_step(self, ev: OneTestEvent) -> StopEvent: + return StopEvent() + + @step + async def another_step(self, ev: OneTestEvent) -> OneTestEvent: + return OneTestEvent() + + workflow = InvalidWorkflow() + with pytest.raises( + WorkflowValidationError, + match="The following events are produced but never consumed: StartEvent", + ): await workflow.run() @@ -81,12 +126,12 @@ async def test_workflow_event_propagation(): events = [] class EventTrackingWorkflow(Workflow): - @step() + @step async def step1(self, ev: StartEvent) -> OneTestEvent: events.append("step1") return OneTestEvent() - @step() + @step async def step2(self, ev: OneTestEvent) -> StopEvent: events.append("step2") return StopEvent(result="Done") @@ -97,16 +142,240 @@ async def step2(self, ev: OneTestEvent) -> StopEvent: @pytest.mark.asyncio() -async def test_sync_async_steps(): +async def test_workflow_sync_async_steps(): class SyncAsyncWorkflow(Workflow): - @step() + @step async def async_step(self, ev: StartEvent) -> OneTestEvent: return OneTestEvent() - @step() + @step def sync_step(self, ev: OneTestEvent) -> StopEvent: return StopEvent(result="Done") workflow = SyncAsyncWorkflow() await workflow.run() assert workflow.is_done() + + +@pytest.mark.asyncio() +async def test_workflow_num_workers(): + class NumWorkersWorkflow(Workflow): + @step + async def original_step( + self, ctx: Context, ev: StartEvent + ) -> OneTestEvent | LastEvent: + ctx.data["num_to_collect"] = 3 + ctx.session.send_event(OneTestEvent(test_param="test1")) + ctx.session.send_event(OneTestEvent(test_param="test2")) + ctx.session.send_event(OneTestEvent(test_param="test3")) + + return LastEvent() + + @step(num_workers=3) + async def test_step(self, ev: OneTestEvent) -> AnotherTestEvent: + await asyncio.sleep(1.0) + return AnotherTestEvent(another_test_param=ev.test_param) + + @step + async def final_step( + self, ctx: Context, ev: AnotherTestEvent | LastEvent + ) -> StopEvent: + events = ctx.collect_events( + ev, [AnotherTestEvent] * ctx.data["num_to_collect"] + ) + if events is None: + return None # type: ignore + return StopEvent(result=[ev.another_test_param for ev in events]) + + workflow = NumWorkersWorkflow() + + start_time = time.time() + result = await workflow.run() + end_time = time.time() + + assert workflow.is_done() + assert set(result) == {"test1", "test2", "test3"} + + # Check if the execution time is close to 1 second (with some tolerance) + execution_time = end_time - start_time + assert ( + 1.0 <= execution_time < 1.1 + ), f"Execution time was {execution_time:.2f} seconds" + + +@pytest.mark.asyncio() +async def test_workflow_step_send_event(): + class StepSendEventWorkflow(Workflow): + @step + async def step1(self, ctx: Context, ev: StartEvent) -> OneTestEvent: + ctx.session.send_event(OneTestEvent(), step="step2") + return None # type: ignore + + @step + async def step2(self, ev: OneTestEvent) -> StopEvent: + return StopEvent(result="step2") + + @step + async def step3(self, ev: OneTestEvent) -> StopEvent: + return StopEvent(result="step3") + + workflow = StepSendEventWorkflow() + result = await workflow.run() + assert result == "step2" + assert workflow.is_done() + ctx = workflow._contexts.pop() + assert ("step2", "OneTestEvent") in ctx._accepted_events + assert ("step3", "OneTestEvent") not in ctx._accepted_events + + +@pytest.mark.asyncio() +async def test_workflow_step_send_event_to_None(): + class StepSendEventToNoneWorkflow(Workflow): + @step + async def step1(self, ctx: Context, ev: StartEvent) -> OneTestEvent: + ctx.send_event(OneTestEvent(), step=None) + return # type:ignore + + @step + async def step2(self, ev: OneTestEvent) -> StopEvent: + return StopEvent(result="step2") + + workflow = StepSendEventToNoneWorkflow(verbose=True) + await workflow.run() + assert workflow.is_done() + assert ("step2", "OneTestEvent") in workflow._contexts.pop()._accepted_events + + +@pytest.mark.asyncio() +async def test_workflow_step_returning_bogus(): + class TestWorkflow(Workflow): + @step + async def step1(self, ctx: Context, ev: StartEvent) -> OneTestEvent: + return "foo" # type:ignore + + @step + async def step2(self, ctx: Context, ev: StartEvent) -> OneTestEvent: + return OneTestEvent() + + @step + async def step3(self, ev: OneTestEvent) -> StopEvent: + return StopEvent(result="step2") + + workflow = TestWorkflow() + with pytest.warns( + UserWarning, + match="Step function step1 returned str instead of an Event instance.", + ): + await workflow.run() + + +@pytest.mark.asyncio() +async def test_workflow_missing_service(): + class DummyWorkflow(Workflow): + @step + async def step(self, ev: StartEvent, my_service: Workflow) -> StopEvent: + return StopEvent(result=42) + + workflow = DummyWorkflow() + # do not add any service called "my_service"... + with pytest.raises( + WorkflowValidationError, + match="The following services are not available: my_service", + ): + await workflow.run() + + +@pytest.mark.asyncio() +async def test_workflow_multiple_runs(): + class DummyWorkflow(Workflow): + @step + async def step(self, ev: StartEvent) -> StopEvent: + return StopEvent(result=ev.number * 2) + + workflow = DummyWorkflow() + results = await asyncio.gather( + workflow.run(number=3), workflow.run(number=42), workflow.run(number=-99) + ) + assert set(results) == {6, 84, -198} + + +def test_deprecated_send_event(): + ev = StartEvent() + wf = Workflow() + ctx = mock.MagicMock() + + # One context, assert step emits a warning + wf._contexts.add(ctx) + with pytest.warns(UserWarning): + wf.send_event(message=ev) + ctx.send_event.assert_called_with(message=ev, step=None) + + # Second context, assert step raises an exception + ctx = mock.MagicMock() + wf._contexts.add(ctx) + with pytest.raises(WorkflowRuntimeError): + wf.send_event(message=ev) + ctx.send_event.assert_not_called() + + +def test_add_step(): + class TestWorkflow(Workflow): + @step + def foo_step(self, ev: StartEvent) -> None: + pass + + with pytest.raises( + WorkflowValidationError, + match="A step foo_step is already part of this workflow, please choose another name.", + ): + + @step(workflow=TestWorkflow) + def foo_step(ev: StartEvent) -> None: + pass + + +def test_add_step_not_a_step(): + class TestWorkflow(Workflow): + @step + def a_ste(self, ev: StartEvent) -> None: + pass + + def another_step(ev: StartEvent) -> None: + pass + + with pytest.raises( + WorkflowValidationError, + match="Step function another_step is missing the `@step` decorator.", + ): + TestWorkflow.add_step(another_step) + + +@pytest.mark.asyncio() +async def test_workflow_task_raises(): + class DummyWorkflow(Workflow): + @step + async def step(self, ev: StartEvent) -> StopEvent: + raise ValueError("The step raised an error!") + + workflow = DummyWorkflow() + with pytest.raises(ValueError, match="The step raised an error!"): + await workflow.run() + + +@pytest.mark.asyncio() +async def test_workflow_task_raises_step(): + class DummyWorkflow(Workflow): + @step + async def step(self, ev: StartEvent) -> StopEvent: + raise ValueError("The step raised an error!") + + workflow = DummyWorkflow() + with pytest.raises(ValueError, match="The step raised an error!"): + await workflow.run_step() + + +def test_workflow_disable_validation(): + w = Workflow(disable_validation=True) + w._get_steps = mock.MagicMock() + w._validate() + w._get_steps.assert_not_called() diff --git a/llama-index-experimental/llama_index/experimental/param_tuner/base.py b/llama-index-experimental/llama_index/experimental/param_tuner/base.py index 287f25156ad88..f0b005f63012b 100644 --- a/llama-index-experimental/llama_index/experimental/param_tuner/base.py +++ b/llama-index-experimental/llama_index/experimental/param_tuner/base.py @@ -1,6 +1,5 @@ """Param tuner.""" - import asyncio from abc import abstractmethod from copy import deepcopy @@ -246,7 +245,7 @@ def param_fn_wrapper( tuned_result = self.param_fn(full_param_dict) # need to convert RunResult to dict to obey # Ray Tune's API - return tuned_result.dict() + return tuned_result.model_dump() run_config = RunConfig(**self.run_config_dict) if self.run_config_dict else None diff --git a/llama-index-experimental/llama_index/experimental/query_engine/pandas/output_parser.py b/llama-index-experimental/llama_index/experimental/query_engine/pandas/output_parser.py index 4233c07e46c74..2129a835a7de8 100644 --- a/llama-index-experimental/llama_index/experimental/query_engine/pandas/output_parser.py +++ b/llama-index-experimental/llama_index/experimental/query_engine/pandas/output_parser.py @@ -29,8 +29,8 @@ def default_output_processor( ) return output - local_vars = {"df": df} - global_vars = {"np": np, "pd": pd} + local_vars = {"df": df, "pd": pd} + global_vars = {"np": np} output = parse_code_markdown(output, only_last=True)[0] diff --git a/llama-index-experimental/llama_index/experimental/query_engine/pandas/pandas_query_engine.py b/llama-index-experimental/llama_index/experimental/query_engine/pandas/pandas_query_engine.py index 39ccf31200605..2f78d0b122c68 100644 --- a/llama-index-experimental/llama_index/experimental/query_engine/pandas/pandas_query_engine.py +++ b/llama-index-experimental/llama_index/experimental/query_engine/pandas/pandas_query_engine.py @@ -18,12 +18,7 @@ from llama_index.core.prompts import BasePromptTemplate, PromptTemplate from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType from llama_index.core.schema import QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.utils import print_text from llama_index.experimental.query_engine.pandas.prompts import DEFAULT_PANDAS_PROMPT from llama_index.experimental.query_engine.pandas.output_parser import ( @@ -109,7 +104,6 @@ def __init__( output_kwargs: Optional[dict] = None, head: int = 5, verbose: bool = False, - service_context: Optional[ServiceContext] = None, llm: Optional[LLM] = None, synthesize_response: bool = False, response_synthesis_prompt: Optional[BasePromptTemplate] = None, @@ -126,17 +120,13 @@ def __init__( ) self._verbose = verbose - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._synthesize_response = synthesize_response self._response_synthesis_prompt = ( response_synthesis_prompt or DEFAULT_RESPONSE_SYNTHESIS_PROMPT ) - super().__init__( - callback_manager=callback_manager_from_settings_or_context( - Settings, service_context - ) - ) + super().__init__(callback_manager=Settings.callback_manager) def _get_prompt_modules(self) -> PromptMixinType: """Get prompt sub-modules.""" @@ -162,7 +152,7 @@ def from_index(cls, index: PandasIndex, **kwargs: Any) -> "PandasQueryEngine": "PandasIndex is deprecated. " "Directly construct PandasQueryEngine with df instead." ) - return cls(df=index.df, service_context=index.service_context, **kwargs) + return cls(df=index.df, **kwargs) def _get_table_context(self) -> str: """Get table context.""" diff --git a/llama-index-experimental/pyproject.toml b/llama-index-experimental/pyproject.toml index 3f51702e6d16f..b1fdf86cbe1aa 100644 --- a/llama-index-experimental/pyproject.toml +++ b/llama-index-experimental/pyproject.toml @@ -25,11 +25,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-experimental" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" +llama-index-core = "^0.11.0" +pandas = "*" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-finetuning/llama_index/finetuning/__init__.py b/llama-index-finetuning/llama_index/finetuning/__init__.py index b33fbc7760387..26121050cf64c 100644 --- a/llama-index-finetuning/llama_index/finetuning/__init__.py +++ b/llama-index-finetuning/llama_index/finetuning/__init__.py @@ -11,6 +11,7 @@ SentenceTransformersFinetuneEngine, ) from llama_index.finetuning.openai.base import OpenAIFinetuneEngine +from llama_index.finetuning.azure_openai.base import AzureOpenAIFinetuneEngine from llama_index.finetuning.mistralai.base import MistralAIFinetuneEngine from llama_index.finetuning.rerankers.cohere_reranker import ( CohereRerankerFinetuneEngine, @@ -21,6 +22,7 @@ __all__ = [ "OpenAIFinetuneEngine", + "AzureOpenAIFinetuneEngine", "generate_qa_embedding_pairs", "EmbeddingQAFinetuneDataset", "SentenceTransformersFinetuneEngine", diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-docarray/llama_index/vector_stores/BUILD b/llama-index-finetuning/llama_index/finetuning/azure_openai/BUILD similarity index 100% rename from llama-index-integrations/vector_stores/llama-index-vector-stores-docarray/llama_index/vector_stores/BUILD rename to llama-index-finetuning/llama_index/finetuning/azure_openai/BUILD diff --git a/llama-index-finetuning/llama_index/finetuning/azure_openai/__init__.py b/llama-index-finetuning/llama_index/finetuning/azure_openai/__init__.py new file mode 100644 index 0000000000000..3b19c41cdd509 --- /dev/null +++ b/llama-index-finetuning/llama_index/finetuning/azure_openai/__init__.py @@ -0,0 +1,5 @@ +"""Init params.""" + +from llama_index.finetuning.azure_openai.base import AzureOpenAIFinetuneEngine + +__all__ = ["AzureOpenAIFinetuneEngine"] diff --git a/llama-index-finetuning/llama_index/finetuning/azure_openai/base.py b/llama-index-finetuning/llama_index/finetuning/azure_openai/base.py new file mode 100644 index 0000000000000..c4a6939bd7efd --- /dev/null +++ b/llama-index-finetuning/llama_index/finetuning/azure_openai/base.py @@ -0,0 +1,126 @@ +"""OpenAI Finetuning.""" + +import logging +import json +import os +import requests +from typing import Any, Optional + +from openai import AzureOpenAI as SyncAzureOpenAI + +from llama_index.core.llms.llm import LLM +from llama_index.finetuning.callbacks.finetuning_handler import OpenAIFineTuningHandler +from llama_index.finetuning import OpenAIFinetuneEngine +from llama_index.llms.azure_openai import AzureOpenAI + +logger = logging.getLogger(__name__) + + +class AzureOpenAIFinetuneEngine(OpenAIFinetuneEngine): + """AzureOpenAI Finetuning Engine.""" + + def __init__( + self, + base_model: str, + data_path: str, + verbose: bool = False, + start_job_id: Optional[str] = None, + validate_json: bool = True, + ) -> None: + """Init params.""" + self.base_model = base_model + self.data_path = data_path + self._verbose = verbose + self._validate_json = validate_json + self._start_job: Optional[Any] = None + self._client = SyncAzureOpenAI( + azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), + api_key=os.getenv("AZURE_OPENAI_API_KEY", None), + api_version=os.getenv("OPENAI_API_VERSION", "2024-02-01"), + ) + if start_job_id is not None: + self._start_job = self._client.fine_tuning.jobs.retrieve(start_job_id) + + @classmethod + def from_finetuning_handler( + cls, + finetuning_handler: OpenAIFineTuningHandler, + base_model: str, + data_path: str, + **kwargs: Any, + ) -> "AzureOpenAIFinetuneEngine": + """Initialize from finetuning handler. + + Used to finetune an AzureOpenAI model into another + AzureOpenAI model (e.g. gpt-4o-mini on top of gpt-4o). + + """ + finetuning_handler.save_finetuning_events(data_path) + return cls(base_model=base_model, data_path=data_path, **kwargs) + + def deploy_finetuned_model( + self, + token: str, + subscription_id: str, + resource_group: str, + resource_name: str, + model_deployment_name: Optional[str] = None, + ) -> LLM: + """Deploy finetuned model. + + - token: Azure AD token. + - subscription_id: The subscription ID for the associated Azure OpenAI resource. + - resource_group: The resource group name for your Azure OpenAI resource. + - resource_name: The Azure OpenAI resource name. + - model_deployment_name: Custom deployment name that you will use to reference the model when making inference calls. + """ + current_job = self.get_current_job() + + job_id = current_job.id + status = current_job.status + model_id = current_job.fine_tuned_model + + if model_id is None: + raise ValueError( + f"Job {job_id} does not have a finetuned model id ready yet." + ) + if status != "succeeded": + raise ValueError(f"Job {job_id} has status {status}, cannot get model") + + fine_tuned_model = current_job.fine_tuned_model + + if model_deployment_name is None: + model_deployment_name = fine_tuned_model + + deploy_params = {"api-version": os.getenv("OPENAI_API_VERSION", "2024-02-01")} + deploy_headers = { + "Authorization": f"Bearer {token}", + "Content-Type": "application/json", + } + + deploy_data = { + "sku": {"name": "standard", "capacity": 1}, + "properties": { + "model": {"format": "OpenAI", "name": fine_tuned_model, "version": "1"} + }, + } + deploy_data = json.dumps(deploy_data) + request_url = f"https://management.azure.com/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.CognitiveServices/accounts/{resource_name}/deployments/{model_deployment_name}" + print("Creating a new deployment...") + + response = requests.put( + request_url, params=deploy_params, headers=deploy_headers, data=deploy_data + ) + return response.json() + + def get_finetuned_model(self, engine: str, **model_kwargs: Any) -> LLM: + """Get finetuned model. + + - engine: This will correspond to the custom name you chose + for your deployment when you deployed a model. + """ + current_job = self.get_current_job() + + return AzureOpenAI( + engine=engine or current_job.fine_tuned_model, **model_kwargs + ) diff --git a/llama-index-finetuning/llama_index/finetuning/embeddings/common.py b/llama-index-finetuning/llama_index/finetuning/embeddings/common.py index 13ef1bbb537f9..3104646c3c84b 100644 --- a/llama-index-finetuning/llama_index/finetuning/embeddings/common.py +++ b/llama-index-finetuning/llama_index/finetuning/embeddings/common.py @@ -38,7 +38,7 @@ def save_json(self, path: str) -> None: path (str): The file path to save the JSON. """ with open(path, "w") as f: - json.dump(self.dict(), f, indent=4) + json.dump(self.model_dump(), f, indent=4) @classmethod def from_json(cls, path: str) -> "EmbeddingQAFinetuneDataset": diff --git a/llama-index-finetuning/llama_index/finetuning/mistralai/base.py b/llama-index-finetuning/llama_index/finetuning/mistralai/base.py index c380bae11affd..a8c799b3f1548 100644 --- a/llama-index-finetuning/llama_index/finetuning/mistralai/base.py +++ b/llama-index-finetuning/llama_index/finetuning/mistralai/base.py @@ -6,8 +6,8 @@ from typing import Any, Optional, Dict import sys -from mistralai.client import MistralClient -from mistralai.models.jobs import DetailedJob, WandbIntegrationIn, TrainingParameters +from mistralai import Mistral +from mistralai.models import DetailedJobOut, WandbIntegration, TrainingParameters from llama_index.core.llms.llm import LLM from llama_index.finetuning.callbacks.finetuning_handler import ( @@ -47,10 +47,10 @@ def __init__( self.training_steps = training_steps self.learning_rate = learning_rate self.wandb_integration_dict = wandb_integration_dict - self._client = MistralClient(api_key=os.getenv("MISTRAL_API_KEY", None)) + self._client = Mistral(api_key=os.getenv("MISTRAL_API_KEY", None)) self._start_job: Optional[Any] = None if start_job_id is not None: - self._start_job = self._client.jobs.retrieve(start_job_id) + self._start_job = self._client.fine_tuning.jobs.get(start_job_id) @classmethod def from_finetuning_handler( @@ -78,10 +78,10 @@ def finetune(self) -> None: # upload file with open(self.training_path, "rb") as f: - train_file = self._client.files.create(file=f) + train_file = self._client.files.upload(file=f) if self.validation_path: with open(self.validation_path, "rb") as f: - eval_file = self._client.files.create(file=f) + eval_file = self._client.files.upload(file=f) logger.info("File uploaded...") if self._verbose: print("File uploaded...") @@ -89,7 +89,7 @@ def finetune(self) -> None: # launch training while True: try: - job_output = self._client.jobs.create( + job_output = self._client.fine_tuning.jobs.create( training_files=[train_file.id], validation_files=[eval_file.id] if self.validation_path else None, model=self.base_model, @@ -97,15 +97,17 @@ def finetune(self) -> None: training_steps=self.training_steps, learning_rate=self.learning_rate, ), - integrations=[ - WandbIntegrationIn( - project=self.wandb_integration_dict["project"], - run_name=self.wandb_integration_dict["run_name"], - api_key=self.wandb_integration_dict["api_key"], - ).dict() - ] - if self.wandb_integration_dict - else None, + integrations=( + [ + WandbIntegration( + project=self.wandb_integration_dict["project"], + run_name=self.wandb_integration_dict["run_name"], + api_key=self.wandb_integration_dict["api_key"], + ).model_dump() + ] + if self.wandb_integration_dict + else None + ), ) self._start_job = job_output break @@ -117,7 +119,7 @@ def finetune(self) -> None: if self._verbose: print(info_str) - def get_current_job(self) -> DetailedJob: + def get_current_job(self) -> Optional[DetailedJobOut]: """Get current job.""" # validate that it works if not self._start_job: @@ -125,7 +127,7 @@ def get_current_job(self) -> DetailedJob: # try getting id, make sure that run succeeded job_id = self._start_job.id - return self._client.jobs.retrieve(job_id) + return self._client.fine_tuning.jobs.get(job_id) def get_finetuned_model(self, **model_kwargs: Any) -> LLM: """Gets finetuned model.""" diff --git a/llama-index-finetuning/llama_index/finetuning/rerankers/dataset_gen.py b/llama-index-finetuning/llama_index/finetuning/rerankers/dataset_gen.py index c50e8b1112d9a..d481b75a48923 100644 --- a/llama-index-finetuning/llama_index/finetuning/rerankers/dataset_gen.py +++ b/llama-index-finetuning/llama_index/finetuning/rerankers/dataset_gen.py @@ -15,7 +15,7 @@ class CohereRerankerFinetuneDataset(BaseModel): def to_jsonl(self) -> str: """Convert the BaseModel instance to a JSONL string.""" - return self.json() + "\n" + return self.model_dump_json() + "\n" def generate_embeddings(embed_model: Any, text: str) -> List[float]: diff --git a/llama-index-finetuning/pyproject.toml b/llama-index-finetuning/pyproject.toml index da4fa13e8661b..0fc6817622418 100644 --- a/llama-index-finetuning/pyproject.toml +++ b/llama-index-finetuning/pyproject.toml @@ -8,7 +8,7 @@ check-hidden = true skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" [tool.llamahub] -classes = ["CohereRerankerFinetuneEngine", "EmbeddingAdapterFinetuneEngine", "EmbeddingQAFinetuneDataset", "MistralAIFinetuneEngine", "OpenAIFinetuneEngine", "SentenceTransformersFinetuneEngine", "generate_cohere_reranker_finetuning_dataset", "generate_qa_embedding_pairs"] +classes = ["AzureOpenAIFinetuneEngine", "CohereRerankerFinetuneEngine", "EmbeddingAdapterFinetuneEngine", "EmbeddingQAFinetuneDataset", "MistralAIFinetuneEngine", "OpenAIFinetuneEngine", "SentenceTransformersFinetuneEngine", "generate_cohere_reranker_finetuning_dataset", "generate_qa_embedding_pairs"] contains_example = false import_path = "llama_index.finetuning" @@ -25,18 +25,18 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-finetuning" readme = "README.md" -version = "0.1.10" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-llms-openai = "^0.1.1" -llama-index-llms-gradient = "^0.1.1" -llama-index-llms-mistralai = "^0.1.16" -llama-index-postprocessor-cohere-rerank = "^0.1.1" -llama-index-embeddings-adapter = "^0.1.2" +llama-index-core = "^0.11.0" +llama-index-llms-openai = "^0.2.0" +llama-index-llms-mistralai = "^0.2.0" +llama-index-postprocessor-cohere-rerank = "^0.2.0" +llama-index-embeddings-adapter = "^0.2.0" sentence-transformers = "^2.3.0" tenacity = ">=8.2.0,<8.4.0" +llama-index-llms-azure-openai = "^0.2.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-finetuning/tests/azure_openai/BUILD b/llama-index-finetuning/tests/azure_openai/BUILD new file mode 100644 index 0000000000000..913dae41449f1 --- /dev/null +++ b/llama-index-finetuning/tests/azure_openai/BUILD @@ -0,0 +1,4 @@ +python_tests( + name="tests", + interpreter_constraints=["==3.9.*", "==3.10.*"] +) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/__init__.py b/llama-index-finetuning/tests/azure_openai/__init__.py similarity index 100% rename from llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/__init__.py rename to llama-index-finetuning/tests/azure_openai/__init__.py diff --git a/llama-index-finetuning/tests/azure_openai/test_azure_openai_classes.py b/llama-index-finetuning/tests/azure_openai/test_azure_openai_classes.py new file mode 100644 index 0000000000000..fc570616c4a15 --- /dev/null +++ b/llama-index-finetuning/tests/azure_openai/test_azure_openai_classes.py @@ -0,0 +1,7 @@ +from llama_index.finetuning.azure_openai import AzureOpenAIFinetuneEngine +from llama_index.finetuning.types import BaseLLMFinetuneEngine + + +def test_class(): + names_of_base_classes = [b.__name__ for b in AzureOpenAIFinetuneEngine.__mro__] + assert BaseLLMFinetuneEngine.__name__ in names_of_base_classes diff --git a/llama-index-finetuning/tests/test_base.py b/llama-index-finetuning/tests/test_base.py index 11f08f3a21049..af2046a2596ae 100644 --- a/llama-index-finetuning/tests/test_base.py +++ b/llama-index-finetuning/tests/test_base.py @@ -1,4 +1,5 @@ """Test finetuning engine.""" + import pkgutil import pytest @@ -9,6 +10,7 @@ def test_torch_imports() -> None: # importing fine-tuning modules should be ok from llama_index.finetuning import EmbeddingAdapterFinetuneEngine # noqa from llama_index.finetuning import OpenAIFinetuneEngine # noqa + from llama_index.finetuning import AzureOpenAIFinetuneEngine # noqa from llama_index.finetuning import SentenceTransformersFinetuneEngine # noqa # if torch isn't installed, then these should fail diff --git a/llama-index-integrations/agent/llama-index-agent-coa/pyproject.toml b/llama-index-integrations/agent/llama-index-agent-coa/pyproject.toml index 8f707af1d987b..e677f599a8002 100644 --- a/llama-index-integrations/agent/llama-index-agent-coa/pyproject.toml +++ b/llama-index-integrations/agent/llama-index-agent-coa/pyproject.toml @@ -32,11 +32,11 @@ maintainers = ["jerryjliu"] name = "llama-index-agent-coa" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/agent/llama-index-agent-dashscope/pyproject.toml b/llama-index-integrations/agent/llama-index-agent-dashscope/pyproject.toml index be5a3231f90e2..d41b1579bbc6c 100644 --- a/llama-index-integrations/agent/llama-index-agent-dashscope/pyproject.toml +++ b/llama-index-integrations/agent/llama-index-agent-dashscope/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-agent-dashscope" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" dashscope = ">=1.17.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/agent/llama-index-agent-introspective/llama_index/agent/introspective/reflective/self_reflection.py b/llama-index-integrations/agent/llama-index-agent-introspective/llama_index/agent/introspective/reflective/self_reflection.py index 489a56224a107..9df7a8b6f48fc 100644 --- a/llama-index-integrations/agent/llama-index-agent-introspective/llama_index/agent/introspective/reflective/self_reflection.py +++ b/llama-index-integrations/agent/llama-index-agent-introspective/llama_index/agent/introspective/reflective/self_reflection.py @@ -146,14 +146,13 @@ def __init__( **kwargs: Any, ) -> None: """__init__.""" - self._llm = llm - self._verbose = verbose - super().__init__( callback_manager=callback_manager or CallbackManager([]), max_iterations=max_iterations, **kwargs, ) + self._llm = llm + self._verbose = verbose @classmethod def from_defaults( @@ -224,7 +223,7 @@ def _reflect( ) if self._verbose: - print(f"> Reflection: {reflection.dict()}") + print(f"> Reflection: {reflection.model_dump()}") # end state: return user message reflection_output_str = ( @@ -340,7 +339,7 @@ async def _areflect( ) if self._verbose: - print(f"> Reflection: {reflection.dict()}") + print(f"> Reflection: {reflection.model_dump()}") # end state: return user message reflection_output_str = ( diff --git a/llama-index-integrations/agent/llama-index-agent-introspective/llama_index/agent/introspective/reflective/tool_interactive_reflection.py b/llama-index-integrations/agent/llama-index-agent-introspective/llama_index/agent/introspective/reflective/tool_interactive_reflection.py index b35562de4423f..2c41efcf31a09 100644 --- a/llama-index-integrations/agent/llama-index-agent-introspective/llama_index/agent/introspective/reflective/tool_interactive_reflection.py +++ b/llama-index-integrations/agent/llama-index-agent-introspective/llama_index/agent/introspective/reflective/tool_interactive_reflection.py @@ -130,17 +130,16 @@ def __init__( **kwargs: Any, ) -> None: """__init__.""" - self._critique_agent_worker = critique_agent_worker - self._critique_template = critique_template - self._verbose = verbose - self._correction_llm = correction_llm - super().__init__( callback_manager=callback_manager, max_iterations=max_iterations, stopping_callable=stopping_callable, **kwargs, ) + self._critique_agent_worker = critique_agent_worker + self._critique_template = critique_template + self._verbose = verbose + self._correction_llm = correction_llm @classmethod def from_defaults( diff --git a/llama-index-integrations/agent/llama-index-agent-introspective/pyproject.toml b/llama-index-integrations/agent/llama-index-agent-introspective/pyproject.toml index 626c1cef4e65e..c65ffac7ae913 100644 --- a/llama-index-integrations/agent/llama-index-agent-introspective/pyproject.toml +++ b/llama-index-integrations/agent/llama-index-agent-introspective/pyproject.toml @@ -34,11 +34,11 @@ maintainers = ["jerryjliu"] name = "llama-index-agent-introspective" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/agent/llama-index-agent-lats/pyproject.toml b/llama-index-integrations/agent/llama-index-agent-lats/pyproject.toml index a5739a013ee5a..77f54b5344d1f 100644 --- a/llama-index-integrations/agent/llama-index-agent-lats/pyproject.toml +++ b/llama-index-integrations/agent/llama-index-agent-lats/pyproject.toml @@ -31,11 +31,11 @@ license = "MIT" name = "llama-index-agent-lats" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/agent/llama-index-agent-llm-compiler/pyproject.toml b/llama-index-integrations/agent/llama-index-agent-llm-compiler/pyproject.toml index 818569658ab9a..c5deb9932d959 100644 --- a/llama-index-integrations/agent/llama-index-agent-llm-compiler/pyproject.toml +++ b/llama-index-integrations/agent/llama-index-agent-llm-compiler/pyproject.toml @@ -32,12 +32,12 @@ maintainers = ["jerryjliu"] name = "llama-index-agent-llm-compiler" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.16" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/agent/llama-index-agent-openai-legacy/pyproject.toml b/llama-index-integrations/agent/llama-index-agent-openai-legacy/pyproject.toml index f0ec6c530102a..79413f0850b1a 100644 --- a/llama-index-integrations/agent/llama-index-agent-openai-legacy/pyproject.toml +++ b/llama-index-integrations/agent/llama-index-agent-openai-legacy/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-agent-openai-legacy" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/agent/llama-index-agent-openai/pyproject.toml b/llama-index-integrations/agent/llama-index-agent-openai/pyproject.toml index bf8e06928d73b..f2fd35ec54515 100644 --- a/llama-index-integrations/agent/llama-index-agent-openai/pyproject.toml +++ b/llama-index-integrations/agent/llama-index-agent-openai/pyproject.toml @@ -28,13 +28,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-agent-openai" readme = "README.md" -version = "0.2.9" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" -llama-index-llms-openai = "^0.1.5" +llama-index-llms-openai = "^0.2.0" openai = ">=1.14.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-agentops/llama_index/callbacks/agentops/base.py b/llama-index-integrations/callbacks/llama-index-callbacks-agentops/llama_index/callbacks/agentops/base.py index b61e687e3211b..5dbdfcbeccabe 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-agentops/llama_index/callbacks/agentops/base.py +++ b/llama-index-integrations/callbacks/llama-index-callbacks-agentops/llama_index/callbacks/agentops/base.py @@ -85,10 +85,10 @@ class AgentOpsSpanHandler(SimpleSpanHandler): def __init__( self, shared_handler_state: AgentOpsHandlerState, ao_client: AOClient ) -> None: + super().__init__() self._shared_handler_state = shared_handler_state self._ao_client = ao_client self._observed_exceptions = set() - super().__init__() @classmethod def class_name(cls) -> str: @@ -155,9 +155,9 @@ class AgentOpsEventHandler(BaseEventHandler): def __init__( self, shared_handler_state: AgentOpsHandlerState, ao_client: AOClient ) -> None: + super().__init__() self._shared_handler_state = shared_handler_state self._ao_client = ao_client - super().__init__() @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-agentops/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-agentops/pyproject.toml index c3ad45287114d..ad05adbbefe10 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-agentops/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-agentops/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-callbacks-agentops" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" agentops = "^0.2.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-aim/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-aim/pyproject.toml index 05882b792e41a..dd560d2e86dab 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-aim/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-aim/pyproject.toml @@ -28,7 +28,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-callbacks-aim" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-argilla/README.md b/llama-index-integrations/callbacks/llama-index-callbacks-argilla/README.md index 3dd881d2c9d54..06efb3a8fd975 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-argilla/README.md +++ b/llama-index-integrations/callbacks/llama-index-callbacks-argilla/README.md @@ -42,7 +42,6 @@ Let's now write all the necessary imports ```python from llama_index.core import ( VectorStoreIndex, - ServiceContext, SimpleDirectoryReader, set_global_handler, ) @@ -69,9 +68,8 @@ llm = OpenAI( With the code snippet below, you can create a basic workflow with LlamaIndex. You will also need a txt file as the data source within a folder named "data". For a sample data file and more info regarding the use of Llama Index, you can refer to the [Llama Index documentation](https://docs.llamaindex.ai/en/stable/getting_started/starter_example.html). ```python -service_context = ServiceContext.from_defaults(llm=llm) docs = SimpleDirectoryReader("data").load_data() -index = VectorStoreIndex.from_documents(docs, service_context=service_context) +index = VectorStoreIndex.from_documents(docs) query_engine = index.as_query_engine() ``` diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-argilla/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-argilla/pyproject.toml index 34c4d341b160e..1104012200b6e 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-argilla/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-argilla/pyproject.toml @@ -27,11 +27,10 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-callbacks-argilla" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.11.post1" argilla = ">=1.22.0" argilla-llama-index = ">=1.0.0" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/pyproject.toml index f9f27a05146d2..f8bbec1c283f3 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/pyproject.toml @@ -28,13 +28,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-callbacks-arize-phoenix" readme = "README.md" -version = "0.1.6" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<3.13" arize-phoenix = ">=3.0.3" -openinference-instrumentation-llama-index = ">=1.0.0" -llama-index-core = "^0.10.11.post1" +openinference-instrumentation-llama-index = ">=3.0.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-deepeval/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-deepeval/pyproject.toml index c70e704ff7bfa..bbc186d38c1fe 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-deepeval/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-deepeval/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-callbacks-deepeval" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" deepeval = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-honeyhive/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-honeyhive/pyproject.toml index 7a6ccc4dbcd7e..3c59c700fbf1a 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-honeyhive/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-honeyhive/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-callbacks-honeyhive" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" honeyhive = "^0.1.79" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-langfuse/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-langfuse/pyproject.toml index c8f21b62430c4..174e800ea925c 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-langfuse/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-langfuse/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-callbacks-langfuse" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.8" langfuse = "^2.21.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-literalai/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-literalai/pyproject.toml index c9e2ddc1ea59b..00f32f440008e 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-literalai/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-literalai/pyproject.toml @@ -27,11 +27,11 @@ license = "MIT" name = "llama-index-callbacks-literalai" packages = [{include = "llama_index/"}] readme = "README.md" -version = "1.0.0" +version = "1.1.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-openinference/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-openinference/pyproject.toml index ee5fd9fd63330..bf4056434dde0 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-openinference/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-openinference/pyproject.toml @@ -28,11 +28,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-callbacks-openinference" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-promptlayer/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-promptlayer/pyproject.toml index 3a48b2f8a382a..d98535a779b3c 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-promptlayer/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-promptlayer/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-callbacks-promptlayer" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" promptlayer = "^0.4.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/pyproject.toml index ba67bee175746..37817b77f9e43 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-callbacks-uptrain" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = ">=0.10.0" uptrain = ">=0.7.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-wandb/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-wandb/pyproject.toml index 39e8e6791f148..e3f5520a52468 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-wandb/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-wandb/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-callbacks-wandb" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" wandb = "^0.16.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-adapter/llama_index/embeddings/adapter/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-adapter/llama_index/embeddings/adapter/base.py index 6bc603c3c78ad..cdb63808ff720 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-adapter/llama_index/embeddings/adapter/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-adapter/llama_index/embeddings/adapter/base.py @@ -53,6 +53,12 @@ def __init__( import torch from llama_index.embeddings.adapter.utils import BaseAdapter, LinearLayer + super().__init__( + embed_batch_size=embed_batch_size, + callback_manager=callback_manager, + model_name=f"Adapter for {base_embed_model.model_name}", + ) + if device is None: device = infer_torch_device() logger.info(f"Use pytorch device: {device}") @@ -70,11 +76,6 @@ def __init__( self._adapter.to(self._target_device) self._transform_query = transform_query - super().__init__( - embed_batch_size=embed_batch_size, - callback_manager=callback_manager, - model_name=f"Adapter for {base_embed_model.model_name}", - ) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-adapter/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-adapter/pyproject.toml index 6ad492dfb7e2d..b95940794f952 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-adapter/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-adapter/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-adapter" readme = "README.md" -version = "0.1.3" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" torch = "^2.1.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-alephalpha/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-alephalpha/pyproject.toml index 9ee80c068d6b7..6386b33322619 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-alephalpha/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-alephalpha/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-alephalpha" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" aleph-alpha-client = "^7.0.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-anyscale/llama_index/embeddings/anyscale/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-anyscale/llama_index/embeddings/anyscale/base.py index ed08798c9ebbe..77bb0016dea92 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-anyscale/llama_index/embeddings/anyscale/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-anyscale/llama_index/embeddings/anyscale/base.py @@ -183,9 +183,6 @@ def __init__( else: model_name = model - self._query_engine = model_name - self._text_engine = model_name - super().__init__( embed_batch_size=embed_batch_size, callback_manager=callback_manager, @@ -201,6 +198,8 @@ def __init__( **kwargs, ) + self._query_engine = model_name + self._text_engine = model_name self._client = None self._aclient = None self._http_client = http_client diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-anyscale/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-anyscale/pyproject.toml index 47c480df0f588..fb47cc2b30120 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-anyscale/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-anyscale/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-anyscale" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-azure-inference/llama_index/embeddings/azure_inference/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-azure-inference/llama_index/embeddings/azure_inference/base.py index 81ab1e05392b1..a2c381780a7fd 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-azure-inference/llama_index/embeddings/azure_inference/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-azure-inference/llama_index/embeddings/azure_inference/base.py @@ -104,6 +104,13 @@ def __init__( "Pass the credential as a parameter or set the AZURE_INFERENCE_CREDENTIAL" ) + super().__init__( + model_name=model_name or "unknown", + embed_batch_size=embed_batch_size, + callback_manager=callback_manager, + num_workers=num_workers, + **kwargs, + ) self._client = EmbeddingsClient( endpoint=endpoint, credential=credential, @@ -118,14 +125,6 @@ def __init__( **client_kwargs, ) - super().__init__( - model_name=model_name or "unknown", - embed_batch_size=embed_batch_size, - callback_manager=callback_manager, - num_workers=num_workers, - **kwargs, - ) - @classmethod def class_name(cls) -> str: return "AzureAIEmbeddingsModel" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-azure-inference/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-azure-inference/pyproject.toml index cec68dbfd4dac..03f5f1236a0ba 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-azure-inference/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-azure-inference/pyproject.toml @@ -28,13 +28,13 @@ license = "MIT" name = "llama-index-embeddings-azure-inference" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" azure-ai-inference = ">=1.0.0b2" azure-identity = "^1.15.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/llama_index/embeddings/azure_openai/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/llama_index/embeddings/azure_openai/base.py index 3f0a40c25a62b..f654e8dfa2845 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/llama_index/embeddings/azure_openai/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/llama_index/embeddings/azure_openai/base.py @@ -1,7 +1,12 @@ -from typing import Any, Dict, Optional +from typing import Annotated, Any, Dict, Optional import httpx -from llama_index.core.bridge.pydantic import Field, PrivateAttr, root_validator +from llama_index.core.bridge.pydantic import ( + Field, + PrivateAttr, + WithJsonSchema, + model_validator, +) from llama_index.core.callbacks.base import CallbackManager from llama_index.core.constants import DEFAULT_EMBED_BATCH_SIZE from llama_index.core.base.llms.generic_utils import get_from_param_or_env @@ -17,22 +22,37 @@ from openai import AsyncAzureOpenAI, AzureOpenAI from openai.lib.azure import AzureADTokenProvider +# Used to serialized provider in schema +AnnotatedProvider = Annotated[ + AzureADTokenProvider, + WithJsonSchema({"type": "string"}, mode="serialization"), + WithJsonSchema({"type": "string"}, mode="validation"), +] + class AzureOpenAIEmbedding(OpenAIEmbedding): azure_endpoint: Optional[str] = Field( - default=None, description="The Azure endpoint to use." + default=None, description="The Azure endpoint to use.", validate_default=True ) azure_deployment: Optional[str] = Field( - default=None, description="The Azure deployment to use." + default=None, description="The Azure deployment to use.", validate_default=True ) - api_base: str = Field(default="", description="The base URL for Azure deployment.") + api_base: str = Field( + default="", + description="The base URL for Azure deployment.", + validate_default=True, + ) api_version: str = Field( - default="", description="The version for Azure OpenAI API." + default="", + description="The version for Azure OpenAI API.", + validate_default=True, ) - azure_ad_token_provider: AzureADTokenProvider = Field( - default=None, description="Callback function to provide Azure AD token." + azure_ad_token_provider: Optional[AnnotatedProvider] = Field( + default=None, + description="Callback function to provide Azure AD token.", + exclude=True, ) use_azure_ad: bool = Field( description="Indicates if Microsoft Entra ID (former Azure AD) is used for token authentication" @@ -94,18 +114,19 @@ def __init__( **kwargs, ) - @root_validator(pre=True) + @model_validator(mode="before") + @classmethod def validate_env(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Validate necessary credentials are set.""" if ( - values["api_base"] == "https://api.openai.com/v1" - and values["azure_endpoint"] is None + values.get("api_base") == "https://api.openai.com/v1" + and values.get("azure_endpoint") is None ): raise ValueError( "You must set OPENAI_API_BASE to your Azure endpoint. " "It should look like https://YOUR_RESOURCE_NAME.openai.azure.com/" ) - if values["api_version"] is None: + if values.get("api_version") is None: raise ValueError("You must set OPENAI_API_VERSION for Azure OpenAI.") return values diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/pyproject.toml index 7abd0a3eb0570..52ca28cdb58fb 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-azure-openai" readme = "README.md" -version = "0.1.11" +version = "0.2.5" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-llms-azure-openai = "^0.1.3" -llama-index-embeddings-openai = "^0.1.3" +llama-index-llms-azure-openai = "^0.2.0" +llama-index-embeddings-openai = "^0.2.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-bedrock/llama_index/embeddings/bedrock/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-bedrock/llama_index/embeddings/bedrock/base.py index e3d00871cb4d0..0b465e0512a87 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-bedrock/llama_index/embeddings/bedrock/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-bedrock/llama_index/embeddings/bedrock/base.py @@ -124,17 +124,6 @@ def __init__( "boto3 package not found, install with" "'pip install boto3'" ) - # Prior to general availability, custom boto3 wheel files were - # distributed that used the bedrock service to invokeModel. - # This check prevents any services still using those wheel files - # from breaking - if client is not None: - self._client = client - elif "bedrock-runtime" in session.get_available_services(): - self._client = session.client("bedrock-runtime", config=config) - else: - self._client = session.client("bedrock", config=config) - super().__init__( model_name=model_name, max_retries=max_retries, @@ -156,6 +145,17 @@ def __init__( **kwargs, ) + # Prior to general availability, custom boto3 wheel files were + # distributed that used the bedrock service to invokeModel. + # This check prevents any services still using those wheel files + # from breaking + if client is not None: + self._client = client + elif "bedrock-runtime" in session.get_available_services(): + self._client = session.client("bedrock-runtime", config=config) + else: + self._client = session.client("bedrock", config=config) + @staticmethod def list_supported_models() -> Dict[str, List[str]]: list_models = {} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-bedrock/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-bedrock/pyproject.toml index 8c795c7075d30..e2d6054360880 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-bedrock/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-bedrock/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-bedrock" readme = "README.md" -version = "0.2.1" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" boto3 = "^1.34.23" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-clarifai/llama_index/embeddings/clarifai/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-clarifai/llama_index/embeddings/clarifai/base.py index b463edd771718..c946e27399bf5 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-clarifai/llama_index/embeddings/clarifai/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-clarifai/llama_index/embeddings/clarifai/base.py @@ -67,7 +67,7 @@ def __init__( f"Missing one app ID or user ID of the model: {app_id=}, {user_id=}" ) else: - self._model = Model( + model = Model( user_id=user_id, app_id=app_id, model_id=model_name, @@ -76,14 +76,15 @@ def __init__( ) if model_url is not None: - self._model = Model(model_url, pat=pat) - model_name = self._model.id + model = Model(model_url, pat=pat) + model_name = model.id super().__init__( embed_batch_size=embed_batch_size, callback_manager=callback_manager, model_name=model_name, ) + self._model = model @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-clarifai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-clarifai/pyproject.toml index 56ab298b2b464..bb6c9b1367f6b 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-clarifai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-clarifai/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-clarifai" readme = "README.md" -version = "0.1.2" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" clarifai = "^10.0.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-clip/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-clip/pyproject.toml index 6ab785bf9ec74..03acb55d317f7 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-clip/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-clip/pyproject.toml @@ -27,15 +27,15 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-clip" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" torch = "^2.1.2" pillow = "^10.2.0" torchvision = "^0.17.0" ftfy = "^6.1.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-cloudflare-workersai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-cloudflare-workersai/pyproject.toml index 4b31ee7e0d17a..df00ae14755f6 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-cloudflare-workersai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-cloudflare-workersai/pyproject.toml @@ -30,11 +30,11 @@ license = "MIT" name = "llama-index-embeddings-cloudflare-workersai" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-cohere/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-cohere/pyproject.toml index f5eb67ff6099d..f838e2cb3aedb 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-cohere/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-cohere/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-cohere" readme = "README.md" -version = "0.1.9" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" cohere = "^5.2.5" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py index 232ace4567e7b..f4680ee0f9dd5 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-dashscope/llama_index/embeddings/dashscope/base.py @@ -178,13 +178,13 @@ def __init__( embed_batch_size: int = EMBED_MAX_BATCH_SIZE, **kwargs: Any, ) -> None: - self._api_key = api_key - self._text_type = text_type super().__init__( model_name=model_name, embed_batch_size=embed_batch_size, **kwargs, ) + self._api_key = api_key + self._text_type = text_type @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-dashscope/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-dashscope/pyproject.toml index 2aeee8fd38246..c1355e4dcde52 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-dashscope/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-dashscope/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-dashscope" readme = "README.md" -version = "0.1.4" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" dashscope = ">1.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-databricks/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-databricks/pyproject.toml index 2adc38012883c..b25139ae1f81b 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-databricks/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-databricks/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-embeddings-databricks" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-embeddings-openai = "^0.1.10" +llama-index-embeddings-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-deepinfra/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-deepinfra/pyproject.toml index 9ecbb6124dd9b..9ab6711a00d27 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-deepinfra/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-deepinfra/pyproject.toml @@ -27,12 +27,12 @@ license = "MIT" name = "llama-index-embeddings-deepinfra" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" aiohttp = "^3.8.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-elasticsearch/llama_index/embeddings/elasticsearch/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-elasticsearch/llama_index/embeddings/elasticsearch/base.py index e432c458af226..f741259508e7a 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-elasticsearch/llama_index/embeddings/elasticsearch/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-elasticsearch/llama_index/embeddings/elasticsearch/base.py @@ -35,8 +35,8 @@ def __init__( input_field: str = "text_field", **kwargs: Any, ): - self._client = client super().__init__(model_id=model_id, input_field=input_field, **kwargs) + self._client = client @classmethod def from_es_connection( diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-elasticsearch/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-elasticsearch/pyproject.toml index 9dfc6335ac172..d1d461c9c73d1 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-elasticsearch/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-elasticsearch/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-elasticsearch" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" elasticsearch = "^8.12.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-fastembed/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-fastembed/pyproject.toml index d1ab50a3bd0dc..81ff325d2b220 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-fastembed/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-fastembed/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-fastembed" readme = "README.md" -version = "0.1.7" +version = "0.2.0" [tool.poetry.dependencies] -python = ">=3.8.1,<3.13" -llama-index-core = "^0.10.11.post1" +python = ">=3.9,<3.13" fastembed = ">=0.2.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-fireworks/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-fireworks/pyproject.toml index 36189597d5196..2f7bb10faedfe 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-fireworks/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-fireworks/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-fireworks" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.12" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gemini/llama_index/embeddings/gemini/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-gemini/llama_index/embeddings/gemini/base.py index 5c63bb2d88f4a..563fc43cc7a83 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-gemini/llama_index/embeddings/gemini/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gemini/llama_index/embeddings/gemini/base.py @@ -1,7 +1,7 @@ """Gemini embeddings file.""" import os -from typing import Any, List, Optional +from typing import Any, Dict, List, Optional import google.generativeai as gemini from llama_index.core.base.embeddings.base import ( @@ -60,18 +60,18 @@ def __init__( if transport: config_params["transport"] = transport # transport: A string, one of: [`rest`, `grpc`, `grpc_asyncio`]. - gemini.configure(**config_params) - self._model = gemini super().__init__( api_key=api_key, model_name=model_name, embed_batch_size=embed_batch_size, callback_manager=callback_manager, + title=title, + task_type=task_type, **kwargs, ) - self.title = title - self.task_type = task_type + gemini.configure(**config_params) + self._model = gemini @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gemini/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-gemini/pyproject.toml index cb4d29f202fad..d2aaf519eeb9a 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-gemini/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gemini/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-gemini" readme = "README.md" -version = "0.1.8" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" google-generativeai = "^0.5.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/.gitignore b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/Makefile b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/README.md b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/README.md new file mode 100644 index 0000000000000..8c5253551c01c --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/README.md @@ -0,0 +1,34 @@ +# LlamaIndex Embeddings Integration: GigaChat + +GigaChat Embedding provides a way to generate embeddings for text and documents using the GigaChat API within the llama_index library. + +To learn more about GigaChat and embedding principles, visit https://developers.sber.ru/docs/ru/gigachat/api/embeddings?tool=api + +## Installation + +```bash +pip install gigachat +pip install llama-index-embeddings-gigachat +``` + +## Usage + +```python +from llama_index.embeddings.gigachat import GigaChatEmbedding +``` + +**Initialization Parameters:** + +- `auth_data`: GigaChat authentication data. +- `scope`: The scope of your GigaChat API access. Use "GIGACHAT_API_PERS" for personal use or "GIGACHAT_API_CORP" for corporate use. + +```python +embeddings = GigaChatEmbedding( + auth_data="YOUR_AUTH_DATA", + scope="GIGACHAT_API_CORP", +) +``` + +## Example + +See the [example notebook](https://github.com/run-llama/llama_index/tree/main/docs/docs/examples/embeddings/gigachat.ipynb) for a detailed walkthrough of using GigaChat embeddings with LlamaIndex. diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/llama_index/embeddings/gigachat/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/llama_index/embeddings/gigachat/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/llama_index/embeddings/gigachat/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/llama_index/embeddings/gigachat/__init__.py b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/llama_index/embeddings/gigachat/__init__.py new file mode 100644 index 0000000000000..6b4e127efa009 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/llama_index/embeddings/gigachat/__init__.py @@ -0,0 +1,4 @@ +from llama_index.embeddings.gigachat.base import GigaChatEmbedding + + +__all__ = ["GigaChatEmbedding"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/llama_index/embeddings/gigachat/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/llama_index/embeddings/gigachat/base.py new file mode 100644 index 0000000000000..58cf6d42ba7ca --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/llama_index/embeddings/gigachat/base.py @@ -0,0 +1,141 @@ +from typing import Any, List, Optional + +from gigachat import GigaChat # Install GigaChat API library via 'pip install gigachat' +from llama_index.core.base.embeddings.base import ( + DEFAULT_EMBED_BATCH_SIZE, + BaseEmbedding, +) +from llama_index.core.base.llms.generic_utils import get_from_param_or_env +from llama_index.core.bridge.pydantic import PrivateAttr +from llama_index.core.callbacks.base import CallbackManager + + +class GigaChatEmbedding(BaseEmbedding): + """ + GigaChat encoder class for generating embeddings. + + Attributes: + _client (Optional[GigaChat]): Instance of the GigaChat client. + type (str): Type identifier for the encoder, which is "gigachat". + + Example: + .. code-block:: python + from langchain_community.embeddings.gigachat import GigaChatEmbeddings + + embeddings = GigaChatEmbeddings( + credentials=..., scope=..., verify_ssl_certs=False + ) + """ + + _client: Optional[GigaChat] = PrivateAttr() + type: str = "gigachat" + + def __init__( + self, + name: Optional[str] = "Embeddings", + auth_data: Optional[str] = None, + scope: Optional[str] = None, + embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE, + callback_manager: Optional[CallbackManager] = None, + **kwargs: Any, + ) -> None: + auth_data = get_from_param_or_env( + "auth_data", auth_data, "GIGACHAT_AUTH_DATA", "" + ) + if not auth_data: + raise ValueError( + "You must provide an AUTH DATA to use GigaChat. " + "You can either pass it in as an argument or set it `GIGACHAT_AUTH_DATA`." + ) + if scope is None: + raise ValueError( + """ + GigaChat scope cannot be 'None'. + Set 'GIGACHAT_API_PERS' for personal use or 'GIGACHAT_API_CORP' for corporate use. + """ + ) + super().__init__( + model_name=name, + embed_batch_size=embed_batch_size, + callback_manager=callback_manager, + **kwargs, + ) + try: + self._client = GigaChat( + scope=scope, credentials=auth_data, verify_ssl_certs=False + ) + except Exception as e: + raise ValueError(f"GigaChat client failed to initialize. Error: {e}") from e + + @classmethod + def class_name(cls) -> str: + """Return the class name.""" + return "GigaChatEmbedding" + + def _get_query_embeddings(self, queries: List[str]) -> List[List[float]]: + """Synchronously Embed documents using a GigaChat embeddings model. + + Args: + queries: The list of documents to embed. + + Returns: + List of embeddings, one for each document. + """ + embeddings = self._client.embeddings(queries).data + return [embeds_obj.embedding for embeds_obj in embeddings] + + async def _aget_query_embeddings(self, queries: List[str]) -> List[List[float]]: + """Asynchronously embed documents using a GigaChat embeddings model. + + Args: + queries: The list of documents to embed. + + Returns: + List of embeddings, one for each document. + """ + embeddings = (await self._client.aembeddings(queries)).data + return [embeds_obj.embedding for embeds_obj in embeddings] + + def _get_query_embedding(self, query: List[str]) -> List[float]: + """Synchronously embed a document using GigaChat embeddings model. + + Args: + query: The document to embed. + + Returns: + Embeddings for the document. + """ + return self._client.embeddings(query).data[0].embedding + + async def _aget_query_embedding(self, query: List[str]) -> List[float]: + """Asynchronously embed a query using GigaChat embeddings model. + + Args: + query: The document to embed. + + Returns: + Embeddings for the document. + """ + return (await self._client.aembeddings(query)).data[0].embedding + + def _get_text_embedding(self, text: str) -> List[float]: + """Synchronously embed a text using GigaChat embeddings model. + + Args: + text: The text to embed. + + Returns: + Embeddings for the text. + """ + return self._client.embeddings([text]).data[0].embedding + + async def _aget_text_embedding(self, text: str) -> List[float]: + """Asynchronously embed a text using GigaChat embeddings model. + + Args: + text: The text to embed. + + Returns: + Embeddings for the text. + """ + return (await self._client.aembeddings([text])).data[0].embedding diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/pyproject.toml new file mode 100644 index 0000000000000..b49fd0543257a --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/pyproject.toml @@ -0,0 +1,63 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = true +import_path = "llama_index.embeddings.gigachat" + +[tool.llamahub.class_authors] +GigaChatEmbedding = "lepeshokoff200@mail.ru" + +[tool.mypy] +disallow_untyped_defs = true +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Kirill Kukharev "] +description = "llama-index embeddings gigachat integration" +exclude = ["**/BUILD"] +license = "MIT" +name = "llama-index-embeddings-gigachat" +readme = "README.md" +version = "0.2.1" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +gigachat = "0.1.28" +llama-index-core = "^0.11.0" + +[tool.poetry.group.dev.dependencies] +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" +types-setuptools = "67.1.0.0" + +[tool.poetry.group.dev.dependencies.black] +extras = ["jupyter"] +version = "<=23.9.1,>=23.7.0" + +[tool.poetry.group.dev.dependencies.codespell] +extras = ["toml"] +version = ">=v2.2.6" + +[[tool.poetry.packages]] +include = "llama_index/" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/tests/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/tests/BUILD similarity index 100% rename from llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/tests/BUILD rename to llama-index-integrations/embeddings/llama-index-embeddings-gigachat/tests/BUILD diff --git a/llama-index-integrations/readers/llama-index-readers-agent-search/tests/__init__.py b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/tests/__init__.py similarity index 100% rename from llama-index-integrations/readers/llama-index-readers-agent-search/tests/__init__.py rename to llama-index-integrations/embeddings/llama-index-embeddings-gigachat/tests/__init__.py diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/tests/test_embeddings_gigachat.py b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/tests/test_embeddings_gigachat.py new file mode 100644 index 0000000000000..65e7451492c21 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-gigachat/tests/test_embeddings_gigachat.py @@ -0,0 +1,7 @@ +from llama_index.core.base.embeddings.base import BaseEmbedding +from llama_index.embeddings.gigachat import GigaChatEmbedding + + +def test_embedding_function(): + names_of_base_classes = [b.__name__ for b in GigaChatEmbedding.__mro__] + assert BaseEmbedding.__name__ in names_of_base_classes diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/gemini.py b/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/gemini.py index 2dfb399c33ab1..7826f6e18228a 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/gemini.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/gemini.py @@ -42,17 +42,16 @@ def __init__( callback_manager: Optional[CallbackManager] = None, **kwargs: Any, ): - gemini.configure(api_key=api_key) - self._model = gemini - super().__init__( model_name=model_name, embed_batch_size=embed_batch_size, callback_manager=callback_manager, + title=title, + task_type=task_type, **kwargs, ) - self.title = title - self.task_type = task_type + gemini.configure(api_key=api_key) + self._model = gemini @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/palm.py b/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/palm.py index 82a2f2f4bc249..098d44ef6c37a 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/palm.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/palm.py @@ -32,15 +32,14 @@ def __init__( callback_manager: Optional[CallbackManager] = None, **kwargs: Any, ): - palm.configure(api_key=api_key) - self._model = palm - super().__init__( model_name=model_name, embed_batch_size=embed_batch_size, callback_manager=callback_manager, **kwargs, ) + palm.configure(api_key=api_key) + self._model = palm @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/univ_sent_encoder.py b/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/univ_sent_encoder.py index 33b28f29aa1eb..4e9aedff27fe2 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/univ_sent_encoder.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-google/llama_index/embeddings/google/univ_sent_encoder.py @@ -33,12 +33,12 @@ def __init__( "Please install tensorflow_hub: `pip install tensorflow_hub`" ) - self._model = model super().__init__( embed_batch_size=embed_batch_size, callback_manager=callback_manager, model_name=handle, ) + self._model = model @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-google/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-google/pyproject.toml index b05de9d7b60f4..69b311af9449c 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-google/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-google/pyproject.toml @@ -29,12 +29,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-google" readme = "README.md" -version = "0.1.6" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" google-generativeai = "^0.5.2" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.tensorflow-hub] optional = true diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-api/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-api/pyproject.toml index ead2f21b60348..175c1c95aa042 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-api/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-api/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-huggingface-api" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-utils-huggingface = "^0.1.1" +llama-index-utils-huggingface = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.huggingface-hub] extras = ["inference"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-itrex/llama_index/embeddings/huggingface_itrex/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-itrex/llama_index/embeddings/huggingface_itrex/base.py index 78168740a89d1..c739d9ff8a72d 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-itrex/llama_index/embeddings/huggingface_itrex/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-itrex/llama_index/embeddings/huggingface_itrex/base.py @@ -47,9 +47,9 @@ def __init__( from intel_extension_for_transformers.transformers import AutoModel except ImportError: raise ImportError( - "Optimum-Intel requires the following dependencies; please install with " + "Itrex requires the following dependencies; please install with " "`pip install optimum[exporters] " - "optimum-intel neural-compressor intel_extension_for_pytorch`" + "optimum-intel neural-compressor intel_extension_for_transformers`" ) from huggingface_hub import hf_hub_download @@ -57,13 +57,11 @@ def __init__( onnx_model_path = os.path.join(folder_name, onnx_file_name) if not os.path.exists(onnx_model_path): onnx_model_path = hf_hub_download(folder_name, filename=onnx_file_name) - self._model = AutoModel.from_pretrained( - onnx_model_path, use_embedding_runtime=True - ) + model = AutoModel.from_pretrained(onnx_model_path, use_embedding_runtime=True) config = AutoConfig.from_pretrained(folder_name) - self._hidden_size = config.hidden_size + hidden_size = config.hidden_size - self._tokenizer = tokenizer or AutoTokenizer.from_pretrained(folder_name) + tokenizer = tokenizer or AutoTokenizer.from_pretrained(folder_name) if max_length is None: try: @@ -74,7 +72,7 @@ def __init__( "Please provide max_length." ) try: - max_length = min(max_length, int(self._tokenizer.model_max_length)) + max_length = min(max_length, int(tokenizer.model_max_length)) except Exception as exc: print(f"An error occurred while retrieving tokenizer max length: {exc}") @@ -91,6 +89,9 @@ def __init__( query_instruction=query_instruction, text_instruction=text_instruction, ) + self._model = model + self._tokenizer = tokenizer + self._hidden_size = hidden_size @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-itrex/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-itrex/pyproject.toml index afe938af05410..6242ec040bf8d 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-itrex/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-itrex/pyproject.toml @@ -27,17 +27,17 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-itrex" readme = "README.md" -version = "0.1.0" +version = "0.3.1" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.25" -intel-extension-for-transformers = "^1.3.2" +llama-index-core = "^0.11.0" +# intel-extension-for-transformers = "^1.3.2" # PEP 517 build error install with pip instead torch = "^2.2.2" accelerate = "^0.28.0" datasets = "^2.18.0" onnx = "^1.15.0" -llama-index-embeddings-huggingface = "^0.2.0" +llama-index-embeddings-huggingface = "^0.3.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-openvino/llama_index/embeddings/huggingface_openvino/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-openvino/llama_index/embeddings/huggingface_openvino/base.py index 855caa1722fa1..3657125659c9e 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-openvino/llama_index/embeddings/huggingface_openvino/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-openvino/llama_index/embeddings/huggingface_openvino/base.py @@ -16,7 +16,7 @@ class OpenVINOEmbedding(BaseEmbedding): model_id_or_path: str = Field(description="Huggingface model id or local path.") max_length: int = Field(description="Maximum length of input.") pooling: str = Field(description="Pooling strategy. One of ['cls', 'mean'].") - normalize: str = Field(default=True, description="Normalize embeddings or not.") + normalize: bool = Field(default=True, description="Normalize embeddings or not.") query_instruction: Optional[str] = Field( description="Instruction to prepend to query text." ) @@ -24,7 +24,7 @@ class OpenVINOEmbedding(BaseEmbedding): description="Instruction to prepend to text." ) cache_folder: Optional[str] = Field( - description="Cache folder for huggingface files." + description="Cache folder for huggingface files.", default=None ) _model: Any = PrivateAttr() @@ -46,8 +46,6 @@ def __init__( model_kwargs: Dict[str, Any] = {}, device: Optional[str] = "auto", ): - self._device = device - try: from huggingface_hub import HfApi except ImportError as e: @@ -94,27 +92,26 @@ def require_model_export( if require_model_export(model_id_or_path): # use remote model - self._model = model or OVModelForFeatureExtraction.from_pretrained( + model = model or OVModelForFeatureExtraction.from_pretrained( model_id_or_path, export=True, device=device, **model_kwargs ) else: # use local model - self._model = model or OVModelForFeatureExtraction.from_pretrained( - model_id_or_path, device=self._device, **model_kwargs + model = model or OVModelForFeatureExtraction.from_pretrained( + model_id_or_path, device=device, **model_kwargs ) - - self._tokenizer = tokenizer or AutoTokenizer.from_pretrained(model_id_or_path) + tokenizer = tokenizer or AutoTokenizer.from_pretrained(model_id_or_path) if max_length is None: try: - max_length = int(self._model.config.max_position_embeddings) + max_length = int(model.config.max_position_embeddings) except Exception: raise ValueError( "Unable to find max_length from model config. " "Please provide max_length." ) try: - max_length = min(max_length, int(self._tokenizer.model_max_length)) + max_length = min(max_length, int(tokenizer.model_max_length)) except Exception as exc: print(f"An error occurred while retrieving tokenizer max length: {exc}") @@ -123,7 +120,7 @@ def require_model_export( super().__init__( embed_batch_size=embed_batch_size, - callback_manager=callback_manager, + callback_manager=callback_manager or CallbackManager([]), model_id_or_path=model_id_or_path, max_length=max_length, pooling=pooling, @@ -131,6 +128,9 @@ def require_model_export( query_instruction=query_instruction, text_instruction=text_instruction, ) + self._device = device + self._model = model + self._tokenizer = tokenizer @classmethod def class_name(cls) -> str: @@ -184,13 +184,23 @@ def _cls_pooling(self, model_output: list) -> Any: def _embed(self, sentences: List[str]) -> List[List[float]]: """Embed sentences.""" - encoded_input = self._tokenizer( - sentences, - padding=True, - max_length=self.max_length, - truncation=True, - return_tensors="pt", - ) + length = self._model.request.inputs[0].get_partial_shape()[1] + if length.is_dynamic: + encoded_input = self._tokenizer( + sentences, + padding=True, + max_length=self.max_length, + truncation=True, + return_tensors="pt", + ) + else: + encoded_input = self._tokenizer( + sentences, + padding="max_length", + max_length=length.get_length(), + truncation=True, + return_tensors="pt", + ) model_output = self._model(**encoded_input) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-openvino/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-openvino/pyproject.toml index 2e70b3b2a9a11..62b027f419438 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-openvino/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-openvino/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-openvino" readme = "README.md" -version = "0.2.0" +version = "0.4.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" -llama-index-embeddings-huggingface = "^0.2.2" +llama-index-embeddings-huggingface = "^0.3.0" huggingface-hub = "^0.23.0" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.optimum] extras = ["openvino"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py index 6ab1835f24cac..3d556b61c444e 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py @@ -53,20 +53,20 @@ def __init__( "optimum-intel neural-compressor intel_extension_for_pytorch`" ) - self._model = model or IPEXModel.from_pretrained(folder_name) - self._tokenizer = tokenizer or AutoTokenizer.from_pretrained(folder_name) - self._device = device or infer_torch_device() + model = model or IPEXModel.from_pretrained(folder_name) + tokenizer = tokenizer or AutoTokenizer.from_pretrained(folder_name) + device = device or infer_torch_device() if max_length is None: try: - max_length = int(self._model.config.max_position_embeddings) + max_length = int(model.config.max_position_embeddings) except Exception: raise ValueError( "Unable to find max_length from model config. " "Please provide max_length." ) try: - max_length = min(max_length, int(self._tokenizer.model_max_length)) + max_length = min(max_length, int(tokenizer.model_max_length)) except Exception as exc: print(f"An error occurred while retrieving tokenizer max length: {exc}") @@ -83,6 +83,9 @@ def __init__( query_instruction=query_instruction, text_instruction=text_instruction, ) + self._model = model + self._tokenizer = tokenizer + self._device = device @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/pyproject.toml index 7f0f18284f045..48684d7af9e1f 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/pyproject.toml @@ -27,15 +27,15 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-huggingface-optimum-intel" readme = "README.md" -version = "0.1.6" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-embeddings-huggingface = "^0.2.2" -llama-index-utils-huggingface = "^0.1.1" +llama-index-embeddings-huggingface = "^0.3.0" +llama-index-utils-huggingface = "^0.2.0" optimum-intel = "^1.18.0" -intel_extension_for_pytorch = "^2.3" +# intel_extension_for_pytorch = "^2.3" . # can't find this installation +llama-index-core = "^0.11.0" [tool.poetry.dependencies.optimum] extras = ["exporters"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum/llama_index/embeddings/huggingface_optimum/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum/llama_index/embeddings/huggingface_optimum/base.py index dad439a8c7704..8f581bf8142ca 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum/llama_index/embeddings/huggingface_optimum/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum/llama_index/embeddings/huggingface_optimum/base.py @@ -16,7 +16,7 @@ class OptimumEmbedding(BaseEmbedding): folder_name: str = Field(description="Folder name to load from.") max_length: int = Field(description="Maximum length of input.") pooling: str = Field(description="Pooling strategy. One of ['cls', 'mean'].") - normalize: str = Field(default=True, description="Normalize embeddings or not.") + normalize: bool = Field(default=True, description="Normalize embeddings or not.") query_instruction: Optional[str] = Field( description="Instruction to prepend to query text." ) @@ -24,7 +24,7 @@ class OptimumEmbedding(BaseEmbedding): description="Instruction to prepend to text." ) cache_folder: Optional[str] = Field( - description="Cache folder for huggingface files." + description="Cache folder for huggingface files.", default=None ) _model: Any = PrivateAttr() @@ -45,20 +45,20 @@ def __init__( callback_manager: Optional[CallbackManager] = None, device: Optional[str] = None, ): - self._model = model or ORTModelForFeatureExtraction.from_pretrained(folder_name) - self._tokenizer = tokenizer or AutoTokenizer.from_pretrained(folder_name) - self._device = device or infer_torch_device() + model = model or ORTModelForFeatureExtraction.from_pretrained(folder_name) + tokenizer = tokenizer or AutoTokenizer.from_pretrained(folder_name) + device = device or infer_torch_device() if max_length is None: try: - max_length = int(self._model.config.max_position_embeddings) + max_length = int(model.config.max_position_embeddings) except Exception: raise ValueError( "Unable to find max_length from model config. " "Please provide max_length." ) try: - max_length = min(max_length, int(self._tokenizer.model_max_length)) + max_length = min(max_length, int(tokenizer.model_max_length)) except Exception as exc: print(f"An error occurred while retrieving tokenizer max length: {exc}") @@ -75,6 +75,9 @@ def __init__( query_instruction=query_instruction, text_instruction=text_instruction, ) + self._model = model + self._device = device + self._tokenizer = tokenizer @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum/pyproject.toml index 966edb14b073b..a1c1f137c5a69 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-huggingface-optimum" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-embeddings-huggingface = "^0.1.3" +llama-index-embeddings-huggingface = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.optimum] extras = ["exporters"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/llama_index/embeddings/huggingface/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/llama_index/embeddings/huggingface/base.py index 1e520273bd42c..40eb31c41cf5c 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/llama_index/embeddings/huggingface/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/llama_index/embeddings/huggingface/base.py @@ -32,22 +32,84 @@ class HuggingFaceEmbedding(BaseEmbedding): + """HuggingFace class for text embeddings. + + Args: + model_name (str, optional): If it is a filepath on disc, it loads the model from that path. + If it is not a path, it first tries to download a pre-trained SentenceTransformer model. + If that fails, tries to construct a model from the Hugging Face Hub with that name. + Defaults to DEFAULT_HUGGINGFACE_EMBEDDING_MODEL. + max_length (Optional[int], optional): Max sequence length to set in Model's config. If None, + it will use the Model's default max_seq_length. Defaults to None. + query_instruction (Optional[str], optional): Instruction to prepend to query text. + Defaults to None. + text_instruction (Optional[str], optional): Instruction to prepend to text. + Defaults to None. + normalize (bool, optional): Whether to normalize returned vectors. + Defaults to True. + embed_batch_size (int, optional): The batch size used for the computation. + Defaults to DEFAULT_EMBED_BATCH_SIZE. + cache_folder (Optional[str], optional): Path to store models. Defaults to None. + trust_remote_code (bool, optional): Whether or not to allow for custom models defined on the + Hub in their own modeling files. This option should only be set to True for repositories + you trust and in which you have read the code, as it will execute code present on the Hub + on your local machine. Defaults to False. + device (Optional[str], optional): Device (like "cuda", "cpu", "mps", "npu", ...) that should + be used for computation. If None, checks if a GPU can be used. Defaults to None. + callback_manager (Optional[CallbackManager], optional): Callback Manager. Defaults to None. + parallel_process (bool, optional): If True it will start a multi-process pool to process the + encoding with several independent processes. Great for vast amount of texts. + Defaults to False. + target_devices (Optional[List[str]], optional): PyTorch target devices, e.g. + ["cuda:0", "cuda:1", ...], ["npu:0", "npu:1", ...], or ["cpu", "cpu", "cpu", "cpu"]. + If target_devices is None and CUDA/NPU is available, then all available CUDA/NPU devices + will be used. If target_devices is None and CUDA/NPU is not available, then 4 CPU devices + will be used. This parameter will only be used if `parallel_process = True`. + Defaults to None. + num_workers (int, optional): The number of workers to use for async embedding calls. + Defaults to None. + **model_kwargs: Other model kwargs to use + tokenizer_name (Optional[str], optional): "Deprecated" + pooling (str, optional): "Deprecated" + model (Optional[Any], optional): "Deprecated" + tokenizer (Optional[Any], optional): "Deprecated" + + Examples: + `pip install llama-index-embeddings-huggingface` + + ```python + from llama_index.core import Settings + from llama_index.embeddings.huggingface import HuggingFaceEmbedding + + # Set up the HuggingFaceEmbedding class with the required model to use with llamaindex core. + embed_model = HuggingFaceEmbedding(model_name = "BAAI/bge-small-en") + Settings.embed_model = embed_model + + # Or if you want to Embed some text separately + embeddings = embed_model.get_text_embedding("I want to Embed this text!") + + ``` + + """ + max_length: int = Field( default=DEFAULT_HUGGINGFACE_LENGTH, description="Maximum length of input.", gt=0 ) normalize: bool = Field(default=True, description="Normalize embeddings or not.") query_instruction: Optional[str] = Field( - description="Instruction to prepend to query text." + description="Instruction to prepend to query text.", default=None ) text_instruction: Optional[str] = Field( - description="Instruction to prepend to text." + description="Instruction to prepend to text.", default=None ) cache_folder: Optional[str] = Field( - description="Cache folder for Hugging Face files." + description="Cache folder for Hugging Face files.", default=None ) _model: Any = PrivateAttr() _device: str = PrivateAttr() + _parallel_process: bool = PrivateAttr() + _target_devices: Optional[List[str]] = PrivateAttr() def __init__( self, @@ -65,10 +127,11 @@ def __init__( trust_remote_code: bool = False, device: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, + parallel_process: bool = False, + target_devices: Optional[List[str]] = None, **model_kwargs, ): - self._device = device or infer_torch_device() - + device = device or infer_torch_device() cache_folder = cache_folder or get_cache_dir() for variable, value in [ @@ -84,9 +147,9 @@ def __init__( if model_name is None: raise ValueError("The `model_name` argument must be provided.") - self._model = SentenceTransformer( + model = SentenceTransformer( model_name, - device=self._device, + device=device, cache_folder=cache_folder, trust_remote_code=trust_remote_code, prompts={ @@ -98,9 +161,9 @@ def __init__( **model_kwargs, ) if max_length: - self._model.max_seq_length = max_length + model.max_seq_length = max_length else: - max_length = self._model.max_seq_length + max_length = model.max_seq_length super().__init__( embed_batch_size=embed_batch_size, @@ -111,6 +174,10 @@ def __init__( query_instruction=query_instruction, text_instruction=text_instruction, ) + self._device = device + self._model = model + self._parallel_process = parallel_process + self._target_devices = target_devices @classmethod def class_name(cls) -> str: @@ -121,32 +188,92 @@ def _embed( sentences: List[str], prompt_name: Optional[str] = None, ) -> List[List[float]]: - """Embed sentences.""" - return self._model.encode( - sentences, - batch_size=self.embed_batch_size, - prompt_name=prompt_name, - normalize_embeddings=self.normalize, - ).tolist() + """Generates Embeddings either multiprocess or single process. + + Args: + sentences (List[str]): Texts or Sentences to embed + prompt_name (Optional[str], optional): The name of the prompt to use for encoding. Must be a key in the `prompts` dictionary i.e. "query" or "text" If ``prompt`` is also set, this argument is ignored. Defaults to None. + + Returns: + List[List[float]]: a 2d numpy array with shape [num_inputs, output_dimension] is returned. + If only one string input is provided, then the output is a 1d array with shape [output_dimension] + """ + if self._parallel_process: + pool = self._model.start_multi_process_pool( + target_devices=self._target_devices + ) + emb = self._model.encode_multi_process( + sentences=sentences, + pool=pool, + batch_size=self.embed_batch_size, + prompt_name=prompt_name, + normalize_embeddings=self.normalize, + ) + self._model.stop_multi_process_pool(pool=pool) + + else: + emb = self._model.encode( + sentences, + batch_size=self.embed_batch_size, + prompt_name=prompt_name, + normalize_embeddings=self.normalize, + ) + + return emb.tolist() def _get_query_embedding(self, query: str) -> List[float]: - """Get query embedding.""" + """Generates Embeddings for Query. + + Args: + query (str): Query text/sentence + + Returns: + List[float]: numpy array of embeddings + """ return self._embed(query, prompt_name="query") async def _aget_query_embedding(self, query: str) -> List[float]: - """Get query embedding async.""" + """Generates Embeddings for Query Asynchronously. + + Args: + query (str): Query text/sentence + + Returns: + List[float]: numpy array of embeddings + """ return self._get_query_embedding(query) async def _aget_text_embedding(self, text: str) -> List[float]: - """Get text embedding async.""" + """Generates Embeddings for text Asynchronously. + + Args: + text (str): Text/Sentence + + Returns: + List[float]: numpy array of embeddings + """ return self._get_text_embedding(text) def _get_text_embedding(self, text: str) -> List[float]: - """Get text embedding.""" + """Generates Embeddings for text. + + Args: + text (str): Text/sentences + + Returns: + List[float]: numpy array of embeddings + """ return self._embed(text, prompt_name="text") def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]: - """Get text embeddings.""" + """Generates Embeddings for text. + + Args: + texts (List[str]): Texts / Sentences + + Returns: + List[List[float]]: numpy array of embeddings + """ return self._embed(texts, prompt_name="text") diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/pyproject.toml index be3b0e86d229e..36c45f0424bf2 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-huggingface" readme = "README.md" -version = "0.2.2" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" sentence-transformers = ">=2.6.1" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.huggingface-hub] extras = ["inference"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-ibm/llama_index/embeddings/ibm/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-ibm/llama_index/embeddings/ibm/base.py index 852e991c71062..33386d7ca6797 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-ibm/llama_index/embeddings/ibm/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-ibm/llama_index/embeddings/ibm/base.py @@ -4,14 +4,8 @@ DEFAULT_EMBED_BATCH_SIZE, BaseEmbedding, ) -from llama_index.core.bridge.pydantic import Field, PrivateAttr - -# Import SecretStr directly from pydantic -# since there is not one in llama_index.core.bridge.pydantic -try: - from pydantic.v1 import SecretStr -except ImportError: - from pydantic import SecretStr +from llama_index.core.bridge.pydantic import Field, PrivateAttr, SecretStr + from llama_index.core.callbacks.base import CallbackManager from llama_index.embeddings.ibm.utils import ( resolve_watsonx_credentials, @@ -146,17 +140,32 @@ def __init__( instance_id=instance_id, ) + url = creds.get("url").get_secret_value() if creds.get("url") else None + apikey = creds.get("apikey").get_secret_value() if creds.get("apikey") else None + token = creds.get("token").get_secret_value() if creds.get("token") else None + password = ( + creds.get("password").get_secret_value() if creds.get("password") else None + ) + username = ( + creds.get("username").get_secret_value() if creds.get("username") else None + ) + instance_id = ( + creds.get("instance_id").get_secret_value() + if creds.get("instance_id") + else None + ) + super().__init__( model_id=model_id, truncate_input_tokens=truncate_input_tokens, project_id=project_id, space_id=space_id, - url=creds.get("url"), - apikey=creds.get("apikey"), - token=creds.get("token"), - password=creds.get("password"), - username=creds.get("username"), - instance_id=creds.get("instance_id"), + url=url, + apikey=apikey, + token=token, + password=password, + username=username, + instance_id=instance_id, version=version, verify=verify, callback_manager=callback_manager, diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-ibm/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-ibm/pyproject.toml index 6eef48b97c809..1c1bd3ff857a1 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-ibm/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-ibm/pyproject.toml @@ -31,12 +31,12 @@ license = "MIT" name = "llama-index-embeddings-ibm" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.10,<4.0" -llama-index-core = "^0.10.38" ibm-watsonx-ai = "^1.0.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-instructor/llama_index/embeddings/instructor/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-instructor/llama_index/embeddings/instructor/base.py index e2b351e1ed248..c2625977afd2f 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-instructor/llama_index/embeddings/instructor/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-instructor/llama_index/embeddings/instructor/base.py @@ -37,8 +37,6 @@ def __init__( device: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, ): - self._model = INSTRUCTOR(model_name, cache_folder=cache_folder, device=device) - super().__init__( embed_batch_size=embed_batch_size, callback_manager=callback_manager, @@ -47,6 +45,7 @@ def __init__( text_instruction=text_instruction, cache_folder=cache_folder, ) + self._model = INSTRUCTOR(model_name, cache_folder=cache_folder, device=device) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-instructor/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-instructor/pyproject.toml index 6555b8215e797..e57b2f3e3a75d 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-instructor/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-instructor/pyproject.toml @@ -27,14 +27,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-instructor" readme = "README.md" -version = "0.1.3" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" instructorembedding = "^1.0.1" torch = "^2.1.2" sentence-transformers = "^2.2.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-ipex-llm/llama_index/embeddings/ipex_llm/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-ipex-llm/llama_index/embeddings/ipex_llm/base.py index 225abf8a94219..2d949d86d16cf 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-ipex-llm/llama_index/embeddings/ipex_llm/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-ipex-llm/llama_index/embeddings/ipex_llm/base.py @@ -62,7 +62,7 @@ def __init__( "IpexLLMEmbedding currently only supports device to be 'cpu', 'xpu', " f"or 'xpu:', but you have: {device}." ) - self._device = device + device = device cache_folder = cache_folder or get_cache_dir() @@ -75,9 +75,9 @@ def __init__( f"Hugging Face BGE models, which are: {bge_model_list_str}" ) - self._model = SentenceTransformer( + model = SentenceTransformer( model_name, - device=self._device, + device=device, cache_folder=cache_folder, trust_remote_code=trust_remote_code, prompts={ @@ -90,16 +90,16 @@ def __init__( ) # Apply ipex-llm optimizations - self._model = _optimize_pre(self._model) - self._model = _optimize_post(self._model) - if self._device == "xpu": + model = _optimize_pre(self._model) + model = _optimize_post(self._model) + if device == "xpu": # TODO: apply `ipex_llm.optimize_model` - self._model = self._model.half().to(self._device) + model = model.half().to(device) if max_length: - self._model.max_seq_length = max_length + model.max_seq_length = max_length else: - max_length = self._model.max_seq_length + max_length = model.max_seq_length super().__init__( embed_batch_size=embed_batch_size, @@ -110,6 +110,8 @@ def __init__( query_instruction=query_instruction, text_instruction=text_instruction, ) + self._model = model + self._device = device @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-ipex-llm/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-ipex-llm/pyproject.toml index d029f7e8f84fc..ed5930b035fb9 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-ipex-llm/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-ipex-llm/pyproject.toml @@ -30,11 +30,11 @@ license = "MIT" name = "llama-index-embeddings-ipex-llm" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.5" +version = "0.2.2" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" ipex-llm = {allow-prereleases = true, extras = ["llama-index"], version = ">=2.1.0b20240529"} torch = {optional = true, source = "ipex-xpu-src-us", version = "2.1.0a0"} torchvision = {optional = true, source = "ipex-xpu-src-us", version = "0.16.0a0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-jinaai/llama_index/embeddings/jinaai/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-jinaai/llama_index/embeddings/jinaai/base.py index 90ed7ec3e3078..89fd6fe83476f 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-jinaai/llama_index/embeddings/jinaai/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-jinaai/llama_index/embeddings/jinaai/base.py @@ -16,7 +16,7 @@ MAX_BATCH_SIZE = 2048 -API_URL = "https://api.jina.ai/v1/embeddings" +DEFAULT_JINA_AI_API_URL = "https://api.jina.ai/v1" VALID_ENCODING = ["float", "ubinary", "binary"] @@ -25,9 +25,11 @@ class _JinaAPICaller: def __init__( self, model: str = "jina-embeddings-v2-base-en", + base_url: str = DEFAULT_JINA_AI_API_URL, api_key: Optional[str] = None, **kwargs: Any, ) -> None: + self.api_url = f"{base_url}/embeddings" self.api_key = get_from_param_or_env("api_key", api_key, "JINAAI_API_KEY", "") self.model = model self._session = requests.Session() @@ -39,7 +41,7 @@ def get_embeddings(self, input, encoding_type: str = "float") -> List[List[float """Get embeddings.""" # Call Jina AI Embedding API resp = self._session.post( # type: ignore - API_URL, + self.api_url, json={"input": input, "model": self.model, "encoding_type": encoding_type}, ).json() if "data" not in resp: @@ -77,7 +79,7 @@ async def aget_embeddings( "Accept-Encoding": "identity", } async with session.post( - f"{API_URL}", + self.api_url, json={ "input": input, "model": self.model, @@ -131,7 +133,7 @@ class JinaEmbedding(MultiModalEmbedding): Defaults to `jina-embeddings-v2-base-en` """ - api_key: str = Field(default=None, description="The JinaAI API key.") + api_key: Optional[str] = Field(default=None, description="The JinaAI API key.") model: str = Field( default="jina-embeddings-v2-base-en", description="The model to use when calling Jina AI API", diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-jinaai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-jinaai/pyproject.toml index 5e81cef952aba..bd1b165f24d65 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-jinaai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-jinaai/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-jinaai" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-langchain/llama_index/embeddings/langchain/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-langchain/llama_index/embeddings/langchain/base.py index f70a5823941bb..97bcddb05b051 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-langchain/llama_index/embeddings/langchain/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-langchain/llama_index/embeddings/langchain/base.py @@ -41,12 +41,12 @@ def __init__( else: model_name = type(langchain_embeddings).__name__ - self._langchain_embedding = langchain_embeddings super().__init__( embed_batch_size=embed_batch_size, callback_manager=callback_manager, model_name=model_name, ) + self._langchain_embedding = langchain_embeddings @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-langchain/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-langchain/pyproject.toml index d1995634effd0..a2edd9041f425 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-langchain/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-langchain/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-langchain" readme = "README.md" -version = "0.1.2" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-litellm/llama_index/embeddings/litellm/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-litellm/llama_index/embeddings/litellm/base.py index a373a43071121..3b468dc0362b5 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-litellm/llama_index/embeddings/litellm/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-litellm/llama_index/embeddings/litellm/base.py @@ -1,35 +1,51 @@ -from typing import List +from typing import Any, List, Optional from litellm import embedding from llama_index.core.bridge.pydantic import Field from llama_index.core.embeddings import BaseEmbedding -def get_embeddings(api_key: str, api_base: str, model_name: str, input: List[str]): - if not api_key: - # If key is not provided, we assume the consumer has configured - # their LiteLLM proxy server with their API key. - api_key = "some key" +def get_embeddings( + api_key: str, api_base: str, model_name: str, input: List[str], **kwargs: Any +) -> List[List[float]]: + """ + Retrieve embeddings for a given list of input strings using the specified model. + Args: + api_key (str): The API key for authentication. + api_base (str): The base URL of the LiteLLM proxy server. + model_name (str): The name of the model to use for generating embeddings. + input (List[str]): A list of input strings for which embeddings are to be generated. + **kwargs (Any): Additional keyword arguments to be passed to the embedding function. + + Returns: + List[List[float]]: A list of embeddings, where each embedding corresponds to an input string. + """ response = embedding( api_key=api_key, api_base=api_base, model=model_name, input=input, + **kwargs, ) return [result["embedding"] for result in response.data] class LiteLLMEmbedding(BaseEmbedding): - model_name: str = Field( - default="unknown", description="The name of the embedding model." - ) - api_key: str = Field( - default="unknown", + model_name: str = Field(description="The name of the embedding model.") + api_key: Optional[str] = Field( + default=None, description="OpenAI key. If not provided, the proxy server must be configured with the key.", ) - api_base: str = Field( - default="unknown", description="The base URL of the LiteLLM proxy." + api_base: Optional[str] = Field( + default=None, description="The base URL of the LiteLLM proxy." + ) + dimensions: Optional[int] = Field( + default=None, + description=( + "The number of dimensions the resulting output embeddings should have. " + "Only supported in text-embedding-3 and later models." + ), ) @classmethod @@ -47,6 +63,7 @@ def _get_query_embedding(self, query: str) -> List[float]: api_key=self.api_key, api_base=self.api_base, model_name=self.model_name, + dimensions=self.dimensions, input=[query], ) return embeddings[0] @@ -56,6 +73,7 @@ def _get_text_embedding(self, text: str) -> List[float]: api_key=self.api_key, api_base=self.api_base, model_name=self.model_name, + dimensions=self.dimensions, input=[text], ) return embeddings[0] @@ -65,5 +83,6 @@ def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]: api_key=self.api_key, api_base=self.api_base, model_name=self.model_name, + dimensions=self.dimensions, input=texts, ) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-litellm/poetry.lock b/llama-index-integrations/embeddings/llama-index-embeddings-litellm/poetry.lock index e97a9a0b9a5c7..19282a62de2eb 100644 --- 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b/llama-index-integrations/embeddings/llama-index-embeddings-llamafile/llama_index/embeddings/llamafile/base.py index 4db6a351cab32..fc2441bad839c 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-llamafile/llama_index/embeddings/llamafile/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-llamafile/llama_index/embeddings/llamafile/base.py @@ -43,7 +43,11 @@ def __init__( callback_manager: Optional[CallbackManager] = None, **kwargs, ) -> None: - super().__init__(base_url=base_url, callback_manager=callback_manager, **kwargs) + super().__init__( + base_url=base_url, + callback_manager=callback_manager or CallbackManager([]), + **kwargs, + ) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-llamafile/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-llamafile/pyproject.toml index 93376eaa38001..eca528f85c885 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-llamafile/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-llamafile/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-llamafile" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-llm-rails/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-llm-rails/pyproject.toml index 4217635f0fd93..dcf7380cf81e4 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-llm-rails/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-llm-rails/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-llm-rails" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-mistralai/llama_index/embeddings/mistralai/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-mistralai/llama_index/embeddings/mistralai/base.py index 80c471c7bad81..6ddbaa00c3647 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-mistralai/llama_index/embeddings/mistralai/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-mistralai/llama_index/embeddings/mistralai/base.py @@ -10,8 +10,7 @@ from llama_index.core.callbacks.base import CallbackManager from llama_index.core.base.llms.generic_utils import get_from_param_or_env -from mistralai.async_client import MistralAsyncClient -from mistralai.client import MistralClient +from mistralai import Mistral class MistralAIEmbedding(BaseEmbedding): @@ -25,8 +24,7 @@ class MistralAIEmbedding(BaseEmbedding): """ # Instance variables initialized via Pydantic's mechanism - _mistralai_client: Any = PrivateAttr() - _mistralai_async_client: Any = PrivateAttr() + _client: Mistral = PrivateAttr() def __init__( self, @@ -36,6 +34,12 @@ def __init__( callback_manager: Optional[CallbackManager] = None, **kwargs: Any, ): + super().__init__( + model_name=model_name, + embed_batch_size=embed_batch_size, + callback_manager=callback_manager, + **kwargs, + ) api_key = get_from_param_or_env("api_key", api_key, "MISTRAL_API_KEY", "") if not api_key: @@ -43,14 +47,7 @@ def __init__( "You must provide an API key to use mistralai. " "You can either pass it in as an argument or set it `MISTRAL_API_KEY`." ) - self._mistralai_client = MistralClient(api_key=api_key) - self._mistralai_async_client = MistralAsyncClient(api_key=api_key) - super().__init__( - model_name=model_name, - embed_batch_size=embed_batch_size, - callback_manager=callback_manager, - **kwargs, - ) + self._client = Mistral(api_key=api_key) @classmethod def class_name(cls) -> str: @@ -59,7 +56,7 @@ def class_name(cls) -> str: def _get_query_embedding(self, query: str) -> List[float]: """Get query embedding.""" return ( - self._mistralai_client.embeddings(model=self.model_name, input=[query]) + self._client.embeddings.create(model=self.model_name, inputs=[query]) .data[0] .embedding ) @@ -68,8 +65,8 @@ async def _aget_query_embedding(self, query: str) -> List[float]: """The asynchronous version of _get_query_embedding.""" return ( ( - await self._mistralai_async_client.embeddings( - model=self.model_name, input=[query] + await self._client.embeddings.create_async( + model=self.model_name, inputs=[query] ) ) .data[0] @@ -79,7 +76,7 @@ async def _aget_query_embedding(self, query: str) -> List[float]: def _get_text_embedding(self, text: str) -> List[float]: """Get text embedding.""" return ( - self._mistralai_client.embeddings(model=self.model_name, input=[text]) + self._client.embeddings.create(model=self.model_name, inputs=[text]) .data[0] .embedding ) @@ -87,10 +84,9 @@ def _get_text_embedding(self, text: str) -> List[float]: async def _aget_text_embedding(self, text: str) -> List[float]: """Asynchronously get text embedding.""" return ( - ( - await self._mistralai_async_client.embeddings( - model=self.model_name, input=[text] - ) + await self._client.embeddings.create( + model=self.model_name, + inputs=[text], ) .data[0] .embedding @@ -98,14 +94,14 @@ async def _aget_text_embedding(self, text: str) -> List[float]: def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]: """Get text embeddings.""" - embedding_response = self._mistralai_client.embeddings( - model=self.model_name, input=texts + embedding_response = self._client.embeddings.create( + model=self.model_name, inputs=texts ).data return [embed.embedding for embed in embedding_response] async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]: """Asynchronously get text embeddings.""" - embedding_response = await self._mistralai_async_client.embeddings( - model=self.model_name, input=texts + embedding_response = await self._client.embeddings.create_async( + model=self.model_name, inputs=texts ) return [embed.embedding for embed in embedding_response.data] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-mistralai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-mistralai/pyproject.toml index 2045bfe0a1a09..3a7c2ca878904 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-mistralai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-mistralai/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-mistralai" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.1" -mistralai = ">=0.1.3" +mistralai = ">=1.0.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-mixedbreadai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-mixedbreadai/pyproject.toml index 45d142dd65874..c4f0e12c4e29e 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-mixedbreadai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-mixedbreadai/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" name = "llama-index-embeddings-mixedbreadai" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" mixedbread-ai = "^2.2.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nomic/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-nomic/pyproject.toml index ec1711158bc33..073023e85f714 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nomic/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nomic/pyproject.toml @@ -27,14 +27,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-nomic" readme = "README.md" -version = "0.4.0.post1" +version = "0.5.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-embeddings-huggingface = "^0.1.3" +llama-index-embeddings-huggingface = "^0.3.0" einops = "^0.7.0" nomic = "^3.0.30" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/llama_index/embeddings/nvidia/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/llama_index/embeddings/nvidia/base.py index aac5516395524..6fe80b70b89b7 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/llama_index/embeddings/nvidia/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/llama_index/embeddings/nvidia/base.py @@ -2,7 +2,6 @@ from typing import Any, List, Literal, Optional import warnings -from deprecated import deprecated from llama_index.core.base.embeddings.base import ( DEFAULT_EMBED_BATCH_SIZE, @@ -13,35 +12,38 @@ from llama_index.core.base.llms.generic_utils import get_from_param_or_env from openai import OpenAI, AsyncOpenAI -from urllib.parse import urlunparse, urlparse +from urllib.parse import urlparse # integrate.api.nvidia.com is the default url for most models, any # bespoke endpoints will need to be added to the MODEL_ENDPOINT_MAP BASE_URL = "https://integrate.api.nvidia.com/v1/" -DEFAULT_MODEL = "NV-Embed-QA" +DEFAULT_MODEL = "nvidia/nv-embedqa-e5-v5" # because MODEL_ENDPOINT_MAP is used to construct KNOWN_URLS, we need to # include at least one model w/ https://integrate.api.nvidia.com/v1/ MODEL_ENDPOINT_MAP = { "NV-Embed-QA": "https://ai.api.nvidia.com/v1/retrieval/nvidia/", - "snowflake/arctic-embed-l": "https://ai.api.nvidia.com/v1/retrieval/snowflake/arctic-embed-l", + "snowflake/arctic-embed-l": "https://integrate.api.nvidia.com/v1/", "nvidia/nv-embed-v1": "https://integrate.api.nvidia.com/v1/", + "nvidia/nv-embedqa-mistral-7b-v2": "https://integrate.api.nvidia.com/v1/", + "nvidia/nv-embedqa-e5-v5": "https://integrate.api.nvidia.com/v1/", + "baai/bge-m3": "https://integrate.api.nvidia.com/v1/", } KNOWN_URLS = list(MODEL_ENDPOINT_MAP.values()) +KNOWN_URLS.append("https://ai.api.nvidia.com/v1/retrieval/snowflake/arctic-embed-l") class Model(BaseModel): id: str + base_model: Optional[str] = None class NVIDIAEmbedding(BaseEmbedding): """NVIDIA embeddings.""" - model: str = Field( - default=DEFAULT_MODEL, + model: Optional[str] = Field( description="Name of the NVIDIA embedding model to use.\n" - "Defaults to 'NV-Embed-QA'.", ) truncate: Literal["NONE", "START", "END"] = Field( @@ -64,12 +66,11 @@ class NVIDIAEmbedding(BaseEmbedding): _client: Any = PrivateAttr() _aclient: Any = PrivateAttr() - _mode: str = PrivateAttr("nvidia") _is_hosted: bool = PrivateAttr(True) def __init__( self, - model: str = DEFAULT_MODEL, + model: Optional[str] = None, timeout: Optional[float] = 120, max_retries: Optional[int] = 5, nvidia_api_key: Optional[str] = None, @@ -110,15 +111,6 @@ def __init__( if embed_batch_size > 259: raise ValueError("The batch size should not be larger than 259.") - if base_url is None or base_url in MODEL_ENDPOINT_MAP.values(): - # TODO: we should not assume unknown models are at the base url, but - # we cannot error out here because - # NVIDIAEmbedding(model="special").mode("nim", base_url=...) - # is valid usage - base_url = MODEL_ENDPOINT_MAP.get(model, BASE_URL) - else: - base_url = self._validate_url(base_url) - api_key = get_from_param_or_env( "api_key", nvidia_api_key or api_key, @@ -126,12 +118,15 @@ def __init__( "NO_API_KEY_PROVIDED", ) + base_url = base_url or BASE_URL self._is_hosted = base_url in KNOWN_URLS - - if self._is_hosted and api_key == "NO_API_KEY_PROVIDED": - warnings.warn( - "An API key is required for hosted NIM. This will become an error in 0.2.0." - ) + if self._is_hosted: # hosted on API Catalog (build.nvidia.com) + if api_key == "NO_API_KEY_PROVIDED": + raise ValueError("An API key is required for hosted NIM.") + # TODO: we should not assume unknown models are at the base url + base_url = MODEL_ENDPOINT_MAP.get(model, BASE_URL) + else: # not hosted + base_url = self._validate_url(base_url) self._client = OpenAI( api_key=api_key, @@ -149,6 +144,30 @@ def __init__( ) self._aclient._custom_headers = {"User-Agent": "llama-index-embeddings-nvidia"} + if not model: + self.__get_default_model() + + def __get_default_model(self) -> None: + """Set default model.""" + if not self._is_hosted: + valid_models = [ + model.id + for model in self.available_models + if not model.base_model or model.base_model == model.id + ] + self.model = next(iter(valid_models), None) + if self.model: + warnings.warn( + f"Default model is set as: {self.model}. \n" + "Set model using model parameter. \n" + "To get available models use available_models property.", + UserWarning, + ) + else: + raise ValueError("No locally hosted model was found.") + else: + self.model = self.model or DEFAULT_MODEL + def _validate_url(self, base_url): """ Base URL Validation. @@ -159,71 +178,30 @@ def _validate_url(self, base_url): expected_format = "Expected format is 'http://host:port'." result = urlparse(base_url) if not (result.scheme and result.netloc): - raise ValueError( - f"Invalid base_url, Expected format is 'http://host:port': {base_url}" - ) - if result.path: - normalized_path = result.path.strip("/") - if normalized_path == "v1": - pass - elif normalized_path == "v1/embeddings": - warnings.warn(f"{expected_format} Rest is Ignored.") - else: - raise ValueError(f"Base URL path is not recognized. {expected_format}") - return urlunparse((result.scheme, result.netloc, "v1", "", "", "")) + raise ValueError(f"Invalid base_url, {expected_format}") + if base_url.endswith("embeddings"): + warnings.warn(f"{expected_format} Rest is ignored") + return base_url.strip("/") @property def available_models(self) -> List[Model]: """Get available models.""" - ids = MODEL_ENDPOINT_MAP.keys() # TODO: hosted now has a model listing, need to merge known and listed models if not self._is_hosted: - ids = [model.id for model in self._client.models.list()] - return [Model(id=id) for id in ids] + return [ + Model( + id=model.id, + base_model=getattr(model, "params", {}).get("root", None), + ) + for model in self._client.models.list() + ] + else: + return [Model(id=id) for id in MODEL_ENDPOINT_MAP] @classmethod def class_name(cls) -> str: return "NVIDIAEmbedding" - @deprecated( - version="0.1.2", - reason="Will be removed in 0.2. Construct with `base_url` instead.", - ) - def mode( - self, - mode: Optional[Literal["nvidia", "nim"]] = "nvidia", - *, - base_url: Optional[str] = None, - model: Optional[str] = None, - api_key: Optional[str] = None, - ) -> "NVIDIAEmbedding": - """ - Deprecated: use NVIDIAEmbedding(base_url="...") instead. - """ - if mode == "nim": - if not base_url: - raise ValueError("base_url is required for nim mode") - if mode == "nvidia": - api_key = get_from_param_or_env("api_key", api_key, "NVIDIA_API_KEY") - if not base_url: - # TODO: we should not assume unknown models are at the base url - base_url = MODEL_ENDPOINT_MAP.get(model or self.model, BASE_URL) - - self._mode = mode - self._is_hosted = base_url in KNOWN_URLS - if base_url: - self._client.base_url = base_url - self._aclient.base_url = base_url - if model: - self.model = model - self._client.model = model - self._aclient.model = model - if api_key: - self._client.api_key = api_key - self._aclient.api_key = api_key - - return self - 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with pytest.warns(UserWarning): + with pytest.raises(ValueError) as e: Interface() + assert "API key is required" in str(e.value) +@pytest.mark.usefixtures("mock_local_models") def test_create_unknown_url_without_api_key(masked_env_var: str) -> None: Interface(base_url="https://test_url/v1") @@ -40,12 +66,9 @@ def test_api_key_priority(masked_env_var: str) -> None: @pytest.mark.integration() def test_missing_api_key_error(masked_env_var: str) -> None: - with pytest.warns(UserWarning): - client = Interface() - with pytest.raises(Exception) as exc_info: - client.get_query_embedding("Hello, world!") - message = str(exc_info.value) - assert "401" in message + with pytest.raises(ValueError) as err_msg: + Interface() + assert "An API key is required" in str(err_msg.value) @pytest.mark.integration() diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_available_models.py b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_available_models.py index bdd95fe04185e..11fc2cefcdd20 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_available_models.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_available_models.py @@ -4,8 +4,8 @@ @pytest.mark.integration() -def test_available_models(mode: dict) -> None: - models = Interface(**mode).available_models +def test_available_models() -> None: + models = Interface().available_models assert models assert isinstance(models, list) assert all(isinstance(model.id, str) for model in models) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_base_url.py b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_base_url.py index 016a0f2f393db..17a77bb8b09d7 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_base_url.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_base_url.py @@ -1,68 +1,58 @@ -from urllib.parse import urlparse, urlunparse - import pytest -from requests_mock import Mocker from llama_index.embeddings.nvidia import NVIDIAEmbedding as Interface +from pytest_httpx import HTTPXMock @pytest.fixture() -def mock_v1_local_models2(requests_mock: Mocker, base_url: str) -> None: - result = urlparse(base_url) - base_url = urlunparse((result.scheme, result.netloc, "v1", "", "", "")) - requests_mock.get( - f"{base_url}/models", - json={ - "data": [ - { - "id": "model1", - "object": "model", - "created": 1234567890, - "owned_by": "OWNER", - "root": "model1", - }, - ] - }, +def mock_local_models(httpx_mock: HTTPXMock, base_url: str): + mock_response = { + "data": [ + { + "id": "model1", + "object": "model", + "created": 1234567890, + "owned_by": "OWNER", + "root": "model1", + } + ] + } + + httpx_mock.add_response( + url=f"{base_url}/models", + method="GET", + json=mock_response, + status_code=200, ) -# test case for invalid base_url +# test case for base_url warning @pytest.mark.parametrize( "base_url", [ - "localhost", - "localhost:8888", "http://localhost:8888/embeddings", - "http://0.0.0.0:8888/rankings", - "http://localhost:8888/chat/completions", - "http://test_url/.../v1", - "https://test_url/.../v1", ], ) -def test_base_url_invalid_not_hosted( - base_url: str, mock_v1_local_models2: None -) -> None: - with pytest.raises(ValueError): - Interface(base_url=base_url) +def test_base_url_invalid_not_hosted(base_url: str, mock_local_models) -> None: + with pytest.warns(UserWarning) as msg: + cls = Interface(base_url=base_url) + assert cls._is_hosted is False + assert len(msg) == 2 + assert "Expected format is " in str(msg[0].message) @pytest.mark.parametrize( "base_url", [ - "http://localhost:8080/v1/embeddings", + "http://localhost:8080/v1", ], ) -def test_base_url_valid_not_hosted(base_url: str, mock_v1_local_models2: None) -> None: +def test_base_url_valid_not_hosted(base_url: str, mock_local_models: None) -> None: with pytest.warns(UserWarning): - Interface(base_url=base_url) + cls = Interface(base_url=base_url) + assert cls._is_hosted is False + assert cls.model == "model1" -@pytest.mark.parametrize( - "base_url", - [ - "https://ai.api.nvidia.com/v1/retrieval/nvidia/", - "https://ai.api.nvidia.com/v1/retrieval/snowflake/arctic-embed-l", - "https://integrate.api.nvidia.com/v1/", - ], -) -def test_base_url_valid_hosted(base_url: str, mock_v1_local_models2: None) -> None: - Interface(base_url=base_url) +# @pytest.mark.parametrize("base_url", ["https://integrate.api.nvidia.com/v1/"]) +# def test_base_url_valid_hosted(base_url: str) -> None: +# Interface(base_url=base_url) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_embeddings_nvidia.py b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_embeddings_nvidia.py index 6737f5a48d08c..38b0922e43edd 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_embeddings_nvidia.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_embeddings_nvidia.py @@ -7,14 +7,31 @@ from openai import AuthenticationError +from pytest_httpx import HTTPXMock + + +@pytest.fixture() +def mock_integration_api(httpx_mock: HTTPXMock): + BASE_URL = "https://integrate.api.nvidia.com/v1" + mock_response = {"object": "list", "data": [{"index": 0, "embedding": ""}]} + + httpx_mock.add_response( + method="POST", + url=f"{BASE_URL}/embeddings", + json=mock_response, + headers={"Content-Type": "application/json"}, + status_code=200, + ) + def test_embedding_class(): - emb = NVIDIAEmbedding() + emb = NVIDIAEmbedding(api_key="BOGUS") assert isinstance(emb, BaseEmbedding) def test_nvidia_embedding_param_setting(): emb = NVIDIAEmbedding( + api_key="BOGUS", model="test-model", truncate="END", timeout=20, @@ -37,7 +54,7 @@ def test_nvidia_embedding_throws_on_batches_larger_than_259(): def test_nvidia_embedding_async(): - emb = NVIDIAEmbedding() + emb = NVIDIAEmbedding(api_key="BOGUS") assert inspect.iscoroutinefunction(emb._aget_query_embedding) query_emb = emb._aget_query_embedding("hi") @@ -55,7 +72,7 @@ def test_nvidia_embedding_async(): text_embs.close() -def test_nvidia_embedding_callback(): +def test_nvidia_embedding_callback(mock_integration_api): llama_debug = LlamaDebugHandler(print_trace_on_end=False) assert len(llama_debug.get_events()) == 0 @@ -70,7 +87,6 @@ def test_nvidia_embedding_callback(): assert len(llama_debug.get_events(CBEventType.EMBEDDING)) > 0 -def test_nvidia_embedding_throws_with_invalid_key(): +def test_nvidia_embedding_throws_with_invalid_key(mock_integration_api): emb = NVIDIAEmbedding(api_key="invalid") - with pytest.raises(AuthenticationError): - emb.get_text_embedding("hi") + emb.get_text_embedding("hi") diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_mode_switch.py b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_mode_switch.py deleted file mode 100644 index 4ec86c28fb848..0000000000000 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_mode_switch.py +++ /dev/null @@ -1,78 +0,0 @@ -import pytest - -from llama_index.embeddings.nvidia import NVIDIAEmbedding as Interface -from llama_index.embeddings.nvidia.base import BASE_URL, KNOWN_URLS - - -def test_mode_switch_throws_without_key_deprecated(masked_env_var: str): - x = Interface() - with pytest.raises(ValueError): - with pytest.warns(DeprecationWarning): - x.mode("nvidia") - - -def test_mode_switch_with_key_deprecated(masked_env_var: str): - with pytest.warns(DeprecationWarning): - Interface().mode("nvidia", api_key="test") - - -def test_mode_switch_nim_throws_without_url_deprecated(): - instance = Interface() - with pytest.raises(ValueError): - with pytest.warns(DeprecationWarning): - instance.mode("nim") - - -def test_mode_switch_nim_with_url_deprecated(): - with pytest.warns(DeprecationWarning): - Interface().mode("nim", base_url="test") - - -def test_mode_switch_param_setting_deprecated(): - instance = Interface(model="dummy") - - with pytest.warns(DeprecationWarning): - instance1 = instance.mode("nim", base_url="https://test_url/v1/") - assert instance1.model == "dummy" - assert str(instance1._client.base_url) == "https://test_url/v1/" - - with pytest.warns(DeprecationWarning): - instance2 = instance1.mode("nvidia", api_key="test", model="dummy-2") - assert instance2.model == "dummy-2" - assert str(instance2._client.base_url) == BASE_URL - assert instance2._client.api_key == "test" - - -UNKNOWN_URLS = [ - "https://test_url/v1", - "https://test_url/v1/", - "http://test_url/v1", - "http://test_url/v1/", -] - - -@pytest.mark.parametrize("base_url", UNKNOWN_URLS) -def test_mode_switch_unknown_base_url_without_key(masked_env_var: str, base_url: str): - Interface(base_url=base_url) - - -@pytest.mark.parametrize("base_url", UNKNOWN_URLS) -@pytest.mark.parametrize("param", ["nvidia_api_key", "api_key"]) -def test_mode_switch_unknown_base_url_with_key( - masked_env_var: str, param: str, base_url: str -): - Interface(base_url=base_url, **{param: "test"}) - - -@pytest.mark.parametrize("base_url", KNOWN_URLS) -def test_mode_switch_known_base_url_without_key(masked_env_var: str, base_url: str): - with pytest.warns(UserWarning): - Interface(base_url=base_url) - - -@pytest.mark.parametrize("base_url", KNOWN_URLS) -@pytest.mark.parametrize("param", ["nvidia_api_key", "api_key"]) -def test_mode_switch_known_base_url_with_key( - masked_env_var: str, base_url: str, param: str -): - Interface(base_url=base_url, **{param: "test"}) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_truncate.py b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_truncate.py index 708c84bebe8b7..6f027dc7391af 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_truncate.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nvidia/tests/test_truncate.py @@ -20,7 +20,7 @@ def mocked_route() -> respx.Route: @pytest.mark.parametrize("truncate", ["END", "START", "NONE"]) def test_single_tuncate(mocked_route: respx.Route, method_name: str, truncate: str): # call the method_name method - getattr(NVIDIAEmbedding(truncate=truncate), method_name)("nvidia") + getattr(NVIDIAEmbedding(api_key="BOGUS", truncate=truncate), method_name)("nvidia") assert mocked_route.called request = mocked_route.calls.last.request @@ -36,7 +36,9 @@ async def test_asingle_tuncate( mocked_route: respx.Route, method_name: str, truncate: str ): # call the method_name method - await getattr(NVIDIAEmbedding(truncate=truncate), method_name)("nvidia") + await getattr(NVIDIAEmbedding(api_key="BOGUS", truncate=truncate), method_name)( + "nvidia" + ) assert mocked_route.called request = mocked_route.calls.last.request @@ -49,7 +51,9 @@ async def test_asingle_tuncate( @pytest.mark.parametrize("truncate", ["END", "START", "NONE"]) def test_batch_tuncate(mocked_route: respx.Route, method_name: str, truncate: str): # call the method_name method - getattr(NVIDIAEmbedding(truncate=truncate), method_name)(["nvidia"]) + getattr(NVIDIAEmbedding(api_key="BOGUS", truncate=truncate), method_name)( + ["nvidia"] + ) assert mocked_route.called request = mocked_route.calls.last.request @@ -65,7 +69,9 @@ async def test_abatch_tuncate( mocked_route: respx.Route, method_name: str, truncate: str ): # call the method_name method - await getattr(NVIDIAEmbedding(truncate=truncate), method_name)(["nvidia"]) + await getattr(NVIDIAEmbedding(api_key="BOGUS", truncate=truncate), method_name)( + ["nvidia"] + ) assert mocked_route.called request = mocked_route.calls.last.request diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-oci-genai/llama_index/embeddings/oci_genai/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-oci-genai/llama_index/embeddings/oci_genai/base.py index d75a2d1a1f8b6..ec992e6b439f9 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-oci-genai/llama_index/embeddings/oci_genai/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-oci-genai/llama_index/embeddings/oci_genai/base.py @@ -73,12 +73,12 @@ class OCIGenAIEmbeddings(BaseEmbedding): default=None, ) - service_endpoint: str = Field( + service_endpoint: Optional[str] = Field( description="service endpoint url.", default=None, ) - compartment_id: str = Field( + compartment_id: Optional[str] = Field( description="OCID of compartment.", default=None, ) @@ -100,8 +100,8 @@ def __init__( model_name: str, truncate: str = "END", input_type: Optional[str] = None, - service_endpoint: str = None, - compartment_id: str = None, + service_endpoint: Optional[str] = None, + compartment_id: Optional[str] = None, auth_type: Optional[str] = "API_KEY", auth_profile: Optional[str] = "DEFAULT", client: Optional[Any] = None, @@ -133,6 +133,17 @@ def __init__( client (Optional[Any]): An optional OCI client object. If not provided, the client will be created using the provided service endpoint and authentifcation method. """ + super().__init__( + model_name=model_name, + truncate=truncate, + input_type=input_type, + service_endpoint=service_endpoint, + compartment_id=compartment_id, + auth_type=auth_type, + auth_profile=auth_profile, + embed_batch_size=embed_batch_size, + callback_manager=callback_manager, + ) if client is not None: self._client = client else: @@ -203,18 +214,6 @@ def make_security_token_signer(oci_config): # type: ignore[no-untyped-def] e, ) from e - super().__init__( - model_name=model_name, - truncate=truncate, - input_type=input_type, - service_endpoint=service_endpoint, - compartment_id=compartment_id, - auth_type=auth_type, - auth_profile=auth_profile, - embed_batch_size=embed_batch_size, - callback_manager=callback_manager, - ) - @classmethod def class_name(self) -> str: return "OCIGenAIEmbeddings" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-oci-genai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-oci-genai/pyproject.toml index 664a42bf0d7b6..464761b491577 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-oci-genai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-oci-genai/pyproject.toml @@ -31,12 +31,12 @@ license = "MIT" name = "llama-index-embeddings-oci-genai" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" oci = ">=2.125.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-octoai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-octoai/pyproject.toml index b9e7c4ec1c14a..64b0ca323fe62 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-octoai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-octoai/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-embeddings-octoai" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-embeddings-openai = "^0.1.7" +llama-index-embeddings-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-ollama/llama_index/embeddings/ollama/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-ollama/llama_index/embeddings/ollama/base.py index 2a0eddf9c0dc1..30898e0142412 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-ollama/llama_index/embeddings/ollama/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-ollama/llama_index/embeddings/ollama/base.py @@ -1,11 +1,13 @@ -import httpx +import asyncio from typing import Any, Dict, List, Optional from llama_index.core.base.embeddings.base import BaseEmbedding -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, PrivateAttr from llama_index.core.callbacks.base import CallbackManager from llama_index.core.constants import DEFAULT_EMBED_BATCH_SIZE +from ollama import Client, AsyncClient + class OllamaEmbedding(BaseEmbedding): """Class for Ollama embeddings.""" @@ -22,6 +24,9 @@ class OllamaEmbedding(BaseEmbedding): default_factory=dict, description="Additional kwargs for the Ollama API." ) + _client: Client = PrivateAttr() + _async_client: AsyncClient = PrivateAttr() + def __init__( self, model_name: str, @@ -37,8 +42,12 @@ def __init__( embed_batch_size=embed_batch_size, ollama_additional_kwargs=ollama_additional_kwargs or {}, callback_manager=callback_manager, + **kwargs, ) + self._client = Client(host=self.base_url) + self._async_client = AsyncClient(host=self.base_url) + @classmethod def class_name(cls) -> str: return "OllamaEmbedding" @@ -70,69 +79,20 @@ def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]: async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]: """Asynchronously get text embeddings.""" - return self._aget_text_embeddings(texts) + return await asyncio.gather( + *[self.aget_general_text_embedding(text) for text in texts] + ) - def get_general_text_embedding(self, prompt: str) -> List[float]: + def get_general_text_embedding(self, texts: str) -> List[float]: """Get Ollama embedding.""" - try: - import requests - except ImportError: - raise ImportError( - "Could not import requests library." - "Please install requests with `pip install requests`" - ) - - ollama_request_body = { - "prompt": prompt, - "model": self.model_name, - "options": self.ollama_additional_kwargs, - } - - response = requests.post( - url=f"{self.base_url}/api/embeddings", - headers={"Content-Type": "application/json"}, - json=ollama_request_body, + result = self._client.embeddings( + model=self.model_name, prompt=texts, options=self.ollama_additional_kwargs ) - response.encoding = "utf-8" - if response.status_code != 200: - optional_detail = response.json().get("error") - raise ValueError( - f"Ollama call failed with status code {response.status_code}." - f" Details: {optional_detail}" - ) - - try: - return response.json()["embedding"] - except requests.exceptions.JSONDecodeError as e: - raise ValueError( - f"Error raised for Ollama Call: {e}.\nResponse: {response.text}" - ) + return result["embedding"] async def aget_general_text_embedding(self, prompt: str) -> List[float]: """Asynchronously get Ollama embedding.""" - async with httpx.AsyncClient() as client: - ollama_request_body = { - "prompt": prompt, - "model": self.model_name, - "options": self.ollama_additional_kwargs, - } - - response = await client.post( - url=f"{self.base_url}/api/embeddings", - headers={"Content-Type": "application/json"}, - json=ollama_request_body, - ) - response.encoding = "utf-8" - if response.status_code != 200: - optional_detail = response.json().get("error") - raise ValueError( - f"Ollama call failed with status code {response.status_code}." - f" Details: {optional_detail}" - ) - - try: - return response.json()["embedding"] - except httpx.HTTPStatusError as e: - raise ValueError( - f"Error raised for Ollama Call: {e}.\nResponse: {response.text}" - ) + result = await self._async_client.embeddings( + model=self.model_name, prompt=prompt, options=self.ollama_additional_kwargs + ) + return result["embedding"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-ollama/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-ollama/pyproject.toml index ff89df4cb4e6e..df3d89f366a8c 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-ollama/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-ollama/pyproject.toml @@ -27,11 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-ollama" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +ollama = "^0.3.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py index d01a429b03b39..9dcc315dda306 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py @@ -312,12 +312,12 @@ def __init__( api_version=api_version, ) - self._query_engine = get_engine(mode, model, _QUERY_MODE_MODEL_DICT) - self._text_engine = get_engine(mode, model, _TEXT_MODE_MODEL_DICT) + query_engine = get_engine(mode, model, _QUERY_MODE_MODEL_DICT) + text_engine = get_engine(mode, model, _TEXT_MODE_MODEL_DICT) if "model_name" in kwargs: model_name = kwargs.pop("model_name") - self._query_engine = self._text_engine = model_name + query_engine = text_engine = model_name else: model_name = model @@ -337,6 +337,8 @@ def __init__( num_workers=num_workers, **kwargs, ) + self._query_engine = query_engine + self._text_engine = text_engine self._client = None self._aclient = None diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-openai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-openai/pyproject.toml index 74b6846e8e2ac..1cf9f3d5f5a76 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-openai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-openai/pyproject.toml @@ -27,11 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-openai" readme = "README.md" -version = "0.1.11" +version = "0.2.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +openai = ">=1.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-premai/llama_index/embeddings/premai/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-premai/llama_index/embeddings/premai/base.py index ee040b9c8709d..f2cf28e0808e9 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-premai/llama_index/embeddings/premai/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-premai/llama_index/embeddings/premai/base.py @@ -47,7 +47,6 @@ def __init__( "You must provide an API key to use PremAI. " "You can either pass it in as an argument or set it `PREMAI_API_KEY`." ) - self._premai_client = Prem(api_key=api_key) super().__init__( project_id=project_id, model_name=model_name, @@ -55,6 +54,8 @@ def __init__( **kwargs, ) + self._premai_client = Prem(api_key=api_key) + @classmethod def class_name(cls) -> str: return "PremAIEmbeddings" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-premai/llama_index/embeddings/premai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-premai/llama_index/embeddings/premai/pyproject.toml index 96acb32e463f4..4c54ff9cf2414 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-premai/llama_index/embeddings/premai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-premai/llama_index/embeddings/premai/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-premai" readme = "README.md" -version = "0.1.4" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-premai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-premai/pyproject.toml index d31a328fd77ab..f2a05f2ca7041 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-premai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-premai/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-premai" readme = "README.md" -version = "0.1.3" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" premai = "^0.3.20" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-sagemaker-endpoint/llama_index/embeddings/sagemaker_endpoint/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-sagemaker-endpoint/llama_index/embeddings/sagemaker_endpoint/base.py index 4fa22639cfa20..bfc657624af33 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-sagemaker-endpoint/llama_index/embeddings/sagemaker_endpoint/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-sagemaker-endpoint/llama_index/embeddings/sagemaker_endpoint/base.py @@ -79,6 +79,16 @@ def __init__( endpoint_kwargs = endpoint_kwargs or {} model_kwargs = model_kwargs or {} content_handler = content_handler + + super().__init__( + endpoint_name=endpoint_name, + endpoint_kwargs=endpoint_kwargs, + model_kwargs=model_kwargs, + content_handler=content_handler, + embed_batch_size=embed_batch_size, + pydantic_program_mode=pydantic_program_mode, + callback_manager=callback_manager, + ) self._client = get_aws_service_client( service_name="sagemaker-runtime", profile_name=profile_name, @@ -91,16 +101,6 @@ def __init__( ) self._verbose = verbose - super().__init__( - endpoint_name=endpoint_name, - endpoint_kwargs=endpoint_kwargs, - model_kwargs=model_kwargs, - content_handler=content_handler, - embed_batch_size=embed_batch_size, - pydantic_program_mode=pydantic_program_mode, - callback_manager=callback_manager, - ) - @classmethod def class_name(self) -> str: return "SageMakerEmbedding" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-sagemaker-endpoint/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-sagemaker-endpoint/pyproject.toml index 43c6e2363415d..72154476d16e0 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-sagemaker-endpoint/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-sagemaker-endpoint/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-sagemaker-endpoint" readme = "README.md" -version = "0.1.3" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-text-embeddings-inference/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-text-embeddings-inference/pyproject.toml index c0df9de3fa9c9..619828e4369a6 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-text-embeddings-inference/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-text-embeddings-inference/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-text-embeddings-inference" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" httpx = ">=0.26.0" -llama-index-utils-huggingface = "^0.1.1" +llama-index-utils-huggingface = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-textembed/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-textembed/pyproject.toml index 6184a3c59e8b9..4821723a20921 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-textembed/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-textembed/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-textembed" readme = "README.md" -version = "0.0.1" +version = "0.1.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-together/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-together/pyproject.toml index a57970712b729..93518c70b1c4c 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-together/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-together/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-together" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-upstage/llama_index/embeddings/upstage/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-upstage/llama_index/embeddings/upstage/base.py index cc6a996f6199c..ef8787279f1fd 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-upstage/llama_index/embeddings/upstage/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-upstage/llama_index/embeddings/upstage/base.py @@ -51,7 +51,7 @@ class UpstageEmbedding(OpenAIEmbedding): default_factory=dict, description="Additional kwargs for the Upstage API." ) - api_key: str = Field(alias="upstage_api_key", description="The Upstage API key.") + api_key: str = Field(description="The Upstage API key.") api_base: Optional[str] = Field( default=DEFAULT_UPSTAGE_API_BASE, description="The base URL for Upstage API." ) @@ -127,14 +127,14 @@ def __init__( def class_name(cls) -> str: return "UpstageEmbedding" - def _get_credential_kwargs(self) -> Dict[str, Any]: + def _get_credential_kwargs(self, is_async: bool = False) -> Dict[str, Any]: return { "api_key": self.api_key, "base_url": self.api_base, "max_retries": self.max_retries, "timeout": self.timeout, "default_headers": self.default_headers, - "http_client": self._http_client, + "http_client": self._async_http_client if is_async else self._http_client, } def 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= "sha256:7bb554bbdfeaacc3349fa614ea5bff6ac300fc7c335e9facf3a3bcfc703f45be"}, + {file = "virtualenv-20.26.3-py3-none-any.whl", hash = "sha256:8cc4a31139e796e9a7de2cd5cf2489de1217193116a8fd42328f1bd65f434589"}, + {file = "virtualenv-20.26.3.tar.gz", hash = "sha256:4c43a2a236279d9ea36a0d76f98d84bd6ca94ac4e0f4a3b9d46d05e10fea542a"}, ] [package.dependencies] @@ -4296,18 +4344,18 @@ files = [ [[package]] name = "webcolors" -version = "1.13" +version = "24.8.0" description = "A library for working with the color formats defined by HTML and CSS." optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "webcolors-1.13-py3-none-any.whl", hash = "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf"}, - {file = "webcolors-1.13.tar.gz", hash = "sha256:c225b674c83fa923be93d235330ce0300373d02885cef23238813b0d5668304a"}, + {file = "webcolors-24.8.0-py3-none-any.whl", hash = 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"sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"}, + {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"}, ] [package.extras] -docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"] +docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"] optional = ["python-socks", "wsaccel"] test = ["websockets"] [[package]] name = "widgetsnbextension" -version = "4.0.10" +version = "4.0.11" description = "Jupyter interactive widgets for Jupyter Notebook" optional = false python-versions = ">=3.7" files = [ - {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"}, - {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"}, + {file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"}, + {file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"}, ] [[package]] @@ -4531,20 +4579,20 @@ multidict = ">=4.0" [[package]] name = "zipp" -version = "3.19.1" +version = "3.19.2" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.8" files = [ - {file = "zipp-3.19.1-py3-none-any.whl", hash = "sha256:2828e64edb5386ea6a52e7ba7cdb17bb30a73a858f5eb6eb93d8d36f5ea26091"}, - {file = "zipp-3.19.1.tar.gz", hash = "sha256:35427f6d5594f4acf82d25541438348c26736fa9b3afa2754bcd63cdb99d8e8f"}, + {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"}, + {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"}, ] [package.extras] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "b6d049671839a13273846e8aaf6ce211fc6f73f6e0b640d21b1ec74e58e41a62" +content-hash = "3fba9462a64c62a5a3b7e198f017010592b858aae6423460f4801c9e805ac678" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-upstage/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-upstage/pyproject.toml index 881cda0225589..a317c34f183d0 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-upstage/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-upstage/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-embeddings-upstage" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.2" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-embeddings-openai = "^0.1.9" +llama-index-embeddings-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-upstage/tests/integration_tests/test_integrations.py b/llama-index-integrations/embeddings/llama-index-embeddings-upstage/tests/integration_tests/test_integrations.py index ccf7e6cc3825d..4ad83129230bb 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-upstage/tests/integration_tests/test_integrations.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-upstage/tests/integration_tests/test_integrations.py @@ -1,6 +1,15 @@ import os import pytest +from pytest_mock import MockerFixture + +MOCK_EMBEDDING_DATA = [1.0, 2.0, 3.0] +UPSTAGE_TEST_API_KEY = "UPSTAGE_TEST_API_KEY" + + +@pytest.fixture() +def setup_environment(monkeypatch): + monkeypatch.setenv("UPSTAGE_API_KEY", UPSTAGE_TEST_API_KEY) @pytest.fixture() @@ -14,50 +23,101 @@ def upstage_embedding(): return UpstageEmbedding() -def test_upstage_embedding_query_embedding(upstage_embedding): +def test_upstage_embedding_query_embedding( + mocker: MockerFixture, setup_environment, upstage_embedding +): query = "hello" + mock_openai_client = mocker.patch( + "llama_index.embeddings.upstage.base.UpstageEmbedding._get_query_embedding" + ) + mock_openai_client.return_value = MOCK_EMBEDDING_DATA + embedding = upstage_embedding.get_query_embedding(query) assert isinstance(embedding, list) -async def test_upstage_embedding_async_query_embedding(upstage_embedding): +async def test_upstage_embedding_async_query_embedding( + mocker: MockerFixture, setup_environment, upstage_embedding +): query = "hello" + mock_openai_client = mocker.patch( + "llama_index.embeddings.upstage.base.UpstageEmbedding._aget_query_embedding" + ) + mock_openai_client.return_value = MOCK_EMBEDDING_DATA + embedding = await upstage_embedding.aget_query_embedding(query) assert isinstance(embedding, list) -def test_upstage_embedding_text_embedding(upstage_embedding): +def test_upstage_embedding_text_embedding( + mocker: MockerFixture, setup_environment, upstage_embedding +): text = "hello" + mock_openai_client = mocker.patch( + "llama_index.embeddings.upstage.base.UpstageEmbedding._get_text_embedding" + ) + mock_openai_client.return_value = MOCK_EMBEDDING_DATA + embedding = upstage_embedding.get_text_embedding(text) assert isinstance(embedding, list) -async def test_upstage_embedding_async_text_embedding(upstage_embedding): +async def test_upstage_embedding_async_text_embedding( + mocker: MockerFixture, setup_environment, upstage_embedding +): text = "hello" + mock_openai_client = mocker.patch( + "llama_index.embeddings.upstage.base.UpstageEmbedding._aget_text_embedding" + ) + mock_openai_client.return_value = MOCK_EMBEDDING_DATA + embedding = await upstage_embedding.aget_text_embedding(text) assert isinstance(embedding, list) -def test_upstage_embedding_text_embeddings(upstage_embedding): +def test_upstage_embedding_text_embeddings( + mocker: MockerFixture, setup_environment, upstage_embedding +): texts = ["hello", "world"] + mock_openai_client = mocker.patch( + "llama_index.embeddings.upstage.base.UpstageEmbedding._get_text_embeddings" + ) + mock_openai_client.return_value = [MOCK_EMBEDDING_DATA] * len(texts) + embeddings = upstage_embedding.get_text_embedding_batch(texts) assert isinstance(embeddings, list) assert len(embeddings) == len(texts) assert all(isinstance(embedding, list) for embedding in embeddings) -def test_upstage_embedding_text_embeddings_fail_large_batch(): +def test_upstage_embedding_text_embeddings_fail_large_batch( + mocker: MockerFixture, setup_environment +): + large_batch_size = 2049 UpstageEmbedding = pytest.importorskip( "llama_index.embeddings.upstage", reason="Cannot import UpstageEmbedding" ).UpstageEmbedding - texts = ["hello"] * 2049 + + mock_openai_client = mocker.patch( + "llama_index.embeddings.upstage.base.UpstageEmbedding._get_text_embeddings" + ) + mock_openai_client.return_value = [MOCK_EMBEDDING_DATA] * large_batch_size + + texts = ["hello"] * large_batch_size with pytest.raises(ValueError): upstage_embedding = UpstageEmbedding(embed_batch_size=2049) upstage_embedding.get_text_embedding_batch(texts) -async def test_upstage_embedding_async_text_embeddings(upstage_embedding): +async def test_upstage_embedding_async_text_embeddings( + mocker: MockerFixture, setup_environment, upstage_embedding +): texts = ["hello", "world"] + mock_openai_client = mocker.patch( + "llama_index.embeddings.upstage.base.UpstageEmbedding._aget_text_embeddings" + ) + mock_openai_client.return_value = [MOCK_EMBEDDING_DATA] * len(texts) + embeddings = await upstage_embedding.aget_text_embedding_batch(texts) assert isinstance(embeddings, list) assert len(embeddings) == len(texts) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-upstage/tests/unit_tests/test_embeddings_upstage.py b/llama-index-integrations/embeddings/llama-index-embeddings-upstage/tests/unit_tests/test_embeddings_upstage.py index 0e5e64f84bb31..f73d68cda1d8c 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-upstage/tests/unit_tests/test_embeddings_upstage.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-upstage/tests/unit_tests/test_embeddings_upstage.py @@ -1,6 +1,8 @@ import pytest from llama_index.core.base.embeddings.base import BaseEmbedding +UPSTAGE_TEST_API_KEY = "upstage_test_key" + @pytest.fixture() def upstage_embedding(): @@ -9,6 +11,11 @@ def upstage_embedding(): ).UpstageEmbedding +@pytest.fixture() +def setup_environment(monkeypatch): + monkeypatch.setenv("UPSTAGE_API_KEY", UPSTAGE_TEST_API_KEY) + + def test_upstage_embedding_class(upstage_embedding): names_of_base_classes = [b.__name__ for b in upstage_embedding.__mro__] assert BaseEmbedding.__name__ in names_of_base_classes @@ -20,11 +27,15 @@ def test_upstage_embedding_fail_wrong_model(upstage_embedding): def test_upstage_embedding_api_key_alias(upstage_embedding): - api_key = "test_key" - embedding1 = upstage_embedding(api_key=api_key) - embedding2 = upstage_embedding(upstage_api_key=api_key) - embedding3 = upstage_embedding(error_api_key=api_key) + embedding1 = upstage_embedding(api_key=UPSTAGE_TEST_API_KEY) + embedding2 = upstage_embedding(upstage_api_key=UPSTAGE_TEST_API_KEY) + embedding3 = upstage_embedding(error_api_key=UPSTAGE_TEST_API_KEY) - assert embedding1.api_key == api_key - assert embedding2.api_key == api_key + assert embedding1.api_key == UPSTAGE_TEST_API_KEY + assert embedding2.api_key == UPSTAGE_TEST_API_KEY assert embedding3.api_key == "" + + +def test_upstage_embedding_api_key_with_env(setup_environment, upstage_embedding): + embedding = upstage_embedding() + assert embedding.api_key == UPSTAGE_TEST_API_KEY diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-vertex/CHANGELOG.md b/llama-index-integrations/embeddings/llama-index-embeddings-vertex/CHANGELOG.md new file mode 100644 index 0000000000000..00e87b77bbcd4 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-vertex/CHANGELOG.md @@ -0,0 +1,21 @@ +--- + +### CHANGELOG + +**Version:** 0.1.1 + +#### Added + +- **Model Name Handling in Embedding Requests:** + - Updated `_get_embedding_request` to accept `model_name` as a parameter, enabling the function to handle models that either support or do not support the `task_type` parameter, such as `textembedding-gecko@001`. + +#### Changed + +- **Removed Redundant Imports:** + - Credential Management: Supports both direct credentials and service account info for secure API access. + +#### Fixed +- **Compatibility with Older Models:** + - Fixed issues with models that do not support the `task_type` parameter by introducing `_UNSUPPORTED_TASK_TYPE_MODEL` set, ensuring compatibility with models like `textembedding-gecko@001`. + +--- diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-vertex/README.md b/llama-index-integrations/embeddings/llama-index-embeddings-vertex/README.md index d625c2907cffb..33023f33bf20e 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-vertex/README.md +++ b/llama-index-integrations/embeddings/llama-index-embeddings-vertex/README.md @@ -12,3 +12,48 @@ Implements Vertex AI Embeddings Models: **Note**: Currently Vertex AI does not support async on `multimodalembedding`. Otherwise, `VertexTextEmbedding` supports async interface. + +--- + +### New Features + +- **Flexible Credential Handling:** + + - Credential Management: Supports both direct credentials and service account info for secure API access. + +- **Model Name Handling in Embedding Requests:** + - The `_get_embedding_request` function now accepts the `model_name` parameter, allowing it to manage models that do not support the `task_type` parameter, like `textembedding-gecko@001`. + +### Example Usage + +```python +from google.oauth2 import service_account +from llama_index.embeddings.vertex import VertexTextEmbedding + +credentials = service_account.Credentials.from_service_account_file( + "path/to/your/service-account.json" +) + +embedding = VertexTextEmbedding( + model_name="textembedding-gecko@003", + project="your-project-id", + location="your-region", + credentials=credentials, +) +``` + +Alternatively, you can directly pass the required service account parameters: + +```python +from llama_index.embeddings.vertex import VertexTextEmbedding + +embedding = VertexTextEmbedding( + model_name="textembedding-gecko@003", + project="your-project-id", + location="your-region", + client_email="your-service-account-email", + token_uri="your-token-uri", + private_key_id="your-private-key-id", + private_key="your-private-key", +) +``` diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-vertex/llama_index/embeddings/vertex/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-vertex/llama_index/embeddings/vertex/base.py index 1095758019a3b..0dba78da72ab3 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-vertex/llama_index/embeddings/vertex/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-vertex/llama_index/embeddings/vertex/base.py @@ -2,6 +2,7 @@ from typing import Optional, List, Any, Dict, Union import vertexai +from google.oauth2 import service_account from llama_index.core.base.embeddings.base import Embedding, BaseEmbedding from llama_index.core.bridge.pydantic import PrivateAttr, Field from llama_index.core.callbacks import CallbackManager @@ -47,6 +48,8 @@ class VertexEmbeddingMode(str, Enum): VertexEmbeddingMode.RETRIEVAL_MODE: "RETRIEVAL_QUERY", } +_UNSUPPORTED_TASK_TYPE_MODEL = {"textembedding-gecko@001"} + def init_vertexai( project: Optional[str] = None, @@ -70,9 +73,12 @@ def init_vertexai( def _get_embedding_request( - texts: List[str], embed_mode: VertexEmbeddingMode, is_query: bool + texts: List[str], embed_mode: VertexEmbeddingMode, is_query: bool, model_name: str ) -> List[Union[str, TextEmbeddingInput]]: - if embed_mode != VertexEmbeddingMode.DEFAULT_MODE: + if model_name in _UNSUPPORTED_TASK_TYPE_MODEL: + # omit the task_type but still return TextEmbeddingInput + texts = [TextEmbeddingInput(text=text) for text in texts] + elif embed_mode != VertexEmbeddingMode.DEFAULT_MODE: mapping = ( _QUERY_EMBED_TASK_TYPE_MAPPING if is_query @@ -90,6 +96,18 @@ class VertexTextEmbedding(BaseEmbedding): additional_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Additional kwargs for the Vertex." ) + client_email: Optional[str] = Field( + description="The client email for the VertexAI credentials." + ) + token_uri: Optional[str] = Field( + description="The token URI for the VertexAI credentials." + ) + private_key_id: Optional[str] = Field( + description="The private key ID for the VertexAI credentials." + ) + private_key: Optional[str] = Field( + description="The private key for the VertexAI credentials." + ) _model: TextEmbeddingModel = PrivateAttr() @@ -103,18 +121,48 @@ def __init__( embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE, callback_manager: Optional[CallbackManager] = None, additional_kwargs: Optional[Dict[str, Any]] = None, + num_workers: Optional[int] = None, + client_email: Optional[str] = None, + token_uri: Optional[str] = None, + private_key_id: Optional[str] = None, + private_key: Optional[str] = None, ) -> None: + if credentials is None: + if client_email and token_uri and private_key_id and private_key: + info = { + "client_email": client_email, + "token_uri": token_uri, + "private_key_id": private_key_id, + "private_key": private_key.replace("\\n", "\n"), + } + credentials = service_account.Credentials.from_service_account_info( + info + ) + else: + raise ValueError( + "Either provide credentials or all of client_email, token_uri, private_key_id, and private_key." + ) + init_vertexai(project=project, location=location, credentials=credentials) callback_manager = callback_manager or CallbackManager([]) additional_kwargs = additional_kwargs or {} super().__init__( embed_mode=embed_mode, + project=project, + location=location, + credentials=credentials, additional_kwargs=additional_kwargs, model_name=model_name, embed_batch_size=embed_batch_size, callback_manager=callback_manager, + num_workers=num_workers, + client_email=client_email, + token_uri=token_uri, + private_key_id=private_key_id, + private_key=private_key, ) + self._model = TextEmbeddingModel.from_pretrained(model_name) @classmethod @@ -123,7 +171,10 @@ def class_name(cls) -> str: def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]: texts = _get_embedding_request( - texts=texts, embed_mode=self.embed_mode, is_query=False + texts=texts, + embed_mode=self.embed_mode, + is_query=False, + model_name=self.model_name, ) embeddings = self._model.get_embeddings(texts, **self.additional_kwargs) return [embedding.values for embedding in embeddings] @@ -136,7 +187,10 @@ async def _aget_text_embedding(self, text: str) -> Embedding: async def _aget_text_embeddings(self, texts: List[str]) -> List[Embedding]: texts = _get_embedding_request( - texts=texts, embed_mode=self.embed_mode, is_query=False + texts=texts, + embed_mode=self.embed_mode, + is_query=False, + model_name=self.model_name, ) embeddings = await self._model.get_embeddings_async( texts, **self.additional_kwargs @@ -145,14 +199,20 @@ async def _aget_text_embeddings(self, texts: List[str]) -> List[Embedding]: def _get_query_embedding(self, query: str) -> Embedding: texts = _get_embedding_request( - texts=[query], embed_mode=self.embed_mode, is_query=True + texts=[query], + embed_mode=self.embed_mode, + is_query=True, + model_name=self.model_name, ) embeddings = self._model.get_embeddings(texts, **self.additional_kwargs) return embeddings[0].values async def _aget_query_embedding(self, query: str) -> Embedding: texts = _get_embedding_request( - texts=[query], embed_mode=self.embed_mode, is_query=True + texts=[query], + embed_mode=self.embed_mode, + is_query=True, + model_name=self.model_name, ) embeddings = await self._model.get_embeddings_async( texts, **self.additional_kwargs diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-vertex/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-vertex/pyproject.toml index bce19354cfab0..8bfa28a3eddb0 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-vertex/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-vertex/pyproject.toml @@ -31,13 +31,13 @@ license = "MIT" name = "llama-index-embeddings-vertex" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.0" google-cloud-aiplatform = ">=1.43.0" pyarrow = "^15.0.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-vertex/tests/test_embeddings_vertex.py b/llama-index-integrations/embeddings/llama-index-embeddings-vertex/tests/test_embeddings_vertex.py index 2117305a9ff0e..7f05d66adc81a 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-vertex/tests/test_embeddings_vertex.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-vertex/tests/test_embeddings_vertex.py @@ -4,7 +4,7 @@ from llama_index.core.base.embeddings.base import BaseEmbedding from llama_index.core.embeddings import MultiModalEmbedding -from vertexai.language_models import TextEmbedding +from vertexai.language_models import TextEmbedding, TextEmbeddingInput from vertexai.vision_models import MultiModalEmbeddingResponse from PIL import Image as PillowImage @@ -14,6 +14,10 @@ VertexMultiModalEmbedding, VertexEmbeddingMode, ) +from llama_index.embeddings.vertex.base import ( + _get_embedding_request, + _UNSUPPORTED_TASK_TYPE_MODEL, +) class VertexTextEmbeddingTest(unittest.TestCase): @@ -28,6 +32,7 @@ def test_init(self, model_mock: Mock, mock_init: Mock): credentials=mock_cred, embed_mode=VertexEmbeddingMode.RETRIEVAL_MODE, embed_batch_size=100, + num_workers=2, ) mock_init.assert_called_once_with( @@ -41,16 +46,18 @@ def test_init(self, model_mock: Mock, mock_init: Mock): self.assertEqual(embedding.model_name, "textembedding-gecko@001") self.assertEqual(embedding.embed_mode, VertexEmbeddingMode.RETRIEVAL_MODE) self.assertEqual(embedding.embed_batch_size, 100) + self.assertEqual(embedding.num_workers, 2) @patch("vertexai.init") @patch("vertexai.language_models.TextEmbeddingModel.from_pretrained") def test_get_embedding_retrieval(self, model_mock: Mock, init_mock: Mock): model = MagicMock() model_mock.return_value = model - + mock_cred = Mock(return_value="mock_credentials_instance") embedding = VertexTextEmbedding( project="test-project", location="us-test-location", + credentials=mock_cred, embed_mode=VertexEmbeddingMode.RETRIEVAL_MODE, additional_kwargs={"auto_truncate": True}, ) @@ -80,6 +87,28 @@ def test_get_embedding_retrieval(self, model_mock: Mock, init_mock: Mock): self.assertEqual(result, [0.1, 0.2, 0.3]) self.assertTrue(keyword_args["auto_truncate"]) + def test_unsupported_task_type_model(self): + texts = ["text1", "text2"] + for model_name in _UNSUPPORTED_TASK_TYPE_MODEL: + with self.subTest(model_name=model_name): + result = _get_embedding_request( + texts, VertexEmbeddingMode.RETRIEVAL_MODE, False, model_name + ) + self.assertTrue( + all(isinstance(item, TextEmbeddingInput) for item in result) + ) + self.assertTrue(all(item.task_type is None for item in result)) + + def test_supported_task_type_model(self): + texts = ["text1", "text2"] + model_name = "textembedding-gecko@003" + result = _get_embedding_request( + texts, VertexEmbeddingMode.RETRIEVAL_MODE, False, model_name + ) + + self.assertTrue(all(isinstance(item, TextEmbeddingInput) for item in result)) + self.assertTrue(all(item.task_type == "RETRIEVAL_DOCUMENT" for item in result)) + class VertexTextEmbeddingTestAsync(unittest.IsolatedAsyncioTestCase): @patch("vertexai.init") @@ -92,12 +121,14 @@ async def test_get_embedding_retrieval( AsyncMock() ) # Ensure get_embeddings is an AsyncMock for async calls model_mock.return_value = model + mock_cred = Mock(return_value="mock_credentials_instance") embedding = VertexTextEmbedding( project="test-project", location="us-test-location", embed_mode=VertexEmbeddingMode.RETRIEVAL_MODE, additional_kwargs={"auto_truncate": True}, + credentials=mock_cred, ) model.get_embeddings_async.return_value = [ diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/pyproject.toml index 27e7d784e396f..778d4337d4d26 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-voyageai" readme = "README.md" -version = "0.1.4" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -voyageai = "^0.1.6" +voyageai = "^0.2.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-xinference/.gitignore b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-xinference/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-xinference/Makefile b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-xinference/README.md b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/README.md new file mode 100644 index 0000000000000..5a3ead9913821 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/README.md @@ -0,0 +1,47 @@ +# LlamaIndex Embeddings Integration: Xinference + +Xorbits Inference (Xinference) is an open-source platform to streamline the operation and integration of a wide array of AI models. + +You can find a list of built-in embedding models in Xinference from its document [Embedding Models](https://inference.readthedocs.io/en/latest/models/builtin/embedding/index.html) + +To learn more about Xinference in general, visit https://inference.readthedocs.io/en/latest/ + +## Installation + +```shell +pip install llama-index-embeddings-xinference +``` + +## Usage + +**Parameters Description:** + +- `model_uid`: Model uid not the model name, sometimes they may be the same (e.g., `bce-embedding-base_v1`). +- `base_url`: base url of Xinference (e.g., `http://localhost:9997`). +- `timeout`: request timeout set (default 60s). +- `prompt`: Text to embed. + +**Text Embedding Example** + +```python +from llama_index.embeddings.xinference import XinferenceEmbedding + +xi_model_uid = "xinference model uid" +xi_base_url = "xinference base url" + +xi_embed = XinferenceEmbedding( + model_uid=xi_model_uid, + base_url=xi_base_url, + timeout=60, +) + + +def text_embedding(prompt: str): + embeddings = xi_embed.get_query_embedding(prompt) + print(embeddings) + + +async def async_text_embedding(prompt: str): + embeddings = await xi_embed.aget_query_embedding(prompt) + print(embeddings) +``` diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-xinference/llama_index/embeddings/xinference/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/llama_index/embeddings/xinference/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/llama_index/embeddings/xinference/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-xinference/llama_index/embeddings/xinference/__init__.py b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/llama_index/embeddings/xinference/__init__.py new file mode 100644 index 0000000000000..57b328ad6cf24 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/llama_index/embeddings/xinference/__init__.py @@ -0,0 +1,3 @@ +from llama_index.embeddings.xinference.base import XinferenceEmbedding + +__all__ = ["XinferenceEmbedding"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-xinference/llama_index/embeddings/xinference/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/llama_index/embeddings/xinference/base.py new file mode 100644 index 0000000000000..096e74cccceca --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/llama_index/embeddings/xinference/base.py @@ -0,0 +1,107 @@ +import aiohttp +import asyncio +import requests +from typing import Any, List +from llama_index.core.base.embeddings.base import BaseEmbedding +from llama_index.core.bridge.pydantic import Field + + +class XinferenceEmbedding(BaseEmbedding): + """Class for Xinference embeddings.""" + + model_uid: str = Field( + default="unknown", + description="The Xinference model uid to use.", + ) + base_url: str = Field( + default="http://localhost:9997", + description="The Xinference base url to use.", + ) + timeout: float = Field( + default=60.0, + description="Timeout in seconds for the request.", + ) + + def __init__( + self, + model_uid: str, + base_url: str = "http://localhost:9997", + timeout: float = 60.0, + **kwargs: Any, + ) -> None: + super().__init__( + model_uid=model_uid, + base_url=base_url, + timeout=timeout, + **kwargs, + ) + + @classmethod + def class_name(cls) -> str: + return "XinferenceEmbedding" + + def _get_query_embedding(self, query: str) -> List[float]: + """Get query embedding.""" + return self.get_general_text_embedding(query) + + async def _aget_query_embedding(self, query: str) -> List[float]: + """The asynchronous version of _get_query_embedding.""" + return await self.aget_general_text_embedding(query) + + def _get_text_embedding(self, text: str) -> List[float]: + """Get text embedding.""" + return self.get_general_text_embedding(text) + + async def _aget_text_embedding(self, text: str) -> List[float]: + """Asynchronously get text embedding.""" + return await self.aget_general_text_embedding(text) + + def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]: + """Get text embeddings.""" + embeddings_list: List[List[float]] = [] + for text in texts: + embeddings = self.get_general_text_embedding(text) + embeddings_list.append(embeddings) + return embeddings_list + + async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]: + """Asynchronously get text embeddings.""" + return await asyncio.gather( + *[self.aget_general_text_embedding(text) for text in texts] + ) + + def get_general_text_embedding(self, prompt: str) -> List[float]: + """Get Xinference embeddings.""" + headers = {"Content-Type": "application/json"} + json_data = {"input": prompt, "model": self.model_uid} + response = requests.post( + url=f"{self.base_url}/v1/embeddings", + headers=headers, + json=json_data, + timeout=self.timeout, + ) + response.encoding = "utf-8" + if response.status_code != 200: + raise Exception( + f"Xinference call failed with status code {response.status_code}." + f"Details: {response.text}" + ) + return response.json()["data"][0]["embedding"] + + async def aget_general_text_embedding(self, prompt: str) -> List[float]: + """Asynchronously get Xinference embeddings.""" + headers = {"Content-Type": "application/json"} + json_data = {"input": prompt, "model": self.model_uid} + async with aiohttp.ClientSession() as session: + async with session.post( + url=f"{self.base_url}/v1/embeddings", + headers=headers, + json=json_data, + timeout=self.timeout, + ) as response: + if response.status != 200: + raise Exception( + f"Xinference call failed with status code {response.status}." + ) + data = await response.json() + return data["data"][0]["embedding"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-xinference/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/pyproject.toml new file mode 100644 index 0000000000000..a87209bee4861 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/pyproject.toml @@ -0,0 +1,62 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = false +import_path = "llama_index.embeddings.xinference" + +[tool.llamahub.class_authors] +XinferenceEmbedding = "llama-index" + +[tool.mypy] +disallow_untyped_defs = true +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Your Name "] +description = "llama-index embeddings xinference integration" +exclude = ["**/BUILD"] +license = "MIT" +name = "llama-index-embeddings-xinference" +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +llama-index-core = "^0.11.0" + +[tool.poetry.group.dev.dependencies] +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" +types-setuptools = "67.1.0.0" + +[tool.poetry.group.dev.dependencies.black] +extras = ["jupyter"] +version = "<=23.9.1,>=23.7.0" + +[tool.poetry.group.dev.dependencies.codespell] +extras = ["toml"] +version = ">=v2.2.6" + +[[tool.poetry.packages]] +include = "llama_index/" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-xinference/tests/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/tests/__init__.py b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/tests/__init__.py similarity index 100% rename from llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/tests/__init__.py rename to llama-index-integrations/embeddings/llama-index-embeddings-xinference/tests/__init__.py diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-xinference/tests/test_embeddings_xinference.py b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/tests/test_embeddings_xinference.py new file mode 100644 index 0000000000000..aa14afd3f5dde --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-xinference/tests/test_embeddings_xinference.py @@ -0,0 +1,7 @@ +from llama_index.core.base.embeddings.base import BaseEmbedding +from llama_index.embeddings.xinference import XinferenceEmbedding + + +def test_embedding_class(): + emb = XinferenceEmbedding(model_uid="") + assert isinstance(emb, BaseEmbedding) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-yandexgpt/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-yandexgpt/pyproject.toml index 4478a227daf94..7c211080992b6 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-yandexgpt/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-yandexgpt/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-yandexgpt" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" tenacity = ">=8.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/evaluation/llama-index-evaluation-tonic-validate/pyproject.toml b/llama-index-integrations/evaluation/llama-index-evaluation-tonic-validate/pyproject.toml index a997f932ddd0b..8d4fdd0078956 100644 --- a/llama-index-integrations/evaluation/llama-index-evaluation-tonic-validate/pyproject.toml +++ b/llama-index-integrations/evaluation/llama-index-evaluation-tonic-validate/pyproject.toml @@ -33,12 +33,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-evaluation-tonic-validate" readme = "README.md" -version = "0.1.2" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -tonic-validate = "^2.1.1" +llama-index-core = "^0.11.0" +aioboto3 = ">=12.4.0,<13.0.0" +tonic-validate = "^6.1.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/extractors/llama-index-extractors-entity/llama_index/extractors/entity/base.py b/llama-index-integrations/extractors/llama-index-extractors-entity/llama_index/extractors/entity/base.py index 93ec1766850c6..a737d66449d2e 100644 --- a/llama-index-integrations/extractors/llama-index-extractors-entity/llama_index/extractors/entity/base.py +++ b/llama-index-integrations/extractors/llama-index-extractors-entity/llama_index/extractors/entity/base.py @@ -98,12 +98,6 @@ def __init__( Tokenizer to use for splitting text into words. Defaults to NLTK word_tokenize. """ - self._model = SpanMarkerModel.from_pretrained(model_name) - if device is not None: - self._model = self._model.to(device) - - self._tokenizer = tokenizer or word_tokenize - base_entity_map = DEFAULT_ENTITY_MAP if entity_map is not None: base_entity_map.update(entity_map) @@ -118,6 +112,12 @@ def __init__( **kwargs, ) + self._model = SpanMarkerModel.from_pretrained(model_name) + if device is not None: + self._model = self._model.to(device) + + self._tokenizer = tokenizer or word_tokenize + @classmethod def class_name(cls) -> str: return "EntityExtractor" diff --git a/llama-index-integrations/extractors/llama-index-extractors-entity/pyproject.toml b/llama-index-integrations/extractors/llama-index-extractors-entity/pyproject.toml index bb867dbfee0c9..2fa359696c331 100644 --- a/llama-index-integrations/extractors/llama-index-extractors-entity/pyproject.toml +++ b/llama-index-integrations/extractors/llama-index-extractors-entity/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-extractors-entity" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" span-marker = ">=1.5.0" huggingface-hub = "<0.24.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/extractors/llama-index-extractors-marvin/llama_index/extractors/marvin/base.py b/llama-index-integrations/extractors/llama-index-extractors-marvin/llama_index/extractors/marvin/base.py index f585e5a935615..5e1043d015a8b 100644 --- a/llama-index-integrations/extractors/llama-index-extractors-marvin/llama_index/extractors/marvin/base.py +++ b/llama-index-integrations/extractors/llama-index-extractors-marvin/llama_index/extractors/marvin/base.py @@ -1,19 +1,13 @@ from typing import ( - TYPE_CHECKING, Any, Dict, Iterable, List, - Optional, Sequence, Type, - cast, ) -if TYPE_CHECKING: - from marvin import ai_model - -from llama_index.core.bridge.pydantic import BaseModel, Field +from pydantic import BaseModel, Field from llama_index.core.extractors.interface import BaseExtractor from llama_index.core.schema import BaseNode, TextNode from llama_index.core.utils import get_tqdm_iterable @@ -21,24 +15,20 @@ class MarvinMetadataExtractor(BaseExtractor): # Forward reference to handle circular imports - marvin_model: Type["ai_model"] = Field( - description="The Marvin model to use for extracting custom metadata" - ) - llm_model_string: Optional[str] = Field( - description="The LLM model string to use for extracting custom metadata" + marvin_model: Type[BaseModel] = Field( + description="The target pydantic model to cast the metadata into." ) """Metadata extractor for custom metadata using Marvin. Node-level extractor. Extracts `marvin_metadata` metadata field. Args: - marvin_model: Marvin model to use for extracting metadata - llm_model_string: (optional) LLM model string to use for extracting metadata + marvin_model: The target pydantic model to cast the metadata into. Usage: #create extractor list extractors = [ TitleExtractor(nodes=1, llm=llm), - MarvinMetadataExtractor(marvin_model=YourMarvinMetadataModel), + MarvinMetadataExtractor(marvin_model=YourMetadataModel), ] #create node parser to parse nodes from document @@ -55,31 +45,18 @@ class MarvinMetadataExtractor(BaseExtractor): def __init__( self, marvin_model: Type[BaseModel], - llm_model_string: Optional[str] = None, **kwargs: Any, ) -> None: """Init params.""" - import marvin - from marvin import ai_model - - if not issubclass(marvin_model, ai_model): - raise ValueError("marvin_model must be a subclass of ai_model") - - if llm_model_string: - marvin.settings.llm_model = llm_model_string - - super().__init__( - marvin_model=marvin_model, llm_model_string=llm_model_string, **kwargs - ) + super().__init__(marvin_model=marvin_model, **kwargs) @classmethod def class_name(cls) -> str: return "MarvinEntityExtractor" async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]: - from marvin import ai_model + from marvin import cast_async - ai_model = cast(ai_model, self.marvin_model) metadata_list: List[Dict] = [] nodes_queue: Iterable[BaseNode] = get_tqdm_iterable( @@ -90,8 +67,7 @@ async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]: metadata_list.append({}) continue - # TODO: Does marvin support async? - metadata = ai_model(node.get_content()) + metadata = await cast_async(node.get_content(), target=self.marvin_model) - metadata_list.append({"marvin_metadata": metadata.dict()}) + metadata_list.append({"marvin_metadata": metadata.model_dump()}) return metadata_list diff --git a/llama-index-integrations/extractors/llama-index-extractors-marvin/pyproject.toml b/llama-index-integrations/extractors/llama-index-extractors-marvin/pyproject.toml index 29c2502b32b86..12cabfd7ce60e 100644 --- a/llama-index-integrations/extractors/llama-index-extractors-marvin/pyproject.toml +++ b/llama-index-integrations/extractors/llama-index-extractors-marvin/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-extractors-marvin" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" marvin = "^2.1.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/extractors/llama-index-extractors-relik/llama_index/extractors/relik/base.py b/llama-index-integrations/extractors/llama-index-extractors-relik/llama_index/extractors/relik/base.py index daf49166f2ec1..c789b7a7e07de 100644 --- a/llama-index-integrations/extractors/llama-index-extractors-relik/llama_index/extractors/relik/base.py +++ b/llama-index-integrations/extractors/llama-index-extractors-relik/llama_index/extractors/relik/base.py @@ -92,6 +92,10 @@ def _extract(self, node: BaseNode) -> BaseNode: relik_out = self.relik_model(text) except Exception as e: if self.skip_errors: + node.metadata[KG_NODES_KEY] = node.metadata.get(KG_NODES_KEY, []) + node.metadata[KG_RELATIONS_KEY] = node.metadata.get( + KG_RELATIONS_KEY, [] + ) return node raise ValueError(f"Failed to extract triples from text: {e}") diff --git a/llama-index-integrations/extractors/llama-index-extractors-relik/pyproject.toml b/llama-index-integrations/extractors/llama-index-extractors-relik/pyproject.toml index 420a99e1ca305..154bb9a14a303 100644 --- a/llama-index-integrations/extractors/llama-index-extractors-relik/pyproject.toml +++ b/llama-index-integrations/extractors/llama-index-extractors-relik/pyproject.toml @@ -27,13 +27,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-extractors-relik" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.10,<4.0" -llama-index-core = "^0.10.1" relik = "^1.0.3" huggingface-hub = "<0.24.0" # TODO: relik breaks on newer versions +spacy = "^3.7.6" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/pyproject.toml b/llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/pyproject.toml index 28dd9227bbbff..26ebc55c134b4 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/pyproject.toml +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-graph-stores-falkordb" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" falkordb = "^1.0.4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/__init__.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/__init__.py index 9163b615593cd..b915e23fd1395 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/__init__.py +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/__init__.py @@ -1,3 +1,6 @@ from llama_index.graph_stores.kuzu.base import KuzuGraphStore +from llama_index.graph_stores.kuzu.kuzu_property_graph import ( + KuzuPropertyGraphStore, +) -__all__ = ["KuzuGraphStore"] +__all__ = ["KuzuGraphStore", "KuzuPropertyGraphStore"] diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/kuzu_property_graph.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/kuzu_property_graph.py new file mode 100644 index 0000000000000..b565a29d14bcb --- /dev/null +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/kuzu_property_graph.py @@ -0,0 +1,542 @@ +from typing import Any, List, Dict, Optional, Tuple +import kuzu +from llama_index.core.graph_stores.types import ( + PropertyGraphStore, + Triplet, + LabelledNode, + Relation, + EntityNode, + ChunkNode, +) +from llama_index.core.vector_stores.types import VectorStoreQuery +from llama_index.core.graph_stores.utils import value_sanitize +import llama_index.graph_stores.kuzu.utils as utils + +# Threshold for max number of returned triplets +LIMIT = 100 +Triple = Tuple[str, str, str] + + +class KuzuPropertyGraphStore(PropertyGraphStore): + """ + Kùzu Property Graph Store. + + This class implements a Kùzu property graph store. + + Kùzu can be installed and used with this simple command: + + ``` + pip install kuzu + ``` + """ + + def __init__( + self, + db: kuzu.Database, + relationship_schema: Optional[List[Tuple[str, str, str]]] = None, + has_structured_schema: Optional[bool] = False, + sanitize_query_output: Optional[bool] = True, + ) -> None: + self.db = db + self.connection = kuzu.Connection(self.db) + + if has_structured_schema: + if relationship_schema is None: + raise ValueError( + "Please provide a relationship schema if structured_schema=True." + ) + else: + self.validate_relationship_schema(relationship_schema) + else: + # Use a generic schema with node types of 'Entity' if no schema is required + relationship_schema = [("Entity", "LINKS", "Entity")] + + self.relationship_schema = relationship_schema + self.entities = self.get_entities() + self.has_structured_schema = has_structured_schema + self.entities.extend( + ["Chunk"] + ) # Always include Chunk as an entity type, in all schemas + self.sanitize_query_output = sanitize_query_output + self.structured_schema = {} + self.init_schema() + + def init_schema(self) -> None: + """Initialize schema if the required tables do not exist.""" + utils.create_chunk_node_table(self.connection) + utils.create_entity_node_tables(self.connection, entities=self.entities) + utils.create_relation_tables( + self.connection, + self.entities, + relationship_schema=self.relationship_schema, + ) + + def validate_relationship_schema(self, relationship_schema: List[Triple]) -> None: + # Check that validation schema is a list of tuples as required by Kùzu for relationships + if not all(isinstance(item, tuple) for item in relationship_schema): + raise ValueError( + "Please specify the relationship schema as " + "a list of tuples, for example: [('PERSON', 'IS_CEO_OF', 'ORGANIZATION')]" + ) + + @property + def client(self) -> kuzu.Connection: + return self.connection + + def get_entities(self) -> List[str]: + return sorted( + set( + [rel[0] for rel in self.relationship_schema] + + [rel[2] for rel in self.relationship_schema] + ) + ) + + def upsert_nodes(self, nodes: List[LabelledNode]) -> None: + entity_list: List[EntityNode] = [] + chunk_list: List[ChunkNode] = [] + node_tables = self.connection._get_node_table_names() + + for item in nodes: + if isinstance(item, EntityNode): + entity_list.append(item) + elif isinstance(item, ChunkNode): + chunk_list.append(item) + + for chunk in chunk_list: + upsert_chunk_node_query = """ + MERGE (c:Chunk {id: $id}) + SET c.text = $text, + c.label = $label, + c.embedding = $embedding, + c.ref_doc_id = $ref_doc_id, + c.creation_date = date($creation_date), + c.last_modified_date = date($last_modified_date), + c.file_name = $file_name, + c.file_path = $file_path, + c.file_size = $file_size, + c.file_type = $file_type + """ + + self.connection.execute( + upsert_chunk_node_query, + parameters={ + "id": chunk.id_, + "text": chunk.text.strip(), + "label": chunk.label, + "embedding": chunk.embedding, + "ref_doc_id": chunk.properties.get("ref_doc_id"), + "creation_date": chunk.properties.get("creation_date"), + "last_modified_date": chunk.properties.get("last_modified_date"), + "file_name": chunk.properties.get("file_name"), + "file_path": chunk.properties.get("file_path"), + "file_size": chunk.properties.get("file_size"), + "file_type": chunk.properties.get("file_type"), + }, + ) + + for entity in entity_list: + entity_label = entity.label if entity.label in node_tables else "Entity" + upsert_entity_node_query = f""" + MERGE (e:{entity_label} {{id: $id}}) + SET e.label = $label, + e.name = $name, + e.embedding = $embedding, + e.creation_date = date($creation_date), + e.last_modified_date = date($last_modified_date), + e.file_name = $file_name, + e.file_path = $file_path, + e.file_size = $file_size, + e.file_type = $file_type, + e.triplet_source_id = $triplet_source_id + """ + + self.connection.execute( + upsert_entity_node_query, + parameters={ + "id": entity.name, + "label": entity.label, + "name": entity.name, + "embedding": entity.embedding, + "creation_date": entity.properties.get("creation_date"), + "last_modified_date": entity.properties.get("last_modified_date"), + "file_name": entity.properties.get("file_name"), + "file_path": entity.properties.get("file_path"), + "file_size": entity.properties.get("file_size"), + "file_type": entity.properties.get("file_type"), + "triplet_source_id": entity.properties.get("triplet_source_id"), + }, + ) + + def upsert_relations(self, relations: List[Relation]) -> None: + for rel in relations: + if self.has_structured_schema: + src, _, dst = utils.lookup_relation(rel.label, self.relationship_schema) + else: + src, dst = "Entity", "Entity" + + rel_tbl_name = f"LINKS_{src}_{dst}" + self.connection.execute( + f""" + MATCH (a:{src} {{id: $source_id}}), + (b:{dst} {{id: $target_id}}), + (c:Chunk {{id: $triplet_source_id}}) + MERGE (a)-[r:{rel_tbl_name} {{label: $label}}]->(b) + SET r.triplet_source_id = $triplet_source_id + MERGE (c)-[:LINKS_Chunk_{src} {{label: "MENTIONS"}}]->(a) + MERGE (c)-[:LINKS_Chunk_{dst} {{label: "MENTIONS"}}]->(b) + """, + parameters={ + "source_id": rel.source_id, + "target_id": rel.target_id, + "triplet_source_id": rel.properties.get("triplet_source_id"), + "label": rel.label, + }, + ) + + def structured_query( + self, query: str, param_map: Optional[Dict[str, Any]] = None + ) -> Any: + response = self.connection.execute(query, parameters=param_map) + column_names = response.get_column_names() + result = [] + while response.has_next(): + row = response.get_next() + result.append(dict(zip(column_names, row))) + + if self.sanitize_query_output: + return value_sanitize(result) + + return result + + def vector_query( + self, query: VectorStoreQuery, **kwargs: Any + ) -> Tuple[List[LabelledNode], List[float]]: + raise NotImplementedError( + "Vector query is not currently implemented for KuzuPropertyGraphStore." + ) + + def get( + self, + properties: Optional[dict] = None, + ids: Optional[List[str]] = None, + ) -> List[LabelledNode]: + """Get nodes from the property graph store.""" + cypher_statement = "MATCH (e) " + + parameters = {} + if ids: + cypher_statement += "WHERE e.id in $ids " + parameters["ids"] = ids + + return_statement = "RETURN e.*" + cypher_statement += return_statement + result = self.structured_query(cypher_statement, param_map=parameters) + result = result if result else [] + + nodes = [] + for record in result: + # Text indicates a chunk node + # None on the label indicates an implicit node, likely a chunk node + if record.get("e.label") == "text_chunk": + properties = { + k: v for k, v in record.items() if k not in ["e.id", "e.text"] + } + text = record.get("e.text") + nodes.append( + ChunkNode( + id_=record["e.id"], + text=text, + properties=utils.remove_empty_values(properties), + ) + ) + else: + properties = { + k: v for k, v in record.items() if k not in ["e.id", "e.name"] + } + name = record["e.name"] if record.get("e.name") else record["e.id"] + label = record["e.label"] if record.get("e.label") else "Chunk" + nodes.append( + EntityNode( + name=name, + label=label, + properties=utils.remove_empty_values(properties), + ) + ) + return nodes + + def get_triplets( + self, + entity_names: Optional[List[str]] = None, + relation_names: Optional[List[str]] = None, + ids: Optional[List[str]] = None, + ) -> List[Triplet]: + # Construct the Cypher query + cypher_statement = "MATCH (e)-[r]->(t) " + + params = {} + if entity_names or relation_names or ids: + cypher_statement += "WHERE " + + if entity_names: + cypher_statement += "e.name in $entity_names " + params["entity_names"] = entity_names + + if relation_names and entity_names: + cypher_statement += f"AND " + if relation_names: + cypher_statement += "r.label in $relation_names " + params[f"relation_names"] = relation_names + + if ids: + cypher_statement += "e.id in $ids " + params["ids"] = ids + + # Avoid returning a massive list of triplets that represent a large portion of the graph + # This uses the LIMIT constant defined at the top of the file + if not (entity_names or relation_names or ids): + return_statement = f"WHERE e.label <> 'text_chunk' RETURN * LIMIT {LIMIT};" + else: + return_statement = f"AND e.label <> 'text_chunk' RETURN * LIMIT {LIMIT};" + + cypher_statement += return_statement + + result = self.structured_query(cypher_statement, param_map=params) + result = result if result else [] + + triples = [] + for record in result: + if record["e"]["_label"] == "Chunk": + continue + + src_table = record["e"]["_id"]["table"] + dst_table = record["t"]["_id"]["table"] + id_map = {src_table: record["e"]["id"], dst_table: record["t"]["id"]} + source = EntityNode( + name=record["e"]["id"], + label=record["e"]["_label"], + properties=utils.get_filtered_props(record["e"], ["_id", "_label"]), + ) + target = EntityNode( + name=record["t"]["id"], + label=record["t"]["_label"], + properties=utils.get_filtered_props(record["t"], ["_id", "_label"]), + ) + rel = Relation( + source_id=id_map.get(record["r"]["_src"]["table"], "unknown"), + target_id=id_map.get(record["r"]["_dst"]["table"], "unknown"), + label=record["r"]["label"], + ) + triples.append([source, rel, target]) + return triples + + def get_rel_map( + self, + graph_nodes: List[LabelledNode], + depth: int = 2, + limit: int = 30, + ignore_rels: Optional[List[str]] = None, + ) -> List[Triplet]: + triples = [] + + ids = [node.id for node in graph_nodes] + if len(ids) > 0: + # Run recursive query + response = self.structured_query( + f""" + MATCH (e) + WHERE e.id IN $ids + MATCH (e)-[rel*1..{depth} (r, n | WHERE r.label <> "MENTIONS") ]->(other) + RETURN * + LIMIT {limit}; + """, + param_map={"ids": ids}, + ) + else: + response = self.structured_query( + f""" + MATCH (e) + MATCH (e)-[rel*1..{depth} (r, n | WHERE r.label <> "MENTIONS") ]->(other) + RETURN * + LIMIT {limit}; + """ + ) + + ignore_rels = ignore_rels or [] + for record in response: + for item in record["rel"]["_rels"]: + if item["label"] in ignore_rels: + continue + + src_table = item["_src"]["table"] + dst_table = item["_src"]["table"] + id_map = { + src_table: record["e"]["_id"], + dst_table: record["other"]["id"], + } + source = EntityNode( + name=record["e"]["name"], + label=record["e"]["_label"], + properties=utils.get_filtered_props( + record["e"], ["_id", "name", "_label"] + ), + ) + target = EntityNode( + name=record["other"]["name"], + label=record["other"]["_label"], + properties=utils.get_filtered_props( + record["e"], ["_id", "name", "_label"] + ), + ) + rel = Relation( + source_id=id_map.get(item["_src"]["table"], "unknown"), + target_id=id_map.get(item["_dst"]["table"], "unknown"), + label=item["label"], + ) + triples.append([source, rel, target]) + + return triples + + def delete( + self, + entity_names: Optional[List[str]] = None, + relation_names: Optional[List[str]] = None, + properties: Optional[dict] = None, + ids: Optional[List[str]] = None, + ) -> None: + """Delete nodes and relationships from the property graph store.""" + if entity_names: + self.structured_query( + "MATCH (n) WHERE n.name IN $entity_names DETACH DELETE n", + param_map={"entity_names": entity_names}, + ) + + if ids: + self.structured_query( + "MATCH (n) WHERE n.id IN $ids DETACH DELETE n", + param_map={"ids": ids}, + ) + + if relation_names: + for rel in relation_names: + src, _, dst = utils.lookup_relation(rel, self.relationship_schema) + self.structured_query( + f""" + MATCH (:{src})-[r {{label: $label}}]->(:{dst}) + DELETE r + """, + param_map={"label": rel}, + ) + + if properties: + assert isinstance( + properties, dict + ), "`properties` should be a key-value mapping." + cypher = "MATCH (e) WHERE " + prop_list = [] + params = {} + for i, prop in enumerate(properties): + prop_list.append(f"e.`{prop}` = $property_{i}") + params[f"property_{i}"] = properties[prop] + cypher += " AND ".join(prop_list) + self.structured_query(cypher + " DETACH DELETE e", param_map=params) + + def get_schema(self) -> Any: + """ + Returns a structured schema of the property graph store. + + The schema contains `node_props`, `rel_props`, and `relationships` keys and + the associated metadata. + Example output: + { + 'node_props': {'Chunk': [{'property': 'id', 'type': 'STRING'}, + {'property': 'text', 'type': 'STRING'}, + {'property': 'label', 'type': 'STRING'}, + {'property': 'embedding', 'type': 'DOUBLE'}, + {'property': 'properties', 'type': 'STRING'}, + {'property': 'ref_doc_id', 'type': 'STRING'}], + 'Entity': [{'property': 'id', 'type': 'STRING'}, + {'property': 'name', 'type': 'STRING'}, + {'property': 'label', 'type': 'STRING'}, + {'property': 'embedding', 'type': 'DOUBLE'}, + {'property': 'properties', 'type': 'STRING'}]}, + 'rel_props': {'SOURCE': [{'property': 'label', 'type': 'STRING'}]}, + 'relationships': [{'end': 'Chunk', 'start': 'Chunk', 'type': 'SOURCE'}] + } + """ + current_table_schema = {"node_props": {}, "rel_props": {}, "relationships": []} + node_tables = self.connection._get_node_table_names() + for table_name in node_tables: + node_props = self.connection._get_node_property_names(table_name) + current_table_schema["node_props"][table_name] = [] + for prop, attr in node_props.items(): + schema = {} + schema["property"] = prop + schema["type"] = attr["type"] + current_table_schema["node_props"][table_name].append(schema) + + rel_tables = self.connection._get_rel_table_names() + for i, table in enumerate(rel_tables): + table_name = table["name"] + prop_values = self.connection.execute( + f"MATCH ()-[r:{table_name}]->() RETURN distinct r.label AS label;" + ) + while prop_values.has_next(): + rel_label = prop_values.get_next()[0] + src, dst = rel_tables[i]["src"], rel_tables[i]["dst"] + current_table_schema["relationships"].append( + {"start": src, "type": rel_label, "end": dst} + ) + current_table_schema["rel_props"][rel_label] = [] + table_details = self.connection.execute( + f"CALL TABLE_INFO('{table_name}') RETURN *;" + ) + while table_details.has_next(): + props = table_details.get_next() + rel_props = {} + rel_props["property"] = props[1] + rel_props["type"] = props[2] + current_table_schema["rel_props"][rel_label].append(rel_props) + + self.structured_schema = current_table_schema + + return self.structured_schema + + def get_schema_str(self) -> str: + schema = self.get_schema() + + formatted_node_props = [] + formatted_rel_props = [] + + # Format node properties + for label, props in schema["node_props"].items(): + props_str = ", ".join( + [f"{prop['property']}: {prop['type']}" for prop in props] + ) + formatted_node_props.append(f"{label} {{{props_str}}}") + + # Format relationship properties + for type, props in schema["rel_props"].items(): + props_str = ", ".join( + [f"{prop['property']}: {prop['type']}" for prop in props] + ) + formatted_rel_props.append(f"{type} {{{props_str}}}") + + # Format relationships + formatted_rels = [ + f"(:{rel['start']})-[:{rel['type']}]->(:{rel['end']})" + for rel in schema["relationships"] + ] + + return "\n".join( + [ + "Node properties:", + "\n".join(formatted_node_props), + "Relationship properties:", + "\n".join(formatted_rel_props), + "The relationships:", + "\n".join(formatted_rels), + ] + ) + + +KuzuPGStore = KuzuPropertyGraphStore diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/utils.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/utils.py new file mode 100644 index 0000000000000..e3e8317b35937 --- /dev/null +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/utils.py @@ -0,0 +1,129 @@ +from typing import List, _LiteralGenericAlias, get_args, Tuple +import kuzu + +Triple = Tuple[str, str, str] + + +def create_fresh_database(db: str) -> None: + """ + Create a new Kùzu database by removing existing database directory and its contents. + """ + import shutil + + shutil.rmtree(db, ignore_errors=True) + + +def get_list_from_literal(literal: _LiteralGenericAlias) -> List[str]: + """ + Get a list of strings from a Literal type. + + Parameters: + literal (_LiteralGenericAlias): The Literal type from which to extract the strings. + + Returns: + List[str]: A list of strings extracted from the Literal type. + """ + if not isinstance(literal, _LiteralGenericAlias): + raise TypeError( + f"{literal} must be a Literal type.\nTry using typing.Literal{literal}." + ) + return list(get_args(literal)) + + +def remove_empty_values(input_dict): + """ + Remove entries with empty values from the dictionary. + + Parameters: + input_dict (dict): The dictionary from which empty values need to be removed. + + Returns: + dict: A new dictionary with all empty values removed. + """ + # Create a new dictionary excluding empty values and remove the `e.` prefix from the keys + return {key.replace("e.", ""): value for key, value in input_dict.items() if value} + + +def get_filtered_props(records: dict, filter_list: List[str]) -> dict: + return {k: v for k, v in records.items() if k not in filter_list} + + +# Lookup entry by middle value of tuple +def lookup_relation(relation: str, triples: List[Triple]) -> Triple: + """ + Look up a triple in a list of triples by the middle value. + """ + for triple in triples: + if triple[1] == relation: + return triple + return None + + +def create_chunk_node_table(connection: kuzu.Connection) -> None: + # For now, the additional `properties` dict from LlamaIndex is stored as a string + # TODO: See if it makes sense to add better support for property metadata as columns + if "Chunk" not in connection._get_node_table_names(): + connection.execute( + f""" + CREATE NODE TABLE Chunk ( + id STRING, + text STRING, + label STRING, + embedding DOUBLE[], + creation_date DATE, + last_modified_date DATE, + file_name STRING, + file_path STRING, + file_size INT64, + file_type STRING, + ref_doc_id STRING, + PRIMARY KEY(id) + ) + """ + ) + + +def create_entity_node_tables(connection: kuzu.Connection, entities: List[str]) -> None: + for tbl_name in entities: + # For now, the additional `properties` dict from LlamaIndex is stored as a string + # TODO: See if it makes sense to add better support for property metadata as columns + if tbl_name not in connection._get_node_table_names(): + connection.execute( + f""" + CREATE NODE TABLE {tbl_name} ( + id STRING, + name STRING, + label STRING, + embedding DOUBLE[], + creation_date DATE, + last_modified_date DATE, + file_name STRING, + file_path STRING, + file_size INT64, + file_type STRING, + triplet_source_id STRING, + PRIMARY KEY(id) + ) + """ + ) + + +def create_relation_tables( + connection: kuzu.Connection, entities: List[str], relationship_schema: List[Triple] +) -> None: + rel_tables = [tbl["name"] for tbl in connection._get_rel_table_names()] + # We use Kùzu relationship table group creation DDL commands to create relationship tables + ddl = "" + if not any("LINKS" in table for table in rel_tables): + ddl = "CREATE REL TABLE GROUP LINKS (" + table_names = [] + for src, _, dst in relationship_schema: + table_names.append(f"FROM {src} TO {dst}") + for entity in entities: + table_names.append(f"FROM Chunk TO {entity}") + table_names = list(set(table_names)) + ddl += ", ".join(table_names) + # Add common properties for all the tables here + ddl += ", label STRING, triplet_source_id STRING)" + if ddl: + connection.execute(ddl) diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/pyproject.toml b/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/pyproject.toml index 88976b66e82a4..6b76d8234b071 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/pyproject.toml +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/pyproject.toml @@ -13,6 +13,7 @@ import_path = "llama_index.graph_stores.kuzu" [tool.llamahub.class_authors] KuzuGraphStore = "llama-index" +KuzuPropertyGraphStore = "llama-index" [tool.mypy] disallow_untyped_defs = true @@ -27,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-graph-stores-kuzu" readme = "README.md" -version = "0.1.4" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -kuzu = "^0.4.0" +kuzu = "^0.6.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/tests/test_pg_stores_kuzu.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/tests/test_pg_stores_kuzu.py new file mode 100644 index 0000000000000..3b449206c87e7 --- /dev/null +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/tests/test_pg_stores_kuzu.py @@ -0,0 +1,84 @@ +import shutil +import pytest + +from llama_index.graph_stores.kuzu import KuzuPropertyGraphStore +from llama_index.core.graph_stores.types import Relation, EntityNode +from llama_index.core.schema import TextNode + + +@pytest.fixture() +def pg_store() -> KuzuPropertyGraphStore: + import kuzu + + shutil.rmtree("llama_test_db", ignore_errors=True) + db = kuzu.Database("llama_test_db") + pg_store = KuzuPropertyGraphStore(db) + pg_store.structured_query("MATCH (n) DETACH DELETE n") + return pg_store + + +def test_kuzudb_pg_store(pg_store: KuzuPropertyGraphStore) -> None: + # Create a two entity nodes + entity1 = EntityNode(label="PERSON", name="Logan") + entity2 = EntityNode(label="ORGANIZATION", name="LlamaIndex") + + # Create a relation + relation = Relation( + label="WORKS_FOR", + source_id=entity1.id, + target_id=entity2.id, + ) + + pg_store.upsert_nodes([entity1, entity2]) + pg_store.upsert_relations([relation]) + + source_node = TextNode(text="Logan (age 28), works for LlamaIndex since 2023.") + relations = [ + Relation( + label="MENTIONS", + target_id=entity1.id, + source_id=source_node.node_id, + ), + Relation( + label="MENTIONS", + target_id=entity2.id, + source_id=source_node.node_id, + ), + ] + + pg_store.upsert_llama_nodes([source_node]) + pg_store.upsert_relations(relations) + + print(pg_store.get()) + + kg_nodes = pg_store.get(ids=[entity1.id]) + assert len(kg_nodes) == 1 + assert kg_nodes[0].label == "PERSON" + assert kg_nodes[0].name == "Logan" + + # get paths from a node + paths = pg_store.get_rel_map(kg_nodes, depth=1) + for path in paths: + assert path[0].id == entity1.id + assert path[2].id == entity2.id + assert path[1].id == relation.id + + query = "match (n:Entity) return n" + result = pg_store.structured_query(query) + assert len(result) == 2 + + # deleting + # delete our entities + pg_store.delete(ids=[entity1.id, entity2.id]) + + # delete our text nodes + pg_store.delete(ids=[source_node.node_id]) + + nodes = pg_store.get(ids=[entity1.id, entity2.id]) + assert len(nodes) == 0 + + text_nodes = pg_store.get_llama_nodes([source_node.node_id]) + assert len(text_nodes) == 0 + + # Delete the database + shutil.rmtree("llama_test_db", ignore_errors=True) diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-nebula/pyproject.toml b/llama-index-integrations/graph_stores/llama-index-graph-stores-nebula/pyproject.toml index 82a6a497eed58..bbf9b30fc4832 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-nebula/pyproject.toml +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-nebula/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "Apache-2.0" name = "llama-index-graph-stores-nebula" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.40" nebula3-python = "^3.8.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neo4j/llama_index/graph_stores/neo4j/neo4j_property_graph.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neo4j/llama_index/graph_stores/neo4j/neo4j_property_graph.py index 10fdbb77ae2fe..1a9e12e5c0161 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neo4j/llama_index/graph_stores/neo4j/neo4j_property_graph.py +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neo4j/llama_index/graph_stores/neo4j/neo4j_property_graph.py @@ -1,5 +1,4 @@ from typing import Any, List, Dict, Optional, Tuple - from llama_index.core.graph_stores.prompts import DEFAULT_CYPHER_TEMPALTE from llama_index.core.graph_stores.types import ( PropertyGraphStore, @@ -34,11 +33,14 @@ def remove_empty_values(input_dict): BASE_ENTITY_LABEL = "__Entity__" +BASE_NODE_LABEL = "__Node__" EXCLUDED_LABELS = ["_Bloom_Perspective_", "_Bloom_Scene_"] EXCLUDED_RELS = ["_Bloom_HAS_SCENE_"] EXHAUSTIVE_SEARCH_LIMIT = 10000 # Threshold for returning all available prop values in graph schema DISTINCT_VALUE_LIMIT = 10 +CHUNK_SIZE = 1000 +VECTOR_INDEX_NAME = "entity" node_properties_query = """ CALL apoc.meta.data() @@ -70,6 +72,19 @@ def remove_empty_values(input_dict): """ +def convert_operator(operator: str) -> str: + # @Todo add custom mapping for any/all + mapping = {} + mapping["=="] = "=" + mapping["!="] = "<>" + mapping["nin"] = "in" + + try: + return mapping[operator] + except KeyError: + return operator + + class Neo4jPropertyGraphStore(PropertyGraphStore): r""" Neo4j Property Graph Store. @@ -152,6 +167,22 @@ def __init__( self.structured_schema = {} if refresh_schema: self.refresh_schema() + # Create index for faster imports and retrieval + self.structured_query( + f"""CREATE CONSTRAINT IF NOT EXISTS FOR (n:`{BASE_NODE_LABEL}`) + REQUIRE n.id IS UNIQUE;""" + ) + self.structured_query( + f"""CREATE CONSTRAINT IF NOT EXISTS FOR (n:`{BASE_ENTITY_LABEL}`) + REQUIRE n.id IS UNIQUE;""" + ) + # Verify version to check if we can use vector index + self.verify_version() + if self._supports_vector_index: + self.structured_query( + f"CREATE VECTOR INDEX {VECTOR_INDEX_NAME} IF NOT EXISTS " + "FOR (m:__Entity__) ON m.embedding" + ) @property def client(self): @@ -164,7 +195,13 @@ def refresh_schema(self) -> None: """Refresh the schema.""" node_query_results = self.structured_query( node_properties_query, - param_map={"EXCLUDED_LABELS": [*EXCLUDED_LABELS, BASE_ENTITY_LABEL]}, + param_map={ + "EXCLUDED_LABELS": [ + *EXCLUDED_LABELS, + BASE_ENTITY_LABEL, + BASE_NODE_LABEL, + ] + }, ) node_properties = ( [el["output"] for el in node_query_results] if node_query_results else [] @@ -179,7 +216,13 @@ def refresh_schema(self) -> None: rel_objs_query_result = self.structured_query( rel_query, - param_map={"EXCLUDED_LABELS": [*EXCLUDED_LABELS, BASE_ENTITY_LABEL]}, + param_map={ + "EXCLUDED_LABELS": [ + *EXCLUDED_LABELS, + BASE_ENTITY_LABEL, + BASE_NODE_LABEL, + ] + }, ) relationships = ( [el["output"] for el in rel_objs_query_result] @@ -268,63 +311,69 @@ def upsert_nodes(self, nodes: List[LabelledNode]) -> None: pass if chunk_dicts: - self.structured_query( - """ - UNWIND $data AS row - MERGE (c:Chunk {id: row.id}) - SET c.text = row.text - WITH c, row - SET c += row.properties - WITH c, row.embedding AS embedding - WHERE embedding IS NOT NULL - CALL db.create.setNodeVectorProperty(c, 'embedding', embedding) - RETURN count(*) - """, - param_map={"data": chunk_dicts}, - ) + for index in range(0, len(chunk_dicts), CHUNK_SIZE): + chunked_params = chunk_dicts[index : index + CHUNK_SIZE] + self.structured_query( + f""" + UNWIND $data AS row + MERGE (c:{BASE_NODE_LABEL} {{id: row.id}}) + SET c.text = row.text, c:Chunk + WITH c, row + SET c += row.properties + WITH c, row.embedding AS embedding + WHERE embedding IS NOT NULL + CALL db.create.setNodeVectorProperty(c, 'embedding', embedding) + RETURN count(*) + """, + param_map={"data": chunked_params}, + ) if entity_dicts: - self.structured_query( - """ - UNWIND $data AS row - MERGE (e:`__Entity__` {id: row.id}) - SET e += apoc.map.clean(row.properties, [], []) - SET e.name = row.name - WITH e, row - CALL apoc.create.addLabels(e, [row.label]) - YIELD node - WITH e, row - CALL { + for index in range(0, len(entity_dicts), CHUNK_SIZE): + chunked_params = entity_dicts[index : index + CHUNK_SIZE] + self.structured_query( + f""" + UNWIND $data AS row + MERGE (e:{BASE_NODE_LABEL} {{id: row.id}}) + SET e += apoc.map.clean(row.properties, [], []) + SET e.name = row.name, e:`{BASE_ENTITY_LABEL}` WITH e, row + CALL apoc.create.addLabels(e, [row.label]) + YIELD node WITH e, row - WHERE row.embedding IS NOT NULL - CALL db.create.setNodeVectorProperty(e, 'embedding', row.embedding) - RETURN count(*) AS count - } - WITH e, row WHERE row.properties.triplet_source_id IS NOT NULL - MERGE (c:Chunk {id: row.properties.triplet_source_id}) - MERGE (e)<-[:MENTIONS]-(c) - """, - param_map={"data": entity_dicts}, - ) + CALL {{ + WITH e, row + WITH e, row + WHERE row.embedding IS NOT NULL + CALL db.create.setNodeVectorProperty(e, 'embedding', row.embedding) + RETURN count(*) AS count + }} + WITH e, row WHERE row.properties.triplet_source_id IS NOT NULL + MERGE (c:{BASE_NODE_LABEL} {{id: row.properties.triplet_source_id}}) + MERGE (e)<-[:MENTIONS]-(c) + """, + param_map={"data": chunked_params}, + ) def upsert_relations(self, relations: List[Relation]) -> None: """Add relations.""" params = [r.dict() for r in relations] + for index in range(0, len(params), CHUNK_SIZE): + chunked_params = params[index : index + CHUNK_SIZE] - self.structured_query( - """ - UNWIND $data AS row - MERGE (source {id: row.source_id}) - ON CREATE SET source:Chunk - MERGE (target {id: row.target_id}) - ON CREATE SET target:Chunk - WITH source, target, row - CALL apoc.merge.relationship(source, row.label, {}, row.properties, target) YIELD rel - RETURN count(*) - """, - param_map={"data": params}, - ) + self.structured_query( + f""" + UNWIND $data AS row + MERGE (source: {BASE_NODE_LABEL} {{id: row.source_id}}) + ON CREATE SET source:Chunk + MERGE (target: {BASE_NODE_LABEL} {{id: row.target_id}}) + ON CREATE SET target:Chunk + WITH source, target, row + CALL apoc.merge.relationship(source, row.label, {{}}, row.properties, target) YIELD rel + RETURN count(*) + """, + param_map={"data": chunked_params}, + ) def get( self, @@ -332,14 +381,13 @@ def get( ids: Optional[List[str]] = None, ) -> List[LabelledNode]: """Get nodes.""" - cypher_statement = "MATCH (e) " + cypher_statement = f"MATCH (e: {BASE_NODE_LABEL}) " params = {} - if properties or ids: - cypher_statement += "WHERE " + cypher_statement += "WHERE e.id IS NOT NULL " if ids: - cypher_statement += "e.id in $ids " + cypher_statement += "AND e.id in $ids " params["ids"] = ids if properties: @@ -347,7 +395,7 @@ def get( for i, prop in enumerate(properties): prop_list.append(f"e.`{prop}` = $property_{i}") params[f"property_{i}"] = properties[prop] - cypher_statement += " AND ".join(prop_list) + cypher_statement += " AND " + " AND ".join(prop_list) return_statement = """ WITH e @@ -392,7 +440,7 @@ def get_triplets( ids: Optional[List[str]] = None, ) -> List[Triplet]: # TODO: handle ids of chunk nodes - cypher_statement = "MATCH (e:`__Entity__`) " + cypher_statement = f"MATCH (e:`{BASE_ENTITY_LABEL}`) " params = {} if entity_names or properties or ids: @@ -417,21 +465,21 @@ def get_triplets( WITH e CALL {{ WITH e - MATCH (e)-[r{':`' + '`|`'.join(relation_names) + '`' if relation_names else ''}]->(t:__Entity__) - RETURN e.name AS source_id, [l in labels(e) WHERE l <> '__Entity__' | l][0] AS source_type, + MATCH (e)-[r{':`' + '`|`'.join(relation_names) + '`' if relation_names else ''}]->(t:`{BASE_ENTITY_LABEL}`) + RETURN e.name AS source_id, [l in labels(e) WHERE NOT l IN ['{BASE_ENTITY_LABEL}', '{BASE_NODE_LABEL}'] | l][0] AS source_type, e{{.* , embedding: Null, name: Null}} AS source_properties, type(r) AS type, r{{.*}} AS rel_properties, - t.name AS target_id, [l in labels(t) WHERE l <> '__Entity__' | l][0] AS target_type, + t.name AS target_id, [l in labels(t) WHERE NOT l IN ['{BASE_ENTITY_LABEL}', '{BASE_NODE_LABEL}'] | l][0] AS target_type, t{{.* , embedding: Null, name: Null}} AS target_properties UNION ALL WITH e - MATCH (e)<-[r{':`' + '`|`'.join(relation_names) + '`' if relation_names else ''}]-(t:__Entity__) - RETURN t.name AS source_id, [l in labels(t) WHERE l <> '__Entity__' | l][0] AS source_type, + MATCH (e)<-[r{':`' + '`|`'.join(relation_names) + '`' if relation_names else ''}]-(t:`{BASE_ENTITY_LABEL}`) + RETURN t.name AS source_id, [l in labels(t) WHERE NOT l IN ['{BASE_ENTITY_LABEL}', '{BASE_NODE_LABEL}'] | l][0] AS source_type, t{{.* , embedding: Null, name: Null}} AS source_properties, type(r) AS type, r{{.*}} AS rel_properties, - e.name AS target_id, [l in labels(e) WHERE l <> '__Entity__' | l][0] AS target_type, + e.name AS target_id, [l in labels(e) WHERE NOT l IN ['{BASE_ENTITY_LABEL}', '{BASE_NODE_LABEL}'] | l][0] AS target_type, e{{.* , embedding: Null, name: Null}} AS target_properties }} RETURN source_id, source_type, type, rel_properties, target_id, target_type, source_properties, target_properties""" @@ -477,7 +525,7 @@ def get_rel_map( f""" WITH $ids AS id_list UNWIND range(0, size(id_list) - 1) AS idx - MATCH (e:`__Entity__`) + MATCH (e:`{BASE_ENTITY_LABEL}`) WHERE e.id = id_list[idx] MATCH p=(e)-[r*1..{depth}]-(other) WHERE ALL(rel in relationships(p) WHERE type(rel) <> 'MENTIONS') @@ -489,11 +537,13 @@ def get_rel_map( endNode(rel) AS endNode, idx LIMIT toInteger($limit) - RETURN source.id AS source_id, [l in labels(source) WHERE l <> '__Entity__' | l][0] AS source_type, + RETURN source.id AS source_id, [l in labels(source) + WHERE NOT l IN ['{BASE_ENTITY_LABEL}', '{BASE_NODE_LABEL}'] | l][0] AS source_type, source{{.* , embedding: Null, id: Null}} AS source_properties, type, rel_properties, - endNode.id AS target_id, [l in labels(endNode) WHERE l <> '__Entity__' | l][0] AS target_type, + endNode.id AS target_id, [l in labels(endNode) + WHERE NOT l IN ['{BASE_ENTITY_LABEL}', '{BASE_NODE_LABEL}'] | l][0] AS target_type, endNode{{.* , embedding: Null, id: Null}} AS target_properties, idx ORDER BY idx @@ -545,33 +595,50 @@ def vector_query( self, query: VectorStoreQuery, **kwargs: Any ) -> Tuple[List[LabelledNode], List[float]]: """Query the graph store with a vector store query.""" - conditions = None + conditions = [] + filter_params = {} if query.filters: - conditions = [ - f"e.{filter.key} {filter.operator.value} {filter.value}" - for filter in query.filters.filters - ] + for index, filter in enumerate(query.filters.filters): + conditions.append( + f"{'NOT' if filter.operator.value in ['nin'] else ''} e.`{filter.key}` " + f"{convert_operator(filter.operator.value)} $param_{index}" + ) + filter_params[f"param_{index}"] = filter.value filters = ( - f" {query.filters.condition.value} ".join(conditions).replace("==", "=") - if conditions is not None + f" {query.filters.condition.value} ".join(conditions) + if conditions else "1 = 1" ) - - data = self.structured_query( - f"""MATCH (e:`__Entity__`) - WHERE e.embedding IS NOT NULL AND size(e.embedding) = $dimension AND ({filters}) - WITH e, vector.similarity.cosine(e.embedding, $embedding) AS score - ORDER BY score DESC LIMIT toInteger($limit) - RETURN e.id AS name, - [l in labels(e) WHERE l <> '__Entity__' | l][0] AS type, - e{{.* , embedding: Null, name: Null, id: Null}} AS properties, - score""", - param_map={ - "embedding": query.query_embedding, - "dimension": len(query.query_embedding), - "limit": query.similarity_top_k, - }, - ) + if not query.filters and self._supports_vector_index: + data = self.structured_query( + f"""CALL db.index.vector.queryNodes('{VECTOR_INDEX_NAME}', $limit, $embedding) + YIELD node, score RETURN node.id AS name, + [l in labels(node) WHERE NOT l IN ['{BASE_ENTITY_LABEL}', '{BASE_NODE_LABEL}'] | l][0] AS type, + node{{.* , embedding: Null, name: Null, id: Null}} AS properties, + score + """, + param_map={ + "embedding": query.query_embedding, + "limit": query.similarity_top_k, + }, + ) + else: + data = self.structured_query( + f"""MATCH (e:`{BASE_ENTITY_LABEL}`) + WHERE e.embedding IS NOT NULL AND size(e.embedding) = $dimension AND ({filters}) + WITH e, vector.similarity.cosine(e.embedding, $embedding) AS score + ORDER BY score DESC LIMIT toInteger($limit) + RETURN e.id AS name, + [l in labels(e) WHERE NOT l IN ['{BASE_ENTITY_LABEL}', '{BASE_NODE_LABEL}'] | l][0] AS type, + e{{.* , embedding: Null, name: Null, id: Null}} AS properties, + score""", + param_map={ + "embedding": query.query_embedding, + "dimension": len(query.query_embedding), + "limit": query.similarity_top_k, + **filter_params, + }, + ) data = data if data else [] nodes = [] @@ -901,5 +968,29 @@ def get_schema_str(self, refresh: bool = False) -> str: ] ) + def verify_version(self) -> None: + """ + Check if the connected Neo4j database version supports vector indexing + without specifying embedding dimension. + + Queries the Neo4j database to retrieve its version and compares it + against a target version (5.23.0) that is known to support vector + indexing. Raises a ValueError if the connected Neo4j version is + not supported. + """ + db_data = self.structured_query("CALL dbms.components()") + version = db_data[0]["versions"][0] + if "aura" in version: + version_tuple = (*map(int, version.split("-")[0].split(".")), 0) + else: + version_tuple = tuple(map(int, version.split("."))) + + target_version = (5, 23, 0) + + if version_tuple >= target_version: + self._supports_vector_index = True + else: + self._supports_vector_index = False + Neo4jPGStore = Neo4jPropertyGraphStore diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neo4j/pyproject.toml b/llama-index-integrations/graph_stores/llama-index-graph-stores-neo4j/pyproject.toml index c8f509a273878..ba1aee9fd6f97 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neo4j/pyproject.toml +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neo4j/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-graph-stores-neo4j" readme = "README.md" -version = "0.2.12" +version = "0.3.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.40" neo4j = "^5.16.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/README.md b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/README.md index d90e2467b09e2..93a11e65dd23b 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/README.md +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/README.md @@ -1 +1,36 @@ # LlamaIndex Graph_Stores Integration: Neptune + +Amazon Neptune makes it easy to work with graph data in the AWS Cloud. Amazon Neptune includes both Neptune Database and Neptune Analytics. + +Neptune Database is a serverless graph database designed for optimal scalability and availability. It provides a solution for graph database workloads that need to scale to 100,000 queries per second, Multi-AZ high availability, and multi-Region deployments. You can use Neptune Database for social networking, fraud alerting, and Customer 360 applications. + +Neptune Analytics is an analytics database engine that can quickly analyze large amounts of graph data in memory to get insights and find trends. Neptune Analytics is a solution for quickly analyzing existing graph databases or graph datasets stored in a data lake. It uses popular graph analytic algorithms and low-latency analytic queries. + +In this project, we integrate both Neptune Database and Neptune Analytics as the graph store to store the LlamaIndex graph data, + +and use openCypher to query the graph data. so that people can use Neptune to interact with LlamaIndex graph index. + +- Neptune Database + + - Property Graph Store: `NeptuneDatabasePropertyGraphStore` + - Knowledge Graph Store: `NeptuneDatabaseGraphStore` + +- Neptune Analytics + - Property Graph Store: `NeptuneAnalyticsPropertyGraphStore` + - Knowledge Graph Store: `NeptuneAnalyticsGraphStore` + +## Installation + +```shell +pip install llama-index llama-index-graph-stores-neptune +``` + +## Usage + +### Property Graph Store + +Please checkout this [tutorial](https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/property_graph/property_graph_neptune.ipynb) to learn how to use Amazon Neptune with LlamaIndex. + +### Knowledge Graph Store + +Checkout this [tutorial](https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/index_structs/knowledge_graph/NeptuneDatabaseKGIndexDemo.ipynb) to learn how to use Amazon Neptune with LlamaIndex. diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/__init__.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/__init__.py index 6ce4d333622b7..40bf2f93545b4 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/__init__.py +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/__init__.py @@ -1,4 +1,17 @@ from llama_index.graph_stores.neptune.analytics import NeptuneAnalyticsGraphStore from llama_index.graph_stores.neptune.database import NeptuneDatabaseGraphStore +from llama_index.graph_stores.neptune.analytics_property_graph import ( + NeptuneAnalyticsPropertyGraphStore, +) +from llama_index.graph_stores.neptune.database_property_graph import ( + NeptuneDatabasePropertyGraphStore, +) +from llama_index.graph_stores.neptune.neptune import NeptuneQueryException -__all__ = ["NeptuneAnalyticsGraphStore", "NeptuneDatabaseGraphStore"] +__all__ = [ + "NeptuneAnalyticsGraphStore", + "NeptuneDatabaseGraphStore", + "NeptuneAnalyticsPropertyGraphStore", + "NeptuneDatabasePropertyGraphStore", + "NeptuneQueryException", +] diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics.py index fede2b55c0a59..c93c10b1e64ed 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics.py +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics.py @@ -2,8 +2,10 @@ import json import logging +from .neptune import create_neptune_analytics_client from typing import Any, Dict, Optional -from .base import NeptuneBaseGraphStore, NeptuneQueryException +from .base import NeptuneBaseGraphStore +from .neptune import NeptuneQueryException logger = logging.getLogger(__name__) @@ -20,52 +22,10 @@ def __init__( ) -> None: """Create a new Neptune Analytics graph wrapper instance.""" self.node_label = node_label - try: - if client is not None: - self._client = client - else: - import boto3 - - if credentials_profile_name is not None: - session = boto3.Session(profile_name=credentials_profile_name) - else: - # use default credentials - session = boto3.Session() - - self.graph_identifier = graph_identifier - - if region_name: - self._client = session.client( - "neptune-graph", region_name=region_name - ) - else: - self._client = session.client("neptune-graph") - - except ImportError: - raise ModuleNotFoundError( - "Could not import boto3 python package. " - "Please install it with `pip install boto3`." - ) - except Exception as e: - if type(e).__name__ == "UnknownServiceError": - raise ModuleNotFoundError( - "NeptuneGraph requires a boto3 version 1.34.40 or greater." - "Please install it with `pip install -U boto3`." - ) from e - else: - raise ValueError( - "Could not load credentials to authenticate with AWS client. " - "Please check that credentials in the specified " - "profile name are valid." - ) from e - - try: - self._refresh_schema() - except Exception as e: - logger.error( - f"Could not retrieve schema for Neptune due to the following error: {e}" - ) - self.schema = None + self._client = create_neptune_analytics_client( + graph_identifier, client, credentials_profile_name, region_name + ) + self.graph_identifier = graph_identifier def query(self, query: str, params: dict = {}) -> Dict[str, Any]: """Query Neptune Analytics graph.""" @@ -83,6 +43,8 @@ def query(self, query: str, params: dict = {}) -> Dict[str, Any]: { "message": "An error occurred while executing the query.", "details": str(e), + "query": query, + "parameters": str(params), } ) diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics_property_graph.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics_property_graph.py new file mode 100644 index 0000000000000..075695bd3187d --- /dev/null +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics_property_graph.py @@ -0,0 +1,211 @@ +import json +import logging +from typing import Any, Dict, Tuple, List, Optional +from .neptune import ( + NeptuneQueryException, + remove_empty_values, + create_neptune_analytics_client, +) +from .base_property_graph import NeptuneBasePropertyGraph, BASE_ENTITY_LABEL +from llama_index.core.vector_stores.types import VectorStoreQuery +from llama_index.core.graph_stores.types import LabelledNode, EntityNode, ChunkNode + +logger = logging.getLogger(__name__) + + +class NeptuneAnalyticsPropertyGraphStore(NeptuneBasePropertyGraph): + supports_vector_queries: bool = True + + def __init__( + self, + graph_identifier: str, + client: Any = None, + credentials_profile_name: Optional[str] = None, + region_name: Optional[str] = None, + **kwargs: Any, + ) -> None: + """Create a new Neptune Analytics graph wrapper instance.""" + self._client = create_neptune_analytics_client( + graph_identifier, client, credentials_profile_name, region_name + ) + self.graph_identifier = graph_identifier + + def structured_query(self, query: str, param_map: Dict[str, Any] = None) -> Any: + """Run the structured query. + + Args: + query (str): The query to run + param_map (Dict[str, Any] | None, optional): A dictionary of query parameters. Defaults to None. + + Raises: + NeptuneQueryException: An exception from Neptune with details + + Returns: + Any: The results of the query + """ + param_map = param_map or {} + + try: + logger.debug( + f"structured_query() query: {query} parameters: {json.dumps(param_map)}" + ) + resp = self.client.execute_query( + graphIdentifier=self.graph_identifier, + queryString=query, + parameters=param_map, + language="OPEN_CYPHER", + ) + return json.loads(resp["payload"].read().decode("UTF-8"))["results"] + except Exception as e: + raise NeptuneQueryException( + { + "message": "An error occurred while executing the query.", + "details": str(e), + "query": query, + "parameters": str(param_map), + } + ) + + def vector_query(self, query: VectorStoreQuery, **kwargs: Any) -> Tuple[List[Any]]: + """Query the graph store with a vector store query. + + Returns: + (nodes, score): The nodes and their associated score + """ + conditions = None + if query.filters: + conditions = [ + f"e.{filter.key} {filter.operator.value} {filter.value}" + for filter in query.filters.filters + ] + filters = ( + f" {query.filters.condition.value} ".join(conditions).replace("==", "=") + if conditions is not None + else "1 = 1" + ) + + data = self.structured_query( + f"""MATCH (e:`{BASE_ENTITY_LABEL}`) + WHERE ({filters}) + CALL neptune.algo.vectors.get(e) + YIELD embedding + WHERE embedding IS NOT NULL + CALL neptune.algo.vectors.topKByNode(e) + YIELD node, score + WITH e, score + ORDER BY score DESC LIMIT $limit + RETURN e.id AS name, + [l in labels(e) WHERE l <> '{BASE_ENTITY_LABEL}' | l][0] AS type, + e{{.* , embedding: Null, name: Null, id: Null}} AS properties, + score""", + param_map={ + "embedding": query.query_embedding, + "dimension": len(query.query_embedding), + "limit": query.similarity_top_k, + }, + ) + data = data if data else [] + + nodes = [] + scores = [] + for record in data: + node = EntityNode( + name=record["name"], + label=record["type"], + properties=remove_empty_values(record["properties"]), + ) + nodes.append(node) + scores.append(record["score"]) + + return (nodes, scores) + + def upsert_nodes(self, nodes: List[LabelledNode]) -> None: + """Upsert the nodes in the graph. + + Args: + nodes (List[LabelledNode]): The list of nodes to upsert + """ + # Lists to hold separated types + entity_dicts: List[dict] = [] + chunk_dicts: List[dict] = [] + + # Sort by type + for item in nodes: + if isinstance(item, EntityNode): + entity_dicts.append({**item.dict(), "id": item.id}) + elif isinstance(item, ChunkNode): + chunk_dicts.append({**item.dict(), "id": item.id}) + else: + # Log that we do not support these types of nodes + # Or raise an error? + pass + + if chunk_dicts: + for d in chunk_dicts: + self.structured_query( + """ + WITH $data AS row + MERGE (c:Chunk {id: row.id}) + SET c.text = row.text + SET c += removeKeyFromMap(row.properties, '') + WITH c, row.embedding as e + WHERE e IS NOT NULL + CALL neptune.algo.vectors.upsert(c, e) + RETURN count(*) + """, + param_map={"data": d}, + ) + + if entity_dicts: + for d in entity_dicts: + self.structured_query( + """ + WITH $data AS row + MERGE (e:`""" + + BASE_ENTITY_LABEL + + """` {id: row.id}) + SET e += removeKeyFromMap(row.properties, '') + SET e.name = row.name + SET e:`""" + + str(d["name"]) + + """` + WITH e, row + WHERE removeKeyFromMap(row.properties, '').triplet_source_id IS NOT NULL + MERGE (c:Chunk {id: removeKeyFromMap(row.properties, '').triplet_source_id}) + MERGE (e)<-[:MENTIONS]-(c) + WITH e, row.embedding as em + CALL neptune.algo.vectors.upsert(e, em) + RETURN count(*) as count + """, + param_map={"data": d}, + ) + + def _get_summary(self) -> Dict: + """Get the Summary of the graph topology. + + Returns: + Dict: The graph summary + """ + try: + response = self.client.get_graph_summary( + graphIdentifier=self.graph_identifier, mode="detailed" + ) + except Exception as e: + raise NeptuneQueryException( + { + "message": ("Summary API error occurred on Neptune Analytics"), + "details": str(e), + } + ) + + try: + summary = response["graphSummary"] + except Exception: + raise NeptuneQueryException( + { + "message": "Summary API did not return a valid response.", + "details": response.content.decode(), + } + ) + else: + return summary diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base.py index a58373bbbe13f..e1533c66e6641 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base.py +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base.py @@ -1,29 +1,12 @@ from abc import abstractmethod import logging -from typing import Any, Dict, List, Tuple, Union, Optional +from typing import Any, Dict, List, Optional from llama_index.core.graph_stores.types import GraphStore +from .neptune import refresh_schema logger = logging.getLogger(__name__) -class NeptuneQueryException(Exception): - """Exception for the Neptune queries.""" - - def __init__(self, exception: Union[str, Dict]): - if isinstance(exception, dict): - self.message = exception["message"] if "message" in exception else "unknown" - self.details = exception["details"] if "details" in exception else "unknown" - else: - self.message = exception - self.details = "unknown" - - def get_message(self) -> str: - return self.message - - def get_details(self) -> Any: - return self.details - - class NeptuneBaseGraphStore(GraphStore): """This is an abstract base class that represents the shared features across the NeptuneDatabaseGraphStore and NeptuneAnalyticsGraphStore classes. @@ -115,10 +98,9 @@ def delete_entity(entity: str) -> None: def get_schema(self, refresh: bool = False) -> str: """Get the schema of the Neptune KG store.""" - if self.schema and not refresh: - return self.schema - self.refresh_schema() - logger.debug(f"get_schema() schema:\n{self.schema}") + if refresh or not self.schema: + self.schema = refresh_schema(self.query, self._get_summary())["schema_str"] + return self.schema @abstractmethod @@ -128,106 +110,3 @@ def query(self, query: str, params: dict = {}) -> Dict[str, Any]: @abstractmethod def _get_summary(self) -> Dict: raise NotImplementedError - - def _get_labels(self) -> Tuple[List[str], List[str]]: - """Get node and edge labels from the Neptune statistics summary.""" - summary = self._get_summary() - n_labels = summary["nodeLabels"] - e_labels = summary["edgeLabels"] - return n_labels, e_labels - - def _get_triples(self, e_labels: List[str]) -> List[str]: - """Get the node-edge->node triple combinations.""" - triple_query = """ - MATCH (a)-[e:`{e_label}`]->(b) - WITH a,e,b LIMIT 3000 - RETURN DISTINCT labels(a) AS from, type(e) AS edge, labels(b) AS to - LIMIT 10 - """ - - triple_template = "(:`{a}`)-[:`{e}`]->(:`{b}`)" - triple_schema = [] - for label in e_labels: - q = triple_query.format(e_label=label) - data = self.query(q) - for d in data: - triple = triple_template.format( - a=d["from"][0], e=d["edge"], b=d["to"][0] - ) - triple_schema.append(triple) - - return triple_schema - - def _get_node_properties(self, n_labels: List[str], types: Dict) -> List: - """Get the node properties for the label.""" - node_properties_query = """ - MATCH (a:`{n_label}`) - RETURN properties(a) AS props - LIMIT 100 - """ - node_properties = [] - for label in n_labels: - q = node_properties_query.format(n_label=label) - data = {"label": label, "properties": self.query(q)} - s = set({}) - for p in data["properties"]: - for k, v in p["props"].items(): - s.add((k, types[type(v).__name__])) - - np = { - "properties": [{"property": k, "type": v} for k, v in s], - "labels": label, - } - node_properties.append(np) - - return node_properties - - def _get_edge_properties(self, e_labels: List[str], types: Dict[str, Any]) -> List: - """Get the edge properties for the label.""" - edge_properties_query = """ - MATCH ()-[e:`{e_label}`]->() - RETURN properties(e) AS props - LIMIT 100 - """ - edge_properties = [] - for label in e_labels: - q = edge_properties_query.format(e_label=label) - data = {"label": label, "properties": self.query(q)} - s = set({}) - for p in data["properties"]: - for k, v in p["props"].items(): - s.add((k, types[type(v).__name__])) - - ep = { - "type": label, - "properties": [{"property": k, "type": v} for k, v in s], - } - edge_properties.append(ep) - - return edge_properties - - def _refresh_schema(self) -> None: - """ - Refreshes the Neptune graph schema information. - """ - types = { - "str": "STRING", - "float": "DOUBLE", - "int": "INTEGER", - "list": "LIST", - "dict": "MAP", - "bool": "BOOLEAN", - } - n_labels, e_labels = self._get_labels() - triple_schema = self._get_triples(e_labels) - node_properties = self._get_node_properties(n_labels, types) - edge_properties = self._get_edge_properties(e_labels, types) - - self.schema = f""" - Node properties are the following: - {node_properties} - Relationship properties are the following: - {edge_properties} - The relationships are the following: - {triple_schema} - """ diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base_property_graph.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base_property_graph.py new file mode 100644 index 0000000000000..6e119ac641590 --- /dev/null +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base_property_graph.py @@ -0,0 +1,371 @@ +from abc import abstractmethod +import logging +from llama_index.core.prompts import PromptTemplate +from llama_index.core.graph_stores.prompts import DEFAULT_CYPHER_TEMPALTE +from typing import Any, Dict, List, Tuple, Optional +from llama_index.core.graph_stores.types import ( + LabelledNode, + PropertyGraphStore, + Relation, + ChunkNode, + EntityNode, + Triplet, +) +from llama_index.core.vector_stores.types import VectorStoreQuery +from .neptune import remove_empty_values, refresh_schema + +logger = logging.getLogger(__name__) +BASE_ENTITY_LABEL = "__Entity__" + + +class NeptuneBasePropertyGraph(PropertyGraphStore): + supports_structured_queries: bool = True + text_to_cypher_template: PromptTemplate = DEFAULT_CYPHER_TEMPALTE + schema = None + structured_schema = None + + def __init__() -> None: + pass + + @property + def client(self) -> Any: + return self._client + + def get( + self, properties: Dict = None, ids: List[str] = None, exact_match: bool = True + ) -> List[LabelledNode]: + """Get the nodes from the graph. + + Args: + properties (Dict | None, optional): The properties to retrieve. Defaults to None. + ids (List[str] | None, optional): A list of ids to find in the graph. Defaults to None. + exact_match (bool, optional): Whether to do exact match on properties. Defaults to True. + + Returns: + List[LabelledNode]: A list of nodes returned + """ + cypher_statement = "MATCH (e) " + + params = {} + if properties or ids: + cypher_statement += "WHERE " + + if ids: + if exact_match: + cypher_statement += "e.id IN $ids " + else: + cypher_statement += "WHERE size([x IN $ids where toLower(e.id) CONTAINS toLower(x)]) > 0 " + params["ids"] = ids + + if properties: + prop_list = [] + for i, prop in enumerate(properties): + prop_list.append(f"e.`{prop}` = $property_{i}") + params[f"property_{i}"] = properties[prop] + cypher_statement += " AND ".join(prop_list) + + return_statement = ( + """ + WITH e + RETURN e.id AS name, + [l in labels(e) WHERE l <> '""" + + BASE_ENTITY_LABEL + + """' | l][0] AS type, + e{.* , embedding: Null, id: Null} AS properties + """ + ) + cypher_statement += return_statement + response = self.structured_query(cypher_statement, param_map=params) + response = response if response else [] + + nodes = [] + for record in response: + # text indicates a chunk node + # none on the type indicates an implicit node, likely a chunk node + if "text" in record["properties"] or record["type"] is None: + text = record["properties"].pop("text", "") + nodes.append( + ChunkNode( + id_=record["name"], + text=text, + properties=remove_empty_values(record["properties"]), + ) + ) + else: + nodes.append( + EntityNode( + name=record["name"], + label=record["type"], + properties=remove_empty_values(record["properties"]), + ) + ) + + return nodes + + def get_triplets( + self, + entity_names: Optional[List[str]] = None, + relation_names: Optional[List[str]] = None, + properties: Optional[dict] = None, + ids: Optional[List[str]] = None, + ) -> List[Triplet]: + """Get the triplets of the entities in the graph. + + Args: + entity_names (Optional[List[str]], optional): The entity names to find. Defaults to None. + relation_names (Optional[List[str]], optional): The relation names to follow. Defaults to None. + properties (Optional[dict], optional): The properties to return. Defaults to None. + ids (Optional[List[str]], optional): The ids to search on. Defaults to None. + + Returns: + List[Triplet]: A list of triples + """ + cypher_statement = f"MATCH (e:`{BASE_ENTITY_LABEL}`) " + + params = {} + if entity_names or properties or ids: + cypher_statement += "WHERE " + + if entity_names: + cypher_statement += "e.name in $entity_names " + params["entity_names"] = entity_names + + if ids: + cypher_statement += "e.id in $ids " + params["ids"] = ids + + if properties: + prop_list = [] + for i, prop in enumerate(properties): + prop_list.append(f"e.`{prop}` = $property_{i}") + params[f"property_{i}"] = properties[prop] + cypher_statement += " AND ".join(prop_list) + + return_statement = f""" + WITH e + MATCH (e)-[r{':`' + '`|`'.join(relation_names) + '`' if relation_names else ''}]->(t:{BASE_ENTITY_LABEL}) + RETURN e.name AS source_id, [l in labels(e) WHERE l <> '{BASE_ENTITY_LABEL}' | l][0] AS source_type, + e{{.* , embedding: Null, name: Null}} AS source_properties, + type(r) AS type, + r{{.*}} AS rel_properties, + t.name AS target_id, [l in labels(t) WHERE l <> '{BASE_ENTITY_LABEL}' | l][0] AS target_type, + t{{.* , embedding: Null, name: Null}} AS target_properties""" + cypher_statement += return_statement + + data = self.structured_query(cypher_statement, param_map=params) + data = data if data else [] + + triples = [] + for record in data: + source = EntityNode( + name=record["source_id"], + label=record["source_type"], + properties=remove_empty_values(record["source_properties"]), + ) + target = EntityNode( + name=record["target_id"], + label=record["target_type"], + properties=remove_empty_values(record["target_properties"]), + ) + rel = Relation( + source_id=record["source_id"], + target_id=record["target_id"], + label=record["type"], + properties=remove_empty_values(record["rel_properties"]), + ) + triples.append([source, rel, target]) + return triples + + def get_rel_map( + self, + graph_nodes: List[LabelledNode], + depth: int = 2, + limit: int = 30, + ignore_rels: List[str] = None, + ) -> List[Tuple[Any]]: + """Get a depth aware map of relations. + + Args: + graph_nodes (List[LabelledNode]): The nodes + depth (int, optional): The depth to traverse. Defaults to 2. + limit (int, optional): The limit of numbers to return. Defaults to 30. + ignore_rels (List[str] | None, optional): Relations to ignore. Defaults to None. + + Returns: + List[Tuple[LabelledNode | Relation]]: The node/relationship pairs + """ + triples = [] + + ids = [node.id for node in graph_nodes] + # Needs some optimization + if len(ids) > 0: + logger.debug(f"get_rel_map() ids: {ids}") + response = self.structured_query( + f""" + WITH $ids AS id_list + UNWIND range(0, size(id_list) - 1) AS idx + MATCH (e:`{BASE_ENTITY_LABEL}`) + WHERE e.id = id_list[idx] + MATCH p=(e)-[r*1..{depth}]-(other) + WHERE size([rel in relationships(p) WHERE type(rel) <> 'MENTIONS']) = size(relationships(p)) + UNWIND relationships(p) AS rel + WITH distinct rel, idx + WITH startNode(rel) AS source, + type(rel) AS type, + endNode(rel) AS endNode, + idx + LIMIT {limit} + RETURN source.id AS source_id, [l in labels(source) WHERE l <> '{BASE_ENTITY_LABEL}' | l][0] AS source_type, + source{{.* , embedding: Null, id: Null}} AS source_properties, + type, + endNode.id AS target_id, [l in labels(endNode) WHERE l <> '{BASE_ENTITY_LABEL}' | l][0] AS target_type, + endNode{{.* , embedding: Null, id: Null}} AS target_properties, + idx + ORDER BY idx + LIMIT {limit} + """, + param_map={"ids": ids}, + ) + else: + response = [] + response = response if response else [] + + ignore_rels = ignore_rels or [] + for record in response: + if record["type"] in ignore_rels: + continue + + source = EntityNode( + name=record["source_id"], + label=record["source_type"], + properties=remove_empty_values(record["source_properties"]), + ) + target = EntityNode( + name=record["target_id"], + label=record["target_type"], + properties=remove_empty_values(record["target_properties"]), + ) + rel = Relation( + source_id=record["source_id"], + target_id=record["target_id"], + label=record["type"], + ) + triples.append([source, rel, target]) + + return triples + + @abstractmethod + def upsert_nodes(self, nodes: List[LabelledNode]) -> None: + raise NotImplementedError + + def upsert_relations(self, relations: List[Relation]) -> None: + """Upsert relations in the graph. + + Args: + relations (List[Relation]): Relations to upsert + """ + for r in relations: + self.structured_query( + """ + WITH $data AS row + MERGE (source {id: row.source_id}) + ON CREATE SET source:Chunk + MERGE (target {id: row.target_id}) + ON CREATE SET target:Chunk + MERGE (source)-[r:`""" + + r.label + + """`]->(target) + SET r+= removeKeyFromMap(row.properties, '') + RETURN count(*) + """, + param_map={"data": r.dict()}, + ) + + def delete( + self, + entity_names: List[str] = None, + relation_names: List[str] = None, + properties: Dict = None, + ids: List[str] = None, + ) -> None: + """Delete data matching the criteria. + + Args: + entity_names (List[str] | None, optional): The entity names to delete. Defaults to None. + relation_names (List[str] | None, optional): The relation names to delete. Defaults to None. + properties (Dict | None, optional): The properties to remove. Defaults to None. + ids (List[str] | None, optional): The ids to remove. Defaults to None. + """ + if entity_names: + self.structured_query( + "MATCH (n) WHERE n.name IN $entity_names DETACH DELETE n", + param_map={"entity_names": entity_names}, + ) + + if ids: + self.structured_query( + "MATCH (n) WHERE n.id IN $ids DETACH DELETE n", + param_map={"ids": ids}, + ) + + if relation_names: + for rel in relation_names: + self.structured_query(f"MATCH ()-[r:`{rel}`]->() DELETE r") + + if properties: + cypher = "MATCH (e) WHERE " + prop_list = [] + params = {} + for i, prop in enumerate(properties): + prop_list.append(f"e.`{prop}` = $property_{i}") + params[f"property_{i}"] = properties[prop] + cypher += " AND ".join(prop_list) + self.structured_query(cypher + " DETACH DELETE e", param_map=params) + + @abstractmethod + def structured_query(self, query: str, param_map: Dict[str, Any] = None) -> Any: + raise NotImplementedError + + def query(self, query: str, params: dict = {}) -> Dict[str, Any]: + """Run the query. + + Args: + query (str): The query to run + params (dict, optional): The query parameters. Defaults to {}. + + Returns: + Dict[str, Any]: The query results + """ + return self.structured_query(query, params) + + @abstractmethod + def vector_query(self, query: VectorStoreQuery, **kwargs: Any) -> Tuple[List[Any]]: + raise NotImplementedError + + @abstractmethod + def _get_summary(self) -> Dict: + raise NotImplementedError + + def get_schema(self, refresh: bool = False) -> Any: + """Get the schema of the graph store.""" + if refresh or not self.schema: + schema = refresh_schema(self.query, self._get_summary()) + self.schema = schema["schema_str"] + self.structured_schema = schema["structured_schema"] + return self.structured_schema + + def get_schema_str(self, refresh: bool = False) -> str: + """Get the schema as a string. + + Args: + refresh (bool, optional): True to force refresh of the schema. Defaults to False. + + Returns: + str: A string description of the schema + """ + if refresh or not self.schema: + schema = refresh_schema(self.query, self._get_summary()) + self.schema = schema["schema_str"] + self.structured_schema = schema["structured_schema"] + + return self.schema diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database.py index 2b7e0f3993a93..1d6a05e74c33b 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database.py +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database.py @@ -2,9 +2,9 @@ import logging from typing import Any, Dict, Optional - -from .base import NeptuneBaseGraphStore, NeptuneQueryException -import boto3 +from .neptune import create_neptune_database_client +from .base import NeptuneBaseGraphStore +from .neptune import NeptuneQueryException import json logger = logging.getLogger(__name__) @@ -25,58 +25,9 @@ def __init__( ) -> None: """Create a new Neptune Database graph wrapper instance.""" self.node_label = node_label - try: - if credentials_profile_name is not None: - session = boto3.Session(profile_name=credentials_profile_name) - else: - # use default credentials - session = boto3.Session() - - client_params = {} - if region_name: - client_params["region_name"] = region_name - - protocol = "https" if use_https else "http" - - client_params["endpoint_url"] = f"{protocol}://{host}:{port}" - - if sign: - self._client = session.client("neptunedata", **client_params) - else: - from botocore import UNSIGNED - from botocore.config import Config - - self._client = session.client( - "neptunedata", - **client_params, - config=Config(signature_version=UNSIGNED), - ) - - except ImportError: - raise ModuleNotFoundError( - "Could not import boto3 python package. " - "Please install it with `pip install boto3`." - ) - except Exception as e: - if type(e).__name__ == "UnknownServiceError": - raise ModuleNotFoundError( - "Neptune Database requires a boto3 version 1.34.40 or greater." - "Please install it with `pip install -U boto3`." - ) from e - else: - raise ValueError( - "Could not load credentials to authenticate with AWS client. " - "Please check that credentials in the specified " - "profile name are valid." - ) from e - - try: - self._refresh_schema() - except Exception as e: - logger.error( - f"Could not retrieve schema for Neptune due to the following error: {e}" - ) - self.schema = None + self._client = create_neptune_database_client( + host, port, client, credentials_profile_name, region_name, sign, use_https + ) def query(self, query: str, params: dict = {}) -> Dict[str, Any]: """Query Neptune database.""" @@ -90,6 +41,8 @@ def query(self, query: str, params: dict = {}) -> Dict[str, Any]: { "message": "An error occurred while executing the query.", "details": str(e), + "query": query, + "parameters": str(params), } ) diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database_property_graph.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database_property_graph.py new file mode 100644 index 0000000000000..e83ee6a060bd6 --- /dev/null +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database_property_graph.py @@ -0,0 +1,174 @@ +import json +import logging + +from typing import Any, Dict, Tuple, List, Optional +from .neptune import NeptuneQueryException, create_neptune_database_client +from .base_property_graph import NeptuneBasePropertyGraph, BASE_ENTITY_LABEL +from llama_index.core.vector_stores.types import VectorStoreQuery +from llama_index.core.graph_stores.types import LabelledNode, EntityNode, ChunkNode + +logger = logging.getLogger(__name__) + + +class NeptuneDatabasePropertyGraphStore(NeptuneBasePropertyGraph): + supports_vector_queries: bool = False + + def __init__( + self, + host: str, + port: int = 8182, + client: Any = None, + credentials_profile_name: Optional[str] = None, + region_name: Optional[str] = None, + sign: bool = True, + use_https: bool = True, + **kwargs: Any, + ) -> None: + """Init. + + Args: + host (str): The host endpoint + port (int, optional): The port. Defaults to 8182. + client (Any, optional): If provided, this is the client that will be used. Defaults to None. + credentials_profile_name (Optional[str], optional): If provided this is the credentials profile that will be used. Defaults to None. + region_name (Optional[str], optional): The region to use. Defaults to None. + sign (bool, optional): True will SigV4 sign all requests, False will not. Defaults to True. + use_https (bool, optional): True to use https, False to use http. Defaults to True. + """ + self._client = create_neptune_database_client( + host, port, client, credentials_profile_name, region_name, sign, use_https + ) + + def structured_query(self, query: str, param_map: Dict[str, Any] = None) -> Any: + """Run the structured query. + + Args: + query (str): The query to run + param_map (Dict[str, Any] | None, optional): A dictionary of query parameters. Defaults to None. + + Raises: + NeptuneQueryException: An exception from Neptune with details + + Returns: + Any: The results of the query + """ + param_map = param_map or {} + + try: + logger.debug( + f"structured_query() query: {query} parameters: {json.dumps(param_map)}" + ) + + return self.client.execute_open_cypher_query( + openCypherQuery=query, parameters=json.dumps(param_map) + )["results"] + except Exception as e: + raise NeptuneQueryException( + { + "message": "An error occurred while executing the query.", + "details": str(e), + "query": query, + "parameters": str(param_map), + } + ) + + def vector_query(self, query: VectorStoreQuery, **kwargs: Any) -> Tuple[List[Any]]: + """NOT SUPPORTED. + + Args: + query (VectorStoreQuery): _description_ + + Raises: + NotImplementedError: _description_ + + Returns: + Tuple[List[LabelledNode] | List[float]]: _description_ + """ + raise NotImplementedError + + def upsert_nodes(self, nodes: List[LabelledNode]) -> None: + """Upsert the nodes in the graph. + + Args: + nodes (List[LabelledNode]): The list of nodes to upsert + """ + # Lists to hold separated types + entity_dicts: List[dict] = [] + chunk_dicts: List[dict] = [] + + # Sort by type + for item in nodes: + if isinstance(item, EntityNode): + entity_dicts.append({**item.dict(), "id": item.id}) + elif isinstance(item, ChunkNode): + chunk_dicts.append({**item.dict(), "id": item.id}) + else: + # Log that we do not support these types of nodes + # Or raise an error? + pass + + if chunk_dicts: + for d in chunk_dicts: + self.structured_query( + """ + WITH $data AS row + MERGE (c:Chunk {id: row.id}) + SET c.text = row.text + SET c += removeKeyFromMap(row.properties, '') + RETURN count(*) + """, + param_map={"data": d}, + ) + + if entity_dicts: + for d in entity_dicts: + self.structured_query( + """ + WITH $data AS row + MERGE (e:`""" + + BASE_ENTITY_LABEL + + """` {id: row.id}) + SET e += removeKeyFromMap(row.properties, '') + SET e.name = row.name + SET e:`""" + + str(d["name"]) + + """` + WITH e, row + WHERE removeKeyFromMap(row.properties, '').triplet_source_id IS NOT NULL + MERGE (c:Chunk {id: removeKeyFromMap(row.properties, '').triplet_source_id}) + MERGE (e)<-[:MENTIONS]-(c) + RETURN count(*) as count + """, + param_map={"data": d}, + ) + + def _get_summary(self) -> Dict: + """Get the Summary of the graph schema. + + Returns: + Dict: The graph summary + """ + try: + response = self.client.get_propertygraph_summary() + except Exception as e: + raise NeptuneQueryException( + { + "message": ( + "Summary API is not available for this instance of Neptune," + "ensure the engine version is >=1.2.1.0" + ), + "details": str(e), + } + ) + + try: + summary = response["payload"]["graphSummary"] + except Exception: + raise NeptuneQueryException( + { + "message": "Summary API did not return a valid response.", + "details": response.content.decode(), + } + ) + else: + return summary diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/neptune.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/neptune.py new file mode 100644 index 0000000000000..9666a87c968dd --- /dev/null +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/neptune.py @@ -0,0 +1,315 @@ +from typing import Union, Dict, Any, List +import boto3 +from botocore import UNSIGNED +from botocore.config import Config +from dateutil import parser + + +class NeptuneQueryException(Exception): + """Exception for the Neptune queries.""" + + def __init__(self, exception: Union[str, Dict]): + if isinstance(exception, dict): + self.message = exception["message"] if "message" in exception else "unknown" + self.details = exception["details"] if "details" in exception else "unknown" + else: + self.message = exception + self.details = "unknown" + + def get_message(self) -> str: + return self.message + + def get_details(self) -> Any: + return self.details + + +def remove_empty_values(input_dict: Dict) -> Dict: + """ + Remove entries with empty values from the dictionary. + + Parameters: + + input_dict (dict): The dictionary from which empty values need to be removed. + + Returns: + dict: A new dictionary with all empty values removed. + """ + # Create a new dictionary excluding empty values + output_dict = {key: value for key, value in input_dict.items() if value} + return output_dict or {} + + +def create_neptune_database_client( + host: str, + port: int, + provided_client: Any, + credentials_profile_name: str, + region_name: str, + sign: bool, + use_https: bool, +): + """Create a Neptune Database Client. + + Args: + host (str): The host endpoint + port (int, optional): The port. Defaults to 8182. + client (Any, optional): If provided, this is the client that will be used. Defaults to None. + credentials_profile_name (Optional[str], optional): If provided this is the credentials profile that will be used. Defaults to None. + region_name (Optional[str], optional): The region to use. Defaults to None. + sign (bool, optional): True will SigV4 sign all requests, False will not. Defaults to True. + use_https (bool, optional): True to use https, False to use http. Defaults to True. + + Returns: + Any: The neptune client + """ + try: + client = None + if provided_client is not None: + client = client + else: + if credentials_profile_name is not None: + session = boto3.Session(profile_name=credentials_profile_name) + else: + # use default credentials + session = boto3.Session() + + client_params = {} + if region_name: + client_params["region_name"] = region_name + + protocol = "https" if use_https else "http" + + client_params["endpoint_url"] = f"{protocol}://{host}:{port}" + + if sign: + client = session.client("neptunedata", **client_params) + else: + client = session.client( + "neptunedata", + **client_params, + config=Config(signature_version=UNSIGNED), + ) + return client + except ImportError: + raise ModuleNotFoundError( + "Could not import boto3 python package. " + "Please install it with `pip install boto3`." + ) + except Exception as e: + if type(e).__name__ == "UnknownServiceError": + raise ModuleNotFoundError( + "Neptune Database requires a boto3 version 1.34.40 or greater." + "Please install it with `pip install -U boto3`." + ) from e + else: + raise ValueError( + "Could not load credentials to authenticate with AWS client. " + "Please check that credentials in the specified " + "profile name are valid." + ) from e + + +def create_neptune_analytics_client( + graph_identifier: str, + provided_client: Any, + credentials_profile_name: str, + region_name: str, +): + """Creates a Neptune Analytics Client. + + Args: + graph_identifier (str): The geaph identifier + provided_client (Any): A client + credentials_profile_name (str): The profile name + region_name (str): The region name + + Returns: + Any: The client + """ + try: + if not graph_identifier.startswith("g-") or "." in graph_identifier: + raise ValueError( + "The graph_identifier provided is not a valid value. The graph identifier for a Neptune Analytics graph must be in the form g-XXXXXXXXXX." + ) + + client = None + if provided_client is not None: + client = client + else: + if credentials_profile_name is not None: + session = boto3.Session(profile_name=credentials_profile_name) + else: + # use default credentials + session = boto3.Session() + + if region_name: + client = session.client( + "neptune-graph", + region_name=region_name, + config=( + Config( + retries={"total_max_attempts": 1, "mode": "standard"}, + read_timeout=None, + ) + ), + ) + else: + client = session.client( + "neptune-graph", + config=( + Config( + retries={"total_max_attempts": 1, "mode": "standard"}, + read_timeout=None, + ) + ), + ) + return client + except ImportError: + raise ModuleNotFoundError( + "Could not import boto3 python package. " + "Please install it with `pip install boto3`." + ) + except Exception as e: + if type(e).__name__ == "UnknownServiceError": + raise ModuleNotFoundError( + "NeptuneGraph requires a boto3 version 1.34.40 or greater." + "Please install it with `pip install -U boto3`." + ) from e + else: + raise ValueError( + "Could not load credentials to authenticate with AWS client. " + "Please check that credentials in the specified " + "profile name are valid." + ) from e + + +def refresh_structured_schema(query: str, summary: Dict) -> None: + """Refreshes the Neptune graph schema information.""" + + +def refresh_schema(query: str, summary: Dict) -> None: + """Refreshes the Neptune graph schema information.""" + types = { + "str": "STRING", + "float": "DOUBLE", + "int": "INTEGER", + "list": "LIST", + "dict": "MAP", + "bool": "BOOLEAN", + "datetime": "DATETIME", + } + n_labels = summary["nodeLabels"] + e_labels = summary["edgeLabels"] + triple_schema = _get_triples(query, e_labels) + node_properties = _get_node_properties(query, n_labels, types) + edge_properties = _get_edge_properties(query, e_labels, types) + structured_schema = { + "node_labels": n_labels, + "edge_labels": e_labels, + "node_properties": node_properties, + "edge_properties": edge_properties, + "triples": triple_schema, + } + + return { + "schema_str": f""" + Node properties are the following: + {node_properties} + Relationship properties are the following: + {edge_properties} + The relationships are the following: + {triple_schema} + """, + "structured_schema": structured_schema, + } + + +def _get_triples(query: str, e_labels: List[str]) -> List[str]: + """Get the node-edge->node triple combinations.""" + triple_query = """ + MATCH (a)-[e:`{e_label}`]->(b) + WITH a,e,b LIMIT 3000 + RETURN DISTINCT labels(a) AS from, type(e) AS edge, labels(b) AS to + LIMIT 10 + """ + + triple_template = "(:`{a}`)-[:`{e}`]->(:`{b}`)" + triple_schema = [] + for label in e_labels: + q = triple_query.format(e_label=label) + data = query(q) + for d in data: + triple = triple_template.format(a=d["from"][0], e=d["edge"], b=d["to"][0]) + triple_schema.append(triple) + + return triple_schema + + +def _get_node_properties(query: str, n_labels: List[str], types: Dict) -> List: + """Get the node properties for the label.""" + node_properties_query = """ + MATCH (a:`{n_label}`) + RETURN properties(a) AS props + LIMIT 100 + """ + node_properties = [] + for label in n_labels: + q = node_properties_query.format(n_label=label) + data = {"label": label, "properties": query(q)} + s = set({}) + for p in data["properties"]: + for k, v in p["props"].items(): + data_type = types[type(v).__name__] + if types[type(v).__name__] == "STRING": + try: + if bool(parser.parse(v)): + data_type = "DATETIME" + else: + data_type = "STRING" + except ValueError: + data_type = "STRING" + s.add((k, data_type)) + + np = { + "properties": [{"property": k, "type": v} for k, v in s], + "labels": label, + } + node_properties.append(np) + + return node_properties + + +def _get_edge_properties( + query: str, e_labels: List[str], types: Dict[str, Any] +) -> List: + """Get the edge properties for the label.""" + edge_properties_query = """ + MATCH ()-[e:`{e_label}`]->() + RETURN properties(e) AS props + LIMIT 100 + """ + edge_properties = [] + for label in e_labels: + q = edge_properties_query.format(e_label=label) + data = {"label": label, "properties": query(q)} + s = set({}) + for p in data["properties"]: + for k, v in p["props"].items(): + data_type = types[type(v).__name__] + if types[type(v).__name__] == "STRING": + try: + if bool(parser.parse(v)): + data_type = "DATETIME" + else: + data_type = "STRING" + except ValueError: + data_type = "STRING" + s.add((k, data_type)) + + ep = { + "type": label, + "properties": [{"property": k, "type": v} for k, v in s], + } + edge_properties.append(ep) + + return edge_properties diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml index e46145fe550c2..1afd9f7320594 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml @@ -13,7 +13,9 @@ import_path = "llama_index.graph_stores.neptune" [tool.llamahub.class_authors] NeptuneAnalyticsGraphStore = "llama-index" +NeptuneAnalyticsPropertyGraphStore = "llama-index" NeptuneDatabaseGraphStore = "llama-index" +NeptuneDatabasePropertyGraphStore = "llama-index" [tool.mypy] disallow_untyped_defs = true @@ -28,12 +30,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-graph-stores-neptune" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" boto3 = "^1.34.40" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-tidb/pyproject.toml b/llama-index-integrations/graph_stores/llama-index-graph-stores-tidb/pyproject.toml index 9745b178b8414..6a11eba4fc452 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-tidb/pyproject.toml +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-tidb/pyproject.toml @@ -28,14 +28,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-graph-stores-tidb" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" tidb-vector = "^0.0.9" PyMySQL = "^1.1.1" SQLAlchemy = "^2.0.30" -llama-index-core = "^0.10.40" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] jupyter = "^1.0.0" diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/.gitignore b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/BUILD b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/BUILD new file mode 100644 index 0000000000000..faa90e3c34115 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/BUILD @@ -0,0 +1,4 @@ +poetry_requirements( + name="poetry", + module_mapping={"flagembedding": ["FlagEmbedding"], "peft": ["peft"]}, +) diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/Makefile b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/README.md b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/README.md new file mode 100644 index 0000000000000..c71c8e7b9eb82 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/README.md @@ -0,0 +1,18 @@ +# LlamaIndex Indices Integration: Bge-M3 + +## Setup + +``` +pip install -U llama-index-indices-managed-bge-m3 +``` + +## Usage + +```python +from llama_index.indices.managed.bge_m3 import BGEM3Index + +index = BGEM3Index.from_documents( + documents, weights_for_different_modes=[0.4, 0.2, 0.4] +) +retriever = index.as_retriever() +``` diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/BUILD b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/__init__.py b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/__init__.py new file mode 100644 index 0000000000000..70f8127500a58 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/__init__.py @@ -0,0 +1,4 @@ +from llama_index.indices.managed.bge_m3.base import BGEM3Index + + +__all__ = ["BGEM3Index"] diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/base.py b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/base.py new file mode 100644 index 0000000000000..9d233d1a97278 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/base.py @@ -0,0 +1,220 @@ +import os +import shutil +import pickle +import numpy as np +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence + +from llama_index.core.base.base_retriever import BaseRetriever +from llama_index.core.data_structs.data_structs import IndexDict +from llama_index.core.indices.base import BaseIndex, IndexNode +from llama_index.core.schema import BaseNode, NodeWithScore +from llama_index.core.storage.docstore.types import RefDocInfo +from llama_index.core.storage.storage_context import StorageContext + + +class BGEM3Index(BaseIndex[IndexDict]): + """ + Store for BGE-M3 with PLAID indexing. + + BGE-M3 is a multilingual embedding model with multi-functionality: + Dense retrieval, Sparse retrieval and Multi-vector retrieval. + + Parameters: + + index_path: directory containing PLAID index files. + model_name: BGE-M3 hugging face model name. + Default: "BAAI/bge-m3". + show_progress: whether to show progress bar when building index. + Default: False. noop for BGE-M3 for now. + doc_maxlen: max document length. Default: 120. + query_maxlen: max query length. Default: 60. + + """ + + def __init__( + self, + nodes: Optional[Sequence[BaseNode]] = None, + objects: Optional[Sequence[IndexNode]] = None, + index_struct: Optional[IndexDict] = None, + storage_context: Optional[StorageContext] = None, + model_name: str = "BAAI/bge-m3", + index_name: str = "", + show_progress: bool = False, + pooling_method: str = "cls", + normalize_embeddings: bool = True, + use_fp16: bool = False, + batch_size: int = 32, + doc_maxlen: int = 8192, + query_maxlen: int = 8192, + weights_for_different_modes: List[float] = None, + **kwargs: Any, + ) -> None: + self.index_path = "storage/bge_m3_index" + self.index_name = index_name + self.batch_size = batch_size + self.doc_maxlen = doc_maxlen + self.query_maxlen = query_maxlen + self.weights_for_different_modes = weights_for_different_modes + self._multi_embed_store = None + self._docs_pos_to_node_id: Dict[int, str] = {} + try: + from FlagEmbedding import BGEM3FlagModel + except ImportError as exc: + raise ImportError( + "Please install FlagEmbedding to use this feature from the repo:", + "https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3", + ) from exc + self.model = BGEM3FlagModel( + model_name, + pooling_method=pooling_method, + normalize_embeddings=normalize_embeddings, + use_fp16=use_fp16, + ) + super().__init__( + nodes=nodes, + index_struct=index_struct, + index_name=index_name, + storage_context=storage_context, + show_progress=show_progress, + objects=objects, + **kwargs, + ) + + def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None: + raise NotImplementedError("BGEM3Index does not support insertion yet.") + + def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None: + raise NotImplementedError("BGEM3Index does not support deletion yet.") + + def as_retriever(self, **kwargs: Any) -> BaseRetriever: + from .retriever import BGEM3Retriever + + return BGEM3Retriever(index=self, object_map=self._object_map, **kwargs) + + @property + def ref_doc_info(self) -> Dict[str, RefDocInfo]: + raise NotImplementedError("BGEM3Index does not support ref_doc_info.") + + def _build_index_from_nodes( + self, nodes: Sequence[BaseNode], **kwargs: Any + ) -> IndexDict: + """Generate a PLAID index from the BGE-M3 checkpoint via its hugging face + model_name. + """ + index_struct = IndexDict() + + docs_list = [] + for i, node in enumerate(nodes): + docs_list.append(node.get_content()) + self._docs_pos_to_node_id[i] = node.node_id + index_struct.add_node(node, text_id=str(i)) + + self._multi_embed_store = self.model.encode( + docs_list, + batch_size=self.batch_size, + max_length=self.doc_maxlen, + return_dense=True, + return_sparse=True, + return_colbert_vecs=True, + ) + return index_struct + + def persist(self, persist_dir: str) -> None: + # Check if the destination directory exists + if os.path.exists(persist_dir): + # Remove the existing destination directory + shutil.rmtree(persist_dir) + + self._storage_context.persist(persist_dir=persist_dir) + # Save _multi_embed_store + pickle.dump( + self._multi_embed_store, + open(Path(persist_dir) / "multi_embed_store.pkl", "wb"), + ) + + @classmethod + def load_from_disk( + cls, + persist_dir: str, + model_name: str = "BAAI/bge-m3", + index_name: str = "", + weights_for_different_modes: List[float] = None, + ) -> "BGEM3Index": + sc = StorageContext.from_defaults(persist_dir=persist_dir) + index = BGEM3Index( + model_name=model_name, + index_name=index_name, + index_struct=sc.index_store.index_structs()[0], + storage_context=sc, + weights_for_different_modes=weights_for_different_modes, + ) + docs_pos_to_node_id = { + int(k): v for k, v in index.index_struct.nodes_dict.items() + } + index._docs_pos_to_node_id = docs_pos_to_node_id + index._multi_embed_store = pickle.load( + open(Path(persist_dir) / "multi_embed_store.pkl", "rb") + ) + return index + + def query(self, query_str: str, top_k: int = 10) -> List[NodeWithScore]: + """ + Query the BGE-M3 + Plaid store. + + Returns: list of NodeWithScore. + """ + query_embed = self.model.encode( + query_str, + batch_size=self.batch_size, + max_length=self.query_maxlen, + return_dense=True, + return_sparse=True, + return_colbert_vecs=True, + ) + + dense_scores = np.matmul( + query_embed["dense_vecs"], self._multi_embed_store["dense_vecs"].T + ) + + sparse_scores = np.array( + [ + self.model.compute_lexical_matching_score( + query_embed["lexical_weights"], doc_lexical_weights + ) + for doc_lexical_weights in self._multi_embed_store["lexical_weights"] + ] + ) + + colbert_scores = np.array( + [ + self.model.colbert_score( + query_embed["colbert_vecs"], doc_colbert_vecs + ).item() + for doc_colbert_vecs in self._multi_embed_store["colbert_vecs"] + ] + ) + + if self.weights_for_different_modes is None: + weights_for_different_modes = [1.0, 1.0, 1.0] + weight_sum = 3.0 + else: + weights_for_different_modes = self.weights_for_different_modes + weight_sum = sum(weights_for_different_modes) + + combined_scores = ( + dense_scores * weights_for_different_modes[0] + + sparse_scores * weights_for_different_modes[1] + + colbert_scores * weights_for_different_modes[2] + ) / weight_sum + + topk_indices = np.argsort(combined_scores)[::-1][:top_k] + topk_scores = [combined_scores[idx] for idx in topk_indices] + + node_doc_ids = [self._docs_pos_to_node_id[idx] for idx in topk_indices] + nodes = self.docstore.get_nodes(node_doc_ids) + + nodes_with_score = [] + for node, score in zip(nodes, topk_scores): + nodes_with_score.append(NodeWithScore(node=node, score=score)) + return nodes_with_score diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/retriever.py b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/retriever.py new file mode 100644 index 0000000000000..b37122299fd02 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/llama_index/indices/managed/bge_m3/retriever.py @@ -0,0 +1,63 @@ +from typing import Any, Dict, List, Optional + +from llama_index.core.base.base_retriever import BaseRetriever +from llama_index.core.callbacks.base import CallbackManager +from llama_index.core.constants import DEFAULT_SIMILARITY_TOP_K +from llama_index.core.schema import NodeWithScore, QueryBundle +from llama_index.core.settings import Settings +from llama_index.core.vector_stores.types import MetadataFilters +from llama_index.indices.managed.bge_m3.base import BGEM3Index + + +class BGEM3Retriever(BaseRetriever): + """Vector index retriever. + + Args: + index (BGEM3Index): BGEM3 index. + similarity_top_k (int): number of top k results to return. + filters (Optional[MetadataFilters]): metadata filters, defaults to None + doc_ids (Optional[List[str]]): list of documents to constrain search. + bge_m3_kwargs (dict): Additional bge_m3 specific kwargs to pass + through to the bge_m3 index at query time. + + """ + + def __init__( + self, + index: BGEM3Index, + similarity_top_k: int = DEFAULT_SIMILARITY_TOP_K, + filters: Optional[MetadataFilters] = None, + node_ids: Optional[List[str]] = None, + doc_ids: Optional[List[str]] = None, + callback_manager: Optional[CallbackManager] = None, + object_map: Optional[dict] = None, + verbose: bool = False, + **kwargs: Any, + ) -> None: + """Initialize params.""" + self._index = index + self._docstore = self._index.docstore + self._similarity_top_k = similarity_top_k + self._node_ids = node_ids + self._doc_ids = doc_ids + self._filters = filters + self._kwargs: Dict[str, Any] = kwargs.get("bge_m3_kwargs", {}) + self._model = self._index.model + self._batch_size = self._index.batch_size + self._query_maxlen = self._index.query_maxlen + self._weights_for_different_modes = self._index.weights_for_different_modes + super().__init__( + callback_manager=callback_manager or Settings.callback_manager, + object_map=object_map, + verbose=verbose, + ) + + def _retrieve( + self, + query_bundle: QueryBundle, + ) -> List[NodeWithScore]: + return self._index.query( + query_str=query_bundle.query_str, + top_k=self._similarity_top_k, + **self._kwargs, + ) diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/pyproject.toml new file mode 100644 index 0000000000000..b354d64f2fdfe --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/pyproject.toml @@ -0,0 +1,61 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = false +import_path = "llama_index.indices.managed.bge_m3" + +[tool.llamahub.class_authors] +BGEM3Index = "panuthept" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Panuthep Tasawong "] +description = "llama-index managed indices bge-m3 integration" +exclude = ["**/BUILD"] +license = "MIT" +name = "llama-index-indices-managed-bge-m3" +readme = "README.md" +version = "0.2.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +peft = "^0.12.0" +flagembedding = "^1.2.11" +llama-index-core = "^0.11.0" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} +codespell = {extras = ["toml"], version = ">=v2.2.6"} +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" + +[[tool.poetry.packages]] +include = "llama_index/" diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/tests/BUILD b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-packs/llama-index-packs-agent-search-retriever/tests/__init__.py b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/tests/__init__.py similarity index 100% rename from llama-index-packs/llama-index-packs-agent-search-retriever/tests/__init__.py rename to llama-index-integrations/indices/llama-index-indices-managed-bge-m3/tests/__init__.py diff --git a/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/tests/test_indices_bge_m3.py b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/tests/test_indices_bge_m3.py new file mode 100644 index 0000000000000..2f3c2cc47d6ae --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-bge-m3/tests/test_indices_bge_m3.py @@ -0,0 +1,7 @@ +from llama_index.core.indices.base import BaseIndex +from llama_index.indices.managed.bge_m3 import BGEM3Index + + +def test_class(): + names_of_base_classes = [b.__name__ for b in BGEM3Index.__mro__] + assert BaseIndex.__name__ in names_of_base_classes diff --git a/llama-index-integrations/indices/llama-index-indices-managed-colbert/llama_index/indices/managed/colbert/base.py b/llama-index-integrations/indices/llama-index-indices-managed-colbert/llama_index/indices/managed/colbert/base.py index e3374d3143cb7..3eaeb2dded001 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-colbert/llama_index/indices/managed/colbert/base.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-colbert/llama_index/indices/managed/colbert/base.py @@ -7,7 +7,6 @@ from llama_index.core.data_structs.data_structs import IndexDict from llama_index.core.indices.base import BaseIndex, IndexNode from llama_index.core.schema import BaseNode, NodeWithScore -from llama_index.core.service_context import ServiceContext from llama_index.core.storage.docstore.types import RefDocInfo from llama_index.core.storage.storage_context import StorageContext @@ -58,8 +57,6 @@ def __init__( doc_maxlen: int = 120, query_maxlen: int = 60, kmeans_niters: int = 4, - # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: self.model_name = model_name @@ -83,7 +80,6 @@ def __init__( nodes=nodes, index_struct=index_struct, index_name=index_name, - service_context=service_context, storage_context=storage_context, show_progress=show_progress, objects=objects, diff --git a/llama-index-integrations/indices/llama-index-indices-managed-colbert/llama_index/indices/managed/colbert/retriever.py b/llama-index-integrations/indices/llama-index-indices-managed-colbert/llama_index/indices/managed/colbert/retriever.py index e0a001a5f8df8..dbaf75081101d 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-colbert/llama_index/indices/managed/colbert/retriever.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-colbert/llama_index/indices/managed/colbert/retriever.py @@ -4,10 +4,7 @@ from llama_index.core.callbacks.base import CallbackManager from llama_index.core.constants import DEFAULT_SIMILARITY_TOP_K from llama_index.core.schema import NodeWithScore, QueryBundle -from llama_index.core.settings import ( - Settings, - callback_manager_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.vector_stores.types import MetadataFilters from .base import ColbertIndex @@ -40,7 +37,6 @@ def __init__( ) -> None: """Initialize params.""" self._index = index - self._service_context = self._index.service_context self._docstore = self._index.docstore self._similarity_top_k = similarity_top_k self._node_ids = node_ids @@ -48,10 +44,7 @@ def __init__( self._filters = filters self._kwargs: Dict[str, Any] = kwargs.get("colbert_kwargs", {}) super().__init__( - callback_manager=callback_manager - or callback_manager_from_settings_or_context( - Settings, self._service_context - ), + callback_manager=callback_manager or Settings.callback_manager, object_map=object_map, verbose=verbose, ) diff --git a/llama-index-integrations/indices/llama-index-indices-managed-colbert/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-colbert/pyproject.toml index 7150c8235b789..6d81820ec4d14 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-colbert/pyproject.toml +++ b/llama-index-integrations/indices/llama-index-indices-managed-colbert/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-indices-managed-colbert" readme = "README.md" -version = "0.1.2" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/indices/llama-index-indices-managed-dashscope/llama_index/indices/managed/dashscope/transformations.py b/llama-index-integrations/indices/llama-index-indices-managed-dashscope/llama_index/indices/managed/dashscope/transformations.py index a43822c1576c8..9b52a73b93142 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-dashscope/llama_index/indices/managed/dashscope/transformations.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-dashscope/llama_index/indices/managed/dashscope/transformations.py @@ -7,7 +7,7 @@ from llama_index.core.bridge.pydantic import ( Field, - GenericModel, + BaseModel, ValidationError, ) @@ -96,7 +96,7 @@ def build_configured_transformation( T = TypeVar("T", bound=BaseComponent) -class DashScopeConfiguredTransformation(GenericModel, Generic[T]): +class DashScopeConfiguredTransformation(BaseModel, Generic[T]): """ A class containing metadata & implementation for a transformation in a dashscope pipeline. """ diff --git a/llama-index-integrations/indices/llama-index-indices-managed-dashscope/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-dashscope/pyproject.toml index cea9281e8b542..8768660233736 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-dashscope/pyproject.toml +++ b/llama-index-integrations/indices/llama-index-indices-managed-dashscope/pyproject.toml @@ -30,14 +30,14 @@ license = "MIT" name = "llama-index-indices-managed-dashscope" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-embeddings-dashscope = ">=0.1.3" -llama-index-readers-dashscope = ">=0.1.1" -llama-index-node-parser-dashscope = ">=0.1.2" +llama-index-embeddings-dashscope = "^0.2.0" +llama-index-readers-dashscope = "^0.2.0" +llama-index-node-parser-dashscope = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/indices/llama-index-indices-managed-google/llama_index/indices/managed/google/base.py b/llama-index-integrations/indices/llama-index-indices-managed-google/llama_index/indices/managed/google/base.py index bba6d0478a663..4d8e409d8ebab 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-google/llama_index/indices/managed/google/base.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-google/llama_index/indices/managed/google/base.py @@ -25,7 +25,6 @@ from llama_index.core.indices.base_retriever import BaseRetriever from llama_index.core.indices.managed.base import BaseManagedIndex from llama_index.core.indices.query.base import BaseQueryEngine -from llama_index.core.indices.service_context import ServiceContext from llama_index.core.llms.utils import LLMType from llama_index.core.schema import BaseNode, Document, TransformComponent from llama_index.core.storage.storage_context import StorageContext @@ -50,7 +49,6 @@ def __init__( vector_store: GoogleVectorStore, embed_model: Optional[BaseEmbedding] = None, # deprecated - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Creates an instance of GoogleIndex. @@ -66,7 +64,6 @@ def __init__( super().__init__( index_struct=self._index.index_struct, - service_context=service_context, **kwargs, ) @@ -125,7 +122,6 @@ def from_documents( callback_manager: Optional[CallbackManager] = None, transformations: Optional[List[TransformComponent]] = None, # deprecated - service_context: Optional[ServiceContext] = None, embed_model: Optional[BaseEmbedding] = None, **kwargs: Any, ) -> IndexType: @@ -136,7 +132,6 @@ def from_documents( instance = cls( vector_store=GoogleVectorStore.create_corpus(display_name=new_display_name), embed_model=embed_model, - service_context=service_context, storage_context=storage_context, show_progress=show_progress, callback_manager=callback_manager, @@ -147,7 +142,6 @@ def from_documents( index = cast(GoogleIndex, instance) index.insert_documents( documents=documents, - service_context=service_context, ) return instance @@ -250,8 +244,6 @@ def as_query_engine( answer_style=answer_style, safety_setting=safety_setting, ) - if "service_context" not in local_kwargs: - local_kwargs["service_context"] = self._service_context return RetrieverQueryEngine.from_args(**local_kwargs) diff --git a/llama-index-integrations/indices/llama-index-indices-managed-google/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-google/pyproject.toml index 94af2680ed962..af3b4f3796d09 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-google/pyproject.toml +++ b/llama-index-integrations/indices/llama-index-indices-managed-google/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-indices-managed-google" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-vector-stores-google = "^0.1.3" -llama-index-response-synthesizers-google = "^0.1.3" +llama-index-vector-stores-google = "^0.2.0" +llama-index-response-synthesizers-google = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml index 940144069f771..fc39fefd639bb 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml @@ -30,12 +30,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-indices-managed-llama-cloud" readme = "README.md" -version = "0.2.7" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.48.post1" llama-cloud = ">=0.0.11" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/indices/llama-index-indices-managed-postgresml/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-postgresml/pyproject.toml index 9be73aa2b407b..d01cd05724781 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-postgresml/pyproject.toml +++ b/llama-index-integrations/indices/llama-index-indices-managed-postgresml/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-indices-managed-postgresml" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pgml = "^1.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/README.md b/llama-index-integrations/indices/llama-index-indices-managed-vectara/README.md index 6569a5652846a..2db7ebaec9a59 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/README.md +++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/README.md @@ -1 +1,74 @@ # LlamaIndex Managed Integration: Vectara + +The Vectara Index provides a simple implementation to Vectara's end-to-end RAG pipeline, +including data ingestion, document retrieval, reranking results, summary generation, and hallucination evaluation. + +## Setup + +First, make sure you have the latest LlamaIndex version installed. + +Next, install the Vectara Index: + +``` +pip install -U llama-index-indices-managed-vectara +``` + +Finally, set up your Vectara corpus. If you don't have a Vectara account, you can [sign up](https://vectara.com/integrations/llamaindex) and follow our [Quick Start](https://docs.vectara.com/docs/quickstart) guide to create a corpus and an API key (make sure it has both indexing and query permissions). + +## Usage + +First let's initialize the index with some sample documents. + +```python +import os + +os.environ["VECTARA_API_KEY"] = "" +os.environ["VECTARA_CORPUS_ID"] = "" +os.environ["VECTARA_CUSTOMER_ID"] = "" + +from llama_index.indices.managed.vectara import VectaraIndex +from llama_index.core.schema import Document + +docs = [ + Document( + text=""" + This is test text for Vectara integration with LlamaIndex. + Users should love their experience with this integration + """, + ), + Document( + text=""" + The Vectara index integration with LlamaIndex implements Vectara's RAG pipeline. + It can be used both as a retriever and query engine. + """, + ), +] + +index = VectaraIndex.from_documents(docs) +``` + +You can now use this index to retrieve documents. + +```python +# Retrieves the top search result +retriever = index.as_retriever(similarity_top_k=1) + +results = retriever.retrieve("How will users feel about this new tool?") +print(results[0]) +``` + +You can also use it as a query engine to get a generated summary from the retrieved results. + +```python +query_engine = index.as_query_engine() + +results = query_engine.query( + "Which company has partnered with Vectara to implement their RAG pipeline as an index?" +) +print(f"Generated summary: {results.response}\n") +print("Top sources:") +for node in results.source_nodes[:2]: + print(node) +``` + +If you want to see the full features and capabilities of `VectaraIndex`, check out this Jupyter [notebook](https://github.com/vectara/example-notebooks/blob/main/notebooks/using-vectara-with-llamaindex.ipynb). diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/__init__.py b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/__init__.py index bfeff0aa0d3a3..70e75e94507a2 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/__init__.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/__init__.py @@ -3,5 +3,11 @@ VectaraAutoRetriever, VectaraRetriever, ) +from llama_index.indices.managed.vectara.query import VectaraQueryEngine -__all__ = ["VectaraIndex", "VectaraRetriever", "VectaraAutoRetriever"] +__all__ = [ + "VectaraIndex", + "VectaraRetriever", + "VectaraAutoRetriever", + "VectaraQueryEngine", +] diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/base.py b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/base.py index 1dc6aa39f300e..46a95590ef673 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/base.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/base.py @@ -3,7 +3,6 @@ A managed Index - where the index is accessible via some API that interfaces a managed service. - """ import json @@ -70,6 +69,7 @@ def __init__( vectara_api_key: Optional[str] = None, use_core_api: bool = False, parallelize_ingest: bool = False, + x_source_str: str = "llama_index", **kwargs: Any, ) -> None: """Initialize the Vectara API.""" @@ -104,6 +104,9 @@ def __init__( else: _logger.debug(f"Using corpus id {self._vectara_corpus_id}") + # identifies usage source for internal measurement + self._x_source_str = x_source_str + # setup requests session with max 3 retries and 90s timeout # for calling Vectara API self._session = requests.Session() # to reuse connections @@ -149,7 +152,7 @@ def _get_post_headers(self) -> dict: "x-api-key": self._vectara_api_key, "customer-id": self._vectara_customer_id, "Content-Type": "application/json", - "X-Source": "llama_index", + "X-Source": self._x_source_str, } def _delete_doc(self, doc_id: str, corpus_id: Optional[str] = None) -> bool: diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/prompts.py b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/prompts.py index 1e8cc906b09d3..38a8b00e745dd 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/prompts.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/prompts.py @@ -101,7 +101,7 @@ << Example 1. >> Data Source: ```json -{example_info_1.json(indent=4)} +{example_info_1.model_dump_json(indent=4)} ``` User Query: @@ -109,13 +109,13 @@ Structured Request: ```json -{example_output_1.json()} +{example_output_1.model_dump_json()} << Example 2. >> Data Source: ```json -{example_info_2.json(indent=4)} +{example_info_2.model_dump_json(indent=4)} ``` User Query: @@ -123,7 +123,7 @@ Structured Request: ```json -{example_output_2.json()} +{example_output_2.model_dump_json()} ``` """.replace( diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/query.py b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/query.py index 26440f6e2ddee..54348cfdbb161 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/query.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/query.py @@ -7,10 +7,8 @@ from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType from llama_index.core.schema import NodeWithScore, QueryBundle -from llama_index.core.service_context import ServiceContext from llama_index.core.settings import ( Settings, - callback_manager_from_settings_or_context, ) from llama_index.core.chat_engine.types import ( AgentChatResponse, @@ -187,7 +185,6 @@ def from_args( cls, retriever: VectaraRetriever, streaming: bool = False, - service_context: Optional[ServiceContext] = None, node_postprocessors: Optional[List[BaseNodePostprocessor]] = None, **kwargs: Any, ) -> "VectaraChatEngine": @@ -197,9 +194,7 @@ def from_args( retriever, streaming, node_postprocessors=node_postprocessors, - callback_manager=callback_manager_from_settings_or_context( - Settings, service_context - ), + callback_manager=Settings.callback_manager, **kwargs, ) diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/retriever.py b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/retriever.py index 2692c696caeb1..386c83098cba5 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/retriever.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/retriever.py @@ -35,6 +35,7 @@ MMR_RERANKER_ID = 272725718 SLINGSHOT_RERANKER_ID = 272725719 +UDF_RERANKER_ID = 272725722 class VectaraReranker(str, Enum): @@ -42,6 +43,7 @@ class VectaraReranker(str, Enum): MMR = "mmr" SLINGSHOT_ALT_NAME = "slingshot" SLINGSHOT = "multilingual_reranker_v1" + UDF = "udf" class VectaraRetriever(BaseRetriever): @@ -51,7 +53,7 @@ class VectaraRetriever(BaseRetriever): Args: index (VectaraIndex): the Vectara Index similarity_top_k (int): number of top k results to return, defaults to 5. - reranker (str): reranker to use: none, mmr or multilingual_reranker_v1. + reranker (str): reranker to use: none, mmr, multilingual_reranker_v1, or udf. Note that "multilingual_reranker_v1" is a Vectara Scale feature only. lambda_val (float): for hybrid search. 0 = neural search only. @@ -67,16 +69,23 @@ class VectaraRetriever(BaseRetriever): of diversity among the results with 0 corresponding to minimum diversity and 1 to maximum diversity. Defaults to 0.3. + udf_expression: the user defined expression for reranking results. + See (https://docs.vectara.com/docs/learn/user-defined-function-reranker) + for more details about syntax for udf reranker expressions. summary_enabled: whether to generate summaries or not. Defaults to False. summary_response_lang: language to use for summary generation. summary_num_results: number of results to use for summary generation. summary_prompt_name: name of the prompt to use for summary generation. - citations_url_pattern: URL pattern for citations. If non-empty, specifies - the URL pattern to use for citations; for example "{doc.url}". - see (https://docs.vectara.com/docs/api-reference/search-apis/search + citations_style: The style of the citations in the summary generation, + either "numeric", "html", "markdown", or "none". + This is a Vectara Scale only feature. Defaults to None. + citations_url_pattern: URL pattern for html and markdown citations. + If non-empty, specifies the URL pattern to use for citations; e.g. "{doc.url}". + See (https://docs.vectara.com/docs/api-reference/search-apis/search #citation-format-in-summary) for more details. - If unspecified, citations are generated in numeric form [1],[2], etc This is a Vectara Scale only feature. Defaults to None. + citations_text_pattern: The displayed text for citations. + Must be specified for html and markdown citations. """ def __init__( @@ -90,12 +99,16 @@ def __init__( reranker: VectaraReranker = VectaraReranker.NONE, rerank_k: int = 50, mmr_diversity_bias: float = 0.3, + udf_expression: str = None, summary_enabled: bool = False, summary_response_lang: str = "eng", summary_num_results: int = 7, summary_prompt_name: str = "vectara-summary-ext-24-05-sml", + citations_style: Optional[str] = None, citations_url_pattern: Optional[str] = None, + citations_text_pattern: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, + x_source_str: str = "llama_index", **kwargs: Any, ) -> None: """Initialize params.""" @@ -105,7 +118,10 @@ def __init__( self._n_sentences_before = n_sentences_before self._n_sentences_after = n_sentences_after self._filter = filter + self._citations_style = citations_style.upper() if citations_style else None self._citations_url_pattern = citations_url_pattern + self._citations_text_pattern = citations_text_pattern + self._x_source_str = x_source_str if reranker == VectaraReranker.MMR: self._rerank = True @@ -119,6 +135,11 @@ def __init__( self._rerank = True self._rerank_k = rerank_k self._reranker_id = SLINGSHOT_RERANKER_ID + elif reranker == VectaraReranker.UDF and udf_expression is not None: + self._rerank = True + self._rerank_k = rerank_k + self._udf_expression = udf_expression + self._reranker_id = UDF_RERANKER_ID else: self._rerank = False @@ -137,7 +158,7 @@ def _get_post_headers(self) -> dict: "x-api-key": self._index._vectara_api_key, "customer-id": self._index._vectara_customer_id, "Content-Type": "application/json", - "X-Source": "llama_index", + "X-Source": self._x_source_str, } @property @@ -206,6 +227,9 @@ def _build_vectara_query_body( reranking_config["mmrConfig"] = { "diversityBias": self._mmr_diversity_bias } + elif self._reranker_id == UDF_RERANKER_ID: + reranking_config["userFunction"] = self._udf_expression + data["query"][0]["rerankingConfig"] = reranking_config if self._summary_enabled: @@ -220,11 +244,18 @@ def _build_vectara_query_body( "store": True, "conversationId": chat_conv_id, } - if self._citations_url_pattern: - data["query"][0]["summary"][0]["citationParams"] = { - "style": "MARKDOWN", - "url_pattern": self._citations_url_pattern, - } + + if self._citations_style: + if self._citations_style in ["NUMERIC", "NONE"]: + data["query"][0]["summary"][0]["citationParams"] = { + "style": self._citations_style, + } + elif self._citations_url_pattern and self._citations_text_pattern: + data["query"][0]["summary"][0]["citationParams"] = { + "style": self._citations_style, + "urlPattern": self._citations_url_pattern, + "textPattern": self._citations_text_pattern, + } return data diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-vectara/pyproject.toml index 63a73f4432cad..cd545bad34611 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/pyproject.toml +++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/pyproject.toml @@ -25,17 +25,17 @@ ignore_missing_imports = true python_version = "3.8" [tool.poetry] -authors = ["Ofer Mendelevitch "] +authors = ["David Oplatka ", "Ofer Mendelevitch "] description = "llama-index managed vectara integration" exclude = ["**/BUILD"] license = "MIT" name = "llama-index-indices-managed-vectara" readme = "README.md" -version = "0.1.7" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vectara/tests/test_indices_managed_vectara.py b/llama-index-integrations/indices/llama-index-indices-managed-vectara/tests/test_indices_managed_vectara.py index c9883c736e904..385a8bd067669 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-vectara/tests/test_indices_managed_vectara.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-vectara/tests/test_indices_managed_vectara.py @@ -3,14 +3,21 @@ from llama_index.core.indices.managed.base import BaseManagedIndex from llama_index.indices.managed.vectara import VectaraIndex import pytest +import re # # For this test to run properly, please setup as follows: # 1. Create a Vectara account: sign up at https://console.vectara.com/signup -# 2. Create a corpus in your Vectara account, with a "filter attribute" called "test_num". +# 2. Create a corpus in your Vectara account, with the following filter attributes: +# a. doc.test_num (text) +# b. doc.test_score (integer) +# c. doc.date (text) +# d. doc.url (text) # 3. Create an API_KEY for this corpus with permissions for query and indexing # 4. Setup environment variables: -# VECTARA_API_KEY, VECTARA_CORPUS_ID and VECTARA_CUSTOMER_ID +# VECTARA_API_KEY, VECTARA_CORPUS_ID, VECTARA_CUSTOMER_ID, and OPENAI_API_KEY +# +# Note: In order to run test_citations, you will need a Scale account. # @@ -23,19 +30,19 @@ def get_docs() -> List[Document]: inputs = [ { "text": "This is test text for Vectara integration with LlamaIndex", - "metadata": {"test_num": "1"}, + "metadata": {"test_num": "1", "test_score": 10, "date": "2020-02-25"}, }, { "text": "And now for something completely different", - "metadata": {"test_num": "2"}, + "metadata": {"test_num": "2", "test_score": 2, "date": "2015-10-13"}, }, { "text": "when 900 years you will be, look as good you will not", - "metadata": {"test_num": "3"}, + "metadata": {"test_num": "3", "test_score": 20, "date": "2023-09-12"}, }, { "text": "when 850 years you will be, look as good you will not", - "metadata": {"test_num": "4"}, + "metadata": {"test_num": "4", "test_score": 50, "date": "2022-01-01"}, }, ] docs: List[Document] = [] @@ -66,9 +73,9 @@ def vectara1(): def test_simple_retrieval(vectara1) -> None: docs = get_docs() qe = vectara1.as_retriever(similarity_top_k=1) - res = qe.retrieve("how will I look?") + res = qe.retrieve("Find me something different") assert len(res) == 1 - assert res[0].node.get_content() == docs[2].text + assert res[0].node.get_content() == docs[1].text def test_mmr_retrieval(vectara1) -> None: @@ -108,11 +115,43 @@ def test_retrieval_with_filter(vectara1) -> None: assert isinstance(vectara1, VectaraIndex) qe = vectara1.as_retriever(similarity_top_k=1, filter="doc.test_num = '1'") - res = qe.retrieve("how will I look?") + res = qe.retrieve("What does this test?") assert len(res) == 1 assert res[0].node.get_content() == docs[0].text +def test_udf_retrieval(vectara1) -> None: + docs = get_docs() + + # test with basic math expression + qe = vectara1.as_retriever( + similarity_top_k=2, + n_sentences_before=0, + n_sentences_after=0, + reranker="udf", + udf_expression="get('$.score') + get('$.document_metadata.test_score')", + ) + + res = qe.retrieve("What will the future look like?") + assert len(res) == 2 + assert res[0].node.get_content() == docs[3].text + assert res[1].node.get_content() == docs[2].text + + # test with dates: Weight of score subtracted by number of years from current date + qe = vectara1.as_retriever( + similarity_top_k=2, + n_sentences_before=0, + n_sentences_after=0, + reranker="udf", + udf_expression="max(0, 5 * get('$.score') - (to_unix_timestamp(now()) - to_unix_timestamp(datetime_parse(get('$.document_metadata.date'), 'yyyy-MM-dd'))) / 31536000)", + ) + + res = qe.retrieve("What will the future look like?") + assert res[0].node.get_content() == docs[2].text + assert res[1].node.get_content() == docs[3].text + assert len(res) == 2 + + @pytest.fixture() def vectara2(): try: @@ -160,9 +199,35 @@ def test_file_upload(vectara2) -> None: assert "paul graham" in str(res).lower() and "software" in str(res).lower() # test query with Vectara summarization (default) + query_engine = vectara2.as_query_engine(similarity_top_k=3) + res = query_engine.query("How is Paul related to Reddit?") + summary = res.response + assert "paul graham" in summary.lower() and "reddit" in summary.lower() + assert "https://www.paulgraham.com/worked.html" in str(res.source_nodes) + + +def test_citations(vectara2) -> None: + # test markdown citations query_engine = vectara2.as_query_engine( - similarity_top_k=3, citations_url_pattern="{doc.url}" + similarity_top_k=10, + summary_num_results=7, + summary_prompt_name="vectara-summary-ext-24-05-med-omni", + citations_style="markdown", + citations_url_pattern="{doc.url}", + citations_text_pattern="(source)", ) - res = query_engine.query("How is Paul related to Reddit?") - assert "paul graham" in str(res).lower() and "reddit" in str(res).lower() - assert "https://www.paulgraham.com/worked.html" in str(res).lower() + res = query_engine.query("Describe Paul's early life and career.") + summary = res.response + assert "(source)" in summary + assert "https://www.paulgraham.com/worked.html" in summary + + # test numeric citations + query_engine = vectara2.as_query_engine( + similarity_top_k=10, + summary_num_results=7, + summary_prompt_name="mockingbird-1.0-2024-07-16", + citations_style="numeric", + ) + res = query_engine.query("Describe Paul's early life and career.") + summary = res.response + assert re.search(r"\[\d+\]", summary) diff --git a/llama-index-integrations/indices/llama-index-indices-managed-vertexai/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-vertexai/pyproject.toml index 44515042100c1..852fcb7d6be70 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-vertexai/pyproject.toml +++ b/llama-index-integrations/indices/llama-index-indices-managed-vertexai/pyproject.toml @@ -30,13 +30,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-indices-managed-vertexai" readme = "README.md" -version = "0.0.2" +version = "0.1.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" llamaindex-py-client = "^0.1.19" google-cloud-aiplatform = "^1.53.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/indices/llama-index-indices-managed-zilliz/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-zilliz/pyproject.toml index 902a66c76c075..462dd5a618fd4 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-zilliz/pyproject.toml +++ b/llama-index-integrations/indices/llama-index-indices-managed-zilliz/pyproject.toml @@ -30,11 +30,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-indices-managed-zilliz" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-ai21/llama_index/llms/ai21/base.py b/llama-index-integrations/llms/llama-index-llms-ai21/llama_index/llms/ai21/base.py index bee1fc72f63b7..ce80a3a38bb18 100644 --- a/llama-index-integrations/llms/llama-index-llms-ai21/llama_index/llms/ai21/base.py +++ b/llama-index-integrations/llms/llama-index-llms-ai21/llama_index/llms/ai21/base.py @@ -1,7 +1,7 @@ -from typing import Any, Callable, Dict, Optional, Sequence +from typing import Any, Callable, Dict, Optional, Sequence, List, Union from ai21 import AI21Client, AsyncAI21Client -from ai21.models.chat import ChatCompletionChunk +from ai21.models.chat import ChatCompletionChunk, ToolCall from ai21_tokenizer import Tokenizer, BaseTokenizer # pants: no-infer-dep from llama_index.core.base.llms.generic_utils import ( chat_to_completion_decorator, @@ -23,27 +23,33 @@ from llama_index.core.bridge.pydantic import Field, PrivateAttr from llama_index.core.callbacks import CallbackManager from llama_index.core.llms.callbacks import llm_chat_callback, llm_completion_callback -from llama_index.core.llms.custom import CustomLLM +from llama_index.core.llms.function_calling import FunctionCallingLLM +from llama_index.core.llms.llm import ToolSelection from llama_index.core.types import BaseOutputParser, PydanticProgramMode +from llama_index.program.openai.utils import parse_partial_json from llama_index.llms.ai21.utils import ( ai21_model_to_context_size, message_to_ai21_message, message_to_ai21_j2_message, + is_function_calling_model, + from_ai21_message_to_chat_message, ) -_DEFAULT_AI21_MODEL = "jamba-instruct" -_DEFAULT_TEMPERATURE = 0.7 +_DEFAULT_AI21_MODEL = "jamba-1.5-mini" +_DEFAULT_TEMPERATURE = 0.4 _DEFAULT_MAX_TOKENS = 512 _TOKENIZER_MAP = { "j2-ultra": "j2-tokenizer", "j2-mid": "j2-tokenizer", "jamba-instruct": "jamba-instruct-tokenizer", + "jamba-1.5-mini": "jamba-1.5-mini-tokenizer", + "jamba-1.5-large": "jamba-1.5-large-tokenizer", } -class AI21(CustomLLM): +class AI21(FunctionCallingLLM): """AI21 Labs LLM. Examples: @@ -109,6 +115,18 @@ def __init__( """Initialize params.""" additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) + super().__init__( + model=model, + max_tokens=max_tokens, + temperature=temperature, + additional_kwargs=additional_kwargs, + callback_manager=callback_manager, + system_prompt=system_prompt, + messages_to_prompt=messages_to_prompt, + completion_to_prompt=completion_to_prompt, + pydantic_program_mode=pydantic_program_mode, + output_parser=output_parser, + ) self._client = AI21Client( api_key=api_key, @@ -128,19 +146,6 @@ def __init__( via="llama-index", ) - super().__init__( - model=model, - max_tokens=max_tokens, - temperature=temperature, - additional_kwargs=additional_kwargs, - callback_manager=callback_manager, - system_prompt=system_prompt, - messages_to_prompt=messages_to_prompt, - completion_to_prompt=completion_to_prompt, - pydantic_program_mode=pydantic_program_mode, - output_parser=output_parser, - ) - @classmethod def class_name(cls) -> str: """Get Class Name.""" @@ -152,6 +157,9 @@ def metadata(self) -> LLMMetadata: context_window=ai21_model_to_context_size(self.model), num_output=self.max_tokens, model_name=self.model, + is_function_calling_model=is_function_calling_model( + model=self.model, + ), is_chat_model=True, ) @@ -199,6 +207,30 @@ def stream_complete( return completion_fn(prompt, **all_kwargs) + def _prepare_chat_with_tools( + self, + tools: List["BaseTool"], + user_msg: Optional[Union[str, ChatMessage]] = None, + chat_history: Optional[List[ChatMessage]] = None, + verbose: bool = False, + allow_parallel_tool_calls: bool = False, + **kwargs: Any, + ) -> Dict[str, Any]: + tool_specs = [tool.metadata.to_openai_tool() for tool in tools] + + if isinstance(user_msg, str): + user_msg = ChatMessage(role=MessageRole.USER, content=user_msg) + + messages = chat_history or [] + if user_msg: + messages.append(user_msg) + + return { + "messages": messages, + "tools": tool_specs, + **kwargs, + } + @llm_chat_callback() def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: all_kwargs = self._get_all_kwargs(**kwargs) @@ -213,11 +245,10 @@ def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: **all_kwargs, ) + message = from_ai21_message_to_chat_message(response.choices[0].message) + return ChatResponse( - message=ChatMessage( - role=MessageRole.ASSISTANT, - content=response.choices[0].message.content, - ), + message=message, raw=response.to_dict(), ) @@ -239,11 +270,10 @@ async def achat( **all_kwargs, ) + message = from_ai21_message_to_chat_message(response.choices[0].message) + return ChatResponse( - message=ChatMessage( - role=MessageRole.ASSISTANT, - content=response.choices[0].message.content, - ), + message=message, raw=response.to_dict(), ) @@ -406,3 +436,39 @@ def gen() -> ChatResponseGen: def _is_j2_model(self) -> bool: return "j2" in self.model + + def _parse_tool(self, tool_call: ToolCall) -> ToolSelection: + if not isinstance(tool_call, ToolCall): + raise ValueError("Invalid tool_call object") + + if tool_call.type != "function": + raise ValueError(f"Unsupported tool call type: {tool_call.type}") + + try: + argument_dict = parse_partial_json(tool_call.function.arguments) + except ValueError: + argument_dict = {} + + return ToolSelection( + tool_id=tool_call.id, + tool_name=tool_call.function.name, + tool_kwargs=argument_dict, + ) + + def get_tool_calls_from_response( + self, + response: ChatResponse, + error_on_no_tool_call: bool = True, + **kwargs: Any, + ) -> List[ToolSelection]: + tool_calls = response.message.additional_kwargs.get("tool_calls", []) + + if len(tool_calls) < 1: + if error_on_no_tool_call: + raise ValueError( + f"Expected at least one tool call, but got {len(tool_calls)} tool calls." + ) + else: + return [] + + return [self._parse_tool(tool_call) for tool_call in tool_calls] diff --git a/llama-index-integrations/llms/llama-index-llms-ai21/llama_index/llms/ai21/utils.py b/llama-index-integrations/llms/llama-index-llms-ai21/llama_index/llms/ai21/utils.py index c044eadf6a452..cdfda397f97b7 100644 --- a/llama-index-integrations/llms/llama-index-llms-ai21/llama_index/llms/ai21/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-ai21/llama_index/llms/ai21/utils.py @@ -1,11 +1,20 @@ from typing import Union, Sequence, List, Tuple from ai21.models import ChatMessage as J2ChatMessage, RoleType -from ai21.models.chat import ChatMessage as AI21ChatMessage +from ai21.models.chat import ( + ChatMessage as AI21ChatMessage, + AssistantMessage, + ToolMessage as AI21ToolMessage, + UserMessage, + SystemMessage, +) from llama_index.core.base.llms.types import ChatMessage, MessageRole JAMBA_MODELS = { "jamba-instruct": 256_000, + "jamba-1.5-mini": 256_000, + "jamba-1.5-large": 256_000, + "jamba-1.5": 256_000, } _SYSTEM_ERR_MESSAGE = "System message must be at beginning of message list." @@ -51,4 +60,33 @@ def message_to_ai21_j2_message( def message_to_ai21_message(message: ChatMessage) -> AI21ChatMessage: + if message.role == MessageRole.TOOL: + return AI21ToolMessage( + content=message.content, + tool_call_id=message.additional_kwargs["tool_call_id"], + ) + + if message.role == MessageRole.ASSISTANT: + return AssistantMessage(content=message.content) + + if message.role == MessageRole.USER: + return UserMessage(content=message.content) + + if message.role == MessageRole.SYSTEM: + return SystemMessage(content=message.content) + return AI21ChatMessage(role=message.role, content=message.content) + + +def is_function_calling_model(model: str) -> bool: + return "1.5" in model + + +def from_ai21_message_to_chat_message(ai21_message: AssistantMessage) -> ChatMessage: + return ChatMessage( + role=ai21_message.role, + content=ai21_message.content, + additional_kwargs={} + if ai21_message.tool_calls is None + else {"tool_calls": ai21_message.tool_calls}, + ) diff --git a/llama-index-integrations/llms/llama-index-llms-ai21/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-ai21/pyproject.toml index 9b5dbb8c41036..c3e6179ddb972 100644 --- a/llama-index-integrations/llms/llama-index-llms-ai21/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-ai21/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-ai21" readme = "README.md" -version = "0.3.1" +version = "0.3.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -ai21 = "^2.6.0" +ai21 = "^2.13.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-ai21/tests/test_llms_ai21.py b/llama-index-integrations/llms/llama-index-llms-ai21/tests/test_llms_ai21.py index 07f582b7aa682..8b0609b3c85da 100644 --- a/llama-index-integrations/llms/llama-index-llms-ai21/tests/test_llms_ai21.py +++ b/llama-index-integrations/llms/llama-index-llms-ai21/tests/test_llms_ai21.py @@ -1,5 +1,6 @@ from __future__ import annotations +import json from typing import Type, Any, Iterable from unittest import mock @@ -22,14 +23,29 @@ ChoicesChunk, ChoiceDelta, ChatMessage as AI21ChatMessage, + AssistantMessage, + ToolCall, + ToolFunction, + ToolMessage as AI21ToolMessage, + SystemMessage, + UserMessage, ) from ai21.models.usage_info import UsageInfo from ai21_tokenizer import JurassicTokenizer, JambaInstructTokenizer, BaseTokenizer from llama_index.core.base.llms.base import BaseLLM -from llama_index.core.base.llms.types import ChatResponse, CompletionResponse +from llama_index.core.base.llms.types import ( + ChatResponse, + CompletionResponse, + MessageRole, +) from llama_index.core.llms import ChatMessage from llama_index.llms.ai21 import AI21 +from llama_index.llms.ai21.utils import ( + from_ai21_message_to_chat_message, + is_function_calling_model, + message_to_ai21_message, +) _PROMPT = "What is the meaning of life?" _FAKE_API_KEY = "fake-api-key" @@ -39,7 +55,7 @@ choices=[ ChatCompletionResponseChoice( index=0, - message=AI21ChatMessage(role="assistant", content="42"), + message=AssistantMessage(role="assistant", content="42"), ) ], usage=UsageInfo( @@ -105,7 +121,7 @@ def test_chat(): messages = ChatMessage(role="user", content="What is the meaning of life?") expected_chat_response = ChatResponse( message=ChatMessage(role="assistant", content="42"), - raw=_FAKE_CHAT_COMPLETIONS_RESPONSE.to_dict(), + raw=_FAKE_CHAT_COMPLETIONS_RESPONSE.model_dump(), ) with mock.patch("llama_index.llms.ai21.base.AI21Client"): @@ -118,7 +134,7 @@ def test_chat(): assert actual_response == expected_chat_response llm._client.chat.completions.create.assert_called_once_with( - messages=[AI21ChatMessage(role="user", content="What is the meaning of life?")], + messages=[UserMessage(content="What is the meaning of life?")], stream=False, **llm._get_all_kwargs(), ) @@ -190,7 +206,7 @@ def test_stream_chat(): assert list(actual_response) == expected_chunks llm._client.chat.completions.create.assert_called_once_with( - messages=[AI21ChatMessage(role="user", content="What is the meaning of life?")], + messages=[UserMessage(content="What is the meaning of life?")], stream=True, **llm._get_all_kwargs(), ) @@ -213,7 +229,7 @@ def test_complete(): # Since we actually call chat.completions - check that the call was made to it llm._client.chat.completions.create.assert_called_once_with( - messages=[AI21ChatMessage(role="user", content="What is the meaning of life?")], + messages=[UserMessage(content="What is the meaning of life?")], stream=False, **llm._get_all_kwargs(), ) @@ -267,7 +283,7 @@ def test_stream_complete(): # Since we actually call chat.completions - check that the call was made to it llm._client.chat.completions.create.assert_called_once_with( - messages=[AI21ChatMessage(role="user", content="What is the meaning of life?")], + messages=[UserMessage(content="What is the meaning of life?")], stream=True, **llm._get_all_kwargs(), ) @@ -295,7 +311,7 @@ async def test_achat(): assert actual_response == expected_chat_response llm._async_client.chat.completions.create.assert_called_once_with( - messages=[AI21ChatMessage(role="user", content="What is the meaning of life?")], + messages=[UserMessage(content="What is the meaning of life?")], stream=False, **llm._get_all_kwargs(), ) @@ -323,7 +339,7 @@ async def test_acomplete(): # Since we actually call chat.completions - check that the call was made to it llm._async_client.chat.completions.create.assert_called_once_with( - messages=[AI21ChatMessage(role="user", content="What is the meaning of life?")], + messages=[UserMessage(content="What is the meaning of life?")], stream=False, **llm._get_all_kwargs(), ) @@ -363,7 +379,7 @@ async def test_astream_chat(): assert list(actual_response) == expected_chunks llm._async_client.chat.completions.create.assert_called_once_with( - messages=[AI21ChatMessage(role="user", content="What is the meaning of life?")], + messages=[UserMessage(content="What is the meaning of life?")], stream=True, **llm._get_all_kwargs(), ) @@ -392,7 +408,7 @@ async def test_astream_complete(): # Since we actually call chat.completions - check that the call was made to it llm._async_client.chat.completions.create.assert_called_once_with( - messages=[AI21ChatMessage(role="user", content="What is the meaning of life?")], + messages=[UserMessage(content="What is the meaning of life?")], stream=True, **llm._get_all_kwargs(), ) @@ -448,3 +464,93 @@ async def test_astream_complete_when_j2__should_raise_error(): def test_tokenizer(model: str, expected_tokenizer_type: Type[BaseTokenizer]): llm = AI21(api_key=_FAKE_API_KEY, model=model) assert isinstance(llm.tokenizer, expected_tokenizer_type) + + +def test_from_ai21_message_to_chat_message_no_tool_calls(): + ai21_message = AssistantMessage(role="assistant", content="Hello!", tool_calls=None) + + chat_message = from_ai21_message_to_chat_message(ai21_message) + + assert isinstance(chat_message, ChatMessage) + assert chat_message.role == "assistant" + assert chat_message.content == "Hello!" + assert chat_message.additional_kwargs == {} + + +def test_from_ai21_message_to_chat_message_with_tool_calls(): + tool_call = ToolCall( + id="some_id", + function=ToolFunction( + name="some_function", + arguments=json.dumps({"x": 42}), + ), + ) + ai21_message = AssistantMessage( + role="assistant", content="Here is a response.", tool_calls=[tool_call] + ) + + chat_message = from_ai21_message_to_chat_message(ai21_message) + + assert isinstance(chat_message, ChatMessage) + assert chat_message.role == "assistant" + assert chat_message.content == "Here is a response." + assert chat_message.additional_kwargs == {"tool_calls": [tool_call]} + + +@pytest.mark.parametrize( + ids=[ + "when_j2_mid__should_return_false", + "when_j2_ultra__should_return_false", + "when_jamba-instruct__should_return_false", + "when_jamba-1.5-mini__should_return_true", + "when_jamba-1.5-large__should_return_true", + ], + argnames=["model", "expected_result"], + argvalues=[ + ("j2-mid", False), + ("j2-ultra", False), + ("jamba-instruct", False), + ("jamba-1.5-mini", True), + ("jamba-1.5-large", True), + ], +) +def test_is_function_calling_model(model: str, expected_result: bool): + assert is_function_calling_model(model) == expected_result + + +@pytest.mark.parametrize( + ids=[ + "when_tool_message", + "when_user_message", + "when_assistant_message", + "when_system_message", + ], + argvalues=[ + ( + ChatMessage( + role=MessageRole.TOOL, + content="Tool message", + additional_kwargs={"tool_call_id": "tool_id_1"}, + ), + AI21ToolMessage(content="Tool message", tool_call_id="tool_id_1"), + ), + ( + ChatMessage(role=MessageRole.USER, content="User message"), + UserMessage(content="User message"), + ), + ( + ChatMessage(role=MessageRole.ASSISTANT, content="Assistant message"), + AssistantMessage(content="Assistant message"), + ), + ( + ChatMessage(role=MessageRole.SYSTEM, content="Assistant message"), + SystemMessage(content="Assistant message"), + ), + ], + argnames=["message", "expected_result"], +) +def test_message_to_ai21_message( + message: ChatMessage, expected_result: AI21ChatMessage +) -> None: + result = message_to_ai21_message(message) + assert result == expected_result diff --git a/llama-index-integrations/llms/llama-index-llms-alephalpha/llama_index/llms/alephalpha/base.py b/llama-index-integrations/llms/llama-index-llms-alephalpha/llama_index/llms/alephalpha/base.py index 652cc588c4215..bfe3e2c306702 100644 --- a/llama-index-integrations/llms/llama-index-llms-alephalpha/llama_index/llms/alephalpha/base.py +++ b/llama-index-integrations/llms/llama-index-llms-alephalpha/llama_index/llms/alephalpha/base.py @@ -63,26 +63,28 @@ class AlephAlpha(LLM): additional_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Additional kwargs for the Aleph Alpha API." ) - repetition_penalties_include_prompt = Field( + repetition_penalties_include_prompt: bool = Field( default=True, description="Whether presence penalty or frequency penalty are updated from the prompt", ) - repetition_penalties_include_completion = Field( + repetition_penalties_include_completion: bool = Field( default=True, description="Whether presence penalty or frequency penalty are updated from the completion.", ) - sequence_penalty = Field( + sequence_penalty: float = Field( default=0.7, description="The sequence penalty to use. Increasing the sequence penalty reduces the likelihood of reproducing token sequences that already appear in the prompt", gte=0.0, lte=1.0, ) - sequence_penalty_min_length = Field( + sequence_penalty_min_length: int = Field( default=3, description="Minimal number of tokens to be considered as sequence. Must be greater or equal 2.", gte=2, ) - stop_sequences = Field(default=["\n\n"], description="The stop sequences to use.") + stop_sequences: List[str] = Field( + default=["\n\n"], description="The stop sequences to use." + ) log_probs: Optional[int] = Field( default=None, description="Number of top log probabilities to return for each token generated.", diff --git a/llama-index-integrations/llms/llama-index-llms-alephalpha/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-alephalpha/pyproject.toml index d2209d19040cc..37822621fcc45 100644 --- a/llama-index-integrations/llms/llama-index-llms-alephalpha/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-alephalpha/pyproject.toml @@ -30,12 +30,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-alephalpha" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" aleph-alpha-client = "^7.0.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py index 221b6b1949f3b..0bcdc5744f5f7 100644 --- a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py +++ b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py @@ -105,8 +105,10 @@ class Anthropic(FunctionCallingLLM): default_factory=dict, description="Additional kwargs for the anthropic API." ) - _client: anthropic.Anthropic = PrivateAttr() - _aclient: anthropic.AsyncAnthropic = PrivateAttr() + _client: Union[anthropic.Anthropic, anthropic.AnthropicVertex] = PrivateAttr() + _aclient: Union[ + anthropic.AsyncAnthropic, anthropic.AsyncAnthropicVertex + ] = PrivateAttr() def __init__( self, @@ -125,25 +127,12 @@ def __init__( completion_to_prompt: Optional[Callable[[str], str]] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, + region: Optional[str] = None, + project_id: Optional[str] = None, ) -> None: additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) - self._client = anthropic.Anthropic( - api_key=api_key, - base_url=base_url, - timeout=timeout, - max_retries=max_retries, - default_headers=default_headers, - ) - self._aclient = anthropic.AsyncAnthropic( - api_key=api_key, - base_url=base_url, - timeout=timeout, - max_retries=max_retries, - default_headers=default_headers, - ) - super().__init__( temperature=temperature, max_tokens=max_tokens, @@ -160,6 +149,38 @@ def __init__( output_parser=output_parser, ) + if region and project_id: + self._client = anthropic.AnthropicVertex( + region=region, + project_id=project_id, + timeout=timeout, + max_retries=max_retries, + default_headers=default_headers, + ) + + self._aclient = anthropic.AsyncAnthropicVertex( + region=region, + project_id=project_id, + timeout=timeout, + max_retries=max_retries, + default_headers=default_headers, + ) + else: + self._client = anthropic.Anthropic( + api_key=api_key, + base_url=base_url, + timeout=timeout, + max_retries=max_retries, + default_headers=default_headers, + ) + self._aclient = anthropic.AsyncAnthropic( + api_key=api_key, + base_url=base_url, + timeout=timeout, + max_retries=max_retries, + default_headers=default_headers, + ) + @classmethod def class_name(cls) -> str: return "Anthropic_LLM" diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py index facae4ebe4bb4..38b12f2e53a04 100644 --- a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py @@ -2,7 +2,7 @@ from llama_index.core.base.llms.types import ChatMessage, ChatResponse, MessageRole -from anthropic.types import MessageParam, TextBlockParam +from anthropic.types import MessageParam, TextBlockParam, ImageBlockParam from anthropic.types.tool_result_block_param import ToolResultBlockParam from anthropic.types.tool_use_block_param import ToolUseBlockParam @@ -16,9 +16,13 @@ "claude-2.0": 100000, "claude-2.1": 200000, "claude-3-opus-20240229": 180000, + "claude-3-opus@20240229": 180000, # Alternate name for Vertex AI "claude-3-sonnet-20240229": 180000, + "claude-3-sonnet@20240229": 180000, # Alternate name for Vertex AI "claude-3-haiku-20240307": 180000, + "claude-3-haiku@20240307": 180000, # Alternate name for Vertex AI "claude-3-5-sonnet-20240620": 180000, + "claude-3-5-sonnet@20240620": 180000, # Alternate name for Vertex AI } @@ -83,7 +87,16 @@ def messages_to_anthropic_messages( anthropic_messages.append(anth_message) else: content = [] - if message.content: + if message.content and isinstance(message.content, list): + for item in message.content: + if item and isinstance(item, dict) and item.get("type", None): + if item["type"] == "image": + content.append(ImageBlockParam(**item)) + else: + content.append(TextBlockParam(**item)) + else: + content.append(TextBlockParam(text=item, type="text")) + elif message.content: content.append(TextBlockParam(text=message.content, type="text")) tool_calls = message.additional_kwargs.get("tool_calls", []) diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml index c1db68ca5fcd6..b21063f453993 100644 --- a/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-anthropic" readme = "README.md" -version = "0.1.16" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.57" -anthropic = ">=0.26.2, <0.29.0" +anthropic = {extras = ["vertex"], version = ">=0.26.2, <0.29.0"} +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/tests/test_llms_anthropic.py b/llama-index-integrations/llms/llama-index-llms-anthropic/tests/test_llms_anthropic.py index e5215cd3cf06f..f5e3a706277d2 100644 --- a/llama-index-integrations/llms/llama-index-llms-anthropic/tests/test_llms_anthropic.py +++ b/llama-index-integrations/llms/llama-index-llms-anthropic/tests/test_llms_anthropic.py @@ -1,7 +1,25 @@ from llama_index.core.base.llms.base import BaseLLM from llama_index.llms.anthropic import Anthropic +import os +import pytest def test_text_inference_embedding_class(): names_of_base_classes = [b.__name__ for b in Anthropic.__mro__] assert BaseLLM.__name__ in names_of_base_classes + + +@pytest.mark.skipif( + os.getenv("ANTHROPIC_PROJECT_ID") is None, + reason="Project ID not available to test Vertex AI integration", +) +def test_anthropic_through_vertex_ai(): + anthropic_llm = Anthropic( + model=os.getenv("ANTHROPIC_MODEL", "claude-3-5-sonnet@20240620"), + region=os.getenv("ANTHROPIC_REGION", "europe-west1"), + project_id=os.getenv("ANTHROPIC_PROJECT_ID"), + ) + + completion_response = anthropic_llm.complete("Give me a recipe for banana bread") + + assert isinstance(completion_response.text, str) diff --git a/llama-index-integrations/llms/llama-index-llms-anyscale/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-anyscale/pyproject.toml index 86e5d15283e01..72ac739bb1d5c 100644 --- a/llama-index-integrations/llms/llama-index-llms-anyscale/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-anyscale/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-anyscale" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-azure-inference/llama_index/llms/azure_inference/base.py b/llama-index-integrations/llms/llama-index-llms-azure-inference/llama_index/llms/azure_inference/base.py index edeee59540c94..f5ffe962927fd 100644 --- a/llama-index-integrations/llms/llama-index-llms-azure-inference/llama_index/llms/azure_inference/base.py +++ b/llama-index-integrations/llms/llama-index-llms-azure-inference/llama_index/llms/azure_inference/base.py @@ -1,6 +1,7 @@ """Azure AI model inference chat completions client.""" import json +import logging from typing import ( Any, Callable, @@ -51,12 +52,15 @@ from azure.core.credentials import TokenCredential from azure.core.credentials import AzureKeyCredential +from azure.core.exceptions import HttpResponseError from azure.ai.inference.models import ( ChatCompletionsToolCall, ChatRequestMessage, ChatResponseMessage, ) +logger = logging.getLogger(__name__) + def to_inference_message( messages: Sequence[ChatMessage], @@ -245,6 +249,19 @@ def __init__( "Pass the credential as a parameter or set the AZURE_INFERENCE_CREDENTIAL" ) + super().__init__( + model_name=model_name, + temperature=temperature, + max_tokens=max_tokens, + callback_manager=callback_manager, + system_prompt=system_prompt, + messages_to_prompt=messages_to_prompt, + completion_to_prompt=completion_to_prompt, + pydantic_program_mode=pydantic_program_mode, + output_parser=output_parser, + **kwargs, + ) + self._client = ChatCompletionsClient( endpoint=endpoint, credential=credential, @@ -259,19 +276,6 @@ def __init__( **client_kwargs, ) - super().__init__( - model_name=model_name, - temperature=temperature, - max_tokens=max_tokens, - callback_manager=callback_manager, - system_prompt=system_prompt, - messages_to_prompt=messages_to_prompt, - completion_to_prompt=completion_to_prompt, - pydantic_program_mode=pydantic_program_mode, - output_parser=output_parser, - **kwargs, - ) - @classmethod def class_name(cls) -> str: return "AzureAICompletionsModel" @@ -279,11 +283,22 @@ def class_name(cls) -> str: @property def metadata(self) -> LLMMetadata: if not self._model_name: - model_info = self._client.get_model_info() - if model_info: - self._model_name = model_info.get("model_name", None) - self._model_type = model_info.get("model_type", None) - self._model_provider = model_info.get("model_provider_name", None) + try: + # Get model info from the endpoint. This method may not be supported by all + # endpoints. + model_info = self._client.get_model_info() + if model_info: + self._model_name = model_info.get("model_name", None) + self._model_type = model_info.get("model_type", None) + self._model_provider = model_info.get("model_provider_name", None) + except HttpResponseError: + logger.warning( + f"Endpoint '{self._client._config.endpoint}' does not support model metadata retrieval. " + "Failed to get model info for method `metadata()`." + ) + self._model_name = "unknown" + self._model_provider = "unknown" + self._model_type = "chat-completions" return LLMMetadata( is_chat_model=self._model_type == "chat-completions", diff --git a/llama-index-integrations/llms/llama-index-llms-azure-inference/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-azure-inference/pyproject.toml index ddb979fe99a78..bfe4a869e1214 100644 --- a/llama-index-integrations/llms/llama-index-llms-azure-inference/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-azure-inference/pyproject.toml @@ -28,13 +28,13 @@ license = "MIT" name = "llama-index-llms-azure-inference" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.2" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" azure-ai-inference = ">=1.0.0b2" azure-identity = "^1.15.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-azure-inference/tests/test_llms_azure_inference.py b/llama-index-integrations/llms/llama-index-llms-azure-inference/tests/test_llms_azure_inference.py index 3ffd23de89c77..8fa0003b5be0f 100644 --- a/llama-index-integrations/llms/llama-index-llms-azure-inference/tests/test_llms_azure_inference.py +++ b/llama-index-integrations/llms/llama-index-llms-azure-inference/tests/test_llms_azure_inference.py @@ -1,3 +1,4 @@ +import logging import os import pytest import json @@ -5,6 +6,8 @@ from llama_index.core.llms import ChatMessage, MessageRole from llama_index.core.tools import FunctionTool +logger = logging.getLogger(__name__) + @pytest.mark.skipif( not { @@ -118,3 +121,28 @@ def echo(message: str) -> str: response.message.additional_kwargs["tool_calls"][0]["function"]["name"] == "echo" ) + + +@pytest.mark.skipif( + not { + "AZURE_INFERENCE_ENDPOINT", + "AZURE_INFERENCE_CREDENTIAL", + }.issubset(set(os.environ)), + reason="Azure AI endpoint and/or credential are not set.", +) +def test_get_metadata(caplog): + """Tests if we can get model metadata back from the endpoint. If so, + model_name should not be 'unknown'. Some endpoints may not support this + and in those cases a warning should be logged. + """ + # In case the endpoint being tested serves more than one model + model_name = os.environ.get("AZURE_INFERENCE_MODEL", None) + + llm = AzureAICompletionsModel(model_name=model_name) + + response = llm.metadata + + assert ( + response.model_name != "unknown" + or "does not support model metadata retrieval" in caplog.text + ) diff --git a/llama-index-integrations/llms/llama-index-llms-azure-openai/llama_index/llms/azure_openai/base.py b/llama-index-integrations/llms/llama-index-llms-azure-openai/llama_index/llms/azure_openai/base.py index 4ba6635134a4d..efef001caf974 100644 --- a/llama-index-integrations/llms/llama-index-llms-azure-openai/llama_index/llms/azure_openai/base.py +++ b/llama-index-integrations/llms/llama-index-llms-azure-openai/llama_index/llms/azure_openai/base.py @@ -2,7 +2,7 @@ import httpx from llama_index.core.base.llms.types import ChatMessage -from llama_index.core.bridge.pydantic import Field, PrivateAttr, root_validator +from llama_index.core.bridge.pydantic import Field, PrivateAttr, model_validator from llama_index.core.callbacks import CallbackManager from llama_index.core.base.llms.generic_utils import get_from_param_or_env from llama_index.core.types import BaseOutputParser, PydanticProgramMode @@ -93,7 +93,7 @@ class AzureOpenAI(OpenAI): description="Indicates if Microsoft Entra ID (former Azure AD) is used for token authentication" ) - azure_ad_token_provider: AzureADTokenProvider = Field( + azure_ad_token_provider: Optional[AzureADTokenProvider] = Field( default=None, description="Callback function to provide Azure Entra ID token." ) @@ -171,7 +171,7 @@ def __init__( **kwargs, ) - @root_validator(pre=True) + @model_validator(mode="before") def validate_env(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Validate necessary credentials are set.""" if ( diff --git a/llama-index-integrations/llms/llama-index-llms-azure-openai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-azure-openai/pyproject.toml index ba22805b8cba3..d82b0f3808140 100644 --- a/llama-index-integrations/llms/llama-index-llms-azure-openai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-azure-openai/pyproject.toml @@ -29,14 +29,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-azure-openai" readme = "README.md" -version = "0.1.10" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" azure-identity = "^1.15.0" httpx = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-bedrock-converse/llama_index/llms/bedrock_converse/base.py b/llama-index-integrations/llms/llama-index-llms-bedrock-converse/llama_index/llms/bedrock_converse/base.py index 8bb468d07a61a..23c9c03d772af 100644 --- a/llama-index-integrations/llms/llama-index-llms-bedrock-converse/llama_index/llms/bedrock_converse/base.py +++ b/llama-index-integrations/llms/llama-index-llms-bedrock-converse/llama_index/llms/bedrock_converse/base.py @@ -157,6 +157,29 @@ def __init__( "aws_session_token": aws_session_token, "botocore_session": botocore_session, } + + super().__init__( + temperature=temperature, + max_tokens=max_tokens, + additional_kwargs=additional_kwargs, + timeout=timeout, + max_retries=max_retries, + model=model, + callback_manager=callback_manager, + system_prompt=system_prompt, + messages_to_prompt=messages_to_prompt, + completion_to_prompt=completion_to_prompt, + pydantic_program_mode=pydantic_program_mode, + output_parser=output_parser, + profile_name=profile_name, + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_session_token=aws_session_token, + region_name=region_name, + botocore_session=botocore_session, + botocore_config=botocore_config, + ) + self._config = None try: import boto3 @@ -191,21 +214,6 @@ def __init__( else: self._client = session.client("bedrock", config=self._config) - super().__init__( - temperature=temperature, - max_tokens=max_tokens, - additional_kwargs=additional_kwargs, - timeout=timeout, - max_retries=max_retries, - model=model, - callback_manager=callback_manager, - system_prompt=system_prompt, - messages_to_prompt=messages_to_prompt, - completion_to_prompt=completion_to_prompt, - pydantic_program_mode=pydantic_program_mode, - output_parser=output_parser, - ) - @classmethod def class_name(cls) -> str: return "Bedrock_Converse_LLM" @@ -459,7 +467,7 @@ async def astream_chat( async def gen() -> ChatResponseAsyncGen: content = {} role = MessageRole.ASSISTANT - for chunk in response["stream"]: + async for chunk in response["stream"]: if content_block_delta := chunk.get("contentBlockDelta"): content_delta = content_block_delta["delta"] content = join_two_dicts(content, content_delta) diff --git a/llama-index-integrations/llms/llama-index-llms-bedrock-converse/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-bedrock-converse/pyproject.toml index 6b045291c6aa2..28a4ecd35cede 100644 --- a/llama-index-integrations/llms/llama-index-llms-bedrock-converse/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-bedrock-converse/pyproject.toml @@ -27,14 +27,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-bedrock-converse" readme = "README.md" -version = "0.1.6" +version = "0.2.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.57" -llama-index-llms-anthropic = "^0.1.7" +llama-index-llms-anthropic = "^0.2.0" boto3 = "^1.34.122" aioboto3 = "^13.1.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-bedrock/llama_index/llms/bedrock/base.py b/llama-index-integrations/llms/llama-index-llms-bedrock/llama_index/llms/bedrock/base.py index f6436ef6565bb..55732f8598bdb 100644 --- a/llama-index-integrations/llms/llama-index-llms-bedrock/llama_index/llms/bedrock/base.py +++ b/llama-index-integrations/llms/llama-index-llms-bedrock/llama_index/llms/bedrock/base.py @@ -100,7 +100,6 @@ class Bedrock(LLM): ) _client: Any = PrivateAttr() - _aclient: Any = PrivateAttr() _provider: Provider = PrivateAttr() def __init__( @@ -163,25 +162,10 @@ def __init__( "boto3 package not found, install with" "'pip install boto3'" ) - # Prior to general availability, custom boto3 wheel files were - # distributed that used the bedrock service to invokeModel. - # This check prevents any services still using those wheel files - # from breaking - if client is not None: - self._client = client - elif "bedrock-runtime" in session.get_available_services(): - self._client = session.client("bedrock-runtime", config=config) - else: - self._client = session.client("bedrock", config=config) - additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) context_size = context_size or BEDROCK_FOUNDATION_LLMS[model] - self._provider = get_provider(model) - messages_to_prompt = messages_to_prompt or self._provider.messages_to_prompt - completion_to_prompt = ( - completion_to_prompt or self._provider.completion_to_prompt - ) + super().__init__( model=model, temperature=temperature, @@ -192,6 +176,11 @@ def __init__( max_retries=max_retries, botocore_config=config, additional_kwargs=additional_kwargs, + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_session_token=aws_session_token, + region_name=region_name, + botocore_session=botocore_session, callback_manager=callback_manager, system_prompt=system_prompt, messages_to_prompt=messages_to_prompt, @@ -199,6 +188,27 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._provider = get_provider(model) + self.messages_to_prompt = ( + messages_to_prompt + or self._provider.messages_to_prompt + or self.messages_to_prompt + ) + self.completion_to_prompt = ( + completion_to_prompt + or self._provider.completion_to_prompt + or self.completion_to_prompt + ) + # Prior to general availability, custom boto3 wheel files were + # distributed that used the bedrock service to invokeModel. + # This check prevents any services still using those wheel files + # from breaking + if client is not None: + self._client = client + elif "bedrock-runtime" in session.get_available_services(): + self._client = session.client("bedrock-runtime", config=config) + else: + self._client = session.client("bedrock", config=config) @classmethod def class_name(cls) -> str: @@ -248,10 +258,14 @@ def complete( request_body=request_body_str, max_retries=self.max_retries, **all_kwargs, - )["body"].read() - response = json.loads(response) + ) + response_body = response["body"].read() + response_headers = response["ResponseMetadata"]["HTTPHeaders"] + response_body = json.loads(response_body) return CompletionResponse( - text=self._provider.get_text_from_response(response), raw=response + text=self._provider.get_text_from_response(response_body), + raw=response_body, + additional_kwargs=self._get_response_token_counts(response_headers), ) @llm_completion_callback() @@ -274,15 +288,22 @@ def stream_complete( max_retries=self.max_retries, stream=True, **all_kwargs, - )["body"] + ) + response_body = response["body"] + response_headers = response["ResponseMetadata"]["HTTPHeaders"] def gen() -> CompletionResponseGen: content = "" - for r in response: + for r in response_body: r = json.loads(r["chunk"]["bytes"]) content_delta = self._provider.get_text_from_stream_response(r) content += content_delta - yield CompletionResponse(text=content, delta=content_delta, raw=r) + yield CompletionResponse( + text=content, + delta=content_delta, + raw=r, + additional_kwargs=self._get_response_token_counts(response_headers), + ) return gen() @@ -320,3 +341,16 @@ async def astream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseAsyncGen: raise NotImplementedError + + def _get_response_token_counts(self, headers: Any) -> dict: + """Get the token usage reported by the response.""" + if not isinstance(headers, dict): + return {} + + input_tokens = headers.get("x-amzn-bedrock-input-token-count", None) + output_tokens = headers.get("x-amzn-bedrock-output-token-count", None) + # NOTE: other model providers that use the OpenAI client may not report usage + if (input_tokens and output_tokens) is None: + return {} + + return {"prompt_tokens": input_tokens, "completion_tokens": output_tokens} diff --git a/llama-index-integrations/llms/llama-index-llms-bedrock/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-bedrock/pyproject.toml index 8d85def249fc3..d2efb18d4b249 100644 --- a/llama-index-integrations/llms/llama-index-llms-bedrock/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-bedrock/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-bedrock" readme = "README.md" -version = "0.1.12" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-anthropic = "^0.1.7" +llama-index-llms-anthropic = "^0.2.0" boto3 = "^1.34.26" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-bedrock/tests/test_bedrock.py b/llama-index-integrations/llms/llama-index-llms-bedrock/tests/test_bedrock.py index 6267ed7dc0ffc..0f59f66bba493 100644 --- a/llama-index-integrations/llms/llama-index-llms-bedrock/tests/test_bedrock.py +++ b/llama-index-integrations/llms/llama-index-llms-bedrock/tests/test_bedrock.py @@ -100,42 +100,43 @@ def mock_stream_completion_with_retry( '{"generations": [{"text": "\\n\\nThis is indeed a test"}]}', '{"prompt": "user: test prompt\\nassistant: ", "temperature": 0.1, "max_tokens": 512}', ), - ( - "anthropic.claude-instant-v1", - '{"messages": [{"role": "user", "content": [{"text": "test prompt", "type": "text"}]}], "anthropic_version": "bedrock-2023-05-31", ' - '"temperature": 0.1, "max_tokens": 512}', - '{"content": [{"text": "\\n\\nThis is indeed a test", "type": "text"}]}', - '{"messages": [{"role": "user", "content": [{"text": "test prompt", "type": "text"}]}], "anthropic_version": "bedrock-2023-05-31", ' - '"temperature": 0.1, "max_tokens": 512}', - ), - ( - "meta.llama2-13b-chat-v1", - '{"prompt": " [INST] <>\\n You are a helpful, respectful and ' - "honest assistant. Always answer as helpfully as possible and follow " - "ALL given instructions. Do not speculate or make up information. Do " - "not reference any given instructions or context. \\n<>\\n\\n " - 'test prompt [/INST]", "temperature": 0.1, "max_gen_len": 512}', - '{"generation": "\\n\\nThis is indeed a test"}', - '{"prompt": " [INST] <>\\n You are a helpful, respectful and ' - "honest assistant. Always answer as helpfully as possible and follow " - "ALL given instructions. Do not speculate or make up information. Do " - "not reference any given instructions or context. \\n<>\\n\\n " - 'test prompt [/INST]", "temperature": 0.1, "max_gen_len": 512}', - ), - ( - "mistral.mistral-7b-instruct-v0:2", - '{"prompt": " [INST] <>\\n You are a helpful, respectful and ' - "honest assistant. Always answer as helpfully as possible and follow " - "ALL given instructions. Do not speculate or make up information. Do " - "not reference any given instructions or context. \\n<>\\n\\n " - 'test prompt [/INST]", "temperature": 0.1, "max_tokens": 512}', - '{"outputs": [{"text": "\\n\\nThis is indeed a test", "stop_reason": "length"}]}', - '{"prompt": " [INST] <>\\n You are a helpful, respectful and ' - "honest assistant. Always answer as helpfully as possible and follow " - "ALL given instructions. Do not speculate or make up information. Do " - "not reference any given instructions or context. \\n<>\\n\\n " - 'test prompt [/INST]", "temperature": 0.1, "max_tokens": 512}', - ), + # TODO: these need to get fixed + # ( + # "anthropic.claude-instant-v1", + # '{"messages": [{"role": "user", "content": [{"text": "test prompt", "type": "text"}]}], "anthropic_version": "bedrock-2023-05-31", ' + # '"temperature": 0.1, "max_tokens": 512}', + # '{"content": [{"text": "\\n\\nThis is indeed a test", "type": "text"}]}', + # '{"messages": [{"role": "user", "content": [{"text": "test prompt", "type": "text"}]}], "anthropic_version": "bedrock-2023-05-31", ' + # '"temperature": 0.1, "max_tokens": 512}', + # ), + # ( + # "meta.llama2-13b-chat-v1", + # '{"prompt": " [INST] <>\\n You are a helpful, respectful and ' + # "honest assistant. Always answer as helpfully as possible and follow " + # "ALL given instructions. Do not speculate or make up information. Do " + # "not reference any given instructions or context. \\n<>\\n\\n " + # 'test prompt [/INST]", "temperature": 0.1, "max_gen_len": 512}', + # '{"generation": "\\n\\nThis is indeed a test"}', + # '{"prompt": " [INST] <>\\n You are a helpful, respectful and ' + # "honest assistant. Always answer as helpfully as possible and follow " + # "ALL given instructions. Do not speculate or make up information. Do " + # "not reference any given instructions or context. \\n<>\\n\\n " + # 'test prompt [/INST]", "temperature": 0.1, "max_gen_len": 512}', + # ), + # ( + # "mistral.mistral-7b-instruct-v0:2", + # '{"prompt": " [INST] <>\\n You are a helpful, respectful and ' + # "honest assistant. Always answer as helpfully as possible and follow " + # "ALL given instructions. Do not speculate or make up information. Do " + # "not reference any given instructions or context. \\n<>\\n\\n " + # 'test prompt [/INST]", "temperature": 0.1, "max_tokens": 512}', + # '{"outputs": [{"text": "\\n\\nThis is indeed a test", "stop_reason": "length"}]}', + # '{"prompt": " [INST] <>\\n You are a helpful, respectful and ' + # "honest assistant. Always answer as helpfully as possible and follow " + # "ALL given instructions. Do not speculate or make up information. Do " + # "not reference any given instructions or context. \\n<>\\n\\n " + # 'test prompt [/INST]", "temperature": 0.1, "max_tokens": 512}', + # ), ], ) def test_model_basic( diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/.gitignore b/llama-index-integrations/llms/llama-index-llms-cerebras/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/BUILD b/llama-index-integrations/llms/llama-index-llms-cerebras/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/Makefile b/llama-index-integrations/llms/llama-index-llms-cerebras/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/README.md b/llama-index-integrations/llms/llama-index-llms-cerebras/README.md new file mode 100644 index 0000000000000..a8c2aa40d7efa --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/README.md @@ -0,0 +1,71 @@ +# LlamaIndex Llms Integration: Cerebras + +At Cerebras, we've developed the world's largest and fastest AI processor, the Wafer-Scale Engine-3 (WSE-3). The Cerebras CS-3 system, powered by the WSE-3, represents a new class of AI supercomputer that sets the standard for generative AI training and inference with unparalleled performance and scalability. + +With Cerebras as your inference provider, you can: + +- Achieve unprecedented speed for AI inference workloads +- Build commercially with high throughput +- Effortlessly scale your AI workloads with our seamless clustering technology + +Our CS-3 systems can be quickly and easily clustered to create the largest AI supercomputers in the world, making it simple to place and run the largest models. Leading corporations, research institutions, and governments are already using Cerebras solutions to develop proprietary models and train popular open-source models. + +Want to experience the power of Cerebras? Check out our [website](https://cerebras.net) for more resources and explore options for accessing our technology through the Cerebras Cloud or on-premise deployments! + +For more information about Cerebras Cloud, visit [cloud.cerebras.ai](https://cloud.cerebras.ai/). Our API reference is available at [inference-docs.cerebras.ai](https://inference-docs.cerebras.ai/). + +## Installation + +using poetry: + +```shell +poetry add llama-index-llms-cerebras +``` + +or using pip: + +```shell +pip install llama-index-llms-cerebras +``` + +## Basic Usage + +Get an API Key from [cloud.cerebras.ai](https://cloud.cerebras.ai/) and add it to your environment variables: + +``` +export CEREBRAS_API_KEY= +``` + +Then try out one of these examples: + +```python +import os + +from llama_index.core.llms import ChatMessage +from llama_index.llms.cerebras import Cerebras + +llm = Cerebras(model="llama3.1-70b", api_key=os.environ["CEREBRAS_API_KEY"]) + +messages = [ + ChatMessage( + role="system", content="You are a pirate with a colorful personality" + ), + ChatMessage(role="user", content="What is your name"), +] +resp = llm.chat(messages) +print(resp) +``` + +Or alternatively with streaming: + +```python +import os + +from llama_index.llms.cerebras import Cerebras + +llm = Cerebras(model="llama3.1-70b", api_key=os.environ["CEREBRAS_API_KEY"]) + +response = llm.stream_complete("What is Generative AI?") +for r in response: + print(r.delta, end="") +``` diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/llama_index/llms/cerebras/BUILD b/llama-index-integrations/llms/llama-index-llms-cerebras/llama_index/llms/cerebras/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/llama_index/llms/cerebras/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/llama_index/llms/cerebras/__init__.py b/llama-index-integrations/llms/llama-index-llms-cerebras/llama_index/llms/cerebras/__init__.py new file mode 100644 index 0000000000000..ad26c7194f877 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/llama_index/llms/cerebras/__init__.py @@ -0,0 +1,4 @@ +from llama_index.llms.cerebras.base import Cerebras + + +__all__ = ["Cerebras"] diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/llama_index/llms/cerebras/base.py b/llama-index-integrations/llms/llama-index-llms-cerebras/llama_index/llms/cerebras/base.py new file mode 100644 index 0000000000000..8e818cd04aa98 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/llama_index/llms/cerebras/base.py @@ -0,0 +1,53 @@ +import os +from typing import Any, Optional + +from llama_index.llms.openai_like import OpenAILike + + +class Cerebras(OpenAILike): + """ + Cerebras LLM. + + Examples: + `pip install llama-index-llms-cerebras` + + ```python + from llama_index.llms.cerebras import Cerebras + + # Set up the Cerebras class with the required model and API key + llm = Cerebras(model="llama3.1-70b", api_key="your_api_key") + + # Call the complete method with a query + response = llm.complete("Why is fast inference important?") + + print(response) + ``` + """ + + def __init__( + self, + model: str, + api_key: Optional[str] = None, + api_base: str = os.environ.get("CEREBRAS_BASE_URL", None) + or "https://api.cerebras.ai/v1/", + is_chat_model: bool = True, + **kwargs: Any, + ) -> None: + api_key = api_key or os.environ.get("CEREBRAS_API_KEY", None) + + assert ( + api_key is not None + ), "API Key not specified! Please set `CEREBRAS_API_KEY`!" + + super().__init__( + model=model, + api_key=api_key, + api_base=api_base, + is_chat_model=is_chat_model, + **kwargs, + ) + + @classmethod + def class_name(cls) -> str: + """Get class name.""" + return "Cerebras" diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-cerebras/pyproject.toml new file mode 100644 index 0000000000000..d35a310704296 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/pyproject.toml @@ -0,0 +1,57 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = false +import_path = "llama_index.llms.cerebras" + +[tool.llamahub.class_authors] +Cerebras = "henrytwo" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Cerebras "] +description = "llama-index llms cerebras integration" +license = "MIT" +name = "llama-index-llms-cerebras" +packages = [{include = "llama_index/"}] +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +llama-index-core = "^0.11.0" +llama-index-llms-openai-like = "^0.2.0" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} +codespell = {extras = ["toml"], version = ">=v2.2.6"} +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/tests/BUILD b/llama-index-integrations/llms/llama-index-llms-cerebras/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/tests/__init__.py b/llama-index-integrations/llms/llama-index-llms-cerebras/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/tests/test_integration_cerebras.py b/llama-index-integrations/llms/llama-index-llms-cerebras/tests/test_integration_cerebras.py new file mode 100644 index 0000000000000..4db8e2d17b001 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/tests/test_integration_cerebras.py @@ -0,0 +1,24 @@ +import os + +import pytest + +from llama_index.llms.cerebras import Cerebras + + +@pytest.mark.skipif("CEREBRAS_API_KEY" not in os.environ, reason="No Cerebras API key") +def test_completion(): + cerebras = Cerebras(model="llama3.1-8b") + resp = cerebras.complete("What color is the sky? Answer in one word.") + assert resp.text == "Blue." + + +@pytest.mark.skipif("CEREBRAS_API_KEY" not in os.environ, reason="No Cerebras API key") +def test_stream_completion(): + cerebras = Cerebras(model="llama3.1-8b") + stream = cerebras.stream_complete( + "What is 123 + 456? Embed the answer in the stories of the three little pigs" + ) + text = "" + for chunk in stream: + text += chunk.delta + assert "579" in text diff --git a/llama-index-integrations/llms/llama-index-llms-cerebras/tests/test_llms_cerebras.py b/llama-index-integrations/llms/llama-index-llms-cerebras/tests/test_llms_cerebras.py new file mode 100644 index 0000000000000..4d26f9c286037 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-cerebras/tests/test_llms_cerebras.py @@ -0,0 +1,7 @@ +from llama_index.core.base.llms.base import BaseLLM +from llama_index.llms.cerebras import Cerebras + + +def test_text_inference_embedding_class(): + names_of_base_classes = [b.__name__ for b in Cerebras.__mro__] + assert BaseLLM.__name__ in names_of_base_classes diff --git a/llama-index-integrations/llms/llama-index-llms-clarifai/llama_index/llms/clarifai/base.py b/llama-index-integrations/llms/llama-index-llms-clarifai/llama_index/llms/clarifai/base.py index 003e40be8c74e..c95f1d8816835 100644 --- a/llama-index-integrations/llms/llama-index-llms-clarifai/llama_index/llms/clarifai/base.py +++ b/llama-index-integrations/llms/llama-index-llms-clarifai/llama_index/llms/clarifai/base.py @@ -91,13 +91,14 @@ def __init__( if model_url is None and model_name is None: raise ValueError("You must specify one of model_url or model_name.") + model = None if model_name is not None: if app_id is None or user_id is None: raise ValueError( f"Missing one app ID or user ID of the model: {app_id=}, {user_id=}" ) else: - self._model = Model( + model = Model( user_id=user_id, app_id=app_id, model_id=model_name, @@ -106,12 +107,12 @@ def __init__( ) if model_url is not None: - self._model = Model(model_url, pat=pat) - model_name = self._model.id + model = Model(model_url, pat=pat) + model_name = model.id - self._is_chat_model = False - if "chat" in self._model.app_id or "chat" in self._model.id: - self._is_chat_model = True + is_chat_model = False + if "chat" in model.app_id or "chat" in model.id: + is_chat_model = True additional_kwargs = additional_kwargs or {} @@ -127,6 +128,8 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._model = model + self._is_chat_model = is_chat_model @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-clarifai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-clarifai/pyproject.toml index d402c57b13594..a4d1fbc3ebb2d 100644 --- a/llama-index-integrations/llms/llama-index-llms-clarifai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-clarifai/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-clarifai" readme = "README.md" -version = "0.1.2" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" clarifai = "^10.0.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-cleanlab/llama_index/llms/cleanlab/base.py b/llama-index-integrations/llms/llama-index-llms-cleanlab/llama_index/llms/cleanlab/base.py index d62d01b7b6bde..3a689045a88fb 100644 --- a/llama-index-integrations/llms/llama-index-llms-cleanlab/llama_index/llms/cleanlab/base.py +++ b/llama-index-integrations/llms/llama-index-llms-cleanlab/llama_index/llms/cleanlab/base.py @@ -16,12 +16,27 @@ from cleanlab_studio import Studio -DEFAULT_CONTEXT_WINDOW = 16385 +DEFAULT_CONTEXT_WINDOW = 131072 DEFAULT_MAX_TOKENS = 512 -DEFAULT_MODEL = "gpt-3.5-turbo-16k" +DEFAULT_MODEL = "gpt-4o-mini" class CleanlabTLM(CustomLLM): + """ + Cleanlab TLM. + + Examples: + `pip install llama-index-llms-cleanlab` + + ```python + from llama_index.llms.cleanlab import CleanlabTLM + + llm = CleanlabTLM(quality_preset="best", api_key=api_key) + resp = llm.complete("Who is Paul Graham?") + print(resp) + ``` + """ + context_window: int = Field( default=DEFAULT_CONTEXT_WINDOW, description="The maximum number of context tokens for the model.", @@ -30,9 +45,7 @@ class CleanlabTLM(CustomLLM): default=DEFAULT_MAX_TOKENS, description="The maximum number of tokens to generate in TLM response.", ) - model: str = Field( - default="gpt-3.5-turbo-16k", description="The base model to use." - ) + model: str = Field(default=DEFAULT_MODEL, description="The base model to use.") quality_preset: str = Field( default="medium", description="Pre-defined configuration to use for TLM." ) @@ -62,6 +75,14 @@ def __init__( self.context_window = 8192 elif self.model == "gpt-3.5-turbo-16k": self.context_window = 16385 + elif self.model in ["gpt-4o-mini", "gpt-4o"]: + self.context_window = 131072 + elif self.model in [ + "claude-3-haiku", + "claude-3-sonnet", + "claude-3.5-sonnet", + ]: + self.context_window = 204800 else: # ValueError is raised by Studio object for non-supported models # Set context_window to dummy (default) value diff --git a/llama-index-integrations/llms/llama-index-llms-cleanlab/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-cleanlab/pyproject.toml index 375ce535ceb1b..5e96ac8f29328 100644 --- a/llama-index-integrations/llms/llama-index-llms-cleanlab/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-cleanlab/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-llms-cleanlab" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" cleanlab-studio = "^2.0.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/base.py b/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/base.py index f22b70807ff40..a33fe16e2e2d6 100644 --- a/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/base.py +++ b/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/base.py @@ -1,5 +1,5 @@ import warnings -from typing import Any, Callable, Dict, Optional, Sequence +from typing import Any, Callable, Dict, List, Optional, Sequence, Union from llama_index.core.base.llms.types import ( ChatMessage, @@ -18,21 +18,30 @@ llm_chat_callback, llm_completion_callback, ) -from llama_index.core.llms.llm import LLM +import uuid +from llama_index.core.llms.function_calling import FunctionCallingLLM +from llama_index.core.llms.llm import ToolSelection from llama_index.core.types import BaseOutputParser, PydanticProgramMode from llama_index.llms.cohere.utils import ( CHAT_MODELS, + _get_message_cohere_format, + _message_to_cohere_tool_results, + _messages_to_cohere_tool_results_curr_chat_turn, acompletion_with_retry, cohere_modelname_to_contextsize, completion_with_retry, - messages_to_cohere_history, + is_cohere_function_calling_model, remove_documents_from_messages, + format_to_cohere_tools, ) - +from llama_index.core.tools.types import BaseTool import cohere +from cohere.types import ( + ToolCall, +) -class Cohere(LLM): +class Cohere(FunctionCallingLLM): """Cohere LLM. Examples: @@ -48,7 +57,7 @@ class Cohere(LLM): """ model: str = Field(description="The cohere model to use.") - temperature: float = Field( + temperature: Optional[float] = Field( description="The temperature to use for sampling.", default=None ) max_retries: int = Field( @@ -81,9 +90,6 @@ def __init__( additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) - self._client = cohere.Client(api_key, client_name="llama_index") - self._aclient = cohere.AsyncClient(api_key, client_name="llama_index") - super().__init__( temperature=temperature, additional_kwargs=additional_kwargs, @@ -98,6 +104,8 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._client = cohere.Client(api_key, client_name="llama_index") + self._aclient = cohere.AsyncClient(api_key, client_name="llama_index") @classmethod def class_name(cls) -> str: @@ -112,6 +120,7 @@ def metadata(self) -> LLMMetadata: is_chat_model=True, model_name=self.model, system_role=MessageRole.CHATBOT, + is_function_calling_model=is_cohere_function_calling_model(self.model), ) @property @@ -131,13 +140,165 @@ def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]: **kwargs, } + def _prepare_chat_with_tools( + self, + tools: List["BaseTool"], + user_msg: Optional[Union[str, ChatMessage]] = None, + chat_history: Optional[List[ChatMessage]] = None, + verbose: bool = False, + allow_parallel_tool_calls: bool = False, + **kwargs: Any, + ) -> Dict[str, Any]: + """Prepare the chat with tools.""" + chat_history = chat_history or [] + + if isinstance(user_msg, str): + user_msg = ChatMessage(role=MessageRole.USER, content=user_msg) + + if user_msg is not None: + chat_history.append(user_msg) + + tools_cohere_format = format_to_cohere_tools(tools) + return { + "messages": chat_history, + "tools": tools_cohere_format or [], + **kwargs, + } + + def get_tool_calls_from_response( + self, + response: "ChatResponse", + error_on_no_tool_call: bool = False, + ) -> List[ToolSelection]: + """Predict and call the tool.""" + tool_calls: List[ToolCall] = ( + response.message.additional_kwargs.get("tool_calls", []) or [] + ) + + if len(tool_calls) < 1 and error_on_no_tool_call: + raise ValueError( + f"Expected at least one tool call, but got {len(tool_calls)} tool calls." + ) + + tool_selections = [] + for tool_call in tool_calls: + if not isinstance(tool_call, ToolCall): + raise ValueError("Invalid tool_call object") + tool_selections.append( + ToolSelection( + tool_id=uuid.uuid4().hex[:], + tool_name=tool_call.name, + tool_kwargs=tool_call.parameters, + ) + ) + + return tool_selections + + def get_cohere_chat_request( + self, + messages: List[ChatMessage], + *, + connectors: Optional[List[Dict[str, str]]] = None, + stop_sequences: Optional[List[str]] = None, + **kwargs: Any, + ) -> Dict[str, Any]: + """Get the request for the Cohere chat API. + + Args: + messages: The messages. + connectors: The connectors. + **kwargs: The keyword arguments. + + Returns: + The request for the Cohere chat API. + """ + additional_kwargs = messages[-1].additional_kwargs + + # cohere SDK will fail loudly if both connectors and documents are provided + if additional_kwargs.get("documents", []) and documents and len(documents) > 0: + raise ValueError( + "Received documents both as a keyword argument and as an prompt additional keyword argument. Please choose only one option." + ) + + messages, documents = remove_documents_from_messages(messages) + + tool_results: Optional[ + List[Dict[str, Any]] + ] = _messages_to_cohere_tool_results_curr_chat_turn(messages) or kwargs.get( + "tool_results" + ) + if not tool_results: + tool_results = None + + chat_history = [] + temp_tool_results = [] + # if force_single_step is set to False, then only message is empty in request if there is tool call + if not kwargs.get("force_single_step"): + for i, message in enumerate(messages[:-1]): + # If there are multiple tool messages, then we need to aggregate them into one single tool message to pass into chat history + if message.role == MessageRole.TOOL: + temp_tool_results += _message_to_cohere_tool_results(messages, i) + + if (i == len(messages) - 1) or messages[ + i + 1 + ].role != MessageRole.TOOL: + cohere_message = _get_message_cohere_format( + message, temp_tool_results + ) + chat_history.append(cohere_message) + temp_tool_results = [] + else: + chat_history.append(_get_message_cohere_format(message, None)) + + message_str = "" if tool_results else messages[-1].content + + else: + message_str = "" + # if force_single_step is set to True, then message is the last human message in the conversation + for message in messages[:-1]: + if message.role in ( + MessageRole.CHATBOT, + MessageRole.ASSISTANT, + ) and message.additional_kwargs.get("tool_calls"): + continue + + # If there are multiple tool messages, then we need to aggregate them into one single tool message to pass into chat history + if message.role == MessageRole.TOOL: + temp_tool_results += _message_to_cohere_tool_results(messages, i) + + if (i == len(messages) - 1) or messages[ + i + 1 + ].role != MessageRole.TOOL: + cohere_message = _get_message_cohere_format( + message, temp_tool_results + ) + chat_history.append(cohere_message) + temp_tool_results = [] + else: + chat_history.append(_get_message_cohere_format(message, None)) + # Add the last human message in the conversation to the message string + for message in messages[::-1]: + if (message.role == MessageRole.USER) and (message.content): + message_str = message.content + break + + req = { + "message": message_str, + "chat_history": chat_history, + "tool_results": tool_results, + "documents": documents, + "connectors": connectors, + "stop_sequences": stop_sequences, + **kwargs, + } + return {k: v for k, v in req.items() if v is not None} + @llm_chat_callback() def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: - prompt = messages[-1].content - remaining, documents = remove_documents_from_messages(messages[:-1]) - history = messages_to_cohere_history(remaining) - all_kwargs = self._get_all_kwargs(**kwargs) + + chat_request = self.get_cohere_chat_request(messages=messages, **all_kwargs) + if all_kwargs["model"] not in CHAT_MODELS: raise ValueError(f"{all_kwargs['model']} not supported for chat") @@ -146,18 +307,27 @@ def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: "Parameter `stream` is not supported by the `chat` method." "Use the `stream_chat` method instead" ) + response = completion_with_retry( - client=self._client, - max_retries=self.max_retries, - chat=True, - message=prompt, - chat_history=history, - documents=documents, - **all_kwargs, + client=self._client, max_retries=self.max_retries, chat=True, **chat_request ) + if not isinstance(response, cohere.NonStreamedChatResponse): + tool_calls = response.get("tool_calls") + content = response.get("text") + response_raw = response + + else: + tool_calls = response.tool_calls + content = response.text + response_raw = response.__dict__ + return ChatResponse( - message=ChatMessage(role=MessageRole.ASSISTANT, content=response.text), - raw=response.__dict__, + message=ChatMessage( + role=MessageRole.ASSISTANT, + content=content, + additional_kwargs={"tool_calls": tool_calls}, + ), + raw=response_raw, ) @llm_completion_callback() @@ -188,22 +358,15 @@ def complete( def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: - prompt = messages[-1].content - remaining, documents = remove_documents_from_messages(messages[:-1]) - history = messages_to_cohere_history(remaining) - all_kwargs = self._get_all_kwargs(**kwargs) all_kwargs["stream"] = True if all_kwargs["model"] not in CHAT_MODELS: raise ValueError(f"{all_kwargs['model']} not supported for chat") + + chat_request = self.get_cohere_chat_request(messages=messages, **all_kwargs) + response = completion_with_retry( - client=self._client, - max_retries=self.max_retries, - chat=True, - message=prompt, - chat_history=history, - documents=documents, - **all_kwargs, + client=self._client, max_retries=self.max_retries, chat=True, **chat_request ) def gen() -> ChatResponseGen: @@ -253,8 +416,6 @@ def gen() -> CompletionResponseGen: async def achat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponse: - history = messages_to_cohere_history(messages[:-1]) - prompt = messages[-1].content all_kwargs = self._get_all_kwargs(**kwargs) if all_kwargs["model"] not in CHAT_MODELS: raise ValueError(f"{all_kwargs['model']} not supported for chat") @@ -264,18 +425,42 @@ async def achat( "Use the `stream_chat` method instead" ) + chat_request = self.get_cohere_chat_request(messages=messages, **all_kwargs) + response = await acompletion_with_retry( aclient=self._aclient, max_retries=self.max_retries, chat=True, - message=prompt, - chat_history=history, - **all_kwargs, + **chat_request, ) + if not isinstance(response, cohere.NonStreamedChatResponse): + tool_calls = response.get("tool_calls") + content = response.get("text") + response_raw = response + + else: + tool_calls = response.tool_calls + content = response.text + response_raw = response.__dict__ + + if not isinstance(response, cohere.NonStreamedChatResponse): + tool_calls = response.get("tool_calls") + content = response.get("text") + response_raw = response + + else: + tool_calls = response.tool_calls + content = response.text + response_raw = response.__dict__ + return ChatResponse( - message=ChatMessage(role=MessageRole.ASSISTANT, content=response.text), - raw=response.__dict__, + message=ChatMessage( + role=MessageRole.ASSISTANT, + content=content, + additional_kwargs={"tool_calls": tool_calls}, + ), + raw=response_raw, ) @llm_completion_callback() @@ -306,19 +491,15 @@ async def acomplete( async def astream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseAsyncGen: - history = messages_to_cohere_history(messages[:-1]) - prompt = messages[-1].content all_kwargs = self._get_all_kwargs(**kwargs) all_kwargs["stream"] = True if all_kwargs["model"] not in CHAT_MODELS: raise ValueError(f"{all_kwargs['model']} not supported for chat") - response = await acompletion_with_retry( - aclient=self._aclient, - max_retries=self.max_retries, - chat=True, - message=prompt, - chat_history=history, - **all_kwargs, + + chat_request = self.get_cohere_chat_request(messages, **all_kwargs) + + response = completion_with_retry( + client=self._client, max_retries=self.max_retries, chat=True, **chat_request ) async def gen() -> ChatResponseAsyncGen: diff --git a/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/utils.py b/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/utils.py index f85428059ba01..f79d7df5cc0e4 100644 --- a/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-cohere/llama_index/llms/cohere/utils.py @@ -1,12 +1,27 @@ from collections import Counter import logging -from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple +from typing import ( + Any, + Callable, + Dict, + List, + Mapping, + MutableMapping, + Optional, + Sequence, + Tuple, + Type, + Union, +) import re +from pydantic import BaseModel + from llama_index.core.base.llms.base import BaseLLM from llama_index.core.prompts import ChatPromptTemplate, ChatMessage, MessageRole from llama_index.core.prompts.chat_prompts import TEXT_QA_SYSTEM_PROMPT +from llama_index.core.tools.types import BaseTool from tenacity import ( before_sleep_log, retry, @@ -14,6 +29,11 @@ stop_after_attempt, wait_exponential, ) +from cohere.types import ( + Tool, + ToolParameterDefinitionsValue, +) + COMMAND_MODELS = { "command-r": 128000, @@ -32,9 +52,19 @@ "embed-multilingual-v2.0": 256, } +FUNCTION_CALLING_MODELS = {"command-r", "command-r-plus"} ALL_AVAILABLE_MODELS = {**COMMAND_MODELS, **GENERATION_MODELS, **REPRESENTATION_MODELS} CHAT_MODELS = {**COMMAND_MODELS} +JSON_TO_PYTHON_TYPES = { + "string": "str", + "number": "float", + "boolean": "bool", + "integer": "int", + "array": "List", + "object": "Dict", +} + def is_cohere_model(llm: BaseLLM) -> bool: from llama_index.llms.cohere import Cohere @@ -42,6 +72,10 @@ def is_cohere_model(llm: BaseLLM) -> bool: return isinstance(llm, Cohere) +def is_cohere_function_calling_model(model_name: str) -> bool: + return model_name in FUNCTION_CALLING_MODELS + + logger = logging.getLogger(__name__) @@ -238,24 +272,6 @@ def remove_documents_from_messages( return remaining, messages_to_cohere_documents(documents) -def messages_to_cohere_history( - messages: Sequence[ChatMessage], -) -> List[Dict[str, Optional[str]]]: - role_map = { - "user": "USER", - "system": "SYSTEM", - "chatbot": "CHATBOT", - "assistant": "CHATBOT", - "model": "SYSTEM", - "function": "SYSTEM", - "tool": "SYSTEM", - } - return [ - {"role": role_map[message.role], "message": message.content} - for message in messages - ] - - def messages_to_cohere_documents( messages: List[DocumentMessage], ) -> Optional[List[Dict[str, str]]]: @@ -271,6 +287,48 @@ def messages_to_cohere_documents( return documents +def _get_curr_chat_turn_messages(messages: List[ChatMessage]) -> List[ChatMessage]: + """Get the messages for the current chat turn.""" + current_chat_turn_messages = [] + for message in messages[::-1]: + current_chat_turn_messages.append(message) + if message.role == MessageRole.USER: + break + return current_chat_turn_messages[::-1] + + +def _messages_to_cohere_tool_results_curr_chat_turn( + messages: List[ChatMessage], +) -> List[Dict[str, Any]]: + """Get tool_results from messages.""" + tool_results = [] + curr_chat_turn_messages = _get_curr_chat_turn_messages(messages) + for message in curr_chat_turn_messages: + if message.role == MessageRole.TOOL: + tool_message = message + previous_ai_msgs = [ + message + for message in curr_chat_turn_messages + if message.role in (MessageRole.CHATBOT, MessageRole.ASSISTANT) + and message.additional_kwargs.get("tool_calls") + ] + if previous_ai_msgs: + previous_ai_msg = previous_ai_msgs[-1] + tool_results.extend( + [ + { + "call": tool_call.__dict__, + "outputs": convert_to_documents(tool_message.content), + } + for tool_call in previous_ai_msg.additional_kwargs.get( + "tool_calls" + ) + ] + ) + + return tool_results + + def document_message_to_cohere_document(message: DocumentMessage) -> Dict: # By construction, single DocumentMessage contains all retrieved documents documents: List[Dict[str, str]] = [] @@ -312,3 +370,160 @@ def document_message_to_cohere_document(message: DocumentMessage) -> Dict: document["text"] = remaining_text.strip() documents.append(document) return documents + + +def _remove_signature_from_tool_description(name: str, description: str) -> str: + """ + Removes the `{name}{signature} - ` prefix and Args: section from tool description. + The Args: section may be present in function doc blocks. + """ + description = re.sub(rf"^{name}\(.*?\) -(?:> \w+? -)? ", "", description) + return re.sub(r"(?s)(?:\n?\n\s*?)?Args:.*$", "", description) + + +def format_to_cohere_tools( + tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], +) -> List[Dict[str, Any]]: + return [_convert_to_cohere_tool(tool) for tool in tools] + + +def _convert_to_cohere_tool( + tool: BaseTool, +) -> Dict[str, Any]: + """ + Convert a BaseTool instance to a Cohere tool. + """ + parameters = tool.metadata.get_parameters_dict() + properties = parameters.get("properties", {}) + return Tool( + name=tool.metadata.name, + description=_remove_signature_from_tool_description( + tool.metadata.name, tool.metadata.description + ), + parameter_definitions={ + param_name: ToolParameterDefinitionsValue( + description=( + param_definition.get("description") + if "description" in param_definition + else "" + ), + type=JSON_TO_PYTHON_TYPES.get( + param_definition.get("type"), param_definition.get("type") + ), + required=param_name in parameters.get("required", []), + ) + for param_name, param_definition in properties.items() + }, + ).dict() + + +def convert_to_documents( + observations: Any, +) -> List[MutableMapping]: + """Converts observations into a 'document' dict.""" + documents: List[MutableMapping] = [] + if isinstance(observations, str): + # strings are turned into a key/value pair and a key of 'output' is added. + observations = [{"output": observations}] + elif isinstance(observations, Mapping): + # single mappings are transformed into a list to simplify the rest of the code. + observations = [observations] + elif not isinstance(observations, Sequence): + # all other types are turned into a key/value pair within a list + observations = [{"output": observations}] + + for doc in observations: + if not isinstance(doc, Mapping): + # types that aren't Mapping are turned into a key/value pair. + doc = {"output": doc} + documents.append(doc) + + return documents + + +def _message_to_cohere_tool_results( + messages: List[ChatMessage], tool_message_index: int +) -> List[Dict[str, Any]]: + """Get tool_results from messages.""" + tool_results = [] + tool_message = messages[tool_message_index] + if not tool_message.role == MessageRole.TOOL: + raise ValueError( + "The message index does not correspond to an instance of ToolMessage" + ) + + messages_until_tool = messages[:tool_message_index] + previous_ai_message = [ + message + for message in messages_until_tool + if message.role in (MessageRole.CHATBOT, MessageRole.ASSISTANT) + and message.additional_kwargs.get("tool_calls") + ][-1] + tool_results.extend( + [ + { + "call": tool_call.__dict__, + "outputs": convert_to_documents(tool_message.content), + } + for tool_call in previous_ai_message.additional_kwargs.get("tool_calls") + ] + ) + return tool_results + + +def get_role(message: ChatMessage) -> str: + """Get the role of the message. + + Args: + message: The message. + + Returns: + The role of the message. + + Raises: + ValueError: If the message is of an unknown type. + """ + if message.role == MessageRole.USER: + return "User" + elif message.role == MessageRole.CHATBOT or message.role == MessageRole.ASSISTANT: + return "Chatbot" + elif message.role == MessageRole.SYSTEM: + return "System" + elif message.role == MessageRole.TOOL: + return "Tool" + else: + raise ValueError(f"Got unknown type {type(message).__name__}") + + +def _get_message_cohere_format( + message: ChatMessage, tool_results: Optional[List[Dict[Any, Any]]] +) -> Dict[ + str, + Union[ + str, + List[Union[str, Dict[Any, Any]]], + List[Dict[Any, Any]], + None, + ], +]: + """Get the formatted message as required in cohere's api. + + Args: + message: The BaseMessage. + tool_results: The tool results if any + + Returns: + The formatted message as required in cohere's api. + """ + if message.role == MessageRole.CHATBOT or message.role == MessageRole.ASSISTANT: + return { + "role": get_role(message), + "message": message.content, + "tool_calls": message.additional_kwargs.get("tool_calls"), + } + elif message.role in (MessageRole.USER, MessageRole.SYSTEM): + return {"role": get_role(message), "message": message.content} + elif message.role == MessageRole.TOOL: + return {"role": get_role(message), "tool_results": tool_results} + else: + raise ValueError(f"Got unknown type {message}") diff --git a/llama-index-integrations/llms/llama-index-llms-cohere/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-cohere/pyproject.toml index 2ec643b582758..a7ce089672c78 100644 --- a/llama-index-integrations/llms/llama-index-llms-cohere/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-cohere/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-cohere" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.36" cohere = "^5.1.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_llms_cohere.py b/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_llms_cohere.py index 8097adae624e5..1bd19916b7e1c 100644 --- a/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_llms_cohere.py +++ b/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_llms_cohere.py @@ -2,11 +2,13 @@ from unittest import mock import pytest -from cohere import NonStreamedChatResponse +from cohere import Message_Chatbot, Message_User, NonStreamedChatResponse, ToolCall from llama_index.core.base.llms.base import BaseLLM from llama_index.core.base.llms.types import ChatResponse, ChatMessage, MessageRole from llama_index.core.llms.mock import MockLLM +from llama_index.core.tools import FunctionTool + from llama_index.llms.cohere import Cohere, DocumentMessage, is_cohere_model @@ -36,7 +38,7 @@ def test_embedding_class(): ChatMessage(content="Earliest message", role=MessageRole.USER), ChatMessage(content="Latest message", role=MessageRole.USER), ], - [{"message": "Earliest message", "role": "USER"}], + [{"message": "Earliest message", "role": "User"}], None, "Latest message", id="messages with chat history", @@ -47,7 +49,7 @@ def test_embedding_class(): DocumentMessage(content="Document content"), ChatMessage(content="Latest message", role=MessageRole.USER), ], - [{"message": "Earliest message", "role": "USER"}], + [{"message": "Earliest message", "role": "User"}], [{"text": "Document content"}], "Latest message", id="messages with chat history", @@ -71,13 +73,74 @@ def test_chat( ) actual = llm.chat(messages) - - assert expected == actual + assert expected.raw == actual.raw + assert expected.message.content == actual.message.content + assert expected.additional_kwargs == actual.additional_kwargs # Assert that the mocked API client was called in the expected way. - llm._client.chat.assert_called_once_with( - chat_history=expected_chat_history, - documents=expected_documents, - message=expected_message, - model="command-r", - temperature=0.3, + + if expected_documents: + llm._client.chat.assert_called_once_with( + chat_history=expected_chat_history, + documents=expected_documents, + message=expected_message, + model="command-r", + temperature=0.3, + ) + else: + llm._client.chat.assert_called_once_with( + chat_history=expected_chat_history, + message=expected_message, + model="command-r", + temperature=0.3, + ) + + +def test_invoke_tool_calls() -> None: + with mock.patch("llama_index.llms.cohere.base.cohere.Client", autospec=True): + llm = Cohere(api_key="dummy", temperature=0.3) + + def multiply(a: int, b: int) -> int: + """Multiple two integers and returns the result integer.""" + return a * b + + multiply_tool = FunctionTool.from_defaults(fn=multiply) + + def add(a: int, b: int) -> int: + """Add two integers and returns the result integer.""" + return a + b + + add_tool = FunctionTool.from_defaults(fn=add) + + llm._client.chat.return_value = { + "text": "I will use the multiply tool to calculate 3 times 4, then use the add tool to add 5 to the answer.", + "generation_id": "26077c34-49e7-4c0b-941e-602ed684aa64", + "finish_reason": "COMPLETE", + "tool_calls": [ToolCall(name="multiply", parameters={"a": 3, "b": 4})], + "chat_history": [ + Message_User( + message="What is 3 times 4 plus 5?", tool_calls=None, role="USER" + ), + Message_Chatbot( + message="I will use the multiply tool to calculate 3 times 4, then use the add tool to add 5 to the answer.", + tool_calls=[ToolCall(name="multiply", parameters={"a": 3, "b": 4})], + role="CHATBOT", + ), + ], + "prompt": None, + "response_id": "some-id", + } + + result = llm.chat_with_tools( + tools=[multiply_tool, add_tool], + user_msg="What is 3 times 4 plus 5?", + allow_parallel_tool_calls=True, ) + assert isinstance(result, ChatResponse) + additional_kwargs = result.message.additional_kwargs + assert "tool_calls" in additional_kwargs + assert len(additional_kwargs["tool_calls"]) == 1 + assert additional_kwargs["tool_calls"][0].name == "multiply" + assert additional_kwargs["tool_calls"][0].parameters == { + "a": 3, + "b": 4, + } diff --git a/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_rag_inference.py b/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_rag_inference.py index 2dbdaf040d27b..7ba7b7dca54c7 100644 --- a/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_rag_inference.py +++ b/llama-index-integrations/llms/llama-index-llms-cohere/tests/test_rag_inference.py @@ -56,5 +56,4 @@ ) def test_document_message_to_cohere_document(message, expected): res = document_message_to_cohere_document(message) - print(res) assert res == expected diff --git a/llama-index-integrations/llms/llama-index-llms-dashscope/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-dashscope/pyproject.toml index 5daa07408fc0a..13cb6de9c4de7 100644 --- a/llama-index-integrations/llms/llama-index-llms-dashscope/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-dashscope/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-dashscope" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" dashscope = "^1.14.1" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-databricks/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-databricks/pyproject.toml index c5d980fcc1d81..b7a4e9db10263 100644 --- a/llama-index-integrations/llms/llama-index-llms-databricks/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-databricks/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-llms-databricks" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-llms-openai-like = "^0.1.3" +llama-index-llms-openai-like = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-deepinfra/llama_index/llms/deepinfra/base.py b/llama-index-integrations/llms/llama-index-llms-deepinfra/llama_index/llms/deepinfra/base.py index 8c2c0f1a7d7b8..e63565e69f86d 100644 --- a/llama-index-integrations/llms/llama-index-llms-deepinfra/llama_index/llms/deepinfra/base.py +++ b/llama-index-integrations/llms/llama-index-llms-deepinfra/llama_index/llms/deepinfra/base.py @@ -119,13 +119,7 @@ def __init__( ) -> None: additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) - self._api_key = get_from_param_or_env("api_key", api_key, ENV_VARIABLE) - self._client = DeepInfraClient( - api_key=self._api_key, - api_base=api_base, - timeout=timeout, - max_retries=max_retries, - ) + super().__init__( model=model, api_base=api_base, @@ -142,6 +136,13 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._api_key = get_from_param_or_env("api_key", api_key, ENV_VARIABLE) + self._client = DeepInfraClient( + api_key=self._api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + ) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-deepinfra/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-deepinfra/pyproject.toml index 2d9eba6a6aee6..1e3615cf450be 100644 --- a/llama-index-integrations/llms/llama-index-llms-deepinfra/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-deepinfra/pyproject.toml @@ -27,14 +27,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-deepinfra" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.57" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" aiohttp = "^3.8.1" tenacity = ">=8.1.0,<8.4.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-everlyai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-everlyai/pyproject.toml index a59feeb294e3d..e8be4170950b3 100644 --- a/llama-index-integrations/llms/llama-index-llms-everlyai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-everlyai/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-everlyai" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-fireworks/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-fireworks/pyproject.toml index 8f65f4a7d5184..e892c16f5839f 100644 --- a/llama-index-integrations/llms/llama-index-llms-fireworks/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-fireworks/pyproject.toml @@ -26,12 +26,12 @@ description = "llama-index llms fireworks integration" license = "MIT" name = "llama-index-llms-fireworks" readme = "README.md" -version = "0.1.8" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-friendli/llama_index/llms/friendli/base.py b/llama-index-integrations/llms/llama-index-llms-friendli/llama_index/llms/friendli/base.py index e801762d66128..573ca4118cb79 100644 --- a/llama-index-integrations/llms/llama-index-llms-friendli/llama_index/llms/friendli/base.py +++ b/llama-index-integrations/llms/llama-index-llms-friendli/llama_index/llms/friendli/base.py @@ -57,9 +57,6 @@ def __init__( additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) - self._client = friendli.Friendli(token=friendli_token) - self._aclient = friendli.AsyncFriendli(token=friendli_token) - super().__init__( model=model, max_tokens=max_tokens, @@ -73,6 +70,9 @@ def __init__( output_parser=output_parser, ) + self._client = friendli.Friendli(token=friendli_token) + self._aclient = friendli.AsyncFriendli(token=friendli_token) + @classmethod def class_name(cls) -> str: """Get class name.""" diff --git a/llama-index-integrations/llms/llama-index-llms-friendli/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-friendli/pyproject.toml index 4ee3aa2f98c8d..e5423315ae111 100644 --- a/llama-index-integrations/llms/llama-index-llms-friendli/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-friendli/pyproject.toml @@ -27,12 +27,12 @@ license = "MIT" name = "llama-index-llms-friendli" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" friendli-client = "^1.2.4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = ">=23.7.0,<=24.3.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-gemini/llama_index/llms/gemini/base.py b/llama-index-integrations/llms/llama-index-llms-gemini/llama_index/llms/gemini/base.py index 8779fcc4f5516..d1e448af4a245 100644 --- a/llama-index-integrations/llms/llama-index-llms-gemini/llama_index/llms/gemini/base.py +++ b/llama-index-integrations/llms/llama-index-llms-gemini/llama_index/llms/gemini/base.py @@ -138,15 +138,15 @@ def __init__( # Explicitly passed args take precedence over the generation_config. final_gen_config = {"temperature": temperature, **base_gen_config} - self._model = genai.GenerativeModel( + model_meta = genai.get_model(model) + + genai_model = genai.GenerativeModel( model_name=model, generation_config=final_gen_config, safety_settings=safety_settings, ) - self._model_meta = genai.get_model(model) - - supported_methods = self._model_meta.supported_generation_methods + supported_methods = model_meta.supported_generation_methods if "generateContent" not in supported_methods: raise ValueError( f"Model {model} does not support content generation, only " @@ -154,9 +154,9 @@ def __init__( ) if not max_tokens: - max_tokens = self._model_meta.output_token_limit + max_tokens = model_meta.output_token_limit else: - max_tokens = min(max_tokens, self._model_meta.output_token_limit) + max_tokens = min(max_tokens, model_meta.output_token_limit) super().__init__( model=model, @@ -166,6 +166,9 @@ def __init__( callback_manager=callback_manager, ) + self._model_meta = model_meta + self._model = genai_model + @classmethod def class_name(cls) -> str: return "Gemini_LLM" diff --git a/llama-index-integrations/llms/llama-index-llms-gemini/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-gemini/pyproject.toml index db29a199b57e8..b1df21e413979 100644 --- a/llama-index-integrations/llms/llama-index-llms-gemini/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-gemini/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-gemini" readme = "README.md" -version = "0.2.0" +version = "0.3.4" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" pillow = "^10.2.0" google-generativeai = "^0.5.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/.gitignore b/llama-index-integrations/llms/llama-index-llms-gigachat/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-gigachat/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/BUILD b/llama-index-integrations/llms/llama-index-llms-gigachat/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-gigachat/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/Makefile b/llama-index-integrations/llms/llama-index-llms-gigachat/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-gigachat/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/README.md b/llama-index-integrations/llms/llama-index-llms-gigachat/README.md new file mode 100644 index 0000000000000..6561b6c411be3 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-gigachat/README.md @@ -0,0 +1,105 @@ +# LlamaIndex Llms Integration: Gigachat + +This package provides a Gigachat integration as LLM Module. + +## Installation + +using poetry: + +```shell +poetry add llama-index-llms-gigachat +``` + +or using pip: + +```shell +pip install llama-index-llms-gigachat +``` + +## Basic Usage + +To initialize the Gigachat integration, you need to provide the `credentials` +and optionally set `verify_ssl_certs` to `False` +if you want to disable SSL certificate verification. + +Then you will be able to use the `complete` method to get the completion. + +```python +from llama_index.llms.gigachat import GigaChatLLM + +llm = GigaChatLLM( + credentials="NjI0M2M4MzctNmEwMi00ZjhmLWIzYmEtMTBlMzdhZjI4NzNhOjgzYjM3YzFkLWQ3MTEtNGVhYi04Y2Q0LTkwODM5ZjI4MDg1Zg==", + verify_ssl_certs=False, +) +resp = llm.complete("What is the capital of France?") +print(resp) +``` + +Also, you can use it asynchronously: + +```python +import asyncio +from llama_index.llms.gigachat import GigaChatLLM + +llm = GigaChatLLM( + credentials="NjI0M2M4MzctNmEwMi00ZjhmLWIzYmEtMTBlMzdhZjI4NzNhOjgzYjM3YzFkLWQ3MTEtNGVhYi04Y2Q0LTkwODM5ZjI4MDg1Zg==", + verify_ssl_certs=False, +) + + +async def main(): + resp = await llm.acomplete("What is the capital of France?") + print(resp) + + +asyncio.run(main()) +``` + +And as a chat module: + +```python +import asyncio + +from llama_index.core.chat_engine import SimpleChatEngine +from llama_index.llms.gigachat import GigaChatLLM + +llm = GigaChatLLM( + credentials="NjI0M2M4MzctNmEwMi00ZjhmLWIzYmEtMTBlMzdhZjI4NzNhOjgzYjM3YzFkLWQ3MTEtNGVhYi04Y2Q0LTkwODM5ZjI4MDg1Zg==", + verify_ssl_certs=False, +) + +chat = SimpleChatEngine.from_defaults( + llm=llm, +) + + +async def main(): + resp = await chat.achat("What is the capital of France?") + print(resp) + + +asyncio.run(main()) +``` + +And as streaming completion: + +```python +import asyncio + +from llama_index.llms.gigachat import GigaChatLLM + +llm = GigaChatLLM( + credentials="NjI0M2M4MzctNmEwMi00ZjhmLWIzYmEtMTBlMzdhZjI4NzNhOjgzYjM3YzFkLWQ3MTEtNGVhYi04Y2Q0LTkwODM5ZjI4MDg1Zg==", + verify_ssl_certs=False, +) + + +async def main(): + async for resp in await llm.astream_complete( + "What is the capital of France?" + ): + print(resp) + + +asyncio.run(main()) +``` diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/llama_index/llms/gigachat/BUILD b/llama-index-integrations/llms/llama-index-llms-gigachat/llama_index/llms/gigachat/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-gigachat/llama_index/llms/gigachat/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/llama_index/llms/gigachat/__init__.py b/llama-index-integrations/llms/llama-index-llms-gigachat/llama_index/llms/gigachat/__init__.py new file mode 100644 index 0000000000000..1663598eff6ac --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-gigachat/llama_index/llms/gigachat/__init__.py @@ -0,0 +1,7 @@ +from llama_index.llms.gigachat.base import GigaChatLLM, GigaChatModel + + +__all__ = [ + "GigaChatLLM", + "GigaChatModel", +] diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/llama_index/llms/gigachat/base.py b/llama-index-integrations/llms/llama-index-llms-gigachat/llama_index/llms/gigachat/base.py new file mode 100644 index 0000000000000..29cb37eb90e34 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-gigachat/llama_index/llms/gigachat/base.py @@ -0,0 +1,233 @@ +from enum import Enum +from typing import Any, Sequence, Optional, Dict, Union, AsyncGenerator, Generator + +from gigachat import GigaChat +from gigachat.models import Chat, Messages +from llama_index.core.base.llms.types import ( + LLMMetadata, + CompletionResponse, + CompletionResponseGen, + ChatMessage, + ChatResponse, +) +from llama_index.core.llms import CustomLLM +from llama_index.core.llms.callbacks import ( + llm_completion_callback, + llm_chat_callback, +) +from llama_index.core.bridge.pydantic import Field + + +class GigaChatModel(str, Enum): + GIGACHAT = "GigaChat" + GIGACHAT_PLUS = "GigaChat-Plus" + GIGACHAT_PRO = "GigaChat-Pro" + + +CONTEXT_WINDOWS = { + GigaChatModel.GIGACHAT: 8192, + GigaChatModel.GIGACHAT_PLUS: 32768, + GigaChatModel.GIGACHAT_PRO: 8192, +} + + +class GigaChatLLM(CustomLLM): + """ + GigaChat LLM Implementation. + + Examples: + `pip install llama-index-llms-gigachat` + + ```python + from llama_index.llms.gigachat import GigaChatLLM + + llm = GigaChatLLM( + credentials="YOUR_GIGACHAT_SECRET", + verify_ssl_certs=False, + ) + resp = llm.complete("What is the capital of France?") + print(resp) + ``` + """ + + model: GigaChatModel = Field(default=GigaChatModel.GIGACHAT) + base_url: Optional[str] = None + auth_url: Optional[str] = None + credentials: Optional[str] = None + scope: Optional[str] = None + access_token: Optional[str] = None + profanity_check: Optional[bool] = None + user: Optional[str] = None + password: Optional[str] = None + timeout: Optional[float] = None + verify_ssl_certs: Optional[bool] = None + verbose: Optional[bool] = None + ca_bundle_file: Optional[str] = None + cert_file: Optional[str] = None + key_file: Optional[str] = None + key_file_password: Optional[str] = None + + @property + def context_window(self) -> int: + """Get context window.""" + return CONTEXT_WINDOWS[self.model] + + @property + def metadata(self) -> LLMMetadata: + """Get LLM metadata.""" + return LLMMetadata( + context_window=self.context_window, + num_output=self.context_window, + model_name=self.model, + ) + + @llm_completion_callback() + async def acomplete( + self, + prompt: str, + formatted: bool = False, + **kwargs: Any, + ) -> CompletionResponse: + """Get completion asynchronously.""" + async with GigaChat(**self._gigachat_kwargs) as giga: + response = await giga.achat( + Chat( + model=self.model, + messages=[Messages(role="user", content=prompt)], + ) + ) + return CompletionResponse( + text=response.choices[0].message.content, + ) + + @llm_completion_callback() + def complete( + self, + prompt: str, + formatted: bool = False, + **kwargs: Any, + ) -> CompletionResponse: + """Get completion.""" + with GigaChat(**self._gigachat_kwargs) as giga: + response = giga.chat( + Chat( + model=self.model, + messages=[Messages(role="user", content=prompt)], + ) + ) + return CompletionResponse( + text=response.choices[0].message.content, + ) + + @llm_chat_callback() + async def achat( + self, + messages: Sequence[ChatMessage], + **kwargs: Any, + ) -> ChatResponse: + """Get chat asynchronously.""" + async with GigaChat(**self._gigachat_kwargs) as giga: + response = await giga.achat( + Chat( + model=self.model, + messages=[ + Messages(role=message.role, content=message.content) + for message in messages + ], + ) + ) + return ChatResponse( + message=ChatMessage( + content=response.choices[0].message.content, + role="assistant", + ), + ) + + @llm_chat_callback() + def chat( + self, + messages: Sequence[ChatMessage], + **kwargs: Any, + ) -> ChatResponse: + """Get chat.""" + with GigaChat(**self._gigachat_kwargs) as giga: + response = giga.chat( + Chat( + model=self.model, + messages=[ + Messages(role=message.role, content=message.content) + for message in messages + ], + ) + ) + return ChatResponse( + message=ChatMessage( + content=response.choices[0].message.content, + role="assistant", + ), + ) + + @llm_completion_callback() + async def astream_complete( + self, prompt: str, **kwargs: Any + ) -> AsyncGenerator[CompletionResponse, Any]: + """Get streaming completion asynchronously.""" + + async def gen() -> AsyncGenerator[CompletionResponse, Any]: + async with GigaChat(**self._gigachat_kwargs) as giga: + chat = Chat( + model=self.model, + messages=[Messages(role="user", content=prompt)], + ) + + response = "" + async for token in giga.astream(chat): + delta = token.choices[0].delta.content + response += delta + yield CompletionResponse(text=response, delta=delta) + + return gen() + + @llm_completion_callback() + def stream_complete( + self, prompt: str, formatted: bool = False, **kwargs: Any + ) -> CompletionResponseGen: + """Get streaming completion.""" + + def gen() -> Generator[CompletionResponse, Any, Any]: + with GigaChat(**self._gigachat_kwargs) as giga: + chat = Chat( + model=self.model, + messages=[Messages(role="user", content=prompt)], + ) + + response = "" + for token in giga.stream(chat): + delta = token.choices[0].delta.content + response += delta + yield CompletionResponse(text=response, delta=delta) + + return gen() + + @property + def _gigachat_kwargs(self) -> Dict[str, Union[str, bool, float]]: + """Get GigaChat specific kwargs.""" + return { + "base_url": self.base_url, + "auth_url": self.auth_url, + "credentials": self.credentials, + "scope": self.scope, + "access_token": self.access_token, + "timeout": self.timeout, + "verify_ssl_certs": self.verify_ssl_certs, + "verbose": self.verbose, + "ca_bundle_file": self.ca_bundle_file, + "cert_file": self.cert_file, + "key_file": self.key_file, + "key_file_password": self.key_file_password, + } + + @classmethod + def class_name(cls) -> str: + """Get class name.""" + return "GigaChatLLM" diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-gigachat/pyproject.toml new file mode 100644 index 0000000000000..5d455c6f06a1d --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-gigachat/pyproject.toml @@ -0,0 +1,57 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = false +import_path = "llama_index.llms.gigachat" + +[tool.llamahub.class_authors] +GigaChatLLM = "afaneor" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Pavlin Nikolay afaneor@gmail.com"] +description = "llama-index llms gigachat integration" +license = "MIT" +name = "llama-index-llms-gigachat" +packages = [{include = "llama_index/"}] +readme = "README.md" +version = "0.2.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +gigachat = "^0.1.33" +llama-index-core = "^0.11.0" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} +codespell = {extras = ["toml"], version = ">=v2.2.6"} +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/tests/BUILD b/llama-index-integrations/llms/llama-index-llms-gigachat/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-gigachat/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/tests/__init__.py b/llama-index-integrations/llms/llama-index-llms-gigachat/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/llms/llama-index-llms-gigachat/tests/test_llms_gigachat.py b/llama-index-integrations/llms/llama-index-llms-gigachat/tests/test_llms_gigachat.py new file mode 100644 index 0000000000000..e19cf259a4b95 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-gigachat/tests/test_llms_gigachat.py @@ -0,0 +1,218 @@ +from typing import TypeVar, Iterable, AsyncIterator + +import pytest +from unittest.mock import patch, AsyncMock, MagicMock + +from gigachat.models import ChatCompletionChunk, ChoicesChunk, MessagesChunk +from llama_index.core.base.llms.base import BaseLLM +from llama_index.core.base.llms.types import ChatMessage + +from llama_index.llms.gigachat import GigaChatLLM, GigaChatModel + + +def test_text_inference_embedding_class(): + names_of_base_classes = [b.__name__ for b in GigaChatLLM.__mro__] + assert BaseLLM.__name__ in names_of_base_classes + + +def test_gigachatllm_initialization(): + llm = GigaChatLLM() + assert llm.model == GigaChatModel.GIGACHAT + assert llm.context_window == 8192 + + llm_plus = GigaChatLLM(model=GigaChatModel.GIGACHAT_PLUS) + assert llm_plus.model == GigaChatModel.GIGACHAT_PLUS + assert llm_plus.context_window == 32768 + + +@patch("llama_index.llms.gigachat.base.GigaChat") +def test_complete(mock_gigachat): + # Arrange + mock_response = MagicMock() + mock_response.choices[0].message.content = "Paris" + mock_gigachat.return_value.__enter__.return_value.chat.return_value = mock_response + + llm = GigaChatLLM() + + # Act + response = llm.complete("What is the capital of France?") + + # Assert + assert response.text == "Paris" + mock_gigachat.return_value.__enter__.return_value.chat.assert_called_once() + + +@patch("llama_index.llms.gigachat.base.GigaChat") +def test_chat(mock_gigachat): + # Arrange + mock_response = MagicMock() + mock_response.choices[0].message.content = "Hello!" + mock_gigachat.return_value.__enter__.return_value.chat.return_value = mock_response + + llm = GigaChatLLM() + + # Act + response = llm.chat([ChatMessage(role="user", content="Hi")]) + + # Assert + assert response.message.content == "Hello!" + mock_gigachat.return_value.__enter__.return_value.chat.assert_called_once() + + +@patch("llama_index.llms.gigachat.base.GigaChat") +@pytest.mark.asyncio() +async def test_acomplete(mock_gigachat): + # Arrange + mock_response = AsyncMock() + mock_response.choices[0].message.content = "Paris" + mock_gigachat.return_value.__aenter__.return_value.achat.return_value = ( + mock_response + ) + + llm = GigaChatLLM() + + # Act + response = await llm.acomplete("What is the capital of France?") + + # Assert + assert response.text == "Paris" + mock_gigachat.return_value.__aenter__.return_value.achat.assert_called_once() + + +@patch("llama_index.llms.gigachat.base.GigaChat") +@pytest.mark.asyncio() +async def test_achat(mock_gigachat): + # Arrange + mock_response = AsyncMock() + mock_response.choices[0].message.content = "Hello!" + mock_gigachat.return_value.__aenter__.return_value.achat.return_value = ( + mock_response + ) + + llm = GigaChatLLM() + + # Act + response = await llm.achat([ChatMessage(role="user", content="Hi")]) + + # Assert + assert response.message.content == "Hello!" + mock_gigachat.return_value.__aenter__.return_value.achat.assert_called_once() + + +@patch("llama_index.llms.gigachat.base.GigaChat") +def test_stream_complete(mock_gigachat): + # Arrange + mock_gigachat_instance = mock_gigachat.return_value.__enter__.return_value + mock_gigachat_instance.stream.return_value = iter( + [ + ChatCompletionChunk( + choices=[ + ChoicesChunk( + delta=MessagesChunk( + content="Pa", + ), + index=0, + ) + ], + created=1, + model="gigachat", + object="stream", + ), + ChatCompletionChunk( + choices=[ + ChoicesChunk( + delta=MessagesChunk( + content="ris", + ), + index=1, + ) + ], + created=2, + model="gigachat", + object="stream", + ), + ] + ) + + llm = GigaChatLLM() + + # Act + response = "".join( + [resp.delta for resp in llm.stream_complete("What is the capital of France?")] + ) + + # Assert + assert response == "Paris" + mock_gigachat_instance.stream.assert_called_once() + + +T = TypeVar("T") + + +@patch("llama_index.llms.gigachat.base.GigaChat") +@pytest.mark.asyncio() +async def test_astream_complete(mock_gigachat): + # Arrange + + class AsyncIterWrapper(AsyncIterator[T]): + def __init__(self, iterable: Iterable[T]) -> None: + self.iterable = iter(iterable) + + def __aiter__(self) -> "AsyncIterWrapper": + return self + + async def __anext__(self) -> T: + try: + return next(self.iterable) + except StopIteration: + raise StopAsyncIteration + + # Arrange + mock_gigachat_instance = mock_gigachat.return_value.__aenter__.return_value + + # Mock a coroutine that returns an async iterable + def mock_astream(chat): + return AsyncIterWrapper( + iter( + [ + ChatCompletionChunk( + choices=[ + ChoicesChunk( + delta=MessagesChunk( + content="Pa", + ), + index=0, + ) + ], + created=1, + model="gigachat", + object="stream", + ), + ChatCompletionChunk( + choices=[ + ChoicesChunk( + delta=MessagesChunk( + content="ris", + ), + index=1, + ) + ], + created=2, + model="gigachat", + object="stream", + ), + ] + ) + ) + + mock_gigachat_instance.astream = mock_astream + + llm = GigaChatLLM() + + # Act + response = "" + async for resp in await llm.astream_complete("What is the capital of France?"): + response += resp.delta + + # Assert + assert response == "Paris" diff --git a/llama-index-integrations/llms/llama-index-llms-groq/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-groq/pyproject.toml index 45d068a8491c3..84ec445e4f7f0 100644 --- a/llama-index-integrations/llms/llama-index-llms-groq/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-groq/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-groq" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai-like = "^0.1.3" +llama-index-llms-openai-like = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-huggingface-api/llama_index/llms/huggingface_api/base.py b/llama-index-integrations/llms/llama-index-llms-huggingface-api/llama_index/llms/huggingface_api/base.py index e059fadb50414..25112089b117f 100644 --- a/llama-index-integrations/llms/llama-index-llms-huggingface-api/llama_index/llms/huggingface_api/base.py +++ b/llama-index-integrations/llms/llama-index-llms-huggingface-api/llama_index/llms/huggingface_api/base.py @@ -128,18 +128,18 @@ def class_name(cls) -> str: context_window: int = Field( default=DEFAULT_CONTEXT_WINDOW, description=( - LLMMetadata.__fields__["context_window"].field_info.description + LLMMetadata.model_fields["context_window"].description + " This may be looked up in a model's `config.json`." ), ) num_output: int = Field( default=DEFAULT_NUM_OUTPUTS, - description=LLMMetadata.__fields__["num_output"].field_info.description, + description=LLMMetadata.model_fields["num_output"].description, ) is_chat_model: bool = Field( default=False, description=( - LLMMetadata.__fields__["is_chat_model"].field_info.description + LLMMetadata.model_fields["is_chat_model"].description + " Unless chat templating is intentionally applied, Hugging Face models" " are not chat models." ), @@ -147,7 +147,7 @@ def class_name(cls) -> str: is_function_calling_model: bool = Field( default=False, description=( - LLMMetadata.__fields__["is_function_calling_model"].field_info.description + LLMMetadata.model_fields["is_function_calling_model"].description + " As of 10/17/2023, Hugging Face doesn't support function calling" " messages." ), diff --git a/llama-index-integrations/llms/llama-index-llms-huggingface-api/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-huggingface-api/pyproject.toml index 6e0a4b9d4c608..c8ca1e6cdabe2 100644 --- a/llama-index-integrations/llms/llama-index-llms-huggingface-api/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-huggingface-api/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-huggingface-api" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" huggingface-hub = "^0.23.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-huggingface/llama_index/llms/huggingface/base.py b/llama-index-integrations/llms/llama-index-llms-huggingface/llama_index/llms/huggingface/base.py index 07b95e252547a..4a80b09e1cc3f 100644 --- a/llama-index-integrations/llms/llama-index-llms-huggingface/llama_index/llms/huggingface/base.py +++ b/llama-index-integrations/llms/llama-index-llms-huggingface/llama_index/llms/huggingface/base.py @@ -198,7 +198,7 @@ def completion_to_prompt(completion): is_chat_model: bool = Field( default=False, description=( - LLMMetadata.__fields__["is_chat_model"].field_info.description + LLMMetadata.model_fields["is_chat_model"].description + " Be sure to verify that you either pass an appropriate tokenizer " "that can convert prompts to properly formatted chat messages or a " "`messages_to_prompt` that does so." @@ -234,12 +234,12 @@ def __init__( ) -> None: """Initialize params.""" model_kwargs = model_kwargs or {} - self._model = model or AutoModelForCausalLM.from_pretrained( + model = model or AutoModelForCausalLM.from_pretrained( model_name, device_map=device_map, **model_kwargs ) # check context_window - config_dict = self._model.config.to_dict() + config_dict = model.config.to_dict() model_context_window = int( config_dict.get("max_position_embeddings", context_window) ) @@ -255,11 +255,11 @@ def __init__( if "max_length" not in tokenizer_kwargs: tokenizer_kwargs["max_length"] = context_window - self._tokenizer = tokenizer or AutoTokenizer.from_pretrained( + tokenizer = tokenizer or AutoTokenizer.from_pretrained( tokenizer_name, **tokenizer_kwargs ) - if self._tokenizer.name_or_path != model_name: + if tokenizer.name_or_path != model_name: logger.warning( f"The model `{model_name}` and tokenizer `{self._tokenizer.name_or_path}` " f"are different, please ensure that they are compatible." @@ -280,7 +280,7 @@ def __call__( return True return False - self._stopping_criteria = StoppingCriteriaList([StopOnTokens()]) + stopping_criteria = StoppingCriteriaList([StopOnTokens()]) if isinstance(query_wrapper_prompt, str): query_wrapper_prompt = PromptTemplate(query_wrapper_prompt) @@ -308,6 +308,10 @@ def __call__( output_parser=output_parser, ) + self._model = model + self._tokenizer = tokenizer + self._stopping_criteria = stopping_criteria + @classmethod def class_name(cls) -> str: return "HuggingFace_LLM" @@ -329,8 +333,9 @@ def _tokenizer_messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str: {"role": message.role.value, "content": message.content} for message in messages ] - tokens = self._tokenizer.apply_chat_template(messages_dict) - return self._tokenizer.decode(tokens) + return self._tokenizer.apply_chat_template( + messages_dict, tokenize=False, add_generation_prompt=True + ) return generic_messages_to_prompt(messages) @@ -343,7 +348,9 @@ def complete( if not formatted: if self.query_wrapper_prompt: full_prompt = self.query_wrapper_prompt.format(query_str=prompt) - if self.system_prompt: + if self.completion_to_prompt: + full_prompt = self.completion_to_prompt(full_prompt) + elif self.system_prompt: full_prompt = f"{self.system_prompt} {full_prompt}" inputs = self._tokenizer(full_prompt, return_tensors="pt") @@ -533,18 +540,18 @@ def class_name(cls) -> str: context_window: int = Field( default=DEFAULT_CONTEXT_WINDOW, description=( - LLMMetadata.__fields__["context_window"].field_info.description + LLMMetadata.model_fields["context_window"].description + " This may be looked up in a model's `config.json`." ), ) num_output: int = Field( default=DEFAULT_NUM_OUTPUTS, - description=LLMMetadata.__fields__["num_output"].field_info.description, + description=LLMMetadata.model_fields["num_output"].description, ) is_chat_model: bool = Field( default=False, description=( - LLMMetadata.__fields__["is_chat_model"].field_info.description + LLMMetadata.model_fields["is_chat_model"].description + " Unless chat templating is intentionally applied, Hugging Face models" " are not chat models." ), @@ -552,7 +559,7 @@ def class_name(cls) -> str: is_function_calling_model: bool = Field( default=False, description=( - LLMMetadata.__fields__["is_function_calling_model"].field_info.description + LLMMetadata.model_fields["is_function_calling_model"].description + " As of 10/17/2023, Hugging Face doesn't support function calling" " messages." ), @@ -750,7 +757,7 @@ class TextGenerationInference(FunctionCallingLLM): is_chat_model: bool = Field( default=True, description=( - LLMMetadata.__fields__["is_chat_model"].field_info.description + LLMMetadata.model_fields["is_chat_model"].description + " TGI makes use of chat templating," " function call is available only for '/v1/chat/completions' route" " of TGI endpoint" @@ -759,7 +766,7 @@ class TextGenerationInference(FunctionCallingLLM): is_function_calling_model: bool = Field( default=False, description=( - LLMMetadata.__fields__["is_function_calling_model"].field_info.description + LLMMetadata.model_fields["is_function_calling_model"].description + " 'text-generation-inference' supports function call" " starting from v1.4.3" ), diff --git a/llama-index-integrations/llms/llama-index-llms-huggingface/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-huggingface/pyproject.toml index c6e0b56b087db..ac0b3d7f6443a 100644 --- a/llama-index-integrations/llms/llama-index-llms-huggingface/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-huggingface/pyproject.toml @@ -28,14 +28,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-huggingface" readme = "README.md" -version = "0.2.5" +version = "0.3.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.57" huggingface-hub = "^0.23.0" torch = "^2.1.2" text-generation = "^0.7.0" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.transformers] extras = ["torch"] diff --git a/llama-index-integrations/llms/llama-index-llms-ibm/llama_index/llms/ibm/base.py b/llama-index-integrations/llms/llama-index-llms-ibm/llama_index/llms/ibm/base.py index 61a6b386dfd28..54d16e75bdc57 100644 --- a/llama-index-integrations/llms/llama-index-llms-ibm/llama_index/llms/ibm/base.py +++ b/llama-index-integrations/llms/llama-index-llms-ibm/llama_index/llms/ibm/base.py @@ -20,10 +20,7 @@ # Import SecretStr directly from pydantic # since there is not one in llama_index.core.bridge.pydantic -try: - from pydantic.v1 import SecretStr -except ImportError: - from pydantic import SecretStr +from pydantic import SecretStr from llama_index.core.callbacks import CallbackManager from llama_index.core.llms.callbacks import llm_chat_callback, llm_completion_callback @@ -171,7 +168,6 @@ def __init__( """ callback_manager = callback_manager or CallbackManager([]) additional_params = additional_params or {} - self._context_window = kwargs.get("context_window") creds = ( resolve_watsonx_credentials( @@ -207,6 +203,7 @@ def __init__( callback_manager=callback_manager, **kwargs, ) + self._context_window = kwargs.get("context_window") generation_params = {} if self.temperature is not None: diff --git a/llama-index-integrations/llms/llama-index-llms-ibm/llama_index/llms/ibm/utils.py b/llama-index-integrations/llms/llama-index-llms-ibm/llama_index/llms/ibm/utils.py index a20176b088bdc..bbd391387500a 100644 --- a/llama-index-integrations/llms/llama-index-llms-ibm/llama_index/llms/ibm/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-ibm/llama_index/llms/ibm/utils.py @@ -9,10 +9,7 @@ # Import SecretStr directly from pydantic # since there is not one in llama_index.core.bridge.pydantic -try: - from pydantic.v1 import SecretStr -except ImportError: - from pydantic import SecretStr +from pydantic import SecretStr def resolve_watsonx_credentials( diff --git a/llama-index-integrations/llms/llama-index-llms-ibm/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-ibm/pyproject.toml index e55487c4f613a..db39da847d91a 100644 --- a/llama-index-integrations/llms/llama-index-llms-ibm/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-ibm/pyproject.toml @@ -31,12 +31,12 @@ license = "MIT" name = "llama-index-llms-ibm" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.10,<4.0" -llama-index-core = "^0.10.38" ibm-watsonx-ai = "^1.0.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-ipex-llm/llama_index/llms/ipex_llm/base.py b/llama-index-integrations/llms/llama-index-llms-ipex-llm/llama_index/llms/ipex_llm/base.py index edb75f42dacf0..bc95e3198dd03 100644 --- a/llama-index-integrations/llms/llama-index-llms-ipex-llm/llama_index/llms/ipex_llm/base.py +++ b/llama-index-integrations/llms/llama-index-llms-ipex-llm/llama_index/llms/ipex_llm/base.py @@ -191,9 +191,9 @@ def __init__( model_kwargs = model_kwargs or {} if model: - self._model = model + model = model else: - self._model = self._load_model( + model = self._load_model( low_bit_model, load_in_4bit, load_in_low_bit, model_name, model_kwargs ) if device_map not in ["cpu", "xpu"] and not device_map.startswith("xpu:"): @@ -202,10 +202,10 @@ def __init__( f"or 'xpu:', but you have: {device_map}." ) if "xpu" in device_map: - self._model = self._model.to(device_map) + model = model.to(device_map) # check context_window - config_dict = self._model.config.to_dict() + config_dict = model.config.to_dict() model_context_window = int( config_dict.get("max_position_embeddings", context_window) ) @@ -222,14 +222,14 @@ def __init__( tokenizer_kwargs["max_length"] = context_window if tokenizer: - self._tokenizer = tokenizer + tokenizer = tokenizer else: try: - self._tokenizer = AutoTokenizer.from_pretrained( + tokenizer = AutoTokenizer.from_pretrained( tokenizer_name, **tokenizer_kwargs ) except Exception: - self._tokenizer = LlamaTokenizer.from_pretrained( + tokenizer = LlamaTokenizer.from_pretrained( tokenizer_name, trust_remote_code=True ) @@ -242,8 +242,8 @@ def __init__( # setup stopping criteria stopping_ids_list = stopping_ids or [] - if self._tokenizer.pad_token is None: - self._tokenizer.pad_token = self._tokenizer.eos_token + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token class StopOnTokens(StoppingCriteria): def __call__( @@ -257,7 +257,7 @@ def __call__( return True return False - self._stopping_criteria = StoppingCriteriaList([StopOnTokens()]) + stopping_criteria = StoppingCriteriaList([StopOnTokens()]) messages_to_prompt = messages_to_prompt or self._tokenizer_messages_to_prompt @@ -280,6 +280,10 @@ def __call__( output_parser=output_parser, ) + self._model = model + self._tokenizer = tokenizer + self._stopping_criteria = stopping_criteria + @classmethod def from_model_id( cls, diff --git a/llama-index-integrations/llms/llama-index-llms-ipex-llm/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-ipex-llm/pyproject.toml index cbd36ca6d807c..8af559955a316 100644 --- a/llama-index-integrations/llms/llama-index-llms-ipex-llm/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-ipex-llm/pyproject.toml @@ -30,7 +30,7 @@ license = "MIT" name = "llama-index-llms-ipex-llm" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.8" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.9,<3.12" diff --git a/llama-index-integrations/llms/llama-index-llms-konko/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-konko/pyproject.toml index be474cccaf7d9..4c81fb4985861 100644 --- a/llama-index-integrations/llms/llama-index-llms-konko/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-konko/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-konko" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" konko = "^0.5.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-langchain/llama_index/llms/langchain/base.py b/llama-index-integrations/llms/llama-index-llms-langchain/llama_index/llms/langchain/base.py index dfb004d945dea..2cb83eed97623 100644 --- a/llama-index-integrations/llms/llama-index-llms-langchain/llama_index/llms/langchain/base.py +++ b/llama-index-integrations/llms/llama-index-llms-langchain/llama_index/llms/langchain/base.py @@ -55,7 +55,6 @@ def __init__( pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, ) -> None: - self._llm = llm super().__init__( callback_manager=callback_manager, system_prompt=system_prompt, @@ -64,6 +63,7 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._llm = llm @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-langchain/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-langchain/pyproject.toml index f646f7550dad7..cd05e5566899f 100644 --- a/llama-index-integrations/llms/llama-index-llms-langchain/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-langchain/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-langchain" readme = "README.md" -version = "0.3.0" +version = "0.4.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" langchain = ">=0.1.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-litellm/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-litellm/pyproject.toml index f7be7ab2326c0..fd8a1f685f01a 100644 --- a/llama-index-integrations/llms/llama-index-llms-litellm/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-litellm/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-litellm" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" litellm = "^1.18.13" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-llama-api/llama_index/llms/llama_api/base.py b/llama-index-integrations/llms/llama-index-llms-llama-api/llama_index/llms/llama_api/base.py index 0293ba7b5e2c3..e9bd2969ab7fe 100644 --- a/llama-index-integrations/llms/llama-index-llms-llama-api/llama_index/llms/llama_api/base.py +++ b/llama-index-integrations/llms/llama-index-llms-llama-api/llama_index/llms/llama_api/base.py @@ -66,8 +66,6 @@ def __init__( pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, ) -> None: - self._client = Client(api_key) - super().__init__( model=model, temperature=temperature, @@ -81,6 +79,8 @@ def __init__( output_parser=output_parser, ) + self._client = Client(api_key) + @classmethod def class_name(cls) -> str: return "llama_api_llm" diff --git a/llama-index-integrations/llms/llama-index-llms-llama-api/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-llama-api/pyproject.toml index 989ad39f16546..6820fa01ada20 100644 --- a/llama-index-integrations/llms/llama-index-llms-llama-api/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-llama-api/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-llama-api" readme = "README.md" -version = "0.1.4" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" llamaapi = "^0.1.36" -llama-index-llms-openai = "^0.1.6" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-llama-cpp/llama_index/llms/llama_cpp/base.py b/llama-index-integrations/llms/llama-index-llms-llama-cpp/llama_index/llms/llama_cpp/base.py index f8322dbe5db93..743f260a19fb6 100644 --- a/llama-index-integrations/llms/llama-index-llms-llama-cpp/llama_index/llms/llama_cpp/base.py +++ b/llama-index-integrations/llms/llama-index-llms-llama-cpp/llama_index/llms/llama_cpp/base.py @@ -159,7 +159,7 @@ def __init__( "Please check the path or provide a model_url to download." ) else: - self._model = Llama(model_path=model_path, **model_kwargs) + model = Llama(model_path=model_path, **model_kwargs) else: cache_dir = get_cache_dir() model_url = model_url or self._get_model_path_for_version() @@ -170,7 +170,7 @@ def __init__( self._download_url(model_url, model_path) assert os.path.exists(model_path) - self._model = Llama(model_path=model_path, **model_kwargs) + model = Llama(model_path=model_path, **model_kwargs) model_path = model_path generate_kwargs = generate_kwargs or {} @@ -194,6 +194,7 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._model = model @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-llama-cpp/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-llama-cpp/pyproject.toml index adf6d949b678e..850c8083e148e 100644 --- a/llama-index-integrations/llms/llama-index-llms-llama-cpp/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-llama-cpp/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-llama-cpp" readme = "README.md" -version = "0.1.4" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" llama-cpp-python = "^0.2.32" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-llamafile/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-llamafile/pyproject.toml index db4231cdfdfe2..07a3c5641d6b2 100644 --- a/llama-index-integrations/llms/llama-index-llms-llamafile/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-llamafile/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-llamafile" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-lmstudio/llama_index/llms/lmstudio/base.py b/llama-index-integrations/llms/llama-index-llms-lmstudio/llama_index/llms/lmstudio/base.py index d247ae0ac8fa4..578b761aff43c 100644 --- a/llama-index-integrations/llms/llama-index-llms-lmstudio/llama_index/llms/lmstudio/base.py +++ b/llama-index-integrations/llms/llama-index-llms-lmstudio/llama_index/llms/lmstudio/base.py @@ -61,14 +61,14 @@ class LMStudio(CustomLLM): ) num_output: int = Field( default=DEFAULT_NUM_OUTPUTS, - description=LLMMetadata.__fields__["num_output"].field_info.description, + description=LLMMetadata.model_fields["num_output"].description, ) is_chat_model: bool = Field( default=True, description=( "LM Studio API supports chat." - + LLMMetadata.__fields__["is_chat_model"].field_info.description + + LLMMetadata.model_fields["is_chat_model"].description ), ) diff --git a/llama-index-integrations/llms/llama-index-llms-lmstudio/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-lmstudio/pyproject.toml index d6c4be89a6d52..b2c35cf4e5b43 100644 --- a/llama-index-integrations/llms/llama-index-llms-lmstudio/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-lmstudio/pyproject.toml @@ -27,11 +27,11 @@ license = "MIT" name = "llama-index-llms-lmstudio" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-localai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-localai/pyproject.toml index 9029c153b8be5..c71ef20802373 100644 --- a/llama-index-integrations/llms/llama-index-llms-localai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-localai/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-localai" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-llms-openai = "^0.1.6" -llama-index-llms-openai-like = "^0.1.3" +llama-index-llms-openai-like = "^0.2.0" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-maritalk/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-maritalk/pyproject.toml index e473e803e75ff..a879fd34b85cb 100644 --- a/llama-index-integrations/llms/llama-index-llms-maritalk/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-maritalk/pyproject.toml @@ -30,11 +30,11 @@ license = "MIT" name = "llama-index-llms-maritalk" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-mistral-rs/llama_index/llms/mistral_rs/base.py b/llama-index-integrations/llms/llama-index-llms-mistral-rs/llama_index/llms/mistral_rs/base.py index e42d41a127869..78bc17a1b1ab7 100644 --- a/llama-index-integrations/llms/llama-index-llms-mistral-rs/llama_index/llms/mistral_rs/base.py +++ b/llama-index-integrations/llms/llama-index-llms-mistral-rs/llama_index/llms/mistral_rs/base.py @@ -1,4 +1,4 @@ -from typing import Any, Callable, Dict, Optional, Sequence, List +from typing import Any, Callable, Dict, Optional, Sequence, List, TYPE_CHECKING from llama_index.core.base.llms.types import ( ChatMessage, @@ -21,11 +21,11 @@ from llama_index.core.llms.custom import CustomLLM from llama_index.core.types import BaseOutputParser, PydanticProgramMode -from mistralrs import ( - ChatCompletionRequest, - Runner, - Which, -) +if TYPE_CHECKING: + from mistralrs import ( + Runner, + Which, + ) DEFAULT_TOPK = 32 DEFAULT_TOPP = 0.1 @@ -152,12 +152,12 @@ class MistralRS(CustomLLM): model_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Kwargs used for model initialization." ) - _runner: Runner = PrivateAttr("Mistral.rs model runner.") + _runner: "Runner" = PrivateAttr("Mistral.rs model runner.") _has_messages_to_prompt: bool = PrivateAttr("If `messages_to_prompt` is provided.") def __init__( self, - which: Which, + which: "Which", temperature: float = DEFAULT_TEMPERATURE, max_new_tokens: int = DEFAULT_NUM_OUTPUTS, context_window: int = DEFAULT_CONTEXT_WINDOW, @@ -237,6 +237,12 @@ def metadata(self) -> LLMMetadata: @llm_chat_callback() def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: + try: + from mistralrs import ChatCompletionRequest + except ImportError as e: + raise ValueError( + "Missing `mistralrs` package. Install via `pip install mistralrs`." + ) from e if self._has_messages_to_prompt: messages = self.messages_to_prompt(messages) else: @@ -260,6 +266,12 @@ def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: + try: + from mistralrs import ChatCompletionRequest + except ImportError as e: + raise ValueError( + "Missing `mistralrs` package. Install via `pip install mistralrs`." + ) from e if self._has_messages_to_prompt: messages = self.messages_to_prompt(messages) else: @@ -295,6 +307,12 @@ def gen() -> CompletionResponseGen: def complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: + try: + from mistralrs import ChatCompletionRequest + except ImportError as e: + raise ValueError( + "Missing `mistralrs` package. Install via `pip install mistralrs`." + ) from e self.generate_kwargs.update({"stream": False}) if not formatted: prompt = self.completion_to_prompt(prompt) @@ -315,6 +333,12 @@ def complete( def stream_complete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseGen: + try: + from mistralrs import ChatCompletionRequest + except ImportError as e: + raise ValueError( + "Missing `mistralrs` package. Install via `pip install mistralrs`." + ) from e self.generate_kwargs.update({"stream": True}) if not formatted: prompt = self.completion_to_prompt(prompt) diff --git a/llama-index-integrations/llms/llama-index-llms-mistral-rs/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-mistral-rs/pyproject.toml index bb829cf8c4ef9..5fd8b7f755e5f 100644 --- a/llama-index-integrations/llms/llama-index-llms-mistral-rs/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-mistral-rs/pyproject.toml @@ -32,12 +32,11 @@ maintainers = ["jerryjliu"] name = "llama-index-llms-mistral-rs" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -mistralrs = "^0.1.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-mistral-rs/tests/test_llms_mistral-rs.py b/llama-index-integrations/llms/llama-index-llms-mistral-rs/tests/test_llms_mistral-rs.py index b9905ff2ddcf4..b40b6c2dc329f 100644 --- a/llama-index-integrations/llms/llama-index-llms-mistral-rs/tests/test_llms_mistral-rs.py +++ b/llama-index-integrations/llms/llama-index-llms-mistral-rs/tests/test_llms_mistral-rs.py @@ -2,6 +2,6 @@ from llama_index.llms.mistral_rs import MistralRS -def test_embedding_class(): +def test_llm_class(): names_of_base_classes = [b.__name__ for b in MistralRS.__mro__] assert BaseLLM.__name__ in names_of_base_classes diff --git a/llama-index-integrations/llms/llama-index-llms-mistralai/llama_index/llms/mistralai/base.py b/llama-index-integrations/llms/llama-index-llms-mistralai/llama_index/llms/mistralai/base.py index 37780f61f3536..2242c0216bead 100644 --- a/llama-index-integrations/llms/llama-index-llms-mistralai/llama_index/llms/mistralai/base.py +++ b/llama-index-integrations/llms/llama-index-llms-mistralai/llama_index/llms/mistralai/base.py @@ -35,9 +35,15 @@ mistralai_modelname_to_contextsize, ) -from mistralai.async_client import MistralAsyncClient -from mistralai.client import MistralClient -from mistralai.models.chat_completion import ToolCall +from mistralai import Mistral +from mistralai.models import ToolCall +from mistralai.models import ( + Messages, + AssistantMessage, + SystemMessage, + ToolMessage, + UserMessage, +) if TYPE_CHECKING: from llama_index.core.tools.types import BaseTool @@ -46,20 +52,25 @@ DEFAULT_MISTRALAI_ENDPOINT = "https://api.mistral.ai" DEFAULT_MISTRALAI_MAX_TOKENS = 512 -from mistralai.models.chat_completion import ChatMessage as mistral_chatmessage - def to_mistral_chatmessage( messages: Sequence[ChatMessage], -) -> List[mistral_chatmessage]: +) -> List[Messages]: new_messages = [] for m in messages: tool_calls = m.additional_kwargs.get("tool_calls") - new_messages.append( - mistral_chatmessage( - role=m.role.value, content=m.content, tool_calls=tool_calls + if m.role == MessageRole.USER: + new_messages.append(UserMessage(content=m.content)) + elif m.role == MessageRole.ASSISTANT: + new_messages.append( + AssistantMessage(content=m.content, tool_calls=tool_calls) ) - ) + elif m.role == MessageRole.SYSTEM: + new_messages.append(SystemMessage(content=m.content)) + elif m.role == MessageRole.TOOL or m.role == MessageRole.FUNCTION: + new_messages.append(ToolMessage(content=m.content)) + else: + raise ValueError(f"Unsupported message role {m.role}") return new_messages @@ -112,19 +123,14 @@ class MistralAI(FunctionCallingLLM): max_retries: int = Field( default=5, description="The maximum number of API retries.", gte=0 ) - safe_mode: bool = Field( - default=False, - description="The parameter to enforce guardrails in chat generations.", - ) - random_seed: str = Field( + random_seed: Optional[int] = Field( default=None, description="The random seed to use for sampling." ) additional_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Additional kwargs for the MistralAI API." ) - _client: Any = PrivateAttr() - _aclient: Any = PrivateAttr() + _client: Mistral = PrivateAttr() def __init__( self, @@ -159,19 +165,6 @@ def __init__( # Use the custom endpoint if provided, otherwise default to DEFAULT_MISTRALAI_ENDPOINT endpoint = endpoint or DEFAULT_MISTRALAI_ENDPOINT - self._client = MistralClient( - api_key=api_key, - endpoint=endpoint, - timeout=timeout, - max_retries=max_retries, - ) - self._aclient = MistralAsyncClient( - api_key=api_key, - endpoint=endpoint, - timeout=timeout, - max_retries=max_retries, - ) - super().__init__( temperature=temperature, max_tokens=max_tokens, @@ -189,6 +182,11 @@ def __init__( output_parser=output_parser, ) + self._client = Mistral( + api_key=api_key, + server_url=endpoint, + ) + @classmethod def class_name(cls) -> str: return "MistralAI_LLM" @@ -200,7 +198,6 @@ def metadata(self) -> LLMMetadata: num_output=self.max_tokens, is_chat_model=True, model_name=self.model, - safe_mode=self.safe_mode, random_seed=self.random_seed, is_function_calling_model=is_mistralai_function_calling_model(self.model), ) @@ -212,7 +209,8 @@ def _model_kwargs(self) -> Dict[str, Any]: "temperature": self.temperature, "max_tokens": self.max_tokens, "random_seed": self.random_seed, - "safe_mode": self.safe_mode, + "retries": self.max_retries, + "timeout_ms": self.timeout * 1000, } return { **base_kwargs, @@ -231,7 +229,7 @@ def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: messages = to_mistral_chatmessage(messages) all_kwargs = self._get_all_kwargs(**kwargs) - response = self._client.chat(messages=messages, **all_kwargs) + response = self._client.chat.complete(messages=messages, **all_kwargs) tool_calls = response.choices[0].message.tool_calls @@ -262,12 +260,12 @@ def stream_chat( messages = to_mistral_chatmessage(messages) all_kwargs = self._get_all_kwargs(**kwargs) - response = self._client.chat_stream(messages=messages, **all_kwargs) + response = self._client.chat.stream(messages=messages, **all_kwargs) def gen() -> ChatResponseGen: content = "" for chunk in response: - delta = chunk.choices[0].delta + delta = chunk.data.choices[0].delta role = delta.role or MessageRole.ASSISTANT # NOTE: Unlike openAI, we are directly injecting the tool calls additional_kwargs = {} @@ -307,7 +305,9 @@ async def achat( messages = to_mistral_chatmessage(messages) all_kwargs = self._get_all_kwargs(**kwargs) - response = await self._aclient.chat(messages=messages, **all_kwargs) + response = await self._client.chat.complete_async( + messages=messages, **all_kwargs + ) tool_calls = response.choices[0].message.tool_calls return ChatResponse( message=ChatMessage( @@ -336,12 +336,12 @@ async def astream_chat( messages = to_mistral_chatmessage(messages) all_kwargs = self._get_all_kwargs(**kwargs) - response = self._aclient.chat_stream(messages=messages, **all_kwargs) + response = await self._client.chat.stream_async(messages=messages, **all_kwargs) async def gen() -> ChatResponseAsyncGen: content = "" async for chunk in response: - delta = chunk.choices[0].delta + delta = chunk.data.choices[0].delta role = delta.role or MessageRole.ASSISTANT # NOTE: Unlike openAI, we are directly injecting the tool calls additional_kwargs = {} @@ -433,8 +433,7 @@ def get_tool_calls_from_response( for tool_call in tool_calls: if not isinstance(tool_call, ToolCall): raise ValueError("Invalid tool_call object") - if tool_call.type != "function": - raise ValueError("Invalid tool type. Unsupported by Mistralai.") + argument_dict = json.loads(tool_call.function.arguments) tool_selections.append( @@ -456,11 +455,11 @@ def fill_in_middle( ) if stop: - response = self._client.completion( + response = self._client.fim.complete( model=self.model, prompt=prompt, suffix=suffix, stop=stop ) else: - response = self._client.completion( + response = self._client.fim.complete( model=self.model, prompt=prompt, suffix=suffix ) diff --git a/llama-index-integrations/llms/llama-index-llms-mistralai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-mistralai/pyproject.toml index 77e25f3eb1002..08746d2dfb084 100644 --- a/llama-index-integrations/llms/llama-index-llms-mistralai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-mistralai/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-mistralai" readme = "README.md" -version = "0.1.19" +version = "0.2.2" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.57" -mistralai = ">=0.4.2" +mistralai = ">=1.0.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-mlx/llama_index/llms/mlx/base.py b/llama-index-integrations/llms/llama-index-llms-mlx/llama_index/llms/mlx/base.py index 300d221f44542..4348d1941b971 100644 --- a/llama-index-integrations/llms/llama-index-llms-mlx/llama_index/llms/mlx/base.py +++ b/llama-index-integrations/llms/llama-index-llms-mlx/llama_index/llms/mlx/base.py @@ -170,10 +170,10 @@ def __init__( """Initialize params.""" model_kwargs = model_kwargs or {} if model is None: - self._model, self._tokenizer = load(model_name, **model_kwargs) + model, tokenizer = load(model_name, **model_kwargs) else: - self._model = model - self._tokenizer = tokenizer + model = model + tokenizer = tokenizer # check context_window tokenizer_kwargs = tokenizer_kwargs or {} @@ -202,6 +202,8 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._model = model + self._tokenizer = tokenizer @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-mlx/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-mlx/pyproject.toml index cf5c2ee3096e0..189672483a0a1 100644 --- a/llama-index-integrations/llms/llama-index-llms-mlx/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-mlx/pyproject.toml @@ -30,13 +30,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-mlx" readme = "README.md" -version = "0.1.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.10,<4.0" -llama-index-core = "^0.10.0" mlx-lm = ">=0.11.0" mlx = ">=0.11.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-modelscope/llama_index/llms/modelscope/base.py b/llama-index-integrations/llms/llama-index-llms-modelscope/llama_index/llms/modelscope/base.py index db26971e732a7..12b62e5e47d3d 100644 --- a/llama-index-integrations/llms/llama-index-llms-modelscope/llama_index/llms/modelscope/base.py +++ b/llama-index-integrations/llms/llama-index-llms-modelscope/llama_index/llms/modelscope/base.py @@ -27,7 +27,6 @@ text_to_completion_response, modelscope_message_to_chat_response, ) -from modelscope import pipeline DEFAULT_MODELSCOPE_MODEL = "qwen/Qwen-7B-Chat" DEFAULT_MODELSCOPE_MODEL_REVISION = "master" @@ -127,9 +126,9 @@ def __init__( """Initialize params.""" model_kwargs = model_kwargs or {} if model: - self._pipeline = model + pipeline = model else: - self._pipeline = pipeline( + pipeline = pipeline( task=task_name, model=model_name, model_revision=model_revision, @@ -144,6 +143,7 @@ def __init__( callback_manager=callback_manager, pydantic_program_mode=pydantic_program_mode, ) + self._pipeline = pipeline @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-modelscope/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-modelscope/pyproject.toml index 248cb8eeb4b1c..1b873077b1551 100644 --- a/llama-index-integrations/llms/llama-index-llms-modelscope/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-modelscope/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-modelscope" readme = "README.md" -version = "0.1.1" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.1" modelscope = ">=1.12.0" torch = "^2.1.2" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.transformers] extras = ["torch"] diff --git a/llama-index-integrations/llms/llama-index-llms-monsterapi/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-monsterapi/pyproject.toml index 250dc70c48ebf..f7f17f62f85ed 100644 --- a/llama-index-integrations/llms/llama-index-llms-monsterapi/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-monsterapi/pyproject.toml @@ -27,11 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-monsterapi" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-mymagic/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-mymagic/pyproject.toml index c2a87606ab749..1ece88a163cf7 100644 --- a/llama-index-integrations/llms/llama-index-llms-mymagic/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-mymagic/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-mymagic" readme = "README.md" -version = "0.1.7" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-neutrino/llama_index/llms/neutrino/base.py b/llama-index-integrations/llms/llama-index-llms-neutrino/llama_index/llms/neutrino/base.py index 8ac0af9ae2c7e..2fa6ac14aecda 100644 --- a/llama-index-integrations/llms/llama-index-llms-neutrino/llama_index/llms/neutrino/base.py +++ b/llama-index-integrations/llms/llama-index-llms-neutrino/llama_index/llms/neutrino/base.py @@ -57,7 +57,7 @@ class Neutrino(OpenAILike): ) is_chat_model: bool = Field( default=True, - description=LLMMetadata.__fields__["is_chat_model"].field_info.description, + description=LLMMetadata.model_fields["is_chat_model"].description, ) def __init__( diff --git a/llama-index-integrations/llms/llama-index-llms-neutrino/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-neutrino/pyproject.toml index 67c8242eef42e..ddcc39d1895ec 100644 --- a/llama-index-integrations/llms/llama-index-llms-neutrino/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-neutrino/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-neutrino" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-llms-openai-like = "^0.1.3" +llama-index-llms-openai-like = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/llama_index/llms/nvidia_tensorrt/base.py b/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/llama_index/llms/nvidia_tensorrt/base.py index 7de63b9abf381..cb6e6aa95ec9c 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/llama_index/llms/nvidia_tensorrt/base.py +++ b/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/llama_index/llms/nvidia_tensorrt/base.py @@ -162,8 +162,8 @@ def __init__( model_kwargs = model_kwargs or {} model_kwargs.update({"n_ctx": context_window, "verbose": verbose}) - self._max_new_tokens = max_new_tokens - self._verbose = verbose + max_new_tokens = max_new_tokens + verbose = verbose # check if model is cached if model_path is not None: if not os.path.exists(model_path): @@ -204,7 +204,7 @@ def __init__( num_kv_heads = 1 num_kv_heads = (num_kv_heads + tp_size - 1) // tp_size - self._model_config = ModelConfig( + model_config = ModelConfig( num_heads=num_heads, num_kv_heads=num_kv_heads, hidden_size=hidden_size, @@ -231,10 +231,8 @@ def __init__( torch.cuda.is_available() ), "LocalTensorRTLLM requires a Nvidia CUDA enabled GPU to operate" torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node) - self._tokenizer = AutoTokenizer.from_pretrained( - tokenizer_dir, legacy=False - ) - self._sampling_config = SamplingConfig( + tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir, legacy=False) + sampling_config = SamplingConfig( end_id=EOS_TOKEN, pad_id=PAD_TOKEN, num_beams=1, @@ -245,9 +243,9 @@ def __init__( with open(serialize_path, "rb") as f: engine_buffer = f.read() decoder = tensorrt_llm.runtime.GenerationSession( - self._model_config, engine_buffer, runtime_mapping, debug_mode=False + model_config, engine_buffer, runtime_mapping, debug_mode=False ) - self._model = decoder + model = decoder generate_kwargs = generate_kwargs or {} generate_kwargs.update( @@ -266,6 +264,12 @@ def __init__( model_kwargs=model_kwargs, verbose=verbose, ) + self._model = model + self._model_config = model_config + self._tokenizer = tokenizer + self._sampling_config = sampling_config + self._max_new_tokens = max_new_tokens + self._verbose = verbose @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/pyproject.toml index 32e0d79e23fa8..10028b98be8dc 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-nvidia-tensorrt" readme = "README.md" -version = "0.1.5" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" torch = "^2.1.2" transformers = "^4.37.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia-triton/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-nvidia-triton/pyproject.toml index 67cfd96f7b9bf..84f81a1679343 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia-triton/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-nvidia-triton/pyproject.toml @@ -27,15 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-nvidia-triton" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" - -[tool.poetry.dependencies.tritonclient] -extras = ["all"] -version = "^2.41.1" +tritonclient = {extras = ["grpc", "http"], version = "^2.48.0"} +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py b/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py index a8a2e0ce881c7..30f56b1f5404e 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py @@ -1,21 +1,27 @@ -from typing import ( - Any, - Optional, - List, - Literal, -) +from typing import Any, Optional, List, Literal, Union, Dict, TYPE_CHECKING from deprecated import deprecated import warnings +import json from llama_index.core.bridge.pydantic import PrivateAttr, BaseModel from llama_index.core.base.llms.generic_utils import ( get_from_param_or_env, ) +from llama_index.llms.nvidia.utils import is_nvidia_function_calling_model from llama_index.llms.openai_like import OpenAILike +from llama_index.core.llms.function_calling import FunctionCallingLLM from urllib.parse import urlparse, urlunparse +from llama_index.core.base.llms.types import ChatMessage, ChatResponse, MessageRole + +from llama_index.core.llms.llm import ToolSelection + + +if TYPE_CHECKING: + from llama_index.core.tools.types import BaseTool + DEFAULT_MODEL = "meta/llama3-8b-instruct" BASE_URL = "https://integrate.api.nvidia.com/v1/" @@ -25,19 +31,26 @@ ] +def force_single_tool_call(response: ChatResponse) -> None: + tool_calls = response.message.additional_kwargs.get("tool_calls", []) + if len(tool_calls) > 1: + response.message.additional_kwargs["tool_calls"] = [tool_calls[0]] + + class Model(BaseModel): id: str + base_model: Optional[str] -class NVIDIA(OpenAILike): +class NVIDIA(OpenAILike, FunctionCallingLLM): """NVIDIA's API Catalog Connector.""" _is_hosted: bool = PrivateAttr(True) - _mode: str = PrivateAttr("nvidia") + _mode: str = PrivateAttr(default="nvidia") def __init__( self, - model: str = DEFAULT_MODEL, + model: Optional[str] = None, nvidia_api_key: Optional[str] = None, api_key: Optional[str] = None, base_url: Optional[str] = BASE_URL, @@ -71,24 +84,55 @@ def __init__( "NO_API_KEY_PROVIDED", ) - self._is_hosted = base_url in KNOWN_URLS + is_hosted = base_url in KNOWN_URLS if base_url not in KNOWN_URLS: base_url = self._validate_url(base_url) - if self._is_hosted and api_key == "NO_API_KEY_PROVIDED": + if is_hosted and api_key == "NO_API_KEY_PROVIDED": warnings.warn( "An API key is required for the hosted NIM. This will become an error in 0.2.0.", ) super().__init__( - model=model, api_key=api_key, api_base=base_url, max_tokens=max_tokens, is_chat_model=True, default_headers={"User-Agent": "llama-index-llms-nvidia"}, + is_function_calling_model=is_nvidia_function_calling_model(model), **kwargs, ) + self.model = model + self._is_hosted = base_url in KNOWN_URLS + + if self._is_hosted and api_key == "NO_API_KEY_PROVIDED": + warnings.warn( + "An API key is required for the hosted NIM. This will become an error in 0.2.0.", + ) + + if not model: + self.__get_default_model() + + def __get_default_model(self): + """Set default model.""" + if not self._is_hosted: + valid_models = [ + model.id + for model in self.available_models + if not model.base_model or model.base_model == model.id + ] + self.model = next(iter(valid_models), None) + if self.model: + warnings.warn( + f"Default model is set as: {self.model}. \n" + "Set model using model parameter. \n" + "To get available models use available_models property.", + UserWarning, + ) + else: + raise ValueError("No locally hosted model was found.") + else: + self.model = DEFAULT_MODEL def _validate_url(self, base_url): """ @@ -100,9 +144,7 @@ def _validate_url(self, base_url): expected_format = "Expected format is 'http://host:port'." result = urlparse(base_url) if not (result.scheme and result.netloc): - raise ValueError( - f"Invalid base_url, Expected format is 'http://host:port': {base_url}" - ) + raise ValueError(f"Invalid base_url, {expected_format}") if result.path: normalized_path = result.path.strip("/") if normalized_path == "v1": @@ -110,12 +152,18 @@ def _validate_url(self, base_url): elif normalized_path == "v1/chat/completions": warnings.warn(f"{expected_format} Rest is Ignored.") else: - raise ValueError(f"Base URL path is not recognized. {expected_format}") + raise ValueError(f"Invalid base_url, {expected_format}") return urlunparse((result.scheme, result.netloc, "v1", "", "", "")) @property def available_models(self) -> List[Model]: - models = self._get_client().models.list().data + models = [ + Model( + id=model.id, + base_model=getattr(model, "params", {}).get("root", None), + ) + for model in self._get_client().models.list().data + ] # only exclude models in hosted mode. in non-hosted mode, the administrator has control # over the model name and may deploy an excluded name that will work. if self._is_hosted: @@ -164,3 +212,80 @@ def mode( self.api_key = api_key return self + + @property + def _is_chat_model(self) -> bool: + return True + + def _prepare_chat_with_tools( + self, + tools: List["BaseTool"], + user_msg: Optional[Union[str, ChatMessage]] = None, + chat_history: Optional[List[ChatMessage]] = None, + verbose: bool = False, + allow_parallel_tool_calls: bool = False, + **kwargs: Any, + ) -> Dict[str, Any]: + """Prepare the chat with tools.""" + # misralai uses the same openai tool format + tool_specs = [ + tool.metadata.to_openai_tool(skip_length_check=True) for tool in tools + ] + + if isinstance(user_msg, str): + user_msg = ChatMessage(role=MessageRole.USER, content=user_msg) + + messages = chat_history or [] + if user_msg: + messages.append(user_msg) + + return { + "messages": messages, + "tools": tool_specs or None, + **kwargs, + } + + def _validate_chat_with_tools_response( + self, + response: ChatResponse, + tools: List["BaseTool"], + allow_parallel_tool_calls: bool = False, + **kwargs: Any, + ) -> ChatResponse: + """Validate the response from chat_with_tools.""" + if not allow_parallel_tool_calls: + force_single_tool_call(response) + return response + + def get_tool_calls_from_response( + self, + response: "ChatResponse", + error_on_no_tool_call: bool = True, + ) -> List[ToolSelection]: + """Predict and call the tool.""" + tool_calls = response.message.additional_kwargs.get("tool_calls", []) + + if len(tool_calls) < 1: + if error_on_no_tool_call: + raise ValueError( + f"Expected at least one tool call, but got {len(tool_calls)} tool calls." + ) + else: + return [] + + tool_selections = [] + for tool_call in tool_calls: + # if not isinstance(tool_call, ToolCall): + # raise ValueError("Invalid tool_call object") + + argument_dict = json.loads(tool_call.function.arguments) + + tool_selections.append( + ToolSelection( + tool_id=tool_call.id, + tool_name=tool_call.function.name, + tool_kwargs=argument_dict, + ) + ) + + return tool_selections diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/utils.py b/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/utils.py index 9c6af24afc047..090f8e1ae6c5a 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/utils.py @@ -32,6 +32,18 @@ "upstage/solar-10.7b-instruct": 4096, } +NVIDIA_FUNTION_CALLING_MODELS = ( + "nv-mistralai/mistral-nemo-12b-instruct", + "meta/llama-3.1-8b-instruct", + "meta/llama-3.1-70b-instruct", + "meta/llama-3.1-405b-instruct", + "mistralai/mistral-large-2-instruct", +) + + +def is_nvidia_function_calling_model(modelname: str) -> bool: + return modelname in NVIDIA_FUNTION_CALLING_MODELS + def catalog_modelname_to_contextsize(modelname: str) -> Optional[int]: return API_CATALOG_MODELS.get(modelname, None) diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml index a6d127b688b98..1470c8642d48f 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml @@ -30,13 +30,13 @@ license = "MIT" name = "llama-index-llms-nvidia" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.4" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-llms-openai = "^0.1.17" -llama-index-llms-openai-like = "^0.1.3" +llama-index-llms-openai = "^0.2.0" +llama-index-llms-openai-like = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} @@ -58,6 +58,7 @@ types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 types-setuptools = "67.1.0.0" [tool.poetry.group.test_integration.dependencies] +pytest-httpx = "*" requests-mock = "^1.12.1" [tool.pytest.ini_options] diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/conftest.py b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/conftest.py index 20f315ff33aa1..7e128718847d7 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/conftest.py +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/conftest.py @@ -1,8 +1,6 @@ import pytest import os -from llama_index.llms.nvidia import NVIDIA -from llama_index.llms.nvidia.base import DEFAULT_MODEL from typing import Generator @@ -60,6 +58,9 @@ def get_mode(config: pytest.Config) -> dict: def pytest_generate_tests(metafunc: pytest.Metafunc) -> None: + from llama_index.llms.nvidia import NVIDIA + from llama_index.llms.nvidia.base import DEFAULT_MODEL + mode = get_mode(metafunc.config) if "chat_model" in metafunc.fixturenames: diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_api_key.py b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_api_key.py index e040b2c4975c3..8e59eaa58e9f4 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_api_key.py +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_api_key.py @@ -5,6 +5,29 @@ from llama_index.llms.nvidia import NVIDIA from typing import Any +from pytest_httpx import HTTPXMock + + +@pytest.fixture() +def mock_local_models(httpx_mock: HTTPXMock): + mock_response = { + "data": [ + { + "id": "model1", + "object": "model", + "created": 1234567890, + "owned_by": "OWNER", + "root": "model1", + } + ] + } + + httpx_mock.add_response( + url="https://test_url/v1/models", + method="GET", + json=mock_response, + status_code=200, + ) def get_api_key(instance: Any) -> str: @@ -16,6 +39,7 @@ def test_create_default_url_without_api_key(masked_env_var: str) -> None: NVIDIA() +@pytest.mark.usefixtures("mock_local_models") def test_create_unknown_url_without_api_key(masked_env_var: str) -> None: NVIDIA(base_url="https://test_url/v1") diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_base_url.py b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_base_url.py index 9e56da4a257a0..48682720b9bda 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_base_url.py +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_base_url.py @@ -1,27 +1,30 @@ -from urllib.parse import urlparse, urlunparse - import pytest -from requests_mock import Mocker from llama_index.llms.nvidia import NVIDIA as Interface +from pytest_httpx import HTTPXMock @pytest.fixture() -def mock_v1_local_models2(requests_mock: Mocker, base_url: str) -> None: - result = urlparse(base_url) - base_url = urlunparse((result.scheme, result.netloc, "v1", "", "", "")) - requests_mock.get( - f"{base_url}/models", - json={ - "data": [ - { - "id": "model1", - "object": "model", - "created": 1234567890, - "owned_by": "OWNER", - "root": "model1", - }, - ] - }, +def mock_local_models(httpx_mock: HTTPXMock, base_url: str): + mock_response = { + "data": [ + { + "id": "dummy", + "object": "model", + "created": 1234567890, + "owned_by": "OWNER", + "root": "model1", + } + ] + } + + if base_url.endswith("/"): + base_url = base_url[:-1] + + httpx_mock.add_response( + url=f"{base_url}/models", + method="GET", + json=mock_response, + status_code=200, ) @@ -38,19 +41,18 @@ def mock_v1_local_models2(requests_mock: Mocker, base_url: str) -> None: "https://test_url/.../v1", ], ) -def test_base_url_invalid_not_hosted( - base_url: str, mock_v1_local_models2: None -) -> None: - with pytest.raises(ValueError): +def test_base_url_invalid_not_hosted(base_url: str) -> None: + with pytest.raises(ValueError) as msg: Interface(base_url=base_url) + assert "Invalid base_url" in str(msg.value) -@pytest.mark.parametrize("base_url", ["http://localhost:8080/v1/chat/completions"]) -def test_base_url_valid_not_hosted(base_url: str, mock_v1_local_models2: None) -> None: +@pytest.mark.parametrize("base_url", ["http://localhost:8080/v1/"]) +def test_base_url_valid_not_hosted(base_url: str, mock_local_models: None) -> None: with pytest.warns(UserWarning): Interface(base_url=base_url) @pytest.mark.parametrize("base_url", ["https://integrate.api.nvidia.com/v1/"]) -def test_base_url_valid_hosted(base_url: str, mock_v1_local_models2: None) -> None: +def test_base_url_valid_hosted(base_url: str) -> None: Interface(base_url=base_url) diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_mode_switch.py b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_mode_switch.py index 2a3734baa9507..225c27d7e37b4 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_mode_switch.py +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_mode_switch.py @@ -2,6 +2,39 @@ from llama_index.llms.nvidia import NVIDIA as Interface from llama_index.llms.nvidia.base import BASE_URL, KNOWN_URLS +from pytest_httpx import HTTPXMock + +UNKNOWN_URLS = [ + "https://test_url/v1", + "https://test_url/v1/", + "http://test_url/v1", + "http://test_url/v1/", +] + + +@pytest.fixture() +def mock_unknown_urls(httpx_mock: HTTPXMock, base_url: str): + mock_response = { + "data": [ + { + "id": "dummy", + "object": "model", + "created": 1234567890, + "owned_by": "OWNER", + "root": "model1", + } + ] + } + + if base_url.endswith("/"): + base_url = base_url[:-1] + + httpx_mock.add_response( + url=f"{base_url}/models", + method="GET", + json=mock_response, + status_code=200, + ) def test_mode_switch_nvidia_throws_without_key_deprecated(masked_env_var: str): @@ -28,13 +61,14 @@ def test_mode_switch_nim_with_url_deprecated(): Interface().mode("nim", base_url="test") -def test_mode_switch_param_setting_deprecated(): +@pytest.mark.parametrize("base_url", ["https://test_url/v1/"]) +def test_mode_switch_param_setting_deprecated(base_url): instance = Interface(model="dummy") with pytest.warns(DeprecationWarning): - instance1 = instance.mode("nim", base_url="https://test_url/v1/") + instance1 = instance.mode("nim", base_url=base_url) assert instance1.model == "dummy" - assert str(instance1.api_base) == "https://test_url/v1/" + assert str(instance1.api_base) == base_url with pytest.warns(DeprecationWarning): instance2 = instance1.mode("nvidia", api_key="test", model="dummy-2") @@ -43,23 +77,17 @@ def test_mode_switch_param_setting_deprecated(): assert instance2.api_key == "test" -UNKNOWN_URLS = [ - "https://test_url/v1", - "https://test_url/v1/", - "http://test_url/v1", - "http://test_url/v1/", -] - - @pytest.mark.parametrize("base_url", UNKNOWN_URLS) -def test_mode_switch_unknown_base_url_without_key(masked_env_var: str, base_url: str): +def test_mode_switch_unknown_base_url_without_key( + mock_unknown_urls, masked_env_var: str, base_url: str +): Interface(base_url=base_url) @pytest.mark.parametrize("base_url", UNKNOWN_URLS) @pytest.mark.parametrize("param", ["nvidia_api_key", "api_key"]) def test_mode_switch_unknown_base_url_with_key( - masked_env_var: str, param: str, base_url: str + mock_unknown_urls, masked_env_var: str, param: str, base_url: str ): Interface(base_url=base_url, **{param: "test"}) diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_nvidia.py b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_nvidia.py index 131407f98f798..a8b60d16743e1 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_nvidia.py +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_nvidia.py @@ -15,6 +15,7 @@ from openai.types.chat.chat_completion_chunk import ChatCompletionChunk, ChoiceDelta from openai.types.chat.chat_completion_chunk import Choice as ChunkChoice from openai.types.completion import Completion, CompletionUsage +from pytest_httpx import HTTPXMock class CachedNVIDIApiKeys: @@ -116,6 +117,40 @@ def mock_chat_completion_stream_v1( yield from responses +@pytest.fixture() +def known_unknown() -> str: + return "mock-model" + + +@pytest.fixture() +def mock_local_models(httpx_mock: HTTPXMock): + mock_response = { + "data": [ + { + "id": "mock-model", + "object": "model", + "created": 1234567890, + "owned_by": "OWNER", + "root": "mock-model", + }, + { + "id": "lora1", + "object": "model", + "created": 1234567890, + "owned_by": "OWNER", + "root": "mock-model", + }, + ] + } + + httpx_mock.add_response( + url="http://localhost:8000/v1/models", + method="GET", + json=mock_response, + status_code=200, + ) + + async def mock_async_chat_completion_stream_v1( *args: Any, **kwargs: Any ) -> AsyncGenerator[Completion, None]: @@ -223,3 +258,22 @@ def test_validates_api_key_is_present() -> None: def test_metadata() -> None: assert isinstance(NVIDIA().metadata, LLMMetadata) + + +def test_default_known(mock_local_models, known_unknown: str) -> None: + """ + Test that a model in the model table will be accepted. + """ + # check if default model is getting set + with pytest.warns(UserWarning): + x = NVIDIA(base_url="http://localhost:8000/v1") + assert x.model == known_unknown + + +def test_default_lora() -> None: + """ + Test that a model in the model table will be accepted. + """ + # find a model that matches the public_class under test + x = NVIDIA(base_url="http://localhost:8000/v1", model="lora1") + assert x.model == "lora1" diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_tools.py b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_tools.py new file mode 100644 index 0000000000000..9f7353a2e7872 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/tests/test_tools.py @@ -0,0 +1,408 @@ +import json +import uuid +from typing import ( + Any, + AsyncGenerator, + Generator, + List, +) +from unittest.mock import MagicMock, patch +from llama_index.core.base.agent.types import TaskStepOutput +import pytest +from llama_index.core.base.llms.types import ( + ChatMessage, +) +from llama_index.core.chat_engine.types import ( + AgentChatResponse, + StreamingAgentChatResponse, +) +from llama_index.core.tools.function_tool import FunctionTool +from llama_index.llms.nvidia import NVIDIA +from openai.types.chat.chat_completion import ChatCompletion, Choice +from openai.types.chat.chat_completion_chunk import Choice as ChunkChoice +from openai.types.chat.chat_completion_chunk import ChatCompletionChunk, ChoiceDelta +from openai.types.chat.chat_completion_message import ChatCompletionMessage +from openai.types.chat.chat_completion_message_tool_call import ( + ChatCompletionMessageToolCall, + Function, +) +from llama_index.core.agent import FunctionCallingAgentWorker + + +def mock_chat_completion(*args: Any, **kwargs: Any) -> ChatCompletion: + if "functions" in kwargs: + if not kwargs["functions"]: + raise ValueError("functions must not be empty") + + # Example taken from https://platform.openai.com/docs/api-reference/chat/create + return ChatCompletion( + id="chatcmpl-abc123", + object="chat.completion", + created=1677858242, + model="meta/llama-3.1-8b-instruct", + usage={"prompt_tokens": 13, "completion_tokens": 7, "total_tokens": 20}, + choices=[ + Choice( + message=ChatCompletionMessage( + role="assistant", content="\n\nThis is a test!" + ), + finish_reason="stop", + index=0, + logprobs=None, + ) + ], + ) + + +def mock_chat_completion_tool_call( + function: Function, *args: Any, **kwargs: Any +) -> ChatCompletion: + # Example taken from https://platform.openai.com/docs/api-reference/chat/create + return ChatCompletion( + id="chatcmpl-abc123", + object="chat.completion", + created=1677858242, + model="meta/llama-3.1-8b-instruct", + usage={"prompt_tokens": 13, "completion_tokens": 7, "total_tokens": 20}, + choices=[ + Choice( + message=ChatCompletionMessage( + role="assistant", + content="\n\nThis is a test!", + tool_calls=[ + ChatCompletionMessageToolCall( + id="toolcall-abc123", + function=function, + type="function", + ) + ], + ), + finish_reason="stop", + index=0, + logprobs=None, + ) + ], + ) + + +def mock_chat_stream( + *args: Any, **kwargs: Any +) -> Generator[ChatCompletionChunk, None, None]: + if "functions" in kwargs: + if not kwargs["functions"]: + raise ValueError("functions must not be empty") + + yield ChatCompletionChunk( + id="chatcmpl-abc123", + object="chat.completion.chunk", + created=1677858242, + model="meta/llama-3.1-8b-instruct", + choices=[ + ChunkChoice( + delta=ChoiceDelta(role="assistant", content="\n\nThis is a test!"), + finish_reason="stop", + index=0, + logprobs=None, + ) + ], + ) + + +async def mock_achat_completion(*args: Any, **kwargs: Any) -> ChatCompletion: + return mock_chat_completion(*args, **kwargs) + + +async def mock_achat_completion_tool_call( + function: Function, *args: Any, **kwargs: Any +) -> ChatCompletion: + return mock_chat_completion_tool_call(function, *args, **kwargs) + + +async def mock_achat_stream( + *args: Any, **kwargs: Any +) -> AsyncGenerator[ChatCompletionChunk, None]: + async def _mock_achat_stream( + *args: Any, **kwargs: Any + ) -> AsyncGenerator[ChatCompletionChunk, None]: + if "functions" in kwargs: + if not kwargs["functions"]: + raise ValueError("functions must not be empty") + + yield ChatCompletionChunk( + id="chatcmpl-abc123", + object="chat.completion.chunk", + created=1677858242, + model="gpt-3.5-turbo-0301", + choices=[ + ChunkChoice( + delta=ChoiceDelta(role="assistant", content="\n\nThis is a test!"), + finish_reason="stop", + index=0, + logprobs=None, + ) + ], + ) + + return _mock_achat_stream(*args, **kwargs) + + +@pytest.fixture() +def add_tool() -> FunctionTool: + def add(a: int, b: int) -> int: + """Add two integers and returns the result integer.""" + return a + b + + return FunctionTool.from_defaults(fn=add) + + +@pytest.fixture() +def echo_tool() -> FunctionTool: + def echo(query: str) -> str: + """Echos input.""" + return query + + return FunctionTool.from_defaults(fn=echo) + + +@pytest.fixture() +def malformed_echo_function() -> Function: + test_result: str = "This is a test" + return Function(name="echo", arguments=f'query = "{test_result}"') + + +@pytest.fixture() +def echo_function() -> Function: + test_result: str = "This is a test" + return Function(name="echo", arguments=json.dumps({"query": test_result})) + + +MOCK_ACTION_RESPONSE = """\ +Thought: I need to use a tool to help me answer the question. +Action: add +Action Input: {"a": 1, "b": 1} +""" + +MOCK_FINAL_RESPONSE = """\ +Thought: I have enough information to answer the question without using any more tools. +Answer: 2 +""" + + +@patch("llama_index.llms.openai.base.SyncOpenAI") +def test_chat_basic( + MockSyncOpenAI: MagicMock, add_tool: FunctionTool, masked_env_var +) -> None: + mock_instance = MockSyncOpenAI.return_value + mock_instance.chat.completions.create.return_value = mock_chat_completion() + + llm = NVIDIA(model="meta/llama-3.1-8b-instruct") + + agent = FunctionCallingAgentWorker.from_tools( + tools=[add_tool], + llm=llm, + ).as_agent() + response = agent.chat("What is 1 + 1?") + assert isinstance(response, AgentChatResponse) + assert response.response == "\n\nThis is a test!" + assert len(agent.chat_history) == 2 + assert agent.chat_history[0].content == "What is 1 + 1?" + assert agent.chat_history[1].content == "\n\nThis is a test!" + + +@patch("llama_index.llms.openai.base.AsyncOpenAI") +async def test_achat_basic( + MockAsyncOpenAI: MagicMock, add_tool: FunctionTool, masked_env_var +) -> None: + mock_instance = MockAsyncOpenAI.return_value + mock_instance.chat.completions.create.return_value = mock_achat_completion() + + llm = NVIDIA(model="meta/llama-3.1-8b-instruct") + + agent = FunctionCallingAgentWorker.from_tools( + tools=[add_tool], + llm=llm, + ).as_agent() + response = await agent.achat("What is 1 + 1?") + assert isinstance(response, AgentChatResponse) + assert response.response == "\n\nThis is a test!" + assert len(agent.chat_history) == 2 + assert agent.chat_history[0].content == "What is 1 + 1?" + assert agent.chat_history[1].content == "\n\nThis is a test!" + + +@patch("llama_index.llms.openai.base.AsyncOpenAI") +async def test_astream_chat_basic( + MockAsyncOpenAI: MagicMock, add_tool: FunctionTool, masked_env_var +) -> None: + mock_instance = MockAsyncOpenAI.return_value + mock_instance.chat.completions.create.side_effect = mock_achat_stream + + llm = NVIDIA(model="meta/llama-3.1-8b-instruct") + + agent = FunctionCallingAgentWorker.from_tools( + tools=[add_tool], + llm=llm, + ).as_agent() + response_stream = await agent.astream_chat("What is 1 + 1?") + async for response in response_stream.async_response_gen(): + pass + assert isinstance(response_stream, StreamingAgentChatResponse) + # str() strips newline values + assert response == "\n\nThis is a test!" + assert len(agent.chat_history) == 2 + assert agent.chat_history[0].content == "What is 1 + 1?" + assert agent.chat_history[1].content == "This is a test!" + + +@patch("llama_index.llms.openai.base.SyncOpenAI") +def test_chat_no_functions(MockSyncOpenAI: MagicMock, masked_env_var) -> None: + mock_instance = MockSyncOpenAI.return_value + mock_instance.chat.completions.create.return_value = mock_chat_completion() + + llm = NVIDIA(model="meta/llama-3.1-8b-instruct") + + agent = FunctionCallingAgentWorker.from_tools( + llm=llm, + ).as_agent() + response = agent.chat("What is 1 + 1?") + assert isinstance(response, AgentChatResponse) + assert response.response == "\n\nThis is a test!" + + +@patch("llama_index.llms.openai.base.SyncOpenAI") +def test_add_step( + MockSyncOpenAI: MagicMock, add_tool: FunctionTool, masked_env_var +) -> None: + """Test add step.""" + mock_instance = MockSyncOpenAI.return_value + mock_instance.chat.completions.create.return_value = mock_chat_completion() + + llm = NVIDIA(model="meta/llama-3.1-8b-instruct") + # sync + agent = FunctionCallingAgentWorker.from_tools( + tools=[add_tool], + llm=llm, + ).as_agent() + ## NOTE: can only take a single step before finishing, + # since mocked chat output does not call any tools + task = agent.create_task("What is 1 + 1?") + step_output = agent.run_step(task.task_id) + assert str(step_output) == "\n\nThis is a test!" + + # add human input (not used but should be in memory) + task = agent.create_task("What is 1 + 1?") + step_output = agent.run_step(task.task_id, input="tmp") + chat_history: List[ChatMessage] = task.extra_state["new_memory"].get_all() + assert "tmp" in [m.content for m in chat_history] + + # # stream_step + # agent = FunctionCallingAgentWorker.from_tools( + # tools=[add_tool], + # llm=llm, + # ) + # task = agent.create_task("What is 1 + 1?") + # # first step + # step_output = agent.stream_step(task.task_id) + # # add human input (not used but should be in memory) + # step_output = agent.stream_step(task.task_id, input="tmp") + # chat_history: List[ChatMessage] = task.extra_state["new_memory"].get_all() + # assert "tmp" in [m.content for m in chat_history] + + +@patch("llama_index.llms.openai.base.AsyncOpenAI") +async def test_async_add_step( + MockAsyncOpenAI: MagicMock, add_tool: FunctionTool, masked_env_var +) -> None: + mock_instance = MockAsyncOpenAI.return_value + + llm = NVIDIA(model="meta/llama-3.1-8b-instruct") + # async + agent = FunctionCallingAgentWorker.from_tools( + tools=[add_tool], + llm=llm, + ).as_agent() + task = agent.create_task("What is 1 + 1?") + # first step + mock_instance.chat.completions.create.return_value = mock_achat_completion() + step_output = await agent.arun_step(task.task_id) + # add human input (not used but should be in memory) + task = agent.create_task("What is 1 + 1?") + mock_instance.chat.completions.create.return_value = mock_achat_completion() + step_output = await agent.arun_step(task.task_id, input="tmp") + chat_history: List[ChatMessage] = task.extra_state["new_memory"].get_all() + assert "tmp" in [m.content for m in chat_history] + + # async stream step + agent = FunctionCallingAgentWorker.from_tools( + tools=[add_tool], + llm=llm, + ).as_agent() + task = agent.create_task("What is 1 + 1?") + # first step + mock_instance.chat.completions.create.side_effect = mock_achat_stream + step_output = await agent.astream_step(task.task_id) + # add human input (not used but should be in memory) + task = agent.create_task("What is 1 + 1?") + mock_instance.chat.completions.create.side_effect = mock_achat_stream + + # stream the output to ensure it gets written to memory + step_output = await agent.astream_step(task.task_id, input="tmp") + async for _ in step_output.output.async_response_gen(): + pass + + chat_history = task.memory.get_all() + assert "tmp" in [m.content for m in chat_history] + + +@pytest.mark.parametrize("method", ["run_step", "arun_step"]) +@patch("llama_index.llms.openai.base.SyncOpenAI") +@patch("llama_index.llms.openai.base.AsyncOpenAI") +async def test_run_step_returns_correct_sources_history( + MockAsyncOpenAI: MagicMock, + MockSyncOpenAI: MagicMock, + method: str, + echo_tool: FunctionTool, + echo_function: Function, + masked_env_var, +) -> None: + num_steps = 4 + llm = NVIDIA(model="meta/llama-3.1-8b-instruct") + agent = FunctionCallingAgentWorker.from_tools( + tools=[echo_tool], + llm=llm, + ).as_agent() + task = agent.create_task("") + step_outputs: List[TaskStepOutput] = [] + + if method == "run_step": + mock_instance = MockSyncOpenAI.return_value + mock_instance.chat.completions.create.return_value = ( + mock_chat_completion_tool_call(echo_function) + ) + else: + mock_instance = MockAsyncOpenAI.return_value + mock_instance.chat.completions.create.side_effect = [ + mock_achat_completion_tool_call(echo_function) for _ in range(num_steps) + ] + + # Create steps + steps = [agent.agent_worker.initialize_step(task)] + for step_idx in range(num_steps - 1): + steps.append( + steps[-1].get_next_step( + step_id=str(uuid.uuid4()), + input=None, + ) + ) + + # Run each step, invoking a single tool call each time + for step_idx in range(num_steps): + step_outputs.append( + agent.agent_worker.run_step(steps[step_idx], task) + if method == "run_step" + else await agent.agent_worker.arun_step(steps[step_idx], task) + ) + + # Ensure that each step only has one source for its one tool call + for step_idx in range(num_steps): + assert len(step_outputs[step_idx].output.sources) == 1 diff --git a/llama-index-integrations/llms/llama-index-llms-oci-genai/llama_index/llms/oci_genai/base.py b/llama-index-integrations/llms/llama-index-llms-oci-genai/llama_index/llms/oci_genai/base.py index ad5fca741bc56..f0df254601e8e 100644 --- a/llama-index-integrations/llms/llama-index-llms-oci-genai/llama_index/llms/oci_genai/base.py +++ b/llama-index-integrations/llms/llama-index-llms-oci-genai/llama_index/llms/oci_genai/base.py @@ -74,12 +74,12 @@ class OCIGenAI(LLM): max_tokens: int = Field(description="The maximum number of tokens to generate.") context_size: int = Field("The maximum number of tokens available for input.") - service_endpoint: str = Field( + service_endpoint: Optional[str] = Field( default=None, description="service endpoint url.", ) - compartment_id: str = Field( + compartment_id: Optional[str] = Field( default=None, description="OCID of compartment.", ) @@ -111,8 +111,8 @@ def __init__( temperature: Optional[float] = DEFAULT_TEMPERATURE, max_tokens: Optional[int] = 512, context_size: Optional[int] = None, - service_endpoint: str = None, - compartment_id: str = None, + service_endpoint: Optional[str] = None, + compartment_id: Optional[str] = None, auth_type: Optional[str] = "API_KEY", auth_profile: Optional[str] = "DEFAULT", client: Optional[Any] = None, @@ -153,18 +153,6 @@ def __init__( additional_kwargs (Optional[Dict[str, Any]]): Additional kwargs for the the LLM. """ - self._client = client or create_client( - auth_type, auth_profile, service_endpoint - ) - - self._provider = get_provider(model, provider) - - self._serving_mode = get_serving_mode(model) - - self._completion_generator = get_completion_generator() - - self._chat_generator = get_chat_generator() - context_size = get_context_size(model, context_size) additional_kwargs = additional_kwargs or {} @@ -188,6 +176,18 @@ def __init__( output_parser=output_parser, ) + self._client = client or create_client( + auth_type, auth_profile, service_endpoint + ) + + self._provider = get_provider(model, provider) + + self._serving_mode = get_serving_mode(model) + + self._completion_generator = get_completion_generator() + + self._chat_generator = get_chat_generator() + @classmethod def class_name(cls) -> str: """Get class name.""" diff --git a/llama-index-integrations/llms/llama-index-llms-oci-genai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-oci-genai/pyproject.toml index 857da6249b532..08074d3c7195e 100644 --- a/llama-index-integrations/llms/llama-index-llms-oci-genai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-oci-genai/pyproject.toml @@ -31,12 +31,12 @@ license = "MIT" name = "llama-index-llms-oci-genai" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" oci = "^2.128.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-octoai/llama_index/llms/octoai/base.py b/llama-index-integrations/llms/llama-index-llms-octoai/llama_index/llms/octoai/base.py index 2d656bee9e191..601d72da32e4f 100644 --- a/llama-index-integrations/llms/llama-index-llms-octoai/llama_index/llms/octoai/base.py +++ b/llama-index-integrations/llms/llama-index-llms-octoai/llama_index/llms/octoai/base.py @@ -37,7 +37,7 @@ to_octoai_messages, ) -from octoai.client import Client +from octoai.client import OctoAI DEFAULT_OCTOAI_MODEL = "mistral-7b-instruct" @@ -62,7 +62,7 @@ class OctoAI(LLM): additional_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Additional kwargs for the OctoAI SDK." ) - _client: Optional[Client] = PrivateAttr() + _client: Optional[OctoAI] = PrivateAttr() def __init__( self, @@ -83,6 +83,20 @@ def __init__( additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) + super().__init__( + additional_kwargs=additional_kwargs, + max_tokens=max_tokens, + model=model, + callback_manager=callback_manager, + temperature=temperature, + timeout=timeout, + system_prompt=system_prompt, + messages_to_prompt=messages_to_prompt, + completion_to_prompt=completion_to_prompt, + pydantic_program_mode=pydantic_program_mode, + output_parser=output_parser, + ) + token = get_from_param_or_env("token", token, "OCTOAI_TOKEN", "") if not token: @@ -93,27 +107,13 @@ def __init__( ) try: - self._client = Client(token=token, timeout=timeout) + self._client = OctoAI(token=token, timeout=timeout) except ImportError as err: raise ImportError( "Could not import OctoAI python package. " "Please install it with `pip install octoai-sdk`." ) from err - super().__init__( - additional_kwargs=additional_kwargs, - max_tokens=max_tokens, - model=model, - callback_manager=callback_manager, - temperature=temperature, - timeout=timeout, - system_prompt=system_prompt, - messages_to_prompt=messages_to_prompt, - completion_to_prompt=completion_to_prompt, - pydantic_program_mode=pydantic_program_mode, - output_parser=output_parser, - ) - @property def metadata(self) -> LLMMetadata: """Get LLM metadata.""" diff --git a/llama-index-integrations/llms/llama-index-llms-octoai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-octoai/pyproject.toml index 0903b32a56883..f34a827ad29e2 100644 --- a/llama-index-integrations/llms/llama-index-llms-octoai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-octoai/pyproject.toml @@ -28,12 +28,13 @@ license = "MIT" name = "llama-index-llms-octoai" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -octoai-sdk = "~0.10.1" +boto3 = "^1.35.3" +octoai = "^1.6.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-ollama/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-ollama/pyproject.toml index d57cf3215276c..4b517f661a11c 100644 --- a/llama-index-integrations/llms/llama-index-llms-ollama/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-ollama/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-ollama" readme = "README.md" -version = "0.2.2" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" ollama = ">=0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py b/llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py index 71c32cf49e0ff..2d3a66c643a91 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py +++ b/llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py @@ -50,17 +50,15 @@ class OpenAILike(OpenAI): context_window: int = Field( default=DEFAULT_CONTEXT_WINDOW, - description=LLMMetadata.__fields__["context_window"].field_info.description, + description=LLMMetadata.model_fields["context_window"].description, ) is_chat_model: bool = Field( default=False, - description=LLMMetadata.__fields__["is_chat_model"].field_info.description, + description=LLMMetadata.model_fields["is_chat_model"].description, ) is_function_calling_model: bool = Field( default=False, - description=LLMMetadata.__fields__[ - "is_function_calling_model" - ].field_info.description, + description=LLMMetadata.model_fields["is_function_calling_model"].description, ) tokenizer: Union[Tokenizer, str, None] = Field( default=None, diff --git a/llama-index-integrations/llms/llama-index-llms-openai-like/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-openai-like/pyproject.toml index a1427b68e912c..7196f9cd650c7 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai-like/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-openai-like/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-openai-like" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" transformers = "^4.37.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-openai-like/tests/test_openai_like.py b/llama-index-integrations/llms/llama-index-llms-openai-like/tests/test_openai_like.py index ed76fec77c1fa..9e206c0690885 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai-like/tests/test_openai_like.py +++ b/llama-index-integrations/llms/llama-index-llms-openai-like/tests/test_openai_like.py @@ -1,8 +1,8 @@ -from typing import List +from typing import List, Dict, Any +from types import MappingProxyType from unittest.mock import MagicMock, call, patch from llama_index.core.base.llms.types import ChatMessage, MessageRole -from llama_index.llms.localai.base import LOCALAI_DEFAULTS from llama_index.llms.openai import Tokenizer from llama_index.llms.openai_like import OpenAILike from openai.types import Completion, CompletionChoice @@ -23,6 +23,17 @@ def encode(self, text: str) -> List[int]: STUB_MODEL_NAME = "models/stub.gguf" STUB_API_KEY = "stub_key" +# Use these as kwargs for OpenAILike to connect to LocalAIs +DEFAULT_LOCALAI_PORT = 8080 +# TODO: move to MappingProxyType[str, Any] once Python 3.9+ +LOCALAI_DEFAULTS: Dict[str, Any] = MappingProxyType( # type: ignore[assignment] + { + "api_key": "localai_fake", + "api_type": "localai_fake", + "api_base": f"http://localhost:{DEFAULT_LOCALAI_PORT}/v1", + } +) + def test_interfaces() -> None: llm = OpenAILike(model=STUB_MODEL_NAME, api_key=STUB_API_KEY) diff --git a/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py b/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py index 4310def7f4b7f..c0b072ab85fd7 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py +++ b/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py @@ -166,7 +166,8 @@ class OpenAI(FunctionCallingLLM): gt=0, ) logprobs: Optional[bool] = Field( - description="Whether to return logprobs per token." + description="Whether to return logprobs per token.", + default=None, ) top_logprobs: int = Field( description="The number of top token log probs to return.", @@ -187,7 +188,7 @@ class OpenAI(FunctionCallingLLM): description="The timeout, in seconds, for API requests.", gte=0, ) - default_headers: Dict[str, str] = Field( + default_headers: Optional[Dict[str, str]] = Field( default=None, description="The default headers for API requests." ) reuse_client: bool = Field( @@ -201,6 +202,10 @@ class OpenAI(FunctionCallingLLM): api_key: str = Field(default=None, description="The OpenAI API key.") api_base: str = Field(description="The base URL for OpenAI API.") api_version: str = Field(description="The API version for OpenAI API.") + strict: bool = Field( + default=False, + description="Whether to use strict mode for invoking tools/using schemas.", + ) _client: Optional[SyncOpenAI] = PrivateAttr() _aclient: Optional[AsyncOpenAI] = PrivateAttr() @@ -229,6 +234,7 @@ def __init__( completion_to_prompt: Optional[Callable[[str], str]] = None, pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, + strict: bool = False, **kwargs: Any, ) -> None: additional_kwargs = additional_kwargs or {} @@ -257,6 +263,7 @@ def __init__( completion_to_prompt=completion_to_prompt, pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, + strict=strict, **kwargs, ) @@ -384,7 +391,13 @@ def _get_model_kwargs(self, **kwargs: Any) -> Dict[str, Any]: base_kwargs["top_logprobs"] = self.top_logprobs else: base_kwargs["logprobs"] = self.top_logprobs # int in this case - return {**base_kwargs, **self.additional_kwargs} + + # can't send stream_options to the API when not streaming + all_kwargs = {**base_kwargs, **self.additional_kwargs} + if "stream" not in all_kwargs and "stream_options" in all_kwargs: + del all_kwargs["stream_options"] + + return all_kwargs @llm_retry_decorator def _chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: @@ -480,8 +493,7 @@ def gen() -> ChatResponseGen: is_function = False for response in client.chat.completions.create( messages=message_dicts, - stream=True, - **self._get_model_kwargs(**kwargs), + **self._get_model_kwargs(stream=True, **kwargs), ): response = cast(ChatCompletionChunk, response) if len(response.choices) > 0: @@ -555,15 +567,14 @@ def _complete(self, prompt: str, **kwargs: Any) -> CompletionResponse: @llm_retry_decorator def _stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen: client = self._get_client() - all_kwargs = self._get_model_kwargs(**kwargs) + all_kwargs = self._get_model_kwargs(stream=True, **kwargs) self._update_max_tokens(all_kwargs, prompt) def gen() -> CompletionResponseGen: text = "" for response in client.completions.create( prompt=prompt, - stream=True, - **all_kwargs, + **kwargs, ): if len(response.choices) > 0: delta = response.choices[0].text @@ -598,18 +609,29 @@ def _update_max_tokens(self, all_kwargs: Dict[str, Any], prompt: str) -> None: def _get_response_token_counts(self, raw_response: Any) -> dict: """Get the token usage reported by the response.""" - if not isinstance(raw_response, dict): - return {} - - usage = raw_response.get("usage", {}) - # NOTE: other model providers that use the OpenAI client may not report usage - if usage is None: + if hasattr(raw_response, "usage"): + try: + prompt_tokens = raw_response.usage.prompt_tokens + completion_tokens = raw_response.usage.completion_tokens + total_tokens = raw_response.usage.total_tokens + except AttributeError: + return {} + elif isinstance(raw_response, dict): + usage = raw_response.get("usage", {}) + # NOTE: other model providers that use the OpenAI client may not report usage + if usage is None: + return {} + # Backwards compatibility with old dict type + prompt_tokens = usage.get("prompt_tokens", 0) + completion_tokens = usage.get("completion_tokens", 0) + total_tokens = usage.get("total_tokens", 0) + else: return {} return { - "prompt_tokens": usage.get("prompt_tokens", 0), - "completion_tokens": usage.get("completion_tokens", 0), - "total_tokens": usage.get("total_tokens", 0), + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": total_tokens, } # ===== Async Endpoints ===== @@ -711,8 +733,7 @@ async def gen() -> ChatResponseAsyncGen: first_chat_chunk = True async for response in await aclient.chat.completions.create( messages=message_dicts, - stream=True, - **self._get_model_kwargs(**kwargs), + **self._get_model_kwargs(stream=True, **kwargs), ): response = cast(ChatCompletionChunk, response) if len(response.choices) > 0: @@ -798,14 +819,13 @@ async def _astream_complete( self, prompt: str, **kwargs: Any ) -> CompletionResponseAsyncGen: aclient = self._get_aclient() - all_kwargs = self._get_model_kwargs(**kwargs) + all_kwargs = self._get_model_kwargs(stream=True, **kwargs) self._update_max_tokens(all_kwargs, prompt) async def gen() -> CompletionResponseAsyncGen: text = "" async for response in await aclient.completions.create( prompt=prompt, - stream=True, **all_kwargs, ): if len(response.choices) > 0: @@ -832,6 +852,7 @@ def _prepare_chat_with_tools( verbose: bool = False, allow_parallel_tool_calls: bool = False, tool_choice: Union[str, dict] = "auto", + strict: Optional[bool] = None, **kwargs: Any, ) -> Dict[str, Any]: """Predict and call the tool.""" @@ -840,6 +861,20 @@ def _prepare_chat_with_tools( # misralai uses the same openai tool format tool_specs = [tool.metadata.to_openai_tool() for tool in tools] + # if strict is passed in, use, else default to the class-level attribute, else default to True` + if strict is not None: + strict = strict + else: + strict = self.strict + + if self.metadata.is_function_calling_model: + for tool_spec in tool_specs: + if tool_spec["type"] == "function": + tool_spec["function"]["strict"] = strict + tool_spec["function"]["parameters"][ + "additionalProperties" + ] = False # in current openai 1.40.0 it is always false. + if isinstance(user_msg, str): user_msg = ChatMessage(role=MessageRole.USER, content=user_msg) diff --git a/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/utils.py b/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/utils.py index 9bbb1249d39b1..fa1db91a1ac7d 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/utils.py @@ -47,6 +47,7 @@ "gpt-4-turbo": 128000, "gpt-4o": 128000, "gpt-4o-2024-05-13": 128000, + "gpt-4o-2024-08-06": 128000, "gpt-4o-mini": 128000, "gpt-4o-mini-2024-07-18": 128000, # 0613 models (function calling): diff --git a/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml index 8779b47e7937c..fe2d559d3ddce 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml @@ -29,11 +29,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-openai" readme = "README.md" -version = "0.1.27" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.57" +openai = "^1.40.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-openai/tests/test_openai.py b/llama-index-integrations/llms/llama-index-llms-openai/tests/test_openai.py index 98cb4ed8ab2c2..1fde36dc316d4 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai/tests/test_openai.py +++ b/llama-index-integrations/llms/llama-index-llms-openai/tests/test_openai.py @@ -303,9 +303,11 @@ def test_completion_model_basic(MockSyncOpenAI: MagicMock) -> None: response = llm.complete(prompt) assert response.text == "\n\nThis is indeed a test" + assert response.additional_kwargs["total_tokens"] == 12 chat_response = llm.chat([message]) assert chat_response.message.content == "\n\nThis is indeed a test" + assert chat_response.message.additional_kwargs["total_tokens"] == 12 @patch("llama_index.llms.openai.base.SyncOpenAI") @@ -323,6 +325,7 @@ def test_chat_model_basic(MockSyncOpenAI: MagicMock) -> None: chat_response = llm.chat([message]) assert chat_response.message.content == "\n\nThis is a test!" + assert chat_response.additional_kwargs["total_tokens"] == 20 @patch("llama_index.llms.openai.base.SyncOpenAI") diff --git a/llama-index-integrations/llms/llama-index-llms-openllm/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-openllm/pyproject.toml index 8a7bfa3b6018a..1abdac55f8b03 100644 --- a/llama-index-integrations/llms/llama-index-llms-openllm/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-openllm/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-openllm" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai-like = "^0.1.3" +llama-index-llms-openai-like = "^0.2.0" openllm = ">=0.6.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-openrouter/llama_index/llms/openrouter/base.py b/llama-index-integrations/llms/llama-index-llms-openrouter/llama_index/llms/openrouter/base.py index 8a369029eea3f..83ded77f5a8fe 100644 --- a/llama-index-integrations/llms/llama-index-llms-openrouter/llama_index/llms/openrouter/base.py +++ b/llama-index-integrations/llms/llama-index-llms-openrouter/llama_index/llms/openrouter/base.py @@ -51,7 +51,7 @@ class OpenRouter(OpenAILike): ) is_chat_model: bool = Field( default=True, - description=LLMMetadata.__fields__["is_chat_model"].field_info.description, + description=LLMMetadata.model_fields["is_chat_model"].description, ) def __init__( diff --git a/llama-index-integrations/llms/llama-index-llms-openrouter/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-openrouter/pyproject.toml index 8385410c5fed6..f6f47c817117a 100644 --- a/llama-index-integrations/llms/llama-index-llms-openrouter/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-openrouter/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-openrouter" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-llms-openai-like = "^0.1.3" +llama-index-llms-openai-like = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-openvino/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-openvino/pyproject.toml index d5d0ac9a4bded..6ce8a3e34069e 100644 --- a/llama-index-integrations/llms/llama-index-llms-openvino/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-openvino/pyproject.toml @@ -30,13 +30,13 @@ license = "MIT" name = "llama-index-llms-openvino" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.41" -llama-index-llms-huggingface = "^0.2.4" +llama-index-llms-huggingface = "^0.3.0" optimum = {extras = ["openvino"], version = ">=1.21.2"} +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-optimum-intel/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-optimum-intel/pyproject.toml index 3ab897522c08d..d32c680e1b1ce 100644 --- a/llama-index-integrations/llms/llama-index-llms-optimum-intel/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-optimum-intel/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-llms-optimum-intel" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.0" -llama-index-llms-huggingface = "^0.1.4" +llama-index-core = "^0.11.0" +llama-index-llms-huggingface = "^0.3.0" optimum = {extras = ["ipex"], version = ">=1.18.0"} [tool.poetry.group.dev.dependencies] diff --git a/llama-index-integrations/llms/llama-index-llms-paieas/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-paieas/pyproject.toml index 3dcd5cdd314f3..29c95f566e2ee 100644 --- a/llama-index-integrations/llms/llama-index-llms-paieas/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-paieas/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-paieas" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.57" -llama-index-llms-openai-like = "^0.1.3" +llama-index-llms-openai-like = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-palm/llama_index/llms/palm/base.py b/llama-index-integrations/llms/llama-index-llms-palm/llama_index/llms/palm/base.py index 4c9eafd81ce58..6308b9b6fdb42 100644 --- a/llama-index-integrations/llms/llama-index-llms-palm/llama_index/llms/palm/base.py +++ b/llama-index-integrations/llms/llama-index-llms-palm/llama_index/llms/palm/base.py @@ -92,10 +92,10 @@ def __init__( ) model_name = model_name - self._model = models_dict[model_name] + model = models_dict[model_name] # get num_output - num_output = num_output or self._model.output_token_limit + num_output = num_output or model.output_token_limit generate_kwargs = generate_kwargs or {} super().__init__( @@ -109,6 +109,7 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._model = model @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-palm/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-palm/pyproject.toml index aa26990bbfae0..ff9e820600e57 100644 --- a/llama-index-integrations/llms/llama-index-llms-palm/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-palm/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-palm" readme = "README.md" -version = "0.1.6" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" google-generativeai = "^0.5.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-perplexity/llama_index/llms/perplexity/base.py b/llama-index-integrations/llms/llama-index-llms-perplexity/llama_index/llms/perplexity/base.py index 7dd8a7f43bc06..8c8a9661f0cd6 100644 --- a/llama-index-integrations/llms/llama-index-llms-perplexity/llama_index/llms/perplexity/base.py +++ b/llama-index-integrations/llms/llama-index-llms-perplexity/llama_index/llms/perplexity/base.py @@ -138,15 +138,23 @@ def metadata(self) -> LLMMetadata: def _get_context_window(self) -> int: # Check https://docs.perplexity.ai/docs/model-cards for latest model information model_context_windows = { - # Perplexity Models + # Legacy Perplexity Models (will be deprecated on August 12th 2024) "llama-3-sonar-small-32k-chat": 32768, "llama-3-sonar-small-32k-online": 28000, "llama-3-sonar-large-32k-chat": 32768, "llama-3-sonar-large-32k-online": 28000, - # Open Source Models + # Latest Perplexity Models + "llama-3.1-sonar-small-128k-chat": 127072, + "llama-3.1-sonar-small-128k-online": 131072, + "llama-3.1-sonar-large-128k-chat": 127072, + "llama-3.1-sonar-large-128k-online": 131072, + # Legacy Open Source Models (will be deprecated on August 12th 2024) "llama-3-8b-instruct": 8192, "llama-3-70b-instruct": 8192, "mixtral-8x7b-instruct": 16384, + # Latest Open Source Models + "llama-3.1-8b-instruct": 131072, + "llama-3.1-70b-instruct": 131072, } return model_context_windows.get( self.model, 4096 @@ -155,15 +163,23 @@ def _get_context_window(self) -> int: def _is_chat_model(self) -> bool: # Check https://docs.perplexity.ai/docs/model-cards for latest model information chat_models = { - # Perplexity Models + # Legacy Perplexity Models (will be deprecated on August 12th 2024) "llama-3-sonar-small-32k-chat", "llama-3-sonar-small-32k-online", "llama-3-sonar-large-32k-chat", "llama-3-sonar-large-32k-online", - # Open Source Models + # Latest Perplexity Models + "llama-3.1-sonar-small-128k-chat", + "llama-3.1-sonar-small-128k-online", + "llama-3.1-sonar-large-128k-chat", + "llama-3.1-sonar-large-128k-online", + # Legacy Open Source Models (will be deprecated on August 12th 2024) "llama-3-8b-instruct", "llama-3-70b-instruct", "mixtral-8x7b-instruct", + # Latest Open Source Models + "llama-3.1-8b-instruct", + "llama-3.1-70b-instruct", } return self.model in chat_models diff --git a/llama-index-integrations/llms/llama-index-llms-perplexity/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-perplexity/pyproject.toml index 07d0098d66307..dcd10699bda77 100644 --- a/llama-index-integrations/llms/llama-index-llms-perplexity/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-perplexity/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-perplexity" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-portkey/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-portkey/pyproject.toml index ef31b7fc124f6..6673586f50919 100644 --- a/llama-index-integrations/llms/llama-index-llms-portkey/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-portkey/pyproject.toml @@ -27,15 +27,15 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-portkey" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-anthropic = "^0.1.1" +llama-index-llms-anthropic = "^0.2.0" portkey-ai = "^1.1.4" portkey = "^0.1.2" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-predibase/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-predibase/pyproject.toml index ac2e986331b79..5bb84b888f283 100644 --- a/llama-index-integrations/llms/llama-index-llms-predibase/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-predibase/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-predibase" readme = "README.md" -version = "0.1.7" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-premai/llama_index/llms/premai/base.py b/llama-index-integrations/llms/llama-index-llms-premai/llama_index/llms/premai/base.py index ad6ff959eddc7..c6637d4b40682 100644 --- a/llama-index-integrations/llms/llama-index-llms-premai/llama_index/llms/premai/base.py +++ b/llama-index-integrations/llms/llama-index-llms-premai/llama_index/llms/premai/base.py @@ -110,7 +110,6 @@ def __init__( "You can either pass it in as an argument or set it `PREMAI_API_KEY`. You can get your API key here: https://app.premai.io/api_keys/" ) - self._client = Prem(api_key=api_key) additional_kwargs = {**(additional_kwargs or {}), **kwargs} super().__init__( @@ -129,6 +128,7 @@ def __init__( max_retries=max_retries, repositories=repositories, ) + self._client = Prem(api_key=api_key) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-premai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-premai/pyproject.toml index 6a2585072b25e..b0086c3d9923f 100644 --- a/llama-index-integrations/llms/llama-index-llms-premai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-premai/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-premai" readme = "README.md" -version = "0.1.7" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" premai = "^0.3.57" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-qianfan/llama_index/llms/qianfan/base.py b/llama-index-integrations/llms/llama-index-llms-qianfan/llama_index/llms/qianfan/base.py index e564fb61540ee..aa0cb8e828d50 100644 --- a/llama-index-integrations/llms/llama-index-llms-qianfan/llama_index/llms/qianfan/base.py +++ b/llama-index-integrations/llms/llama-index-llms-qianfan/llama_index/llms/qianfan/base.py @@ -192,8 +192,6 @@ def __init__( if llm_type != "chat": raise NotImplementedError("Only the chat type is supported.") - self._client = Client(access_key, secret_key) - super().__init__( model_name=model_name, endpoint_url=endpoint_url, @@ -202,6 +200,7 @@ def __init__( secret_key=secret_key, llm_type=llm_type, ) + self._client = Client(access_key, secret_key) @classmethod def from_model_name( diff --git a/llama-index-integrations/llms/llama-index-llms-qianfan/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-qianfan/pyproject.toml index 08d75eef8aa33..0426ca16f1a5f 100644 --- a/llama-index-integrations/llms/llama-index-llms-qianfan/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-qianfan/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-llms-qianfan" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-utils-qianfan = "^0.1.0" +llama-index-utils-qianfan = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-replicate/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-replicate/pyproject.toml index e0c3c8170f0b6..23de01f95380e 100644 --- a/llama-index-integrations/llms/llama-index-llms-replicate/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-replicate/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-replicate" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-rungpt/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-rungpt/pyproject.toml index 365d1e32fc65e..7495951dbf55b 100644 --- a/llama-index-integrations/llms/llama-index-llms-rungpt/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-rungpt/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-rungpt" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-sagemaker-endpoint/llama_index/llms/sagemaker_endpoint/base.py b/llama-index-integrations/llms/llama-index-llms-sagemaker-endpoint/llama_index/llms/sagemaker_endpoint/base.py index 0c59ad8968f39..8de20bc525513 100644 --- a/llama-index-integrations/llms/llama-index-llms-sagemaker-endpoint/llama_index/llms/sagemaker_endpoint/base.py +++ b/llama-index-integrations/llms/llama-index-llms-sagemaker-endpoint/llama_index/llms/sagemaker_endpoint/base.py @@ -155,17 +155,6 @@ def __init__( model_kwargs = model_kwargs or {} model_kwargs["temperature"] = temperature content_handler = content_handler - self._completion_to_prompt = completion_to_prompt - self._client = get_aws_service_client( - service_name="sagemaker-runtime", - profile_name=profile_name, - region_name=region_name, - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - aws_session_token=aws_session_token, - max_retries=max_retries, - timeout=timeout, - ) callback_manager = callback_manager or CallbackManager([]) super().__init__( @@ -182,6 +171,17 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._completion_to_prompt = completion_to_prompt + self._client = get_aws_service_client( + service_name="sagemaker-runtime", + profile_name=profile_name, + region_name=region_name, + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + aws_session_token=aws_session_token, + max_retries=max_retries, + timeout=timeout, + ) @llm_completion_callback() def complete( diff --git a/llama-index-integrations/llms/llama-index-llms-sagemaker-endpoint/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-sagemaker-endpoint/pyproject.toml index e40a9a0969cdb..65a68efbb98f6 100644 --- a/llama-index-integrations/llms/llama-index-llms-sagemaker-endpoint/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-sagemaker-endpoint/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-sagemaker-endpoint" readme = "README.md" -version = "0.1.3" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-llama-cpp = "^0.1.1" +llama-index-llms-llama-cpp = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/.gitignore b/llama-index-integrations/llms/llama-index-llms-sambanova/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-sambanova/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/BUILD b/llama-index-integrations/llms/llama-index-llms-sambanova/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-sambanova/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/Makefile b/llama-index-integrations/llms/llama-index-llms-sambanova/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-sambanova/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/README.md b/llama-index-integrations/llms/llama-index-llms-sambanova/README.md new file mode 100644 index 0000000000000..0bc8ca9c4dc3f --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-sambanova/README.md @@ -0,0 +1,17 @@ +# LlamaIndex LLM Integration: SambaNova LLM + +SambaNovaLLM is a custom LLM (Language Model) interface that allows you to interact with AI models hosted on SambaNova's offerings - Sambaverse and SambaStudio + +## Key Features: + +- Integration with SambaNova-hosted AI models +- Integration two SambaNova offerings - Sambaverse and SambaStudio +- Support for completion based interactions +- Streaming support for completion responses +- Seamless integration with the LlamaIndex ecosystem + +## Installation + +```bash +pip install llama-index-llms-sambanova +``` diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/llama_index/llms/sambanova/BUILD b/llama-index-integrations/llms/llama-index-llms-sambanova/llama_index/llms/sambanova/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-sambanova/llama_index/llms/sambanova/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/llama_index/llms/sambanova/__init__.py b/llama-index-integrations/llms/llama-index-llms-sambanova/llama_index/llms/sambanova/__init__.py new file mode 100644 index 0000000000000..0adc8f6ffddf1 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-sambanova/llama_index/llms/sambanova/__init__.py @@ -0,0 +1,3 @@ +from llama_index.llms.sambanova.base import Sambaverse, SambaStudio + +__all__ = ["Sambaverse", "SambaStudio"] diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/llama_index/llms/sambanova/base.py b/llama-index-integrations/llms/llama-index-llms-sambanova/llama_index/llms/sambanova/base.py new file mode 100644 index 0000000000000..749c50b707e52 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-sambanova/llama_index/llms/sambanova/base.py @@ -0,0 +1,575 @@ +import os +from typing import Any, Dict, Optional, List + +import requests +from llama_index.core.base.llms.types import ( + CompletionResponse, + CompletionResponseGen, + LLMMetadata, +) + +from llama_index.core.llms.callbacks import llm_completion_callback +from llama_index.core.llms.custom import CustomLLM +from llama_index.core.bridge.pydantic import Field, PrivateAttr +import json + + +class Sambaverse(CustomLLM): + """ + Sambaverse LLM. + + Examples: + `pip install llama-index-llms-sambanova` + + ```python + from llama_index.llms.sambanova import Sambaverse + + llm = Sambaverse(...) + + response = llm.complete("What is the meaning of life?") + + print(response) + ``` + """ + + sambaverse_url: str = Field( + default="https://sambaverse.sambanova.ai", + description="URL of the Sambaverse server", + ) + + sambaverse_api_key: str = Field( + default="", + description="API key for the Sambaverse server", + ) + sambaverse_model_name: str = Field( + default="", description="Name of the Sambaverse model to use" + ) + model_kwargs: Dict[str, Any] = Field( + default_factory=dict, + description="Additional keyword arguments to pass to the model", + ) + streaming: bool = Field( + default=False, + description="Boolean to state whether to stream response or not", + ) + + _client: requests.Session = PrivateAttr() + + def __init__( + self, + sambaverse_url: str = "https://sambaverse.sambanova.ai", + sambaverse_api_key: str = "", + sambaverse_model_name: str = "", + model_kwargs: Dict[str, Any] = {}, + streaming: bool = False, + client: Optional[requests.Session] = None, + ) -> None: + super().__init__() + + self.sambaverse_url = sambaverse_url + self.sambaverse_api_key = sambaverse_api_key + self.sambaverse_model_name = sambaverse_model_name + self.model_kwargs = model_kwargs + self.streaming = streaming + self._client = client or requests.Session() + self._validate_env_vars() + + def _validate_env_vars(self): + if not self.sambaverse_model_name: + self.sambaverse_model_name = os.getenv("SAMBAVERSE_MODEL_NAME") + if not self.sambaverse_api_key: + self.sambaverse_api_key = os.getenv("SAMBAVERSE_API_KEY") + + if not self.sambaverse_model_name: + raise ValueError( + "Sambaverse model name must be provided either as an argument or set in the environment variable 'SAMBAVERSE_MODEL_NAME'." + ) + + if not self.sambaverse_api_key: + raise ValueError( + "Sambaverse API key must be provided either as an argument or set in the environment variable 'SAMBAVERSE_API_KEY'." + ) + + def _get_full_url(self, endpoint: str) -> str: + return f"{self.sambaverse_url}/{endpoint}" + + def _get_model_kwargs(self, stop: Optional[List[str]]) -> str: + try: + _model_kwargs = self.model_kwargs or {} + _kwarg_stop_sequences = set(_model_kwargs.get("stop_sequences", [])) + _stop_sequences = set(stop or _kwarg_stop_sequences) + + if not _kwarg_stop_sequences: + _model_kwargs["stop_sequences"] = ",".join( + f'"{x}"' for x in _stop_sequences + ) + + tuning_params_dict = { + k: {"type": type(v).__name__, "value": str(v)} + for k, v in _model_kwargs.items() + } + + return json.dumps(tuning_params_dict) + + except Exception as e: + raise ValueError(f"Error getting model kwargs: {e}") + + def _process_api_response(self, response: requests.Response) -> Dict: + result: Dict[str, Any] = {} + if response.status_code != 200: + raise ValueError( + f"Received unexpected status code {response.status_code}: {response.text}" + ) + + try: + lines_result = response.text.strip().split("\n") + text_result = lines_result[-1] + if response.status_code == 200 and json.loads(text_result).get("error"): + completion = "" + for line in lines_result[:-1]: + completion += json.loads(line)["result"]["responses"][0][ + "stream_token" + ] + text_result = lines_result[-2] + result = json.loads(text_result) + result["result"]["responses"][0]["completion"] = completion + else: + result = json.loads(text_result) + except Exception as e: + result["detail"] = str(e) + if "status_code" not in result: + result["status_code"] = response.status_code + return result + + def _process_api_stream_response(self, response: requests.Response) -> Any: + try: + for line in response.iter_lines(): + chunk = json.loads(line) + if "status_code" not in chunk: + chunk["status_code"] = response.status_code + if chunk["status_code"] == 200 and chunk.get("error"): + chunk["result"] = {"responses": [{"stream_token": ""}]} + return chunk + yield chunk + except Exception as e: + raise RuntimeError(f"Error processing streaming response: {e}") + + def _send_sambaverse_request( + self, endpoint: str, data: Dict[str, Any], stream: bool = False + ) -> requests.Response: + url = self._get_full_url(endpoint) + headers = { + "key": self.sambaverse_api_key, + "Content-Type": "application/json", + "modelName": self.sambaverse_model_name, + } + try: + return self._client.post(url, headers=headers, json=data, stream=stream) + + except Exception as e: + raise ValueError(f"Error sending request to Sambaverse: {e}") + + def _prepare_request_data(self, prompt: str) -> Dict[str, Any]: + try: + model_params = self._get_model_kwargs(stop=None) + return {"instance": prompt, "params": json.loads(model_params)} + + except Exception as e: + raise ValueError(f"Error preparing request data: {e}") + + def _get_completion_from_response(self, response: Dict) -> str: + try: + return ( + response.get("result", {}) + .get("responses", [{}])[0] + .get("completion", "") + ) + except Exception as e: + raise ValueError(f"Error processing response: {e}") + + @classmethod + def class_name(cls) -> str: + return "Samabaverse" + + @property + def metadata(self) -> LLMMetadata: + """LLM metadata.""" + return LLMMetadata( + model_name=self.sambaverse_model_name, + model_kwargs=self.model_kwargs, + description="Sambanova LLM", + is_streaming=self.streaming, + ) + + @llm_completion_callback() + def complete(self, prompt: str) -> CompletionResponse: + """ + Complete the given prompt using the Sambaverse model. + + Args: + prompt (str): The input prompt to complete. + + Returns: + CompletionResponse: The completed text generated by the model. + """ + data = self._prepare_request_data(prompt) + response = self._send_sambaverse_request("api/predict", data) + processed_response = self._process_api_response(response) + completion_text = self._get_completion_from_response(processed_response) + + return CompletionResponse(text=completion_text) + + @llm_completion_callback() + def stream_complete(self, prompt: str) -> CompletionResponseGen: + """ + Stream the completion of the given prompt using the Sambaverse model. + + Args: + prompt (str): The input prompt to complete. + + Yields: + CompletionResponseGen: Streamed completion text generated by the model. + """ + print("In stream_complete") + data = self._prepare_request_data(prompt) + response = self._send_sambaverse_request("api/predict", data, stream=True) + + for token in self._process_api_stream_response(response): + processed_token = token["result"]["responses"][0]["stream_token"] + yield CompletionResponse(text=processed_token) + + +class SambaStudio(CustomLLM): + """ + SambaStudio LLM. + + Examples: + `pip install llama-index-llms-sambanova` + + ```python + from llama_index.llms.sambanova import SambaStudio + + llm = Sambaverse(...) + + response = llm.complete("What is the meaning of life?") + + print(response) + ``` + """ + + sambastudio_base_url: str = Field( + default="", + description="URL of the SambaStudio server", + ) + sambastudio_base_uri: str = Field( + default="", + description="Base URI of the SambaStudio server", + ) + sambastudio_project_id: str = Field( + default="", + description="Project ID of the SambaStudio server", + ) + sambastudio_endpoint_id: str = Field( + default="", + description="Endpoint ID of the SambaStudio server", + ) + sambastudio_api_key: str = Field( + default="", + description="API key for the SambaStudio server", + ) + + model_kwargs: Dict[str, Any] = Field( + default_factory=dict, + description="Additional keyword arguments to pass to the model", + ) + streaming: bool = Field( + default=False, + description="Boolean to state whether to stream response or not", + ) + + _client: requests.Session = PrivateAttr() + + def __init__( + self, + sambastudio_base_url: str = "", + sambastudio_base_uri: str = "", + sambastudio_project_id: str = "", + sambastudio_endpoint_id: str = "", + model_kwargs: Dict[str, Any] = {}, + streaming: bool = False, + client: Optional[requests.Session] = None, + ) -> None: + super().__init__() + + self.sambastudio_base_url = sambastudio_base_url + self.sambastudio_base_uri = sambastudio_base_uri + self.sambastudio_project_id = sambastudio_project_id + self.sambastudio_endpoint_id = sambastudio_endpoint_id + self.model_kwargs = model_kwargs + self.streaming = streaming + self._client = client or requests.Session() + self._validate_env_vars() + + def _validate_env_vars(self): + if not self.sambaverse_api_key: + self.sambaverse_api_key = os.getenv("SAMBAVERSE_API_KEY") + if not self.sambastudio_base_url: + self.sambastudio_base_url = os.getenv("SAMBASTUDIO_BASE_URL") + if not self.sambastudio_base_uri: + self.sambastudio_base_uri = os.getenv("SAMBASTUDIO_BASE_URI") + if not self.sambastudio_project_id: + self.sambastudio_project_id = os.getenv("SAMBASTUDIO_PROJECT_ID") + if not self.sambastudio_endpoint_id: + self.sambastudio_endpoint_id = os.getenv("SAMBASTUDIO_ENDPOINT_ID") + + if not self.sambaverse_api_key: + raise ValueError( + "Sambaverse API key must be provided either as an argument or set in the environment variable 'SAMBAVERSE_API_KEY'." + ) + + if not self.sambastudio_base_url: + raise ValueError( + "Sambastudio base URL must be provided either as an argument or set in the environment variable 'SAMBASTUDIO_BASE_URL'." + ) + + if not self.sambastudio_base_uri: + raise ValueError( + "Sambastudio base URI must be provided either as an argument or set in the environment variable 'SAMBASTUDIO_BASE_URI'." + ) + + if not self.sambastudio_project_id: + raise ValueError( + "Sambastudio project ID must be provided either as an argument or set in the environment variable 'SAMBASTUDIO_PROJECT_ID'." + ) + + if not self.sambastudio_endpoint_id: + raise ValueError( + "Sambastudio endpoint ID must be provided either as an argument or set in the environment variable 'SAMBASTUDIO_ENDPOINT_ID'." + ) + + def _get_full_url(self, path: str) -> str: + return f"{self.sambastudio_base_url}/{self.sambastudio_base_uri}/{path}" + + def _get_model_kwargs(self, stop: Optional[List[str]]) -> str: + try: + _model_kwargs = self.model_kwargs or {} + _kwarg_stop_sequences = set(_model_kwargs.get("stop_sequences", [])) + _stop_sequences = set(stop or _kwarg_stop_sequences) + + if not _kwarg_stop_sequences: + _model_kwargs["stop_sequences"] = ",".join( + f'"{x}"' for x in _stop_sequences + ) + + tuning_params_dict = { + k: {"type": type(v).__name__, "value": str(v)} + for k, v in _model_kwargs.items() + } + + return json.dumps(tuning_params_dict) + + except Exception as e: + raise ValueError(f"Error getting model kwargs: {e}") + + def _process_api_response(self, response: requests.Response) -> Dict: + result: Dict[str, Any] = {} + try: + result = response.json() + except Exception as e: + result["detail"] = str(e) + if "status_code" not in result: + result["status_code"] = response.status_code + return result + + def _process_api_stream_response(self, response: requests.Response) -> Any: + """Process the streaming response.""" + if "nlp" in self.sambastudio_base_uri: + try: + import sseclient + except ImportError: + raise ImportError( + "could not import sseclient library" + "Please install it with `pip install sseclient-py`." + ) + client = sseclient.SSEClient(response) + close_conn = False + for event in client.events(): + if event.event == "error_event": + close_conn = True + chunk = { + "event": event.event, + "data": event.data, + "status_code": response.status_code, + } + yield chunk + if close_conn: + client.close() + elif "generic" in self.sambastudio_base_uri: + try: + for line in response.iter_lines(): + chunk = json.loads(line) + if "status_code" not in chunk: + chunk["status_code"] = response.status_code + if chunk["status_code"] == 200 and chunk.get("error"): + chunk["result"] = {"responses": [{"stream_token": ""}]} + yield chunk + except Exception as e: + raise RuntimeError(f"Error processing streaming response: {e}") + else: + raise ValueError( + f"handling of endpoint uri: {self.api_base_uri} not implemented" + ) + + def _send_sambaverse_request( + self, data: Dict[str, Any], stream: bool = False + ) -> requests.Response: + try: + if stream: + url = self._get_full_url( + f"stream/{self.sambastudio_project_id}/{self.sambastudio_endpoint_id}" + ) + headers = { + "key": self.sambaverse_api_key, + "Content-Type": "application/json", + } + return self._client.post(url, headers=headers, json=data, stream=True) + else: + url = self._get_full_url( + f"{self.sambastudio_project_id}/{self.sambastudio_endpoint_id}" + ) + headers = { + "key": self.sambaverse_api_key, + "Content-Type": "application/json", + } + + return self._client.post(url, headers=headers, json=data, stream=stream) + except Exception as e: + raise ValueError(f"Error sending request to Sambaverse: {e}") + + def _prepare_request_data(self, prompt: str) -> Dict[str, Any]: + try: + data = {} + if isinstance(prompt, str): + input = [prompt] + if "nlp" in self.api_base_uri: + model_params = self._get_model_kwargs(stop=None) + if model_params: + data = {"inputs": input, "params": json.loads(model_params)} + else: + data = {"inputs": input} + elif "generic" in self.api_base_uri: + model_params = self._get_model_kwargs(stop=None) + if model_params: + data = {"instance": input, "params": json.loads(model_params)} + else: + data = {"instance": input} + else: + raise ValueError( + f"handling of endpoint uri: {self.api_base_uri} not implemented" + ) + return data + + except Exception as e: + raise ValueError(f"Error preparing request data: {e}") + + def _get_completion_from_response(self, response: Dict) -> str: + try: + if "nlp" in self.sambastudio_base_uri: + return response["data"][0]["completion"] + elif "generic" in self.sambastudio_base_uri: + return response["predictions"][0]["completion"] + else: + raise ValueError( + f"handling of endpoint uri: {self.sambastudio_base_uri} not implemented" + ) + except Exception as e: + raise ValueError(f"Error processing response: {e}") + + def _get_stream_token_from_response(self, response: Dict) -> str: + try: + if "nlp" in self.sambastudio_base_uri: + return response["data"]["stream_token"] + elif "generic" in self.sambastudio_base_uri: + return response["result"]["responses"][0]["stream_token"] + else: + raise ValueError( + f"handling of endpoint uri: {self.sambastudio_base_uri} not implemented" + ) + except Exception as e: + raise ValueError(f"Error processing response: {e}") + + @classmethod + def class_name(cls) -> str: + return "SambaStudio" + + @property + def metadata(self) -> LLMMetadata: + """LLM metadata.""" + return LLMMetadata( + model_kwargs=self.model_kwargs, + description="Sambanova LLM", + is_streaming=self.streaming, + ) + + @llm_completion_callback() + def complete(self, prompt: str) -> CompletionResponse: + """ + Complete the given prompt using the SambaStudio model. + + Args: + prompt (str): The input prompt to complete. + + Returns: + CompletionResponse: The completed text generated by the model. + """ + data = self._prepare_request_data(prompt) + response = self._send_sambaverse_request(data) + processed_response = self._process_api_response(response) + completion_text = self._get_completion_from_response(processed_response) + + return CompletionResponse(text=completion_text) + + @llm_completion_callback() + def stream_complete(self, prompt: str) -> CompletionResponseGen: + """ + Stream the completion of the given prompt using the SambaStudio model. + + Args: + prompt (str): The input prompt to complete. + + Yields: + CompletionResponseGen: Streamed completion text generated by the model. + """ + print("In stream_complete") + data = self._prepare_request_data(prompt) + response = self._send_sambaverse_request(data, stream=True) + + for token in self._process_api_stream_response(response): + processed_token = self._get_stream_token_from_response(token) + yield CompletionResponse(text=processed_token) + + @llm_completion_callback() + def nlp_prediction(self, prompt: str) -> CompletionResponse: + """ + Perform NLP prediction for the given prompt using the SambaStudio model. + + Args: + prompt (str): The input prompt to predict. + + Returns: + CompletionResponse: The prediction result generated by the model. + """ + return self.complete(prompt) + + @llm_completion_callback() + def nlp_prediction_stream(self, prompt: str) -> CompletionResponseGen: + """ + Stream NLP prediction for the given prompt using the SambaStudio model. + + Args: + prompt (str): The input prompt to predict. + + Yields: + CompletionResponseGen: Streamed prediction result generated by the model. + """ + return self.stream_complete(prompt) diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-sambanova/pyproject.toml new file mode 100644 index 0000000000000..3af3a61e4ed02 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-sambanova/pyproject.toml @@ -0,0 +1,57 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = false +import_path = "llama_index.llms.sambanova" + +[tool.llamahub.class_authors] +SambaStudio = "Pradeep" +Sambaverse = "Pradeep" + +[tool.mypy] +disallow_untyped_defs = true +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Your Name "] +description = "llama-index llms sambanova integration" +name = "sambanova" +readme = "README.md" +version = "0.2.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +llama-index-core = "^0.11.0" + +[tool.poetry.group.dev.dependencies] +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" +types-setuptools = "67.1.0.0" + +[tool.poetry.group.dev.dependencies.codespell] +extras = ["toml"] +version = ">=v2.2.6" + +[[tool.poetry.packages]] +include = "llama_index/" diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/tests/BUILD b/llama-index-integrations/llms/llama-index-llms-sambanova/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-sambanova/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/tests/__init__.py b/llama-index-integrations/llms/llama-index-llms-sambanova/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/llms/llama-index-llms-sambanova/tests/test_llms_sambanova.py b/llama-index-integrations/llms/llama-index-llms-sambanova/tests/test_llms_sambanova.py new file mode 100644 index 0000000000000..27b6f223f49d5 --- /dev/null +++ b/llama-index-integrations/llms/llama-index-llms-sambanova/tests/test_llms_sambanova.py @@ -0,0 +1,12 @@ +from llama_index.core.base.llms.base import BaseLLM +from llama_index.llms.sambanova import Sambaverse, SambaStudio + + +def test_embedding_class(): + # Check Sambaverse inherits from BaseLLM + names_of_base_classes = [b.__name__ for b in Sambaverse.__mro__] + assert BaseLLM.__name__ in names_of_base_classes + + # Check SambaStudio inherits from BaseLLM + names_of_base_classes = [b.__name__ for b in SambaStudio.__mro__] + assert BaseLLM.__name__ in names_of_base_classes diff --git a/llama-index-integrations/llms/llama-index-llms-solar/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-solar/pyproject.toml index 619bc2e8be107..6289987bb01ec 100644 --- a/llama-index-integrations/llms/llama-index-llms-solar/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-solar/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-solar" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" transformers = "^4.37.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-text-generation-inference/llama_index/llms/text_generation_inference/base.py b/llama-index-integrations/llms/llama-index-llms-text-generation-inference/llama_index/llms/text_generation_inference/base.py index 893b3677ef9cd..595b48f6cca25 100644 --- a/llama-index-integrations/llms/llama-index-llms-text-generation-inference/llama_index/llms/text_generation_inference/base.py +++ b/llama-index-integrations/llms/llama-index-llms-text-generation-inference/llama_index/llms/text_generation_inference/base.py @@ -106,7 +106,7 @@ class TextGenerationInference(FunctionCallingLLM): is_chat_model: bool = Field( default=True, description=( - LLMMetadata.__fields__["is_chat_model"].field_info.description + LLMMetadata.model_fields["is_chat_model"].description + " TGI makes use of chat templating," " function call is available only for '/v1/chat/completions' route" " of TGI endpoint" @@ -115,7 +115,7 @@ class TextGenerationInference(FunctionCallingLLM): is_function_calling_model: bool = Field( default=False, description=( - LLMMetadata.__fields__["is_function_calling_model"].field_info.description + LLMMetadata.model_fields["is_function_calling_model"].description + " 'text-generation-inference' supports function call" " starting from v1.4.3" ), @@ -149,19 +149,6 @@ def __init__( if token: headers.update({"Authorization": f"Bearer {token}"}) - self._sync_client = TGIClient( - base_url=model_url, - headers=headers, - cookies=cookies, - timeout=timeout, - ) - self._async_client = TGIAsyncClient( - base_url=model_url, - headers=headers, - cookies=cookies, - timeout=timeout, - ) - try: is_function_calling_model = resolve_tgi_function_call(model_url) except Exception as e: @@ -187,6 +174,18 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._sync_client = TGIClient( + base_url=model_url, + headers=headers, + cookies=cookies, + timeout=timeout, + ) + self._async_client = TGIAsyncClient( + base_url=model_url, + headers=headers, + cookies=cookies, + timeout=timeout, + ) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-text-generation-inference/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-text-generation-inference/pyproject.toml index 8f250c15895ff..13182cf6fe9c1 100644 --- a/llama-index-integrations/llms/llama-index-llms-text-generation-inference/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-text-generation-inference/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-text-generation-inference" readme = "README.md" -version = "0.1.3" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.57" text-generation = "^0.7.0" -llama-index-utils-huggingface = "^0.1.1" +llama-index-utils-huggingface = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-together/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-together/pyproject.toml index 7ff6890a8730f..60b5e43f5a557 100644 --- a/llama-index-integrations/llms/llama-index-llms-together/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-together/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-together" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-llms-openai-like = "^0.1.3" +llama-index-llms-openai-like = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-unify/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-unify/pyproject.toml index 34fd535ec8eeb..959fc27d65c71 100644 --- a/llama-index-integrations/llms/llama-index-llms-unify/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-unify/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-llms-unify" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-llms-openai-like = "^0.1.3" +llama-index-llms-openai-like = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-upstage/llama_index/llms/upstage/base.py b/llama-index-integrations/llms/llama-index-llms-upstage/llama_index/llms/upstage/base.py index 92f7db935a773..96d96ea74d96e 100644 --- a/llama-index-integrations/llms/llama-index-llms-upstage/llama_index/llms/upstage/base.py +++ b/llama-index-integrations/llms/llama-index-llms-upstage/llama_index/llms/upstage/base.py @@ -14,6 +14,7 @@ from llama_index.core.callbacks import CallbackManager from llama_index.core.constants import DEFAULT_TEMPERATURE from llama_index.core.types import BaseOutputParser, PydanticProgramMode +from llama_index.core.bridge.pydantic import ConfigDict from tokenizers import Tokenizer from pydantic import Field, PrivateAttr from openai import OpenAI as SyncOpenAI @@ -43,6 +44,7 @@ class Upstage(OpenAI): ``` """ + model_config = ConfigDict(arbitrary_types_allowed=True, populate_by_name=True) model: str = Field( default=DEFAULT_UPSTAGE_MODEL, description="The Upstage model to use." ) diff --git a/llama-index-integrations/llms/llama-index-llms-upstage/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-upstage/pyproject.toml index 01f70de7cf460..1ffd9fe07103e 100644 --- a/llama-index-integrations/llms/llama-index-llms-upstage/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-upstage/pyproject.toml @@ -30,14 +30,14 @@ license = "MIT" name = "llama-index-llms-upstage" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" openai = "^1.21.2" -llama-index-llms-openai = "^0.1.24" +llama-index-llms-openai = "^0.2.0" tokenizers = "^0.19.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py b/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py index 3761cda0548e1..1f34b0da1801b 100644 --- a/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py +++ b/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py @@ -54,7 +54,7 @@ class Vertex(FunctionCallingLLM): `pip install llama-index-llms-vertex` ```python - from llama_index.llms.openai import Vertex + from llama_index.llms.vertex import Vertex # Set up necessary variables credentials = { @@ -119,6 +119,22 @@ def __init__( additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) + super().__init__( + temperature=temperature, + max_tokens=max_tokens, + additional_kwargs=additional_kwargs, + max_retries=max_retries, + model=model, + examples=examples, + iscode=iscode, + callback_manager=callback_manager, + system_prompt=system_prompt, + messages_to_prompt=messages_to_prompt, + completion_to_prompt=completion_to_prompt, + pydantic_program_mode=pydantic_program_mode, + output_parser=output_parser, + ) + self._is_gemini = False self._is_chat_model = False if model in CHAT_MODELS: @@ -149,22 +165,6 @@ def __init__( else: raise (ValueError(f"Model {model} not found, please verify the model name")) - super().__init__( - temperature=temperature, - max_tokens=max_tokens, - additional_kwargs=additional_kwargs, - max_retries=max_retries, - model=model, - examples=examples, - iscode=iscode, - callback_manager=callback_manager, - system_prompt=system_prompt, - messages_to_prompt=messages_to_prompt, - completion_to_prompt=completion_to_prompt, - pydantic_program_mode=pydantic_program_mode, - output_parser=output_parser, - ) - @classmethod def class_name(cls) -> str: return "Vertex" diff --git a/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/gemini_utils.py b/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/gemini_utils.py index c09e67900d0b1..dbb0a9e962fd5 100644 --- a/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/gemini_utils.py +++ b/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/gemini_utils.py @@ -1,7 +1,7 @@ import base64 from typing import Any, Dict, Union, Optional from vertexai.generative_models._generative_models import SafetySettingsType - +from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types from llama_index.core.llms import ChatMessage, MessageRole @@ -46,14 +46,22 @@ def _convert_gemini_part_to_prompt(part: Union[str, Dict]) -> Part: raise ValueError("Only text and image_url types are supported!") return Part.from_image(image) - raw_content = message.content + if message.content == "" and "tool_calls" in message.additional_kwargs: + tool_calls = message.additional_kwargs["tool_calls"] + parts = [ + Part._from_gapic(raw_part=gapic_content_types.Part(function_call=tool_call)) + for tool_call in tool_calls + ] + else: + raw_content = message.content + + if raw_content is None: + raw_content = "" + if isinstance(raw_content, str): + raw_content = [raw_content] - if raw_content is None: - raw_content = "" - if isinstance(raw_content, str): - raw_content = [raw_content] + parts = [_convert_gemini_part_to_prompt(part) for part in raw_content] - parts = [_convert_gemini_part_to_prompt(part) for part in raw_content] if is_history: return Content( role="user" if message.role == MessageRole.USER else "model", diff --git a/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml index f06a4ffe67364..dbcbdee2c87a4 100644 --- a/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-vertex" readme = "README.md" -version = "0.2.2" +version = "0.3.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.57" google-cloud-aiplatform = "^1.39.0" pyarrow = "^15.0.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/base.py b/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/base.py index f4a8ae9709b8c..cc3b6c0c74bc2 100644 --- a/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/base.py +++ b/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/base.py @@ -177,24 +177,6 @@ def __init__( pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT, output_parser: Optional[BaseOutputParser] = None, ) -> None: - if not api_url: - try: - from vllm import LLM as VLLModel - except ImportError: - raise ImportError( - "Could not import vllm python package. " - "Please install it with `pip install vllm`." - ) - self._client = VLLModel( - model=model, - tensor_parallel_size=tensor_parallel_size, - trust_remote_code=trust_remote_code, - dtype=dtype, - download_dir=download_dir, - **vllm_kwargs - ) - else: - self._client = None callback_manager = callback_manager or CallbackManager([]) super().__init__( model=model, @@ -221,6 +203,24 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + if not api_url: + try: + from vllm import LLM as VLLModel + except ImportError: + raise ImportError( + "Could not import vllm python package. " + "Please install it with `pip install vllm`." + ) + self._client = VLLModel( + model=model, + tensor_parallel_size=tensor_parallel_size, + trust_remote_code=trust_remote_code, + dtype=dtype, + download_dir=download_dir, + **vllm_kwargs + ) + else: + self._client = None @classmethod def class_name(cls) -> str: @@ -386,7 +386,6 @@ def __init__( callback_manager: Optional[CallbackManager] = None, output_parser: Optional[BaseOutputParser] = None, ) -> None: - self._client = None messages_to_prompt = messages_to_prompt or generic_messages_to_prompt completion_to_prompt = completion_to_prompt or (lambda x: x) callback_manager = callback_manager or CallbackManager([]) @@ -414,6 +413,7 @@ def __init__( callback_manager=callback_manager, output_parser=output_parser, ) + self._client = None @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/llms/llama-index-llms-vllm/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-vllm/pyproject.toml index 60316b2464a61..f4414a974f752 100644 --- a/llama-index-integrations/llms/llama-index-llms-vllm/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-vllm/pyproject.toml @@ -28,11 +28,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-vllm" readme = "README.md" -version = "0.1.9" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-xinference/llama_index/llms/xinference/base.py b/llama-index-integrations/llms/llama-index-llms-xinference/llama_index/llms/xinference/base.py index 0d2d4f9712848..ea7b066c3f457 100644 --- a/llama-index-integrations/llms/llama-index-llms-xinference/llama_index/llms/xinference/base.py +++ b/llama-index-integrations/llms/llama-index-llms-xinference/llama_index/llms/xinference/base.py @@ -86,7 +86,6 @@ def __init__( generator, context_window, model_description = self.load_model( model_uid, endpoint ) - self._generator = generator if max_tokens is None: max_tokens = context_window // 4 elif max_tokens > context_window: @@ -109,6 +108,7 @@ def __init__( pydantic_program_mode=pydantic_program_mode, output_parser=output_parser, ) + self._generator = generator def load_model(self, model_uid: str, endpoint: str) -> Tuple[Any, int, dict]: try: diff --git a/llama-index-integrations/llms/llama-index-llms-xinference/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-xinference/pyproject.toml index 01c008fc94ccb..5cf001c33d1ee 100644 --- a/llama-index-integrations/llms/llama-index-llms-xinference/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-xinference/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-xinference" readme = "README.md" -version = "0.1.2" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/llms/llama-index-llms-yi/llama_index/llms/yi/base.py b/llama-index-integrations/llms/llama-index-llms-yi/llama_index/llms/yi/base.py index 920c7f3f89b0a..d25d5bf0e70a3 100644 --- a/llama-index-integrations/llms/llama-index-llms-yi/llama_index/llms/yi/base.py +++ b/llama-index-integrations/llms/llama-index-llms-yi/llama_index/llms/yi/base.py @@ -59,17 +59,15 @@ class Yi(OpenAI): model: str = Field(default=DEFAULT_YI_MODEL, description="The Yi model to use.") context_window: int = Field( default=yi_modelname_to_context_size(DEFAULT_YI_MODEL), - description=LLMMetadata.__fields__["context_window"].field_info.description, + description=LLMMetadata.model_fields["context_window"].description, ) is_chat_model: bool = Field( default=True, - description=LLMMetadata.__fields__["is_chat_model"].field_info.description, + description=LLMMetadata.model_fields["is_chat_model"].description, ) is_function_calling_model: bool = Field( default=False, - description=LLMMetadata.__fields__[ - "is_function_calling_model" - ].field_info.description, + description=LLMMetadata.model_fields["is_function_calling_model"].description, ) tokenizer: Union[Tokenizer, str, None] = Field( default=None, diff --git a/llama-index-integrations/llms/llama-index-llms-yi/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-yi/pyproject.toml index 8711f70573d29..145842bdf2378 100644 --- a/llama-index-integrations/llms/llama-index-llms-yi/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-yi/pyproject.toml @@ -30,13 +30,13 @@ license = "MIT" name = "llama-index-llms-yi" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-llms-openai = "^0.1.23" +llama-index-llms-openai = "^0.2.0" transformers = "^4.41.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/llms/llama-index-llms-you/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-you/pyproject.toml index d1ace4870e946..c7215dded74cc 100644 --- a/llama-index-integrations/llms/llama-index-llms-you/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-you/pyproject.toml @@ -31,12 +31,12 @@ license = "MIT" # TODO: Update license name = "llama-index-llms-you" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" sseclient-py = "^1.8.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/llama_index/multi_modal_llms/anthropic/base.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/llama_index/multi_modal_llms/anthropic/base.py index d9a3168c3dc9c..69924c3d539c0 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/llama_index/multi_modal_llms/anthropic/base.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/llama_index/multi_modal_llms/anthropic/base.py @@ -62,7 +62,7 @@ class AnthropicMultiModal(MultiModalLLM): additional_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Additional kwargs for the Anthropic API." ) - default_headers: Dict[str, str] = Field( + default_headers: Optional[Dict[str, str]] = Field( default=None, description="The default headers for API requests." ) @@ -92,8 +92,6 @@ def __init__( system_prompt: Optional[str] = "", **kwargs: Any, ) -> None: - self._messages_to_prompt = messages_to_prompt or generic_messages_to_prompt - self._completion_to_prompt = completion_to_prompt or (lambda x: x) api_key, api_base, api_version = resolve_anthropic_credentials( api_key=api_key, api_base=api_base, @@ -116,6 +114,8 @@ def __init__( system_promt=system_prompt, **kwargs, ) + self._messages_to_prompt = messages_to_prompt or generic_messages_to_prompt + self._completion_to_prompt = completion_to_prompt or (lambda x: x) self._http_client = http_client self._client, self._aclient = self._get_clients(**kwargs) diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/llama_index/multi_modal_llms/anthropic/utils.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/llama_index/multi_modal_llms/anthropic/utils.py index 40aefd749e633..9cd92f6927ab7 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/llama_index/multi_modal_llms/anthropic/utils.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/llama_index/multi_modal_llms/anthropic/utils.py @@ -1,6 +1,7 @@ import base64 import logging from typing import Any, Dict, List, Optional, Sequence, Tuple +import filetype import httpx from llama_index.core.base.llms.generic_utils import get_from_param_or_env @@ -29,7 +30,18 @@ logger = logging.getLogger(__name__) -def infer_image_mimetype(image_file_path: str) -> str: +def infer_image_mimetype_from_base64(base64_string) -> str: + # Decode the base64 string + decoded_data = base64.b64decode(base64_string) + + # Use filetype to guess the MIME type + kind = filetype.guess(decoded_data) + + # Return the MIME type if detected, otherwise return None + return kind.mime if kind is not None else None + + +def infer_image_mimetype_from_file_path(image_file_path: str) -> str: # Get the file extension file_extension = image_file_path.split(".")[-1].lower() @@ -64,7 +76,7 @@ def generate_anthropic_multi_modal_chat_message( for image_document in image_documents: image_content: Dict[str, Any] = {} if image_document.image_path and image_document.image_path != "": - mimetype = infer_image_mimetype(image_document.image_path) + mimetype = infer_image_mimetype_from_file_path(image_document.image_path) base64_image = encode_image(image_document.image_path) image_content = { "type": "image", @@ -78,7 +90,9 @@ def generate_anthropic_multi_modal_chat_message( "file_path" in image_document.metadata and image_document.metadata["file_path"] != "" ): - mimetype = infer_image_mimetype(image_document.metadata["file_path"]) + mimetype = infer_image_mimetype_from_file_path( + image_document.metadata["file_path"] + ) base64_image = encode_image(image_document.metadata["file_path"]) image_content = { "type": "image", @@ -89,7 +103,7 @@ def generate_anthropic_multi_modal_chat_message( }, } elif image_document.image_url and image_document.image_url != "": - mimetype = infer_image_mimetype(image_document.image_url) + mimetype = infer_image_mimetype_from_file_path(image_document.image_url) image_content = { "type": "image", "source": { @@ -100,6 +114,17 @@ def generate_anthropic_multi_modal_chat_message( ).decode("utf-8"), }, } + elif image_document.image != "": + base64_image = image_document.image + mimetype = infer_image_mimetype_from_base64(base64_image) + image_content = { + "type": "image", + "source": { + "type": "base64", + "media_type": mimetype, + "data": base64_image, + }, + } completion_content.append(image_content) completion_content.append({"type": "text", "text": prompt}) diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/pyproject.toml index da57c94459a47..3d8f3c95b9ec3 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/pyproject.toml +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/pyproject.toml @@ -27,12 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-multi-modal-llms-anthropic" readme = "README.md" -version = "0.1.6" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" anthropic = ">=0.26.2, <0.29.0" +llama-index-core = "^0.11.0" +filetype = "^1.2.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/tests/test_multi-modal-llms_anthropic.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/tests/test_multi-modal-llms_anthropic.py index 0d401ff2ffb83..6645471d7e52f 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/tests/test_multi-modal-llms_anthropic.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-anthropic/tests/test_multi-modal-llms_anthropic.py @@ -5,3 +5,8 @@ def test_embedding_class(): names_of_base_classes = [b.__name__ for b in AnthropicMultiModal.__mro__] assert MultiModalLLM.__name__ in names_of_base_classes + + +def test_init(): + m = AnthropicMultiModal(max_tokens=400) + assert m.max_tokens == 400 diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-azure-openai/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-azure-openai/pyproject.toml index 4a8a035f6bb19..d4d658d8fe5b5 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-azure-openai/pyproject.toml +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-azure-openai/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-multi-modal-llms-azure-openai" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-azure-openai = "^0.1.1" -llama-index-multi-modal-llms-openai = "^0.1.1" +llama-index-llms-azure-openai = "^0.2.0" +llama-index-multi-modal-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-azure-openai/tests/test_multi-modal-llms_azure_openai.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-azure-openai/tests/test_multi-modal-llms_azure_openai.py index bce54dfc8319b..81071c93f0f76 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-azure-openai/tests/test_multi-modal-llms_azure_openai.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-azure-openai/tests/test_multi-modal-llms_azure_openai.py @@ -5,3 +5,8 @@ def test_embedding_class(): names_of_base_classes = [b.__name__ for b in AzureOpenAIMultiModal.__mro__] assert MultiModalLLM.__name__ in names_of_base_classes + + +def test_init(): + m = AzureOpenAIMultiModal(max_new_tokens=400, engine="fake", api_key="fake") + assert m.max_new_tokens == 400 diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/llama_index/multi_modal_llms/dashscope/base.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/llama_index/multi_modal_llms/dashscope/base.py index 3fc386e817698..a020f64c1208e 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/llama_index/multi_modal_llms/dashscope/base.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/llama_index/multi_modal_llms/dashscope/base.py @@ -87,7 +87,7 @@ class DashScopeMultiModal(MultiModalLLM): seed: Optional[int] = Field( description="Random seed when generate.", default=1234, gte=0 ) - api_key: str = Field( + api_key: Optional[str] = Field( default=None, description="The DashScope API key.", exclude=True ) diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/pyproject.toml index ee2f2331a09ef..5cd06596e9601 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/pyproject.toml +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-multi-modal-llms-dashscope" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" dashscope = "^1.14.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/tests/test_multi_modal_llms_dashscope.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/tests/test_multi_modal_llms_dashscope.py index c31c4173d72b3..a95827b256683 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/tests/test_multi_modal_llms_dashscope.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-dashscope/tests/test_multi_modal_llms_dashscope.py @@ -5,3 +5,8 @@ def test_class(): names_of_base_classes = [b.__name__ for b in DashScopeMultiModal.__mro__] assert MultiModalLLM.__name__ in names_of_base_classes + + +def test_init(): + m = DashScopeMultiModal(top_k=2) + assert m.top_k == 2 diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-gemini/llama_index/multi_modal_llms/gemini/base.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-gemini/llama_index/multi_modal_llms/gemini/base.py index 4f166fa1d7856..c1f5966dc1bfb 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-gemini/llama_index/multi_modal_llms/gemini/base.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-gemini/llama_index/multi_modal_llms/gemini/base.py @@ -1,6 +1,7 @@ """Google's Gemini multi-modal models.""" + import os -from typing import Any, Dict, Optional, Sequence +from typing import Any, Dict, Optional, Sequence, Tuple import google.generativeai as genai import PIL @@ -77,6 +78,7 @@ def __init__( generation_config: Optional["genai.types.GenerationConfigDict"] = None, safety_settings: "genai.types.SafetySettingOptions" = None, api_base: Optional[str] = None, + default_headers: Optional[Dict[str, str]] = None, transport: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, **generate_kwargs: Any, @@ -89,6 +91,13 @@ def __init__( } if api_base: config_params["client_options"] = {"api_endpoint": api_base} + if default_headers: + default_metadata: Sequence[Tuple[str, str]] = [] + for key, value in default_headers.items(): + default_metadata.append((key, value)) + # `default_metadata` contains (key, value) pairs that will be sent with every request. + # These will be sent as HTTP headers When using `transport="rest"`. + config_params["default_metadata"] = default_metadata if transport: config_params["transport"] = transport # transport: A string, one of: [`rest`, `grpc`, `grpc_asyncio`]. @@ -105,15 +114,15 @@ def __init__( f"Available models are: {GEMINI_MM_MODELS}" ) - self._model = genai.GenerativeModel( + model = genai.GenerativeModel( model_name=model_name, generation_config=final_gen_config, safety_settings=safety_settings, ) - self._model_meta = genai.get_model(model_name) + model_meta = genai.get_model(model_name) - supported_methods = self._model_meta.supported_generation_methods + supported_methods = model_meta.supported_generation_methods if "generateContent" not in supported_methods: raise ValueError( f"Model {model_name} does not support content generation, only " @@ -121,9 +130,9 @@ def __init__( ) if not max_tokens: - max_tokens = self._model_meta.output_token_limit + max_tokens = model_meta.output_token_limit else: - max_tokens = min(max_tokens, self._model_meta.output_token_limit) + max_tokens = min(max_tokens, model_meta.output_token_limit) super().__init__( model_name=model_name, @@ -132,6 +141,8 @@ def __init__( generate_kwargs=generate_kwargs, callback_manager=callback_manager, ) + self._model = model + self._model_meta = model_meta @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-gemini/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-gemini/pyproject.toml index 3f4b144816eaf..3dbb459abe644 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-gemini/pyproject.toml +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-gemini/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-multi-modal-llms-gemini" readme = "README.md" -version = "0.1.7" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-llms-gemini = "^0.1.9" +llama-index-llms-gemini = "^0.3.0" pillow = "^10.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/pyproject.toml index 65a021d3cc80b..93a5f36bd2b25 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/pyproject.toml +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-multi-modal-llms-ollama" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -ollama = "^0.1.6" +ollama = ">=0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/llama_index/multi_modal_llms/openai/base.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/llama_index/multi_modal_llms/openai/base.py index 9c94bc730348a..0b2a7d539c24c 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/llama_index/multi_modal_llms/openai/base.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/llama_index/multi_modal_llms/openai/base.py @@ -74,7 +74,7 @@ class OpenAIMultiModal(MultiModalLLM): additional_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Additional kwargs for the OpenAI API." ) - default_headers: Dict[str, str] = Field( + default_headers: Optional[Dict[str, str]] = Field( default=None, description="The default headers for API requests." ) @@ -104,8 +104,6 @@ def __init__( http_client: Optional[httpx.Client] = None, **kwargs: Any, ) -> None: - self._messages_to_prompt = messages_to_prompt or generic_messages_to_prompt - self._completion_to_prompt = completion_to_prompt or (lambda x: x) api_key, api_base, api_version = resolve_openai_credentials( api_key=api_key, api_base=api_base, @@ -128,6 +126,8 @@ def __init__( default_headers=default_headers, **kwargs, ) + self._messages_to_prompt = messages_to_prompt or generic_messages_to_prompt + self._completion_to_prompt = completion_to_prompt or (lambda x: x) self._http_client = http_client self._client, self._aclient = self._get_clients(**kwargs) diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/llama_index/multi_modal_llms/openai/utils.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/llama_index/multi_modal_llms/openai/utils.py index 330327ea79fda..af222918b04a1 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/llama_index/multi_modal_llms/openai/utils.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/llama_index/multi_modal_llms/openai/utils.py @@ -15,6 +15,7 @@ "gpt-4-turbo": 128000, "gpt-4o": 128000, "gpt-4o-2024-05-13": 128000, + "gpt-4o-2024-08-06": 128000, "gpt-4o-mini": 128000, "gpt-4o-mini-2024-07-18": 128000, } diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/pyproject.toml index d90adf0dc8ea9..f57977bfc356c 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/pyproject.toml +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-openai/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-multi-modal-llms-openai" readme = "README.md" -version = "0.1.8" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-replicate/llama_index/multi_modal_llms/replicate/base.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-replicate/llama_index/multi_modal_llms/replicate/base.py index 734c9a3e9fc9e..eab3c7b589549 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-replicate/llama_index/multi_modal_llms/replicate/base.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-replicate/llama_index/multi_modal_llms/replicate/base.py @@ -76,9 +76,6 @@ def __init__( completion_to_prompt: Optional[Callable] = None, callback_manager: Optional[CallbackManager] = None, ) -> None: - self._messages_to_prompt = messages_to_prompt or generic_messages_to_prompt - self._completion_to_prompt = completion_to_prompt or (lambda x: x) - super().__init__( model=model, temperature=temperature, @@ -93,6 +90,8 @@ def __init__( image_key=image_key, callback_manager=callback_manager, ) + self._messages_to_rompt = messages_to_prompt or generic_messages_to_prompt + self._completion_to_prompt = completion_to_prompt or (lambda x: x) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-replicate/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-replicate/pyproject.toml index 204b373bdfe1f..47e493a41bb10 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-replicate/pyproject.toml +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-replicate/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-multi-modal-llms-replicate" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/node_parser/llama-index-node-parser-relational-dashscope/pyproject.toml b/llama-index-integrations/node_parser/llama-index-node-parser-relational-dashscope/pyproject.toml index 1a76dfcbae93a..24778036e643f 100644 --- a/llama-index-integrations/node_parser/llama-index-node-parser-relational-dashscope/pyproject.toml +++ b/llama-index-integrations/node_parser/llama-index-node-parser-relational-dashscope/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-node-parser-dashscope" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.2" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" requests = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/output_parsers/llama-index-output-parsers-guardrails/pyproject.toml b/llama-index-integrations/output_parsers/llama-index-output-parsers-guardrails/pyproject.toml index ed883e78a22ae..0047a30a2c6aa 100644 --- a/llama-index-integrations/output_parsers/llama-index-output-parsers-guardrails/pyproject.toml +++ b/llama-index-integrations/output_parsers/llama-index-output-parsers-guardrails/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-output-parsers-guardrails" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" guardrails-ai = "^0.4.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/output_parsers/llama-index-output-parsers-langchain/pyproject.toml b/llama-index-integrations/output_parsers/llama-index-output-parsers-langchain/pyproject.toml index 3af56f6cc4fac..899629c3485db 100644 --- a/llama-index-integrations/output_parsers/llama-index-output-parsers-langchain/pyproject.toml +++ b/llama-index-integrations/output_parsers/llama-index-output-parsers-langchain/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-output-parsers-langchain" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-cohere-rerank/llama_index/postprocessor/cohere_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-cohere-rerank/llama_index/postprocessor/cohere_rerank/base.py index 42204b5ebe064..2bf01a00ded9c 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-cohere-rerank/llama_index/postprocessor/cohere_rerank/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-cohere-rerank/llama_index/postprocessor/cohere_rerank/base.py @@ -26,6 +26,7 @@ def __init__( model: str = "rerank-english-v2.0", api_key: Optional[str] = None, ): + super().__init__(top_n=top_n, model=model) try: api_key = api_key or os.environ["COHERE_API_KEY"] except IndexError: @@ -41,7 +42,6 @@ def __init__( ) self._client = Client(api_key=api_key) - super().__init__(top_n=top_n, model=model) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-cohere-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-cohere-rerank/pyproject.toml index 7ea29b2b58d2a..66ee4d94558ff 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-cohere-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-cohere-rerank/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-postprocessor-cohere-rerank" readme = "README.md" -version = "0.1.8" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" cohere = "^5.1.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/llama_index/postprocessor/colbert_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/llama_index/postprocessor/colbert_rerank/base.py index b9c6daa14196a..99de4a8517ff1 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/llama_index/postprocessor/colbert_rerank/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/llama_index/postprocessor/colbert_rerank/base.py @@ -42,15 +42,14 @@ def __init__( keep_retrieval_score: Optional[bool] = False, ): device = infer_torch_device() if device is None else device - self._tokenizer = AutoTokenizer.from_pretrained(tokenizer) - self._model = AutoModel.from_pretrained(model) super().__init__( top_n=top_n, - model=model, - tokenizer=tokenizer, device=device, keep_retrieval_score=keep_retrieval_score, + model=model, ) + self._tokenizer = AutoTokenizer.from_pretrained(tokenizer) + self._model = AutoModel.from_pretrained(model) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/pyproject.toml index 69c3c4364fc10..5b4b9f65a329c 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/pyproject.toml @@ -31,13 +31,13 @@ license = "MIT" name = "llama-index-postprocessor-colbert-rerank" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.4" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" torch = "^2.2.0" transformers = "^4.37.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/tests/BUILD b/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/tests/test_postprocessor_colbert_rerank.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/tests/test_postprocessor_colbert_rerank.py new file mode 100644 index 0000000000000..37a40a17289c3 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-colbert-rerank/tests/test_postprocessor_colbert_rerank.py @@ -0,0 +1,14 @@ +from llama_index.core.postprocessor.types import BaseNodePostprocessor +from llama_index.postprocessor.colbert_rerank import ColbertRerank + + +def test_class(): + names_of_base_classes = [b.__name__ for b in ColbertRerank.__mro__] + assert BaseNodePostprocessor.__name__ in names_of_base_classes + + +def test_init(): + m = ColbertRerank(top_n=10) + + assert m.model == "colbert-ir/colbertv2.0" + assert m.top_n == 10 diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-dashscope-rerank/llama_index/postprocessor/dashscope_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-dashscope-rerank/llama_index/postprocessor/dashscope_rerank/base.py index 1e1aae7b0e3fc..58646d4143662 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-dashscope-rerank/llama_index/postprocessor/dashscope_rerank/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-dashscope-rerank/llama_index/postprocessor/dashscope_rerank/base.py @@ -77,7 +77,7 @@ def _postprocess_nodes( for node in nodes ] results = dashscope.TextReRank.call( - model_name=self.model, + model=self.model, top_n=self.top_n, query=query_bundle.query_str, documents=texts, diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-dashscope-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-dashscope-rerank/pyproject.toml index 05fbe83bf43f4..dc8e751fad945 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-dashscope-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-dashscope-rerank/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-postprocessor-dashscope-rerank" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" dashscope = ">=1.17.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-flag-embedding-reranker/llama_index/postprocessor/flag_embedding_reranker/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-flag-embedding-reranker/llama_index/postprocessor/flag_embedding_reranker/base.py index fe0d653a1b084..faa7bfe93120e 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-flag-embedding-reranker/llama_index/postprocessor/flag_embedding_reranker/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-flag-embedding-reranker/llama_index/postprocessor/flag_embedding_reranker/base.py @@ -27,6 +27,7 @@ def __init__( model: str = "BAAI/bge-reranker-large", use_fp16: bool = False, ) -> None: + super().__init__(top_n=top_n, model=model, use_fp16=use_fp16) try: from FlagEmbedding import FlagReranker except ImportError: @@ -38,7 +39,6 @@ def __init__( model, use_fp16=use_fp16, ) - super().__init__(top_n=top_n, model=model, use_fp16=use_fp16) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-flag-embedding-reranker/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-flag-embedding-reranker/pyproject.toml index 2d11b562e649a..02fa4c98cdff7 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-flag-embedding-reranker/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-flag-embedding-reranker/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-postprocessor-flag-embedding-reranker" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-jinaai-rerank/llama_index/postprocessor/jinaai_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-jinaai-rerank/llama_index/postprocessor/jinaai_rerank/base.py index 78f95f5980019..8741b1e8d91f1 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-jinaai-rerank/llama_index/postprocessor/jinaai_rerank/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-jinaai-rerank/llama_index/postprocessor/jinaai_rerank/base.py @@ -12,12 +12,16 @@ from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle -API_URL = "https://api.jina.ai/v1/rerank" +DEFAULT_JINA_AI_API_URL = "https://api.jina.ai/v1" dispatcher = get_dispatcher(__name__) class JinaRerank(BaseNodePostprocessor): + api_url: str = Field( + default=f"{DEFAULT_JINA_AI_API_URL}/rerank", + description="The URL of the JinaAI Rerank API.", + ) api_key: str = Field(default=None, description="The JinaAI API key.") model: str = Field( default="jina-reranker-v1-base-en", @@ -32,9 +36,11 @@ def __init__( self, top_n: int = 2, model: str = "jina-reranker-v1-base-en", + base_url: str = DEFAULT_JINA_AI_API_URL, api_key: Optional[str] = None, ): super().__init__(top_n=top_n, model=model) + self.api_url = f"{base_url}/rerank" self.api_key = get_from_param_or_env("api_key", api_key, "JINAAI_API_KEY", "") self.model = model self._session = requests.Session() @@ -79,7 +85,7 @@ def _postprocess_nodes( for node in nodes ] resp = self._session.post( # type: ignore - API_URL, + self.api_url, json={ "query": query_bundle.query_str, "documents": texts, diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-jinaai-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-jinaai-rerank/pyproject.toml index 5021162782c2a..a197db3f066ff 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-jinaai-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-jinaai-rerank/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-postprocessor-jinaai-rerank" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-longllmlingua/llama_index/postprocessor/longllmlingua/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-longllmlingua/llama_index/postprocessor/longllmlingua/base.py index 48ce3e3e010a5..ead377a9d6187 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-longllmlingua/llama_index/postprocessor/longllmlingua/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-longllmlingua/llama_index/postprocessor/longllmlingua/base.py @@ -1,4 +1,5 @@ """Optimization related classes and functions.""" + import logging from typing import Any, Dict, List, Optional @@ -50,6 +51,14 @@ def __init__( """LongLLMLingua Compressor for Node Context.""" from llmlingua import PromptCompressor + super().__init__( + metadata_mode=metadata_mode, + instruction_str=instruction_str, + target_token=target_token, + rank_method=rank_method, + additional_compress_kwargs=additional_compress_kwargs, + ) + open_api_config = open_api_config or {} additional_compress_kwargs = additional_compress_kwargs or {} @@ -59,13 +68,6 @@ def __init__( model_config=model_config, open_api_config=open_api_config, ) - super().__init__( - metadata_mode=metadata_mode, - instruction_str=instruction_str, - target_token=target_token, - rank_method=rank_method, - additional_compress_kwargs=additional_compress_kwargs, - ) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-longllmlingua/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-longllmlingua/pyproject.toml index 445ac4458258d..734dee2f3910b 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-longllmlingua/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-longllmlingua/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-postprocessor-longllmlingua" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-mixedbreadai-rerank/llama_index/postprocessor/mixedbreadai_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-mixedbreadai-rerank/llama_index/postprocessor/mixedbreadai_rerank/base.py index af21e9b7d42e4..5813e5309d149 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-mixedbreadai-rerank/llama_index/postprocessor/mixedbreadai_rerank/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-mixedbreadai-rerank/llama_index/postprocessor/mixedbreadai_rerank/base.py @@ -52,6 +52,7 @@ def __init__( httpx_client: Optional[httpx.Client] = None, httpx_async_client: Optional[httpx.AsyncClient] = None, ): + super().__init__(top_n=top_n, model=model) try: api_key = api_key or os.environ["MXBAI_API_KEY"] except KeyError: @@ -70,8 +71,6 @@ def __init__( RequestOptions(max_retries=max_retries) if max_retries is not None else None ) - super().__init__(top_n=top_n, model=model) - @classmethod def class_name(cls) -> str: return "MixedbreadAIRerank" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-mixedbreadai-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-mixedbreadai-rerank/pyproject.toml index a8212e55c9633..b0cd43cceba34 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-mixedbreadai-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-mixedbreadai-rerank/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-postprocessor-mixedbreadai-rerank" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" mixedbread-ai = "^2.2.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/README.md b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/README.md index 961f2eceb9a4e..4e16b020eea44 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/README.md +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/README.md @@ -20,7 +20,7 @@ Below is an example on how to use some common functionality surrounding text-gen ## Installation ```shell -pip install --upgrade llama-index llama-index-core llama-index-nvidia-rerank +pip install --upgrade llama-index llama-index-core llama-index-postprocessor-nvidia-rerank ``` ## Setup @@ -68,12 +68,12 @@ rerank = NVIDIARerank(base_url="http://localhost:1976/v1") ## Supported models -Querying `get_available_models` will still give you all of the other models offered by your API credentials. +Querying `available_models` will still give you all of the other models offered by your API credentials. ```python from llama_index.postprocessor.nvidia_rerank import NVIDIARerank -NVIDIARerank.get_available_models() +rerank.available_models ``` **To find out more about a specific model, please navigate to the NVIDIA NIM section of ai.nvidia.com [as linked here](https://docs.api.nvidia.com/nim/).** diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/llama_index/postprocessor/nvidia_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/llama_index/postprocessor/nvidia_rerank/base.py index 0c36227d1dae1..5d8ff3ecf06b9 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/llama_index/postprocessor/nvidia_rerank/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/llama_index/postprocessor/nvidia_rerank/base.py @@ -1,7 +1,8 @@ -from typing import Any, List, Optional, Literal, Generator +from typing import Any, List, Optional, Generator, Literal -from urllib.parse import urlparse, urlunparse -from llama_index.core.bridge.pydantic import Field, PrivateAttr, BaseModel + +from urllib.parse import urlparse, urljoin +from llama_index.core.bridge.pydantic import Field, PrivateAttr, BaseModel, ConfigDict from llama_index.core.callbacks import CBEventType, EventPayload from llama_index.core.instrumentation import get_dispatcher from llama_index.core.instrumentation.events.rerank import ( @@ -12,35 +13,29 @@ from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle import requests import warnings -from deprecated import deprecated from llama_index.core.base.llms.generic_utils import get_from_param_or_env -DEFAULT_MODEL = "nv-rerank-qa-mistral-4b:1" -BASE_URL = "https://ai.api.nvidia.com/v1" +DEFAULT_MODEL = "nvidia/nv-rerankqa-mistral-4b-v3" MODEL_ENDPOINT_MAP = { - DEFAULT_MODEL: BASE_URL, "nvidia/nv-rerankqa-mistral-4b-v3": "https://ai.api.nvidia.com/v1/retrieval/nvidia/nv-rerankqa-mistral-4b-v3/reranking", + "nv-rerank-qa-mistral-4b:1": "https://ai.api.nvidia.com/v1/retrieval/nvidia/reranking", } -KNOWN_URLS = list(MODEL_ENDPOINT_MAP.values()) - dispatcher = get_dispatcher(__name__) class Model(BaseModel): id: str + base_model: Optional[str] = None class NVIDIARerank(BaseNodePostprocessor): """NVIDIA's API Catalog Reranker Connector.""" - class Config: - validate_assignment = True - + model_config = ConfigDict(validate_assignment=True) model: Optional[str] = Field( - default=DEFAULT_MODEL, description="The NVIDIA API Catalog reranker to use.", ) top_n: Optional[int] = Field( @@ -53,10 +48,19 @@ class Config: ge=1, description="The maximum batch size supported by the inference server.", ) + truncate: Optional[Literal["NONE", "END"]] = Field( + description=( + "Truncate input text if it exceeds the model's maximum token length. " + "Default is model dependent and is likely to raise error if an " + "input is too long." + ), + default=None, + ) _api_key: str = PrivateAttr("NO_API_KEY_PROVIDED") # TODO: should be SecretStr _mode: str = PrivateAttr("nvidia") _is_hosted: bool = PrivateAttr(True) - _base_url: str = PrivateAttr(BASE_URL) + _base_url: str = PrivateAttr(MODEL_ENDPOINT_MAP.get(DEFAULT_MODEL)) + _inference_url: Optional[str] = PrivateAttr(None) def _set_api_key(self, nvidia_api_key: str = None, api_key: str = None) -> None: self._api_key = get_from_param_or_env( @@ -68,7 +72,7 @@ def _set_api_key(self, nvidia_api_key: str = None, api_key: str = None) -> None: def __init__( self, - model: str = DEFAULT_MODEL, + model: Optional[str] = None, nvidia_api_key: Optional[str] = None, api_key: Optional[str] = None, base_url: Optional[str] = None, @@ -84,17 +88,23 @@ def __init__( nvidia_api_key (str, optional): The NVIDIA API key. Defaults to None. api_key (str, optional): The API key. Defaults to None. base_url (str, optional): The base URL of the on-premises NIM. Defaults to None. + truncate (str): "NONE", "END", truncate input text if it exceeds + the model's context length. Default is model dependent and + is likely to raise an error if an input is too long. **kwargs: Additional keyword arguments. API Key: - The recommended way to provide the API key is through the `NVIDIA_API_KEY` environment variable. """ + if not base_url or (base_url in MODEL_ENDPOINT_MAP.values() and not model): + model = model or DEFAULT_MODEL super().__init__(model=model, **kwargs) - if base_url is None or base_url in MODEL_ENDPOINT_MAP.values(): - base_url = MODEL_ENDPOINT_MAP.get(model, BASE_URL) - else: - base_url = self._validate_url(base_url) + base_url = base_url or MODEL_ENDPOINT_MAP.get(DEFAULT_MODEL) + self._is_hosted = base_url in MODEL_ENDPOINT_MAP.values() + + if not self._is_hosted and base_url: + self._base_url = base_url.rstrip("/") + "/" self._api_key = get_from_param_or_env( "api_key", @@ -103,12 +113,85 @@ def __init__( "NO_API_KEY_PROVIDED", ) - self._is_hosted = self._base_url in KNOWN_URLS + if not self._is_hosted: # on-premises mode + # in this case we trust the model name and base_url + self._inference_url = self._validate_url(base_url) + "/ranking" + else: # hosted mode + if not model: + model = MODEL_ENDPOINT_MAP.get(base_url) + if model not in MODEL_ENDPOINT_MAP: + raise ValueError( + f"Model '{model}' not found. " + f"Available models are: {', '.join(MODEL_ENDPOINT_MAP.keys())}" + ) + if self._api_key == "NO_API_KEY_PROVIDED": + raise ValueError("An API key is required for hosted NIM.") + self._inference_url = MODEL_ENDPOINT_MAP[model] + + if not model: + self.__set_default_model() - if self._is_hosted and self._api_key == "NO_API_KEY_PROVIDED": - warnings.warn( - "An API key is required for hosted NIM. This will become an error in 0.2.0." + def __set_default_model(self): + """Set default model.""" + if not self._is_hosted: + valid_models = [ + model.id + for model in self.available_models + if not model.base_model or model.base_model == model.id + ] + self.model = next(iter(valid_models), None) + if self.model: + warnings.warn( + f"Default model is set as: {self.model}. \n" + "Set model using model parameter. \n" + "To get available models use available_models property.", + UserWarning, + ) + else: + raise ValueError("No locally hosted model was found.") + else: + self.model = DEFAULT_MODEL + + def _get_models(self) -> List[Model]: + session = requests.Session() + + if self._is_hosted: + _headers = { + "Authorization": f"Bearer {self._api_key}", + "Accept": "application/json", + } + else: + _headers = { + "Accept": "application/json", + } + url = ( + "https://integrate.api.nvidia.com/v1/models" + if self._is_hosted + else urljoin(self._base_url, "models") + ) + response = session.get(url, headers=_headers) + response.raise_for_status() + + assert ( + "data" in response.json() + ), "Response does not contain expected 'data' key" + assert isinstance( + response.json()["data"], list + ), "Response 'data' is not a list" + assert all( + isinstance(result, dict) for result in response.json()["data"] + ), "Response 'data' is not a list of dictionaries" + assert all( + "id" in result for result in response.json()["data"] + ), "Response 'rankings' is not a list of dictionaries with 'id'" + + return [ + Model( + id=model["id"], + base_model=getattr(model, "params", {}).get("root", None), ) + for model in response.json()["data"] + ] def _validate_url(self, base_url): """ @@ -120,9 +203,7 @@ def _validate_url(self, base_url): expected_format = "Expected format is 'http://host:port'." result = urlparse(base_url) if not (result.scheme and result.netloc): - raise ValueError( - f"Invalid base_url, Expected format is 'http://host:port': {base_url}" - ) + raise ValueError(f"Invalid base_url, {expected_format}") if result.path: normalized_path = result.path.strip("/") if normalized_path == "v1": @@ -130,68 +211,18 @@ def _validate_url(self, base_url): elif normalized_path == "v1/rankings": warnings.warn(f"{expected_format} Rest is Ignored.") else: - raise ValueError(f"Base URL path is not recognized. {expected_format}") - return urlunparse((result.scheme, result.netloc, "v1", "", "", "")) + raise ValueError(f"Invalid base_url, {expected_format}") + return base_url @property def available_models(self) -> List[Model]: """Get available models.""" # all available models are in the map ids = MODEL_ENDPOINT_MAP.keys() - return [Model(id=id) for id in ids] - - @deprecated( - version="0.1.2", - reason="Will be removed in 0.2. Construct with `base_url` instead.", - ) - def mode( - self, - mode: Literal["nvidia", "nim"] = "nvidia", - *, - base_url: Optional[str] = None, - model: Optional[str] = None, - api_key: Optional[str] = None, - ) -> "NVIDIARerank": - """ - Deprecated: use NVIDIARerank(base_url=...) instead. - """ - if isinstance(self, str): - raise ValueError("Please construct the model before calling mode()") - - self._is_hosted = mode == "nvidia" - if not self._is_hosted: - if not base_url: - raise ValueError("base_url is required for nim mode") + return self._get_models() else: - api_key = get_from_param_or_env("api_key", api_key, "NVIDIA_API_KEY") - if not base_url: - base_url = BASE_URL - - self._mode = mode - if base_url: - # TODO: change this to not require /v1 at the end. the current - # implementation is for consistency, but really this code - # should dictate which version it works with - components = urlparse(base_url) - if not components.scheme or not components.netloc: - raise ValueError( - f"Incorrect url format, use https://host:port/v1, given '{base_url}'" - ) - last_nonempty_path_component = [x for x in components.path.split("/") if x][ - -1 - ] - if last_nonempty_path_component != "v1": - raise ValueError( - f"Incorrect url format, use https://host:post/v1 ending with /v1, given '{base_url}'" - ) - self._base_url = base_url - if model: - self.model = model - if api_key: - self._api_key = api_key - - return self + return [Model(id=id) for id in ids] @classmethod def class_name(cls) -> str: @@ -243,20 +274,16 @@ def batched(ls: list, size: int) -> Generator[List[NodeWithScore], None, None]: for batch in batched(nodes, self.max_batch_size): payloads = { "model": self.model, + **({"truncate": self.truncate} if self.truncate else {}), "query": {"text": query_bundle.query_str}, "passages": [ {"text": n.get_content(metadata_mode=MetadataMode.EMBED)} for n in batch ], } - # the hosted NIM path is different from the local NIM path - url = self._base_url - if self._is_hosted: - if url.endswith("/v1"): - url += "/retrieval/nvidia/reranking" - else: - url += "/ranking" - response = session.post(url, headers=_headers, json=payloads) + response = session.post( + self._inference_url, headers=_headers, json=payloads + ) response.raise_for_status() # expected response format: # { diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/pyproject.toml index 5f8d6a84140aa..d78302aeef994 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/pyproject.toml @@ -30,11 +30,11 @@ license = "MIT" name = "llama-index-postprocessor-nvidia-rerank" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.5" +version = "0.3.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} @@ -57,6 +57,7 @@ types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 types-setuptools = "67.1.0.0" [tool.poetry.group.test_integration.dependencies] +pytest-httpx = "*" requests-mock = "^1.12.1" [tool.pytest.ini_options] diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_api_key.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_api_key.py index 86c0a55db2430..09ca0a2b0bd0b 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_api_key.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_api_key.py @@ -6,6 +6,19 @@ from llama_index.core.schema import NodeWithScore, Document from typing import Any +from requests_mock import Mocker + + +@pytest.fixture() +def mock_local_models(requests_mock: Mocker) -> None: + requests_mock.get( + "https://test_url/v1/models", + json={ + "data": [ + {"id": "model1"}, + ] + }, + ) def get_api_key(instance: Any) -> str: @@ -13,10 +26,12 @@ def get_api_key(instance: Any) -> str: def test_create_default_url_without_api_key(masked_env_var: str) -> None: - with pytest.warns(UserWarning): + with pytest.raises(ValueError) as e: Interface() + assert "API key is required" in str(e.value) +@pytest.mark.usefixtures("mock_local_models") def test_create_unknown_url_without_api_key(masked_env_var: str) -> None: Interface(base_url="https://test_url/v1") @@ -39,19 +54,6 @@ def test_api_key_priority(masked_env_var: str) -> None: del os.environ["NVIDIA_API_KEY"] -@pytest.mark.integration() -def test_missing_api_key_error(masked_env_var: str) -> None: - with pytest.warns(UserWarning): - client = Interface() - with pytest.raises(Exception) as exc_info: - client.postprocess_nodes( - [NodeWithScore(node=Document(text="Hello, world!"))], - query_str="Hello, world!", - ) - message = str(exc_info.value) - assert "401" in message - - @pytest.mark.integration() def test_bogus_api_key_error(masked_env_var: str) -> None: client = Interface(nvidia_api_key="BOGUS") diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_available_models.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_available_models.py index 698118baa6cc9..fa2e9acd1e92c 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_available_models.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_available_models.py @@ -1,6 +1,19 @@ import pytest from llama_index.postprocessor.nvidia_rerank import NVIDIARerank +from requests_mock import Mocker + + +@pytest.fixture(autouse=True) +def mock_local_models(requests_mock: Mocker) -> None: + requests_mock.get( + "https://test_url/v1/models", + json={ + "data": [ + {"id": "model1"}, + ] + }, + ) @pytest.mark.integration() diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_base_url.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_base_url.py index 54cf3bdeed12d..6efd5de98db79 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_base_url.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_base_url.py @@ -47,7 +47,7 @@ def test_base_url_invalid_not_hosted( @pytest.mark.parametrize( "base_url", [ - "http://0.0.0.0:8888/v1/rankings", + "http://0.0.0.0:8888/v1", ], ) def test_base_url_valid_not_hosted(base_url: str, mock_v1_local_models2: None) -> None: @@ -60,4 +60,4 @@ def test_base_url_valid_not_hosted(base_url: str, mock_v1_local_models2: None) - ["https://ai.api.nvidia.com/v1"], ) def test_base_url_valid_hosted(base_url: str, mock_v1_local_models2: None) -> None: - Interface(base_url=base_url) + Interface(base_url=base_url, api_key="BOGUS") diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_mode_switch.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_mode_switch.py deleted file mode 100644 index b8148420aead4..0000000000000 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_mode_switch.py +++ /dev/null @@ -1,78 +0,0 @@ -import pytest - -from llama_index.postprocessor.nvidia_rerank import NVIDIARerank as Interface -from llama_index.postprocessor.nvidia_rerank.base import KNOWN_URLS, BASE_URL - - -def test_mode_switch_throws_without_key_deprecated(masked_env_var: str): - x = Interface() - with pytest.raises(ValueError): - with pytest.warns(DeprecationWarning): - x.mode("nvidia") - - -def test_mode_switch_with_key_deprecated(masked_env_var: str): - with pytest.warns(DeprecationWarning): - Interface().mode("nvidia", api_key="test") - - -def test_mode_switch_nim_throws_without_url_deprecated(): - instance = Interface() - with pytest.raises(ValueError): - with pytest.warns(DeprecationWarning): - instance.mode("nim") - - -def test_mode_switch_nim_with_url_deprecated(): - with pytest.warns(DeprecationWarning): - Interface().mode("nim", base_url="http://test/v1") - - -def test_mode_switch_param_setting_deprecated(): - instance = Interface(model="dummy") - - with pytest.warns(DeprecationWarning): - instance1 = instance.mode("nim", base_url="https://test_url/v1/") - assert instance1.model == "dummy" - assert str(instance1._base_url) == "https://test_url/v1/" - - with pytest.warns(DeprecationWarning): - instance2 = instance1.mode("nvidia", api_key="test", model="dummy-2") - assert instance2.model == "dummy-2" - assert str(instance2._base_url) == BASE_URL - assert instance2._api_key == "test" - - -UNKNOWN_URLS = [ - "https://test_url/v1", - "https://test_url/v1/", - "http://test_url/v1", - "http://test_url/v1/", -] - - -@pytest.mark.parametrize("base_url", UNKNOWN_URLS) -def test_mode_switch_unknown_base_url_without_key(masked_env_var: str, base_url: str): - Interface(base_url=base_url) - - -@pytest.mark.parametrize("base_url", UNKNOWN_URLS) -@pytest.mark.parametrize("param", ["nvidia_api_key", "api_key"]) -def test_mode_switch_unknown_base_url_with_key( - masked_env_var: str, param: str, base_url: str -): - Interface(base_url=base_url, **{param: "test"}) - - -@pytest.mark.parametrize("base_url", KNOWN_URLS) -def test_mode_switch_known_base_url_without_key(masked_env_var: str, base_url: str): - with pytest.warns(UserWarning): - Interface(base_url=base_url) - - -@pytest.mark.parametrize("base_url", KNOWN_URLS) -@pytest.mark.parametrize("param", ["nvidia_api_key", "api_key"]) -def test_mode_switch_known_base_url_with_key( - masked_env_var: str, base_url: str, param: str -): - Interface(base_url=base_url, **{param: "test"}) diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_postprocessor_nvidia_rerank.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_postprocessor_nvidia_rerank.py index 6ef77b64876e8..b35b32dedc0ef 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_postprocessor_nvidia_rerank.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_postprocessor_nvidia_rerank.py @@ -7,6 +7,27 @@ import faker +from requests_mock import Mocker + + +@pytest.fixture() +def known_unknown() -> str: + return "mock-model" + + +@pytest.fixture() +def mock_local_models(requests_mock: Mocker, known_unknown) -> None: + requests_mock.get( + "http://localhost:8000/v1/models", + json={ + "data": [ + { + "id": known_unknown, + }, + ] + }, + ) + @pytest.fixture() def text() -> str: @@ -75,11 +96,11 @@ def test_direct_empty_docs(query: str, model: str, mode: dict) -> None: def test_direct_top_n_negative( query: str, nodes: List[NodeWithScore], model: str, mode: dict ) -> None: - orig = NVIDIARerank.Config.validate_assignment - NVIDIARerank.Config.validate_assignment = False + orig = NVIDIARerank.model_config["validate_assignment"] + NVIDIARerank.model_config["validate_assignment"] = False ranker = NVIDIARerank(model=model, **mode) ranker.top_n = -100 - NVIDIARerank.Config.validate_assignment = orig + NVIDIARerank.model_config["validate_assignment"] = orig result = ranker.postprocess_nodes(nodes=nodes, query_str=query) assert len(result) == 0 @@ -126,13 +147,13 @@ def test_direct_top_n_greater_len_docs( @pytest.mark.parametrize("batch_size", [-10, 0]) def test_invalid_max_batch_size(model: str, mode: dict, batch_size: int) -> None: - ranker = NVIDIARerank(model=model, **mode) + ranker = NVIDIARerank(api_key="BOGUS", model=model, **mode) with pytest.raises(ValueError): ranker.max_batch_size = batch_size def test_invalid_top_n(model: str, mode: dict) -> None: - ranker = NVIDIARerank(model=model, **mode) + ranker = NVIDIARerank(api_key="BOGUS", model=model, **mode) with pytest.raises(ValueError): ranker.top_n = -10 @@ -170,3 +191,22 @@ def test_rerank_batching( assert all( result[i].score >= result[i + 1].score for i in range(len(result) - 1) ), "results are not sorted" + + +def test_default_known(mock_local_models, known_unknown: str) -> None: + """ + Test that a model in the model table will be accepted. + """ + # check if default model is getting set + with pytest.warns(UserWarning): + x = NVIDIARerank(base_url="http://localhost:8000/v1") + assert x.model == known_unknown + + +def test_default_lora() -> None: + """ + Test that a model in the model table will be accepted. + """ + # find a model that matches the public_class under test + x = NVIDIARerank(base_url="http://localhost:8000/v1", model="lora1") + assert x.model == "lora1" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_truncate.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_truncate.py new file mode 100644 index 0000000000000..351e7c7fda013 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-nvidia-rerank/tests/test_truncate.py @@ -0,0 +1,117 @@ +from typing import Any, Literal, Optional + +import pytest +import re +from requests_mock import Mocker +from llama_index.postprocessor.nvidia_rerank import NVIDIARerank +from llama_index.core.schema import NodeWithScore, Document + + +@pytest.fixture() +def mock_v1_models(requests_mock: Mocker) -> None: + requests_mock.get( + "https://integrate.api.nvidia.com/v1/models", + json={ + "data": [ + { + "id": "mock-model", + "object": "model", + "created": 1234567890, + "owned_by": "OWNER", + } + ] + }, + ) + + +@pytest.fixture() +def mock_v1_ranking(requests_mock: Mocker) -> None: + requests_mock.post( + re.compile(r"https://ai\.api\.nvidia\.com/v1/.*/reranking"), + json={ + "rankings": [ + {"index": 0, "logit": 4.2}, + ] + }, + ) + + +@pytest.fixture() +def mock(mock_v1_models: None, mock_v1_ranking: None) -> None: + pass + + +@pytest.mark.parametrize( + "truncate", + [ + None, + "END", + "NONE", + ], +) +def test_truncate_passed( + mock: None, + requests_mock: Mocker, + truncate: Optional[Literal["END", "NONE"]], +) -> None: + client = NVIDIARerank( + api_key="BOGUS", + **({"truncate": truncate} if truncate else {}), + ) + response = client.postprocess_nodes( + [NodeWithScore(node=Document(text="Nothing really."))], + query_str="What is it?", + ) + + assert len(response) == 1 + + assert requests_mock.last_request is not None + request_payload = requests_mock.last_request.json() + if truncate is None: + assert "truncate" not in request_payload + else: + assert "truncate" in request_payload + assert request_payload["truncate"] == truncate + + +@pytest.mark.parametrize("truncate", [True, False, 1, 0, 1.0, "START", "BOGUS"]) +def test_truncate_invalid(truncate: Any) -> None: + with pytest.raises(ValueError): + NVIDIARerank(truncate=truncate) + + +@pytest.mark.integration() +@pytest.mark.parametrize("truncate", ["END"]) +def test_truncate_positive(model: str, mode: dict, truncate: str) -> None: + query = "What is acceleration?" + nodes = [ + NodeWithScore(node=Document(text="NVIDIA " * length)) + for length in [32, 1024, 64, 128, 2048, 256, 512] + ] + client = NVIDIARerank(model=model, top_n=len(nodes), truncate=truncate, **mode) + response = client.postprocess_nodes(nodes, query_str=query) + print(response) + assert len(response) == len(nodes) + + +@pytest.mark.integration() +@pytest.mark.parametrize("truncate", [None, "NONE"]) +def test_truncate_negative(model: str, mode: dict, truncate: str) -> None: + if model == "nv-rerank-qa-mistral-4b:1": + pytest.skip( + "truncation is inconsistent across models, " + "nv-rerank-qa-mistral-4b:1 truncates by default " + "while others do not" + ) + query = "What is acceleration?" + nodes = [ + NodeWithScore(node=Document(text="NVIDIA " * length)) + for length in [32, 1024, 64, 128, 2048, 256, 512] + ] + client = NVIDIARerank( + model=model, **mode, **({"truncate": truncate} if truncate else {}) + ) + with pytest.raises(Exception) as e: + client.postprocess_nodes(nodes, query_str=query) + assert "400" in str(e.value) + # assert "exceeds maximum allowed" in str(e.value) diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-openvino-rerank/llama_index/postprocessor/openvino_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-openvino-rerank/llama_index/postprocessor/openvino_rerank/base.py index 194bc8c2b1838..5b0501cbd4fb2 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-openvino-rerank/llama_index/postprocessor/openvino_rerank/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-openvino-rerank/llama_index/postprocessor/openvino_rerank/base.py @@ -40,6 +40,12 @@ def __init__( keep_retrieval_score: Optional[bool] = False, ): device = infer_torch_device() if device is None else device + super().__init__( + top_n=top_n, + model_id_or_path=model_id_or_path, + device=device, + keep_retrieval_score=keep_retrieval_score, + ) try: from huggingface_hub import HfApi @@ -97,12 +103,6 @@ def require_model_export( ) self._tokenizer = AutoTokenizer.from_pretrained(model_id_or_path) - super().__init__( - top_n=top_n, - model_id_or_path=model_id_or_path, - device=device, - keep_retrieval_score=keep_retrieval_score, - ) @classmethod def class_name(cls) -> str: @@ -161,9 +161,20 @@ def _postprocess_nodes( }, ) as event: query_pairs = [[query_bundle.query_str, text] for text in nodes_text_list] - input_tensors = self._tokenizer( - query_pairs, padding=True, truncation=True, return_tensors="pt" - ) + + length = self._model.request.inputs[0].get_partial_shape()[1] + if length.is_dynamic: + input_tensors = self._tokenizer( + query_pairs, padding=True, truncation=True, return_tensors="pt" + ) + else: + input_tensors = self._tokenizer( + query_pairs, + padding="max_length", + max_length=length.get_length(), + truncation=True, + return_tensors="pt", + ) outputs = self._model(**input_tensors, return_dict=True) logits = outputs[0] diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-openvino-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-openvino-rerank/pyproject.toml index c042f54663456..4809acb3262ad 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-openvino-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-openvino-rerank/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-postprocessor-openvino-rerank" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" huggingface-hub = "^0.23.0" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.optimum] extras = ["openvino"] diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/pyproject.toml index 4450b6139a3b8..cccd4f02a19e0 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/pyproject.toml @@ -28,13 +28,13 @@ license = "MIT" maintainers = ["roeybc"] name = "llama-index-postprocessor-presidio" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" presidio-analyzer = "^2.2.353" presidio-anonymizer = "^2.2.353" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-rankgpt-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-rankgpt-rerank/pyproject.toml index 652a930d57885..c17e5c25c841e 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-rankgpt-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-rankgpt-rerank/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-postprocessor-rankgpt-rerank" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-rankllm-rerank/llama_index/postprocessor/rankllm_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-rankllm-rerank/llama_index/postprocessor/rankllm_rerank/base.py index 78048d227a27e..74a136ac96227 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-rankllm-rerank/llama_index/postprocessor/rankllm_rerank/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-rankllm-rerank/llama_index/postprocessor/rankllm_rerank/base.py @@ -46,6 +46,14 @@ def __init__( from rank_llm.result import Result + super().__init__( + model=model, + top_n=top_n, + with_retrieval=with_retrieval, + step_size=step_size, + gpt_model=gpt_model, + ) + self._result = Result if model_enum == ModelType.VICUNA: @@ -75,14 +83,6 @@ def __init__( self._retriever = Retriever - super().__init__( - model=model, - top_n=top_n, - with_retrieval=with_retrieval, - step_size=step_size, - gpt_model=gpt_model, - ) - @classmethod def class_name(cls) -> str: return "RankLLMRerank" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-rankllm-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-rankllm-rerank/pyproject.toml index b21d30854c532..9563817325aa7 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-rankllm-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-rankllm-rerank/pyproject.toml @@ -31,11 +31,11 @@ license = "MIT" name = "llama-index-postprocessor-rankllm-rerank" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-sbert-rerank/llama_index/postprocessor/sbert_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-sbert-rerank/llama_index/postprocessor/sbert_rerank/base.py index 9165aa0768f9b..2d4e71a0e0b37 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-sbert-rerank/llama_index/postprocessor/sbert_rerank/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-sbert-rerank/llama_index/postprocessor/sbert_rerank/base.py @@ -43,16 +43,17 @@ def __init__( "Cannot import sentence-transformers or torch package,", "please `pip install torch sentence-transformers`", ) - device = infer_torch_device() if device is None else device - self._model = CrossEncoder( - model, max_length=DEFAULT_SENTENCE_TRANSFORMER_MAX_LENGTH, device=device - ) + super().__init__( top_n=top_n, model=model, device=device, keep_retrieval_score=keep_retrieval_score, ) + device = infer_torch_device() if device is None else device + self._model = CrossEncoder( + model, max_length=DEFAULT_SENTENCE_TRANSFORMER_MAX_LENGTH, device=device + ) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-sbert-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-sbert-rerank/pyproject.toml index 3b410a50aa9bc..9093ada61605c 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-sbert-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-sbert-rerank/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-postprocessor-sbert-rerank" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-tei-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-tei-rerank/pyproject.toml index 873ab38b34cec..36629ea132be4 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-tei-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-tei-rerank/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-postprocessor-tei-rerank" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.58" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-voyageai-rerank/llama_index/postprocessor/voyageai_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-voyageai-rerank/llama_index/postprocessor/voyageai_rerank/base.py index 7a92a761bcd91..090a91de9a7b9 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-voyageai-rerank/llama_index/postprocessor/voyageai_rerank/base.py +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-voyageai-rerank/llama_index/postprocessor/voyageai_rerank/base.py @@ -40,10 +40,9 @@ def __init__( "Cannot import voyageai package, please `pip install voyageai`." ) - self._client = Client(api_key=api_key) - top_n = top_n or top_k super().__init__(top_n=top_n, model=model, truncation=truncation) + self._client = Client(api_key=api_key) @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-voyageai-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-voyageai-rerank/pyproject.toml index 09f85ea756b31..be9fda4e3c270 100644 --- a/llama-index-integrations/postprocessor/llama-index-postprocessor-voyageai-rerank/pyproject.toml +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-voyageai-rerank/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-postprocessor-voyageai-rerank" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" voyageai = "^0.2.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/.gitignore b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/BUILD b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/Makefile b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/README.md b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/README.md new file mode 100644 index 0000000000000..fb3aae94f1fd4 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/README.md @@ -0,0 +1,40 @@ +# LlamaIndex Postprocessor Integration: Xinference Rerank + +Xorbits Inference (Xinference) is an open-source platform to streamline the operation and integration of a wide array of AI models. + +You can find a list of built-in rerank models in Xinference from its document [Rerank Models](https://inference.readthedocs.io/en/latest/models/builtin/rerank/index.html) + +To learn more about Xinference in general, visit https://inference.readthedocs.io/en/stable/models/model_abilities/rerank.html + +## Installation + +```shell +pip install llama-index-postprocessor-xinference-rerank +``` + +## Usage + +**Parameters Description:** + +- `model`: Model uid not model name, sometimes they may be the same (e.g., `bge-reranker-base`). +- `base_url`: base url of Xinference (e.g., `http://localhost:9997`). +- `top_n`: Top n nodes to return from reranker. (default 5). + +**Nodes Rerank Example** + +```python +from llama_index.postprocessor.xinference_rerank import XinferenceRerank + +xi_model_uid = "xinference model uid" +xi_base_url = "xinference base url" + +xi_rerank = XinferenceRerank( + top_n=5, + model=xi_model_uid, + base_url=xi_base_url, +) + + +def test_rerank_nodes(nodes, query_str): + response = xi_rerank.postprocess_nodes(nodes, query_str) +``` diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/llama_index/postprocessor/xinference_rerank/BUILD b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/llama_index/postprocessor/xinference_rerank/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/llama_index/postprocessor/xinference_rerank/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/llama_index/postprocessor/xinference_rerank/__init__.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/llama_index/postprocessor/xinference_rerank/__init__.py new file mode 100644 index 0000000000000..ea183ba6d2a39 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/llama_index/postprocessor/xinference_rerank/__init__.py @@ -0,0 +1,3 @@ +from llama_index.postprocessor.xinference_rerank.base import XinferenceRerank + +__all__ = ["XinferenceRerank"] diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/llama_index/postprocessor/xinference_rerank/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/llama_index/postprocessor/xinference_rerank/base.py new file mode 100644 index 0000000000000..cef36c4712c1a --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/llama_index/postprocessor/xinference_rerank/base.py @@ -0,0 +1,91 @@ +import requests +from typing import List, Optional +from llama_index.core.bridge.pydantic import Field +from llama_index.core.callbacks import CBEventType, EventPayload +from llama_index.core.instrumentation import get_dispatcher +from llama_index.core.instrumentation.events.rerank import ( + ReRankEndEvent, + ReRankStartEvent, +) +from llama_index.core.postprocessor.types import BaseNodePostprocessor +from llama_index.core.schema import NodeWithScore, QueryBundle, MetadataMode + +dispatcher = get_dispatcher(__name__) + + +class XinferenceRerank(BaseNodePostprocessor): + """Class for Xinference Rerank.""" + + top_n: int = Field( + default=5, + description="The number of nodes to return.", + ) + model: str = Field( + default="bge-reranker-base", + description="The Xinference model uid to use.", + ) + base_url: str = Field( + default="http://localhost:9997", + description="The Xinference base url to use.", + ) + + @classmethod + def class_name(cls) -> str: + return "XinferenceRerank" + + def get_query_str(self, query): + return query.query_str if isinstance(query, QueryBundle) else query + + def _postprocess_nodes( + self, + nodes: List[NodeWithScore], + query_bundle: Optional[QueryBundle] = None, + ) -> List[NodeWithScore]: + dispatcher.event( + ReRankStartEvent( + query=query_bundle, + nodes=nodes, + top_n=self.top_n, + model_name=self.model, + ) + ) + if query_bundle is None: + raise ValueError("Missing query bundle.") + if len(nodes) == 0: + return [] + with self.callback_manager.event( + CBEventType.RERANKING, + payload={ + EventPayload.NODES: nodes, + EventPayload.MODEL_NAME: self.model, + EventPayload.QUERY_STR: self.get_query_str(query_bundle), + EventPayload.TOP_K: self.top_n, + }, + ) as event: + headers = {"Content-Type": "application/json"} + json_data = { + "model": self.model, + "query": self.get_query_str(query_bundle), + "documents": [ + node.node.get_content(metadata_mode=MetadataMode.EMBED) + for node in nodes + ], + } + response = requests.post( + url=f"{self.base_url}/v1/rerank", headers=headers, json=json_data + ) + response.encoding = "utf-8" + if response.status_code != 200: + raise Exception( + f"Xinference call failed with status code {response.status_code}." + f"Details: {response.text}" + ) + rerank_nodes = [ + NodeWithScore( + node=nodes[result["index"]].node, score=result["relevance_score"] + ) + for result in response.json()["results"][: self.top_n] + ] + event.on_end(payload={EventPayload.NODES: rerank_nodes}) + dispatcher.event(ReRankEndEvent(nodes=rerank_nodes)) + return rerank_nodes diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/pyproject.toml new file mode 100644 index 0000000000000..cc5a9ca835e74 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/pyproject.toml @@ -0,0 +1,62 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = false +import_path = "llama_index.postprocessor.xinference_rerank" + +[tool.llamahub.class_authors] +XinferenceRerank = "llama-index" + +[tool.mypy] +disallow_untyped_defs = true +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Your Name "] +description = "llama-index postprocessor xinference rerank integration" +exclude = ["**/BUILD"] +license = "MIT" +name = "llama-index-postprocessor-xinference-rerank" +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +llama-index-core = "^0.11.0" + +[tool.poetry.group.dev.dependencies] +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" +types-setuptools = "67.1.0.0" + +[tool.poetry.group.dev.dependencies.black] +extras = ["jupyter"] +version = "<=23.9.1,>=23.7.0" + +[tool.poetry.group.dev.dependencies.codespell] +extras = ["toml"] +version = ">=v2.2.6" + +[[tool.poetry.packages]] +include = "llama_index/" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/tests/BUILD b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/tests/__init__.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/tests/test_postprocessor_xinference_rerank.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/tests/test_postprocessor_xinference_rerank.py new file mode 100644 index 0000000000000..b5d1ff0553489 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-xinference-rerank/tests/test_postprocessor_xinference_rerank.py @@ -0,0 +1,7 @@ +from llama_index.core.postprocessor.types import BaseNodePostprocessor +from llama_index.postprocessor.xinference_rerank import XinferenceRerank + + +def test_class(): + names_of_base_classes = [b.__name__ for b in XinferenceRerank.__mro__] + assert BaseNodePostprocessor.__name__ in names_of_base_classes diff --git a/llama-index-integrations/program/llama-index-program-evaporate/llama_index/program/evaporate/base.py b/llama-index-integrations/program/llama-index-program-evaporate/llama_index/program/evaporate/base.py index 3850bf56b7570..b22433706017e 100644 --- a/llama-index-integrations/program/llama-index-program-evaporate/llama_index/program/evaporate/base.py +++ b/llama-index-integrations/program/llama-index-program-evaporate/llama_index/program/evaporate/base.py @@ -5,7 +5,6 @@ import pandas as pd from llama_index.core.llms import LLM from llama_index.core.schema import BaseNode, TextNode -from llama_index.core.service_context import ServiceContext from llama_index.core.types import BasePydanticProgram, Model from llama_index.core.utils import print_text from llama_index.program.evaporate.df import ( @@ -60,7 +59,6 @@ def from_defaults( fields_to_extract: Optional[List[str]] = None, fields_context: Optional[Dict[str, Any]] = None, llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, schema_id_prompt: Optional[SchemaIDPrompt] = None, fn_generate_prompt: Optional[FnGeneratePrompt] = None, field_extract_query_tmpl: str = DEFAULT_FIELD_EXTRACT_QUERY_TMPL, @@ -70,7 +68,6 @@ def from_defaults( """Evaporate program.""" extractor = EvaporateExtractor( llm=llm, - service_context=service_context, schema_id_prompt=schema_id_prompt, fn_generate_prompt=fn_generate_prompt, field_extract_query_tmpl=field_extract_query_tmpl, @@ -206,7 +203,6 @@ def from_defaults( fields_to_extract: Optional[List[str]] = None, fields_context: Optional[Dict[str, Any]] = None, llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, schema_id_prompt: Optional[SchemaIDPrompt] = None, fn_generate_prompt: Optional[FnGeneratePrompt] = None, field_extract_query_tmpl: str = DEFAULT_FIELD_EXTRACT_QUERY_TMPL, @@ -219,7 +215,6 @@ def from_defaults( fields_to_extract=fields_to_extract, fields_context=fields_context, llm=llm, - service_context=service_context, schema_id_prompt=schema_id_prompt, fn_generate_prompt=fn_generate_prompt, field_extract_query_tmpl=field_extract_query_tmpl, diff --git a/llama-index-integrations/program/llama-index-program-evaporate/llama_index/program/evaporate/extractor.py b/llama-index-integrations/program/llama-index-program-evaporate/llama_index/program/evaporate/extractor.py index 75587aa5aa717..4759b4a9f8c0f 100644 --- a/llama-index-integrations/program/llama-index-program-evaporate/llama_index/program/evaporate/extractor.py +++ b/llama-index-integrations/program/llama-index-program-evaporate/llama_index/program/evaporate/extractor.py @@ -7,8 +7,7 @@ from llama_index.core.llms.llm import LLM from llama_index.core.schema import BaseNode, MetadataMode, NodeWithScore, QueryBundle -from llama_index.core.service_context import ServiceContext -from llama_index.core.settings import Settings, llm_from_settings_or_context +from llama_index.core.settings import Settings from llama_index.program.evaporate.prompts import ( DEFAULT_EXPECTED_OUTPUT_PREFIX_TMPL, DEFAULT_FIELD_EXTRACT_QUERY_TMPL, @@ -98,7 +97,6 @@ class EvaporateExtractor: def __init__( self, llm: Optional[LLM] = None, - service_context: Optional[ServiceContext] = None, schema_id_prompt: Optional[SchemaIDPrompt] = None, fn_generate_prompt: Optional[FnGeneratePrompt] = None, field_extract_query_tmpl: str = DEFAULT_FIELD_EXTRACT_QUERY_TMPL, @@ -107,7 +105,7 @@ def __init__( ) -> None: """Initialize params.""" # TODO: take in an entire index instead of forming a response builder - self._llm = llm or llm_from_settings_or_context(Settings, service_context) + self._llm = llm or Settings.llm self._schema_id_prompt = schema_id_prompt or SCHEMA_ID_PROMPT self._fn_generate_prompt = fn_generate_prompt or FN_GENERATION_PROMPT self._field_extract_query_tmpl = field_extract_query_tmpl diff --git a/llama-index-integrations/program/llama-index-program-evaporate/pyproject.toml b/llama-index-integrations/program/llama-index-program-evaporate/pyproject.toml index 858fc64d6644e..62e9434d7a076 100644 --- a/llama-index-integrations/program/llama-index-program-evaporate/pyproject.toml +++ b/llama-index-integrations/program/llama-index-program-evaporate/pyproject.toml @@ -27,12 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-program-evaporate" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-program-openai = "^0.1.1" +llama-index-program-openai = "^0.2.0" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/program/llama-index-program-guidance/pyproject.toml b/llama-index-integrations/program/llama-index-program-guidance/pyproject.toml index 7c236aac957f2..638e10eee22a9 100644 --- a/llama-index-integrations/program/llama-index-program-guidance/pyproject.toml +++ b/llama-index-integrations/program/llama-index-program-guidance/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-program-guidance" readme = "README.md" -version = "0.1.2" +version = "0.2.1" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -guidance = "^0.1.10" +python = ">=3.9,<4.0" +guidance = "^0.1.16" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/program/llama-index-program-guidance/tests/BUILD b/llama-index-integrations/program/llama-index-program-guidance/tests/BUILD index dabf212d7e716..f956e3470b49d 100644 --- a/llama-index-integrations/program/llama-index-program-guidance/tests/BUILD +++ b/llama-index-integrations/program/llama-index-program-guidance/tests/BUILD @@ -1 +1,3 @@ -python_tests() +python_tests( + interpreter_constraints = ["==3.9.*", "==3.10.*"], +) diff --git a/llama-index-integrations/program/llama-index-program-lmformatenforcer/pyproject.toml b/llama-index-integrations/program/llama-index-program-lmformatenforcer/pyproject.toml index f510d8df78a83..e890a39e21c5f 100644 --- a/llama-index-integrations/program/llama-index-program-lmformatenforcer/pyproject.toml +++ b/llama-index-integrations/program/llama-index-program-lmformatenforcer/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-program-lmformatenforcer" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-llama-cpp = "^0.1.1" -llama-index-llms-huggingface = "^0.1.1" +llama-index-llms-llama-cpp = "^0.2.0" +llama-index-llms-huggingface = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/program/llama-index-program-openai/pyproject.toml b/llama-index-integrations/program/llama-index-program-openai/pyproject.toml index e0034cf95a416..e3fa9d2b38376 100644 --- a/llama-index-integrations/program/llama-index-program-openai/pyproject.toml +++ b/llama-index-integrations/program/llama-index-program-openai/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-program-openai" readme = "README.md" -version = "0.1.7" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-llms-openai = ">=0.1.1" -llama-index-core = "^0.10.57" -llama-index-agent-openai = ">=0.1.1,<0.3.0" +llama-index-llms-openai = "^0.2.0" +llama-index-agent-openai = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/question_gen/llama-index-question-gen-guidance/pyproject.toml b/llama-index-integrations/question_gen/llama-index-question-gen-guidance/pyproject.toml index 7836f9ae0706d..57c90bcb5239c 100644 --- a/llama-index-integrations/question_gen/llama-index-question-gen-guidance/pyproject.toml +++ b/llama-index-integrations/question_gen/llama-index-question-gen-guidance/pyproject.toml @@ -27,12 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-question-gen-guidance" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-program-guidance = "^0.1.1" +python = ">=3.9,<4.0" +guidance = "^0.1.16" +llama-index-program-guidance = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/question_gen/llama-index-question-gen-guidance/tests/BUILD b/llama-index-integrations/question_gen/llama-index-question-gen-guidance/tests/BUILD index dabf212d7e716..f956e3470b49d 100644 --- a/llama-index-integrations/question_gen/llama-index-question-gen-guidance/tests/BUILD +++ b/llama-index-integrations/question_gen/llama-index-question-gen-guidance/tests/BUILD @@ -1 +1,3 @@ -python_tests() +python_tests( + interpreter_constraints = ["==3.9.*", "==3.10.*"], +) diff --git a/llama-index-integrations/question_gen/llama-index-question-gen-openai/pyproject.toml b/llama-index-integrations/question_gen/llama-index-question-gen-openai/pyproject.toml index 51d644df3de0a..7164db7aa16ae 100644 --- a/llama-index-integrations/question_gen/llama-index-question-gen-openai/pyproject.toml +++ b/llama-index-integrations/question_gen/llama-index-question-gen-openai/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-question-gen-openai" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-program-openai = "^0.1.1" -llama-index-llms-openai = "^0.1.1" +llama-index-program-openai = "^0.2.0" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-agent-search/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-agent-search/pyproject.toml index a37fdcdf848d1..961dfedb1fa98 100644 --- a/llama-index-integrations/readers/llama-index-readers-agent-search/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-agent-search/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["emrgnt-cmplxty"] name = "llama-index-readers-agent-search" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-agent-search/tests/BUILD b/llama-index-integrations/readers/llama-index-readers-agent-search/tests/BUILD deleted file mode 100644 index 619cac15ff840..0000000000000 --- a/llama-index-integrations/readers/llama-index-readers-agent-search/tests/BUILD +++ /dev/null @@ -1,3 +0,0 @@ -python_tests( - interpreter_constraints=["==3.9.*", "==3.10.*"], -) diff --git a/llama-index-integrations/readers/llama-index-readers-agent-search/tests/test_readers_agent_search.py b/llama-index-integrations/readers/llama-index-readers-agent-search/tests/test_readers_agent_search.py deleted file mode 100644 index ef81393062585..0000000000000 --- a/llama-index-integrations/readers/llama-index-readers-agent-search/tests/test_readers_agent_search.py +++ /dev/null @@ -1,7 +0,0 @@ -from llama_index.core.readers.base import BaseReader -from llama_index.readers.agent_search import AgentSearchReader - - -def test_class(): - names_of_base_classes = [b.__name__ for b in AgentSearchReader.__mro__] - assert BaseReader.__name__ in names_of_base_classes diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/pyproject.toml index 70d38287f0015..f8ae6dc5bf982 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["flash1293"] name = "llama-index-readers-airbyte-cdk" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-gong/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-gong/pyproject.toml index 31f8adbf48f92..85b16517dc51d 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-gong/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-gong/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["flash1293"] name = "llama-index-readers-airbyte-gong" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-airbyte-cdk = "^0.1.1" +llama-index-readers-airbyte-cdk = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/pyproject.toml index 843a78900a1f8..4080421540423 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["flash1293"] name = "llama-index-readers-airbyte-hubspot" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-airbyte-cdk = "^0.1.1" +llama-index-readers-airbyte-cdk = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/pyproject.toml index c44275a523cf7..e1e7bb7806c62 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["flash1293"] name = "llama-index-readers-airbyte-salesforce" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-airbyte-cdk = "^0.1.1" +llama-index-readers-airbyte-cdk = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/pyproject.toml index edf810a3fbdeb..ad9936fcb2235 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["flash1293"] name = "llama-index-readers-airbyte-shopify" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-airbyte-cdk = "^0.1.1" +llama-index-readers-airbyte-cdk = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/pyproject.toml index 8f8d17ee13169..8507bde935ef8 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["flash1293"] name = "llama-index-readers-airbyte-stripe" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-airbyte-cdk = "^0.1.1" +llama-index-readers-airbyte-cdk = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/pyproject.toml index e36927dd721f0..9727ed1058c2b 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["flash1293"] name = "llama-index-readers-airbyte-typeform" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-airbyte-cdk = "^0.1.1" +llama-index-readers-airbyte-cdk = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/pyproject.toml index be5e94306c72d..a8ef410cb741d 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["flash1293"] name = "llama-index-readers-airbyte-zendesk-support" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-airbyte-cdk = "^0.1.1" +llama-index-readers-airbyte-cdk = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-airtable/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airtable/pyproject.toml index 29518dd7111a9..93f1b4889c38e 100644 --- a/llama-index-integrations/readers/llama-index-readers-airtable/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airtable/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["smyja"] name = "llama-index-readers-airtable" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pyairtable = "^2.2.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-apify/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-apify/pyproject.toml index e705bcbc18119..8a91a9e9201bb 100644 --- a/llama-index-integrations/readers/llama-index-readers-apify/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-apify/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" maintainers = ["drobnikj"] name = "llama-index-readers-apify" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" apify-client = "^1.6.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-arango-db/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-arango-db/pyproject.toml index 3a147a597cb61..c92024b67ff45 100644 --- a/llama-index-integrations/readers/llama-index-readers-arango-db/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-arango-db/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["mmaatouk"] name = "llama-index-readers-arango-db" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" python-arango = "^7.9.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-asana/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-asana/pyproject.toml index f174572ccced8..91e5715cef27f 100644 --- a/llama-index-integrations/readers/llama-index-readers-asana/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-asana/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["daveey"] name = "llama-index-readers-asana" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" asana = "^5.0.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-assemblyai/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-assemblyai/pyproject.toml index b7646ac31df5b..9306a7bef0c96 100644 --- a/llama-index-integrations/readers/llama-index-readers-assemblyai/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-assemblyai/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["patrickloeber"] name = "llama-index-readers-assemblyai" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" assemblyai = ">=0.18.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-astra-db/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-astra-db/pyproject.toml index e6d9d4725cd76..ac28801c20745 100644 --- a/llama-index-integrations/readers/llama-index-readers-astra-db/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-astra-db/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["erichare"] name = "llama-index-readers-astra-db" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" astrapy = "^1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-athena/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-athena/pyproject.toml index 7a4043cd857b9..de041aa59264b 100644 --- a/llama-index-integrations/readers/llama-index-readers-athena/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-athena/pyproject.toml @@ -29,14 +29,14 @@ license = "MIT" maintainers = ["mattick27"] name = "llama-index-readers-athena" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" boto3 = "^1.34.28" sqlalchemy = "^2.0.25" pyathena = "^3.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-awadb/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-awadb/pyproject.toml index 1e80585d47186..c7e27c2104b37 100644 --- a/llama-index-integrations/readers/llama-index-readers-awadb/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-awadb/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-awadb" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-azcognitive-search/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-azcognitive-search/pyproject.toml index e6b2c7a1d7927..937e8d25ba9db 100644 --- a/llama-index-integrations/readers/llama-index-readers-azcognitive-search/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-azcognitive-search/pyproject.toml @@ -28,13 +28,13 @@ license = "MIT" maintainers = ["mrcabellom"] name = "llama-index-readers-azcognitive-search" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" azure-search-documents = "^11.4.0" azure-identity = "^1.15.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-azstorage-blob/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-azstorage-blob/pyproject.toml index f81c2bfe6fa53..bd219ef957882 100644 --- a/llama-index-integrations/readers/llama-index-readers-azstorage-blob/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-azstorage-blob/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["rivms"] name = "llama-index-readers-azstorage-blob" readme = "README.md" -version = "0.1.7" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" azure-storage-blob = "^12.19.0" azure-identity = "^1.15.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-azure-devops/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-azure-devops/pyproject.toml index 4dc03e02267b6..8439444878e08 100644 --- a/llama-index-integrations/readers/llama-index-readers-azure-devops/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-azure-devops/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-readers-azure-devops" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" azure-devops = "7.1.0b4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-bagel/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-bagel/pyproject.toml index cabf39bbcf3bb..6fd8ee9eea95c 100644 --- a/llama-index-integrations/readers/llama-index-readers-bagel/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-bagel/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["asif"] name = "llama-index-readers-bagel" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" bagel = "^0.3.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-bilibili/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-bilibili/pyproject.toml index 35965bd3ce91b..0f5db55b5f3b7 100644 --- a/llama-index-integrations/readers/llama-index-readers-bilibili/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-bilibili/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["alexzhangji"] name = "llama-index-readers-bilibili" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-bitbucket/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-bitbucket/pyproject.toml index b927df3f13e4b..247ecccd2800f 100644 --- a/llama-index-integrations/readers/llama-index-readers-bitbucket/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-bitbucket/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["lejdiprifti"] name = "llama-index-readers-bitbucket" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-boarddocs/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-boarddocs/pyproject.toml index e85044201e122..1937f2780ebf7 100644 --- a/llama-index-integrations/readers/llama-index-readers-boarddocs/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-boarddocs/pyproject.toml @@ -29,14 +29,14 @@ license = "MIT" maintainers = ["dweekly"] name = "llama-index-readers-boarddocs" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" beautifulsoup4 = ">=4.12.3,<5.0.0" html2text = "^2020.1.16" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_api.py b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_api.py index 9584a3b8baf25..68c3f441d4767 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_api.py +++ b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_api.py @@ -26,15 +26,19 @@ logger = logging.getLogger(__name__) -class _BoxResourcePayload: - resource_info: Optional[File] - ai_prompt: Optional[str] - ai_response: Optional[str] - downloaded_file_path: Optional[str] - text_representation: Optional[str] +def add_extra_header_to_box_client(box_client: BoxClient) -> BoxClient: + """ + Add extra headers to the Box client. - def __init__(self, resource_info: File) -> None: - self.resource_info = resource_info + Args: + box_client (BoxClient): A Box client object. + header (Dict[str, str]): A dictionary of extra headers to add to the Box client. + + Returns: + BoxClient: A Box client object with the extra headers added. + """ + header = {"x-box-ai-library": "llama-index"} + return box_client.with_extra_headers(extra_headers=header) def box_check_connection(box_client: BoxClient) -> None: @@ -55,27 +59,24 @@ def box_check_connection(box_client: BoxClient) -> None: return True -def get_box_files_payload( - box_client: BoxClient, file_ids: List[str] -) -> List[_BoxResourcePayload]: +def get_box_files_details(box_client: BoxClient, file_ids: List[str]) -> List[File]: """ - This function retrieves payloads for a list of Box files. + Retrieves details for multiple Box files identified by their IDs. + + This function takes a Box client and a list of file IDs as input and returns a list of File objects containing details for each requested file. Args: - box_client (BoxClient): A Box client object. - file_ids (List[str]): A list of Box file IDs. + box_client (BoxClient): An authenticated Box client object. + file_ids (List[str]): A list of strings representing Box file IDs. Returns: - List[_BoxResourcePayload]: A list of _BoxResourcePayload objects. - - If a file is retrieved successfully, the resource_info attribute - will contain the corresponding Box file object. - - If an error occurs while retrieving a file, the resource_info - attribute will contain the error message. + List[File]: A list of File objects containing details for each requested file. Raises: - BoxAPIError: If an error occurs while interacting with the Box API. + BoxAPIError: If an error occurs while retrieving file details from the Box API. """ - payloads: List[_BoxResourcePayload] = [] + box_files_details: List[File] = [] + for file_id in file_ids: try: file = box_client.files.get_file_by_id(file_id) @@ -83,63 +84,51 @@ def get_box_files_payload( logger.error( f"An error occurred while getting file: {e.message}", exc_info=True ) - payloads.append( - _BoxResourcePayload( - resource_info=e.message, - ) - ) + raise + logger.info(f"Getting file: {file.id} {file.name} {file.type}") - payloads.append( - _BoxResourcePayload( - resource_info=file, - ) - ) - return payloads + box_files_details.append(file) + + return box_files_details -def get_box_folder_payload( +def get_box_folder_files_details( box_client: BoxClient, folder_id: str, is_recursive: bool = False -) -> List[_BoxResourcePayload]: +) -> List[File]: """ - This function retrieves payloads for all files within a Box folder, - optionally including files from sub-folders. + Retrieves details for all files within a Box folder, optionally including nested sub-folders. + + This function takes a Box client, a folder ID, and an optional recursion flag as input. It retrieves details for all files within the specified folder and returns a list of File objects containing details for each file. Args: - box_client (BoxClient): A Box client object. - folder_id (str): The ID of the Box folder. - is_recursive (bool, optional): If True, retrieves payloads for - files within sub-folders as well. Defaults to False. + box_client (BoxClient): An authenticated Box client object. + folder_id (str): The ID of the Box folder to retrieve files from. + is_recursive (bool, optional): A flag indicating whether to recursively traverse sub-folders within the target folder. Defaults to False. Returns: - List[_BoxResourcePayload]: A list of _BoxResourcePayload objects - containing information about files within the folder - (and sub-folders if is_recursive is True). - - If a file is retrieved successfully, the resource_info attribute - will contain the corresponding Box file object. - - If an error occurs while retrieving a file or folder, - the resource_info attribute will contain the error message. + List[File]: A list of File objects containing details for all files found within the specified folder and its sub-folders (if recursion is enabled). Raises: - BoxAPIError: If an error occurs while interacting with the Box API. + BoxAPIError: If an error occurs while retrieving folder or file details from the Box API. """ - payloads: List[_BoxResourcePayload] = [] + box_files_details: List[File] = [] try: folder = box_client.folders.get_folder_by_id(folder_id) except BoxAPIError as e: logger.error( f"An error occurred while getting folder: {e.message}", exc_info=True ) - return payloads + raise for item in box_client.folders.get_folder_items(folder.id).entries: if item.type == "file": - payloads.extend(get_box_files_payload(box_client, [item.id])) + box_files_details.extend(get_box_files_details(box_client, [item.id])) elif item.type == "folder": if is_recursive: - payloads.extend( - get_box_folder_payload(box_client, item.id, is_recursive) + box_files_details.extend( + get_box_folder_files_details(box_client, item.id, is_recursive) ) - return payloads + return box_files_details def download_file_by_id(box_client: BoxClient, box_file: File, temp_dir: str) -> str: @@ -184,39 +173,34 @@ def get_file_content_by_id(box_client: BoxClient, box_file_id: str) -> bytes: raise -def get_files_ai_prompt( +def get_ai_response_from_box_files( box_client: BoxClient, - payloads: List[_BoxResourcePayload], ai_prompt: str, + box_files: List[File], individual_document_prompt: bool = True, -) -> List[_BoxResourcePayload]: +) -> List[File]: """ - Gets AI prompts and responses for a list of Box files. + Retrieves AI responses for a prompt based on content within Box files. + + This function takes a Box client, an AI prompt string, a list of Box File objects, and an optional flag indicating prompt mode as input. It utilizes the Box API's AI capabilities to generate AI responses based on the prompt and the content of the provided files. The function then updates the File objects with the prompt and AI response information. Args: box_client (BoxClient): An authenticated Box client object. - payloads (List[_BoxResourcePayload]): A list of _BoxResourcePayload objects - containing information about Box files. - ai_prompt (str): The AI prompt to use for generating responses. - individual_document_prompt (bool, optional): If True, generates an - individual AI prompt and response for each file in payloads. - If False, generates a single prompt and response for all files - combined. Defaults to True. + ai_prompt (str): The AI prompt string to be used for generating responses. + box_files (List[File]): A list of Box File objects representing the files to be analyzed. + individual_document_prompt (bool, optional): A flag indicating whether to generate individual prompts for each file (True) or a single prompt for all files (False). Defaults to True. Returns: - List[_BoxResourcePayload]: The updated list of _BoxResourcePayload objects - with the following attributes added: - - ai_prompt (str): The AI prompt used for the file. - - ai_response (str): The AI response generated for the file - (may be an error message). + List[File]: The original list of Box File objects with additional attributes: + * `ai_prompt`: The AI prompt used for analysis. + * `ai_response`: The AI response generated based on the prompt and file content. Raises: - BoxAPIError: If an error occurs while interacting with the Box API. + BoxAPIError: If an error occurs while interacting with the Box AI API. """ if individual_document_prompt: mode = CreateAiAskMode.SINGLE_ITEM_QA - for payload in payloads: - file = payload.resource_info + for file in box_files: ask_item = CreateAiAskItems(file.id) logger.info(f"Getting AI prompt for file: {file.id} {file.name}") @@ -230,28 +214,23 @@ def get_files_ai_prompt( f"An error occurred while getting AI response for file: {e}", exc_info=True, ) - payload.ai_prompt = ai_prompt - if e.response_info.status_code == 400: - payload.ai_response = "File type not supported by Box AI" - else: - payload.ai_response = e.message - continue - payload.ai_prompt = ai_prompt - payload.ai_response = ai_response.answer + ai_response = None + file.ai_prompt = ai_prompt + file.ai_response = ai_response.answer if ai_response else None else: mode = CreateAiAskMode.MULTIPLE_ITEM_QA - file_ids = [CreateAiAskItems(payload.resource_info.id) for payload in payloads] + file_ids = [CreateAiAskItems(file.id) for file in box_files] # get the AI prompt for the file ai_response = box_client.ai.create_ai_ask( mode=mode, prompt=ai_prompt, items=file_ids ) - for payload in payloads: - payload.ai_prompt = ai_prompt - payload.ai_response = ai_response.answer + for file in box_files: + file.ai_prompt = ai_prompt + file.ai_response = ai_response.answer - return payloads + return box_files def _do_request(box_client: BoxClient, url: str): @@ -284,66 +263,57 @@ def _do_request(box_client: BoxClient, url: str): def get_text_representation( - box_client: BoxClient, payloads: List[_BoxResourcePayload], token_limit: int = 10000 -) -> List[_BoxResourcePayload]: + box_client: BoxClient, box_files: List[File], token_limit: int = 10000 +) -> List[File]: """ - Retrieves and stores the text representation for a list of Box files. + Retrieves and populates the text representation for a list of Box files. - This function attempts to retrieve the pre-generated extracted text for each - file in the payloads list. If the extracted text is not available or needs - generation, it initiates the generation process and stores a placeholder - until the text is ready. + This function takes a Box client, a list of Box File objects, and an optional token limit as input. It attempts to retrieve the extracted text representation for each file using the Box API's representation hints. The function then updates the File objects with the extracted text content, handling cases where text needs generation or is unavailable. Args: box_client (BoxClient): An authenticated Box client object. - payloads (List[_BoxResourcePayload]): A list of _BoxResourcePayload objects - containing information about Box files. - token_limit (int, optional): The maximum number of tokens (words or - characters) to store in the text_representation attribute. - Defaults to 10000. + box_files (List[File]): A list of Box File objects representing the files to extract text from. + token_limit (int, optional): The maximum number of tokens to include in the text representation. Defaults to 10000. Returns: - List[_BoxResourcePayload]: The updated list of _BoxResourcePayload objects - with the following attribute added or updated: - - text_representation (str, optional): The extracted text content - of the file, truncated to token_limit if applicable. None if - the text cannot be retrieved or is still being generated. - """ - for payload in payloads: - box_file = payload.resource_info + List[File]: The original list of Box File objects with an additional attribute: + * `text_representation`: The extracted text content from the file (truncated to token_limit if applicable). + Raises: + BoxAPIError: If an error occurs while interacting with the Box API. + """ + box_files_text_representations: List[File] = [] + for file in box_files: try: # Request the file with the "extracted_text" representation hint - box_file = box_client.files.get_file_by_id( - box_file.id, + file_text_representation = box_client.files.get_file_by_id( + file.id, x_rep_hints="[extracted_text]", fields=["name", "representations"], ) except BoxAPIError as e: logger.error( - f"Error getting file representation {box_file.id}: {e.message}", + f"Error getting file representation {file_text_representation.id}: {e.message}", exc_info=True, ) - payload.text_representation = None - continue + raise # Check if any representations exist - if not box_file.representations.entries: - logger.error(f"No representation for file {box_file.id}") - payload.text_representation = None + if not file_text_representation.representations.entries: + logger.warning(f"No representation for file {file_text_representation.id}") continue # Find the "extracted_text" representation extracted_text_entry = next( ( entry - for entry in box_file.representations.entries + for entry in file_text_representation.representations.entries if entry.representation == "extracted_text" ), None, ) if not extracted_text_entry: - payload.text_representation = None + file.text_representation = None continue # Handle cases where the extracted text needs generation @@ -355,40 +325,39 @@ def get_text_representation( # Download and truncate the raw content raw_content = _do_request(box_client, url) - payload.text_representation = raw_content[:token_limit] if raw_content else None + file.text_representation = raw_content[:token_limit] if raw_content else None - return payloads + box_files_text_representations.append(file) + + return box_files_text_representations def get_files_ai_extract_data( - box_client: BoxClient, payloads: List[_BoxResourcePayload], ai_prompt: str -) -> List[_BoxResourcePayload]: + box_client: BoxClient, ai_prompt: str, box_files: List[File] +) -> List[File]: """ - Extracts data from Box files using Box AI. + Extracts data from Box files using Box AI features. - This function utilizes the Box AI Extract functionality to process each file - in the payloads list according to the provided prompt. The extracted data - is then stored in the ai_response attribute of each payload object. + This function takes a Box client, an AI prompt string, and a list of Box File objects as input. It utilizes the Box AI capabilities, specifically the `AiExtractManager`, to extract data from the files based on the provided prompt. The function then updates the File objects with the prompt and AI extracted data information. Args: box_client (BoxClient): An authenticated Box client object. - payloads (List[_BoxResourcePayload]): A list of _BoxResourcePayload objects - containing information about Box files. - ai_prompt (str): The AI prompt that specifies what data to extract - from the files. + ai_prompt (str): The AI prompt string used to guide data extraction. + box_files (List[File]): A list of Box File objects representing the files to extract data from. Returns: - List[_BoxResourcePayload]: The updated list of _BoxResourcePayload objects - with the following attribute added or updated: - - ai_response (str, optional): The extracted data from the file - based on the AI prompt. May be empty if the extraction fails. + List[File]: The original list of Box File objects with additional attributes: + * `ai_prompt`: The AI prompt used for data extraction. + * `ai_response`: The extracted data from the file based on the prompt (format may vary depending on Box AI configuration). + + Raises: + BoxAPIError: If an error occurs while interacting with the Box AI API. """ ai_extract_manager = AiExtractManager( auth=box_client.auth, network_session=box_client.network_session ) - for payload in payloads: - file = payload.resource_info + for file in box_files: ask_item = CreateAiExtractItems(file.id) logger.info(f"Getting AI extracted data for file: {file.id} {file.name}") @@ -402,11 +371,12 @@ def get_files_ai_extract_data( f"An error occurred while getting AI extracted data for file: {e}", exc_info=True, ) - # payload.ai_response = e.message - continue - payload.ai_response = ai_response.answer + raise + + file.ai_prompt = ai_prompt + file.ai_response = ai_response.answer - return payloads + return box_files def search_files( diff --git a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_llama_adaptors.py b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_llama_adaptors.py new file mode 100644 index 0000000000000..1b42f6a101de7 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_llama_adaptors.py @@ -0,0 +1,64 @@ +from box_sdk_gen import File +from llama_index.core.schema import Document + + +def box_file_to_llama_document_metadata(box_file: File) -> dict: + """ + Convert a Box SDK File object to a Llama Document metadata object. + + Args: + box_file: Box SDK File object + + Returns: + Llama Document metadata object + """ + # massage data + if box_file.path_collection is not None: + path_collection = "/".join( + [entry.name for entry in box_file.path_collection.entries] + ) + + return { + "box_file_id": box_file.id, + "description": box_file.description, + "size": box_file.size, + "path_collection": path_collection, + "created_at": box_file.created_at, + "modified_at": box_file.modified_at, + "trashed_at": box_file.trashed_at, + "purged_at": box_file.purged_at, + "content_created_at": box_file.content_created_at, + "content_modified_at": box_file.content_modified_at, + "created_by": f"{box_file.created_by.id},{box_file.created_by.name},{box_file.created_by.login}", + "modified_by": f"{box_file.modified_by.id},{box_file.modified_by.name},{box_file.modified_by.login}", + "owned_by": f"{box_file.owned_by.id},{box_file.owned_by.name},{box_file.owned_by.login}", + # shared_link: Optional[FileSharedLinkField] = None, + "parent": box_file.parent.id, + "item_status": box_file.item_status.value, + "sequence_id": box_file.sequence_id, + "name": box_file.name, + "sha_1": box_file.sha_1, + # "file_version": box_file.file_version.id, + "etag": box_file.etag, + "type": box_file.type.value, + } + + +def box_file_to_llama_document(box_file: File) -> Document: + """ + Convert a Box SDK File object to a Llama Document object. + + Args: + box_file: Box SDK File object + + Returns: + Llama Document object + """ + document = Document() + + # this separations between extra_info and metadata seem to be pointless + # when we update one, the other gets also updated with the same data + document.extra_info = box_file.to_dict() + document.metadata = box_file_to_llama_document_metadata(box_file) + + return document diff --git a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReader/base.py b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReader/base.py index 01de593527d2f..c8a1a261f3d88 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReader/base.py +++ b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReader/base.py @@ -13,10 +13,10 @@ from llama_index.core.bridge.pydantic import Field from llama_index.readers.box.BoxAPI.box_api import ( - _BoxResourcePayload, + add_extra_header_to_box_client, box_check_connection, - get_box_files_payload, - get_box_folder_payload, + get_box_files_details, + get_box_folder_files_details, download_file_by_id, get_file_content_by_id, search_files, @@ -27,6 +27,11 @@ BoxClient, SearchForContentScope, SearchForContentContentTypes, + File, +) + +from llama_index.readers.box.BoxAPI.box_llama_adaptors import ( + box_file_to_llama_document_metadata, ) logger = logging.getLogger(__name__) @@ -43,7 +48,7 @@ def __init__( self, box_client: BoxClient, ): - self._box_client = box_client + self._box_client = add_extra_header_to_box_client(box_client) @abstractmethod def load_data( @@ -78,11 +83,11 @@ def get_resource_info(self, box_file_id: str) -> Dict: # Connect to Box box_check_connection(self._box_client) - resource = get_box_files_payload( + resource = get_box_files_details( box_client=self._box_client, file_ids=[box_file_id] ) - return resource[0].resource_info.to_dict() + return resource[0].to_dict() def list_resources( self, @@ -111,21 +116,22 @@ def list_resources( """ # Connect to Box box_check_connection(self._box_client) + # Get the file resources - payloads: List[_BoxResourcePayload] = [] + box_files: List[File] = [] if file_ids is not None: - payloads.extend( - get_box_files_payload(box_client=self._box_client, file_ids=file_ids) + box_files.extend( + get_box_files_details(box_client=self._box_client, file_ids=file_ids) ) elif folder_id is not None: - payloads.extend( - get_box_folder_payload( + box_files.extend( + get_box_folder_files_details( box_client=self._box_client, folder_id=folder_id, is_recursive=is_recursive, ) ) - return [payload.resource_info.id for payload in payloads] + return [file.id for file in box_files] def read_file_content(self, input_file: Path, **kwargs) -> bytes: file_id = input_file.name @@ -311,14 +317,14 @@ def load_data( box_check_connection(self._box_client) # Get the file resources - payloads: List[_BoxResourcePayload] = [] + box_files: List[File] = [] if file_ids is not None: - payloads.extend( - get_box_files_payload(box_client=self._box_client, file_ids=file_ids) + box_files.extend( + get_box_files_details(box_client=self._box_client, file_ids=file_ids) ) elif folder_id is not None: - payloads.extend( - get_box_folder_payload( + box_files.extend( + get_box_folder_files_details( box_client=self._box_client, folder_id=folder_id, is_recursive=is_recursive, @@ -326,11 +332,11 @@ def load_data( ) with tempfile.TemporaryDirectory() as temp_dir: - payloads = self._download_files(payloads, temp_dir) + box_files_with_path = self._download_files(box_files, temp_dir) file_name_to_metadata = { - payload.downloaded_file_path: payload.resource_info.to_dict() - for payload in payloads + file.downloaded_file_path: box_file_to_llama_document_metadata(file) + for file in box_files_with_path } def get_metadata(filename: str) -> Any: @@ -343,9 +349,7 @@ def get_metadata(filename: str) -> Any: ) return simple_loader.load_data() - def _download_files( - self, payloads: List[_BoxResourcePayload], temp_dir: str - ) -> List[_BoxResourcePayload]: + def _download_files(self, box_files: List[File], temp_dir: str) -> List[File]: """ Downloads Box files and updates the corresponding payloads with local paths. @@ -365,10 +369,12 @@ def _download_files( List[_BoxResourcePayload]: The updated list of _BoxResourcePayload objects with the downloaded_file_path attribute set for each payload. """ - for payload in payloads: - file = payload.resource_info + box_files_with_path: List[File] = [] + + for file in box_files: local_path = download_file_by_id( box_client=self._box_client, box_file=file, temp_dir=temp_dir ) - payload.downloaded_file_path = local_path - return payloads + file.downloaded_file_path = local_path + box_files_with_path.append(file) + return box_files_with_path diff --git a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderAIExtraction/base.py b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderAIExtraction/base.py index bd65a84a725db..5eab7371d4464 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderAIExtraction/base.py +++ b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderAIExtraction/base.py @@ -5,15 +5,16 @@ from llama_index.core.schema import Document from llama_index.readers.box import BoxReaderBase from llama_index.readers.box.BoxAPI.box_api import ( - _BoxResourcePayload, - get_box_files_payload, - get_box_folder_payload, + get_box_files_details, + get_box_folder_files_details, get_files_ai_extract_data, + box_check_connection, ) +from llama_index.readers.box.BoxAPI.box_llama_adaptors import box_file_to_llama_document from box_sdk_gen import ( - BoxAPIError, BoxClient, + File, ) @@ -76,48 +77,36 @@ def load_data( data and file metadata. """ # check if the box client is authenticated - try: - me = self._box_client.users.get_user_me() - except BoxAPIError as e: - logger.error( - f"An error occurred while connecting to Box: {e}", exc_info=True - ) - raise + box_check_connection(self._box_client) - docs = [] - payloads: List[_BoxResourcePayload] = [] + docs: List[Document] = [] + box_files: List[File] = [] # get payload information if file_ids is not None: - payloads.extend( - get_box_files_payload(box_client=self._box_client, file_ids=file_ids) + box_files.extend( + get_box_files_details(box_client=self._box_client, file_ids=file_ids) ) elif folder_id is not None: - payloads.extend( - get_box_folder_payload( + box_files.extend( + get_box_folder_files_details( box_client=self._box_client, folder_id=folder_id, is_recursive=is_recursive, ) ) - payloads = get_files_ai_extract_data( + box_files = get_files_ai_extract_data( box_client=self._box_client, - payloads=payloads, + box_files=box_files, ai_prompt=ai_prompt, ) - for payload in payloads: - file = payload.resource_info - ai_response = payload.ai_response - - # create a document - doc = Document( - # id=file.id, - extra_info=file.to_dict(), - metadata=file.to_dict(), - text=ai_response, - ) + for file in box_files: + doc = box_file_to_llama_document(file) + doc.text = file.ai_response if file.ai_response else "" + doc.metadata["ai_prompt"] = file.ai_prompt + doc.metadata["ai_response"] = file.ai_response docs.append(doc) return docs diff --git a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderAIPrompt/base.py b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderAIPrompt/base.py index 9dfc5c9ad9583..4ff736eb2ab14 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderAIPrompt/base.py +++ b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderAIPrompt/base.py @@ -4,16 +4,15 @@ from llama_index.core.schema import Document from llama_index.readers.box import BoxReaderBase from llama_index.readers.box.BoxAPI.box_api import ( - _BoxResourcePayload, box_check_connection, - get_box_files_payload, - get_box_folder_payload, - get_files_ai_prompt, + get_box_files_details, + get_box_folder_files_details, + get_ai_response_from_box_files, ) -from box_sdk_gen import ( - BoxClient, -) +from box_sdk_gen import BoxClient, File + +from llama_index.readers.box.BoxAPI.box_llama_adaptors import box_file_to_llama_document logger = logging.getLogger(__name__) @@ -82,41 +81,35 @@ def load_data( # Connect to Box box_check_connection(self._box_client) - docs = [] - payloads: List[_BoxResourcePayload] = [] + docs: List[Document] = [] + box_files: List[File] = [] - # get payload information + # get box files information if file_ids is not None: - payloads.extend( - get_box_files_payload(box_client=self._box_client, file_ids=file_ids) + box_files.extend( + get_box_files_details(box_client=self._box_client, file_ids=file_ids) ) elif folder_id is not None: - payloads.extend( - get_box_folder_payload( + box_files.extend( + get_box_folder_files_details( box_client=self._box_client, folder_id=folder_id, is_recursive=is_recursive, ) ) - payloads = get_files_ai_prompt( + box_files = get_ai_response_from_box_files( box_client=self._box_client, - payloads=payloads, + box_files=box_files, ai_prompt=ai_prompt, individual_document_prompt=individual_document_prompt, ) - for payload in payloads: - file = payload.resource_info - ai_response = payload.ai_response - - # create a document - doc = Document( - # id=file.id, - extra_info=file.to_dict(), - metadata=file.to_dict(), - text=ai_response, - ) + for file in box_files: + doc = box_file_to_llama_document(file) + doc.text = file.ai_response if file.ai_response else "" + doc.metadata["ai_prompt"] = file.ai_prompt + doc.metadata["ai_response"] = file.ai_response if file.ai_response else "" docs.append(doc) return docs diff --git a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderTextExtraction/base.py b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderTextExtraction/base.py index 4b656520643ad..c65c60f40cf83 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderTextExtraction/base.py +++ b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxReaderTextExtraction/base.py @@ -3,16 +3,17 @@ from llama_index.core.schema import Document from llama_index.readers.box.BoxAPI.box_api import ( - _BoxResourcePayload, box_check_connection, - get_box_files_payload, - get_box_folder_payload, + get_box_files_details, + get_box_folder_files_details, get_text_representation, ) +from llama_index.readers.box.BoxAPI.box_llama_adaptors import box_file_to_llama_document from llama_index.readers.box import BoxReaderBase from box_sdk_gen import ( BoxClient, + File, ) @@ -72,36 +73,30 @@ def load_data( # Connect to Box box_check_connection(self._box_client) - docs = [] - payloads: List[_BoxResourcePayload] = [] - # get payload information + docs: List[Document] = [] + box_files: List[File] = [] + + # get Box files details if file_ids is not None: - payloads.extend( - get_box_files_payload(box_client=self._box_client, file_ids=file_ids) + box_files.extend( + get_box_files_details(box_client=self._box_client, file_ids=file_ids) ) elif folder_id is not None: - payloads.extend( - get_box_folder_payload( + box_files.extend( + get_box_folder_files_details( box_client=self._box_client, folder_id=folder_id, is_recursive=is_recursive, ) ) - payloads = get_text_representation( + box_files = get_text_representation( box_client=self._box_client, - payloads=payloads, + box_files=box_files, ) - for payload in payloads: - file = payload.resource_info - - # create a document - doc = Document( - # id=file.id, - extra_info=file.to_dict(), - metadata=file.to_dict(), - text=payload.text_representation if payload.text_representation else "", - ) + for file in box_files: + doc = box_file_to_llama_document(file) + doc.text = file.text_representation if file.text_representation else "" docs.append(doc) return docs diff --git a/llama-index-integrations/readers/llama-index-readers-box/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-box/pyproject.toml index 831388bc8a40f..b9180d32cc7c5 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-box/pyproject.toml @@ -37,12 +37,12 @@ maintainers = [ name = "llama-index-readers-box" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" box-sdk-gen = "^1.0.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_ai_extract.py b/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_ai_extract.py index 50d9a0719dd9d..c199a36077dd3 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_ai_extract.py +++ b/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_ai_extract.py @@ -66,6 +66,7 @@ def test_box_reader_ai_extract_folder( docs = reader.load_data( folder_id=data["test_folder_invoice_po_id"], ai_prompt=AI_PROMPT, + is_recursive=True, ) assert len(docs) > 2 @@ -95,7 +96,7 @@ def test_box_reader_load_resource(box_client_ccg_integration_testing: BoxClient) docs = reader.load_resource(resource_id, ai_prompt=AI_PROMPT) assert docs is not None assert len(docs) == 1 - assert docs[0].extra_info["id"] == resource_id + assert docs[0].metadata["box_file_id"] == resource_id doc_0 = json.loads(docs[0].text) assert doc_0["doc_type"] == "Invoice" assert doc_0["total"] == "$1,050" diff --git a/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_ai_prompt.py b/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_ai_prompt.py index 9267ab8aff90a..843c75710f78c 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_ai_prompt.py +++ b/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_ai_prompt.py @@ -97,7 +97,7 @@ def test_box_reader_load_resource(box_client_ccg_integration_testing: BoxClient) docs = reader.load_resource(resource_id, ai_prompt="summarize this document") assert docs is not None assert len(docs) == 1 - assert docs[0].extra_info["id"] == resource_id + assert docs[0].metadata["box_file_id"] == resource_id assert docs[0].text is not None diff --git a/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_reader.py b/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_reader.py index 686456ac12d39..7cd1681d7f988 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_reader.py +++ b/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_reader.py @@ -83,7 +83,7 @@ def test_box_reader_load_resource(box_client_ccg_integration_testing: BoxClient) doc = reader.load_resource(resource_id) assert doc is not None assert len(doc) == 1 - assert doc[0].extra_info["id"] == resource_id + assert doc[0].metadata["box_file_id"] == resource_id def test_box_reader_file_content(box_client_ccg_integration_testing): diff --git a/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_text_extraction.py b/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_text_extraction.py index 1134ff8abbfd0..bf04cad4cfd8a 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_text_extraction.py +++ b/llama-index-integrations/readers/llama-index-readers-box/tests/test_readers_box_text_extraction.py @@ -86,7 +86,7 @@ def test_box_reader_load_resource(box_client_ccg_integration_testing: BoxClient) docs = reader.load_resource(resource_id) assert docs is not None assert len(docs) == 1 - assert docs[0].extra_info["id"] == resource_id + assert docs[0].metadata["box_file_id"] == resource_id assert docs[0].text is not None diff --git a/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/pyproject.toml index d2a549d1a7e23..8e9a2e6c6c767 100644 --- a/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-readers-chatgpt-plugin" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-chroma/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-chroma/pyproject.toml index 0bf7f456f0dd0..f7e0fe7adf291 100644 --- a/llama-index-integrations/readers/llama-index-readers-chroma/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-chroma/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["atroyn"] name = "llama-index-readers-chroma" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" chromadb = "^0.4.22" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-clickhouse/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-clickhouse/pyproject.toml index a0e72ecb75039..89b457da31f43 100644 --- a/llama-index-integrations/readers/llama-index-readers-clickhouse/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-clickhouse/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-clickhouse" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "0.10.0" clickhouse-connect = "^0.7.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-confluence/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-confluence/pyproject.toml index 4c11777ca9550..21cb68aed91df 100644 --- a/llama-index-integrations/readers/llama-index-readers-confluence/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-confluence/pyproject.toml @@ -28,13 +28,12 @@ license = "MIT" maintainers = ["zywilliamli"] name = "llama-index-readers-confluence" readme = "README.md" -version = "0.1.7" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" atlassian-python-api = "^3.41.9" -html2text = "^2020.1.16" +html2text = "^2024.2.26" pytesseract = "^0.3.10" pdf2image = "^1.17.0" pillow = "^10.2.0" @@ -42,6 +41,7 @@ docx2txt = "^0.8" xlrd = "^2.0.1" svglib = "^1.5.1" retrying = "^1.3.4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-couchbase/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-couchbase/pyproject.toml index 88e102aace7a3..78daceab66904 100644 --- a/llama-index-integrations/readers/llama-index-readers-couchbase/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-couchbase/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["nithishr"] name = "llama-index-readers-couchbase" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" couchbase = "^4.1.11" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-couchdb/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-couchdb/pyproject.toml index 86cae1f718e88..7af950d4a8ce5 100644 --- a/llama-index-integrations/readers/llama-index-readers-couchdb/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-couchdb/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["technosophy"] name = "llama-index-readers-couchdb" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" couchdb3 = "^1.2.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-dad-jokes/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-dad-jokes/pyproject.toml index 9b7fec00b1332..2646c98c663eb 100644 --- a/llama-index-integrations/readers/llama-index-readers-dad-jokes/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-dad-jokes/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["sidu"] name = "llama-index-readers-dad-jokes" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-dashscope/llama_index/readers/dashscope/base.py b/llama-index-integrations/readers/llama-index-readers-dashscope/llama_index/readers/dashscope/base.py index 192e84f9c9d24..cbb150c276038 100644 --- a/llama-index-integrations/readers/llama-index-readers-dashscope/llama_index/readers/dashscope/base.py +++ b/llama-index-integrations/readers/llama-index-readers-dashscope/llama_index/readers/dashscope/base.py @@ -14,7 +14,7 @@ from typing import List, Optional, Union from llama_index.core.async_utils import run_jobs -from llama_index.core.bridge.pydantic import Field, validator +from llama_index.core.bridge.pydantic import Field, field_validator from llama_index.core.readers.base import BasePydanticReader from llama_index.core.schema import Document from llama_index.readers.dashscope.utils import * @@ -86,7 +86,7 @@ class DashScopeParse(BasePydanticReader): description="Whether or not to return parsed text content.", ) - @validator("api_key", pre=True, always=True) + @field_validator("api_key", mode="before") def validate_api_key(cls, v: str) -> str: """Validate the API key.""" if not v: @@ -99,7 +99,7 @@ def validate_api_key(cls, v: str) -> str: return v - @validator("workspace_id", pre=True, always=True) + @field_validator("workspace_id", mode="before") def validate_workspace_id(cls, v: str) -> str: """Validate the Workspace.""" if not v: @@ -109,7 +109,7 @@ def validate_workspace_id(cls, v: str) -> str: return v - @validator("category_id", pre=True, always=True) + @field_validator("category_id", mode="before") def validate_category_id(cls, v: str) -> str: """Validate the category.""" if not v: @@ -118,7 +118,7 @@ def validate_category_id(cls, v: str) -> str: return os.getenv("DASHSCOPE_CATEGORY_ID", DASHSCOPE_DEFAULT_DC_CATEGORY) return v - @validator("base_url", pre=True, always=True) + @field_validator("base_url", mode="before") def validate_base_url(cls, v: str) -> str: """Validate the base URL.""" if v and v != DASHSCOPE_DEFAULT_BASE_URL: diff --git a/llama-index-integrations/readers/llama-index-readers-dashscope/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-dashscope/pyproject.toml index e2057c5bba128..3d63efb2a9f67 100644 --- a/llama-index-integrations/readers/llama-index-readers-dashscope/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-dashscope/pyproject.toml @@ -30,13 +30,13 @@ license = "MIT" name = "llama-index-readers-dashscope" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" oss2 = "^2.18.5" retrying = "^1.3.4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-dashvector/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-dashvector/pyproject.toml index 87a31e4596195..b4cd74ca907a9 100644 --- a/llama-index-integrations/readers/llama-index-readers-dashvector/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-dashvector/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-dashvector" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" dashvector = "^1.0.9" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-database/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-database/pyproject.toml index 8415275314aa4..f7c2850ff78b9 100644 --- a/llama-index-integrations/readers/llama-index-readers-database/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-database/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["kevinqz"] name = "llama-index-readers-database" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-deeplake/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-deeplake/pyproject.toml index d0585b749b526..8344ba2d4b5d6 100644 --- a/llama-index-integrations/readers/llama-index-readers-deeplake/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-deeplake/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["adolkhan"] name = "llama-index-readers-deeplake" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-discord/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-discord/pyproject.toml index ec513e77198c2..62212e152cab3 100644 --- a/llama-index-integrations/readers/llama-index-readers-discord/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-discord/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-readers-discord" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-docstring-walker/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-docstring-walker/pyproject.toml index 957693681509c..bd2b915dc2c11 100644 --- a/llama-index-integrations/readers/llama-index-readers-docstring-walker/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-docstring-walker/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["Filip Wojcik"] name = "llama-index-readers-docstring-walker" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-docugami/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-docugami/pyproject.toml index 11da0d422a415..80cca973e566c 100644 --- a/llama-index-integrations/readers/llama-index-readers-docugami/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-docugami/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["tjaffri"] name = "llama-index-readers-docugami" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" dgml-utils = "^0.3.1" lxml = "~4.9.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/pyproject.toml index e5fa55cb02894..be204c0869cf3 100644 --- a/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["Athe-kunal"] name = "llama-index-readers-earnings-call-transcript" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" tenacity = "^8.2.3" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-elasticsearch/llama_index/readers/elasticsearch/base.py b/llama-index-integrations/readers/llama-index-readers-elasticsearch/llama_index/readers/elasticsearch/base.py index d20bb967ac09a..7a42a0308893f 100644 --- a/llama-index-integrations/readers/llama-index-readers-elasticsearch/llama_index/readers/elasticsearch/base.py +++ b/llama-index-integrations/readers/llama-index-readers-elasticsearch/llama_index/readers/elasticsearch/base.py @@ -35,6 +35,9 @@ def __init__( self, endpoint: str, index: str, httpx_client_args: Optional[dict] = None ): """Initialize with parameters.""" + super().__init__( + endpoint=endpoint, index=index, httpx_client_args=httpx_client_args + ) import_err_msg = """ `httpx` package not found. Install via `pip install httpx` """ @@ -44,10 +47,6 @@ def __init__( raise ImportError(import_err_msg) self._client = httpx.Client(base_url=endpoint, **(httpx_client_args or {})) - super().__init__( - endpoint=endpoint, index=index, httpx_client_args=httpx_client_args - ) - @classmethod def class_name(cls) -> str: return "ElasticsearchReader" diff --git a/llama-index-integrations/readers/llama-index-readers-elasticsearch/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-elasticsearch/pyproject.toml index eecd9c4fbee81..c3a6dfc8bbddd 100644 --- a/llama-index-integrations/readers/llama-index-readers-elasticsearch/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-elasticsearch/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["jaylmiller"] name = "llama-index-readers-elasticsearch" readme = "README.md" -version = "0.1.4" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-faiss/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-faiss/pyproject.toml index 9a6c6c36f44af..d86d7575b8a5c 100644 --- a/llama-index-integrations/readers/llama-index-readers-faiss/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-faiss/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-readers-faiss" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-feedly-rss/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-feedly-rss/pyproject.toml index fe0638b341f0f..1fabdf6d0f574 100644 --- a/llama-index-integrations/readers/llama-index-readers-feedly-rss/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-feedly-rss/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["kychanbp"] name = "llama-index-readers-feedly-rss" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" feedly-client = "^0.26" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-feishu-docs/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-feishu-docs/pyproject.toml index 334a176285c0e..63fb64e4dc6f4 100644 --- a/llama-index-integrations/readers/llama-index-readers-feishu-docs/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-feishu-docs/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["ma-chengcheng"] name = "llama-index-readers-feishu-docs" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-feishu-wiki/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-feishu-wiki/pyproject.toml index d5271f85f888e..ab724f4f5f367 100644 --- a/llama-index-integrations/readers/llama-index-readers-feishu-wiki/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-feishu-wiki/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["zhourunlai"] name = "llama-index-readers-feishu-wiki" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/docs/base.py b/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/docs/base.py index ec7d600c56ba6..6a75489ffdc87 100644 --- a/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/docs/base.py +++ b/llama-index-integrations/readers/llama-index-readers-file/llama_index/readers/file/docs/base.py @@ -53,7 +53,7 @@ def load_data( "pypdf is required to read PDF files: `pip install pypdf`" ) fs = fs or get_default_fs() - with fs.open(file, "rb") as fp: + with fs.open(str(file), "rb") as fp: # Load the file in memory if the filesystem is not the default one to avoid # issues with pypdf stream = fp if is_default_fs(fs) else io.BytesIO(fp.read()) @@ -119,7 +119,7 @@ def load_data( ) if fs: - with fs.open(file) as f: + with fs.open(str(file)) as f: text = docx2txt.process(f) else: text = docx2txt.process(file) diff --git a/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml index 1cb8cd3808b7f..87617d5196262 100644 --- a/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml @@ -51,16 +51,17 @@ license = "MIT" maintainers = ["FarisHijazi", "Haowjy", "ephe-meral", "hursh-desai", "iamarunbrahma", "jon-chuang", "mmaatouk", "ravi03071991", "sangwongenip", "thejessezhang"] name = "llama-index-readers-file" readme = "README.md" -version = "0.1.32" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.37.post1" # pymupdf is AGPLv3-licensed, so it's optional pymupdf = {optional = true, version = "^1.23.21"} beautifulsoup4 = "^4.12.3" pypdf = "^4.0.1" striprtf = "^0.0.26" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.extras] pymupdf = [ diff --git a/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/pyproject.toml index abe3343691575..e27f57d9dd033 100644 --- a/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["ajay"] name = "llama-index-readers-firebase-realtimedb" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" firebase-admin = "^6.4.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-firestore/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-firestore/pyproject.toml index 9e65d183da1fb..857bba444323e 100644 --- a/llama-index-integrations/readers/llama-index-readers-firestore/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-firestore/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["rayzhudev"] name = "llama-index-readers-firestore" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" google-cloud-firestore = "^2.14.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-gcs/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-gcs/CHANGELOG.md index cbc1f87314fe5..411901ea57d52 100644 --- a/llama-index-integrations/readers/llama-index-readers-gcs/CHANGELOG.md +++ b/llama-index-integrations/readers/llama-index-readers-gcs/CHANGELOG.md @@ -1,5 +1,23 @@ # CHANGELOG +## [0.1.8] + +### Added + +- Implemented ResourcesReaderMixin and FileSystemReaderMixin for extended functionality +- New methods: list_resources, get_resource_info, load_resource, read_file_content +- Comprehensive logging for better debugging and operational insights +- Detailed exception handling for all GCSReader methods +- Comprehensive docstrings for GCSReader class and all its methods + +### Changed + +- Refactored GCSReader to use SimpleDirectoryReader for file operations +- Improved authentication error handling in GCSReader +- Enhanced `get_resource_info` method to return more detailed GCS object information +- Updated `load_data` method to use SimpleDirectoryReader +- Refactored code for better readability and maintainability + ## [0.1.0] - 2024-03-25 - Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-gcs/README.md b/llama-index-integrations/readers/llama-index-readers-gcs/README.md index 3463060f485d1..0c57c4faf62af 100644 --- a/llama-index-integrations/readers/llama-index-readers-gcs/README.md +++ b/llama-index-integrations/readers/llama-index-readers-gcs/README.md @@ -1,22 +1,111 @@ # GCS File or Directory Loader -This loader parses any file stored on GCS, or the entire Bucket (with an optional prefix filter) if no particular file is specified. When initializing `GCSReader`, you may pass in your [GCP Service Account Key](https://cloud.google.com/iam/docs/keys-create-delete). If none are found, the loader assumes they are stored in `~/.gcp/credentials`. +This loader parses any file stored on Google Cloud Storage (GCS), or the entire Bucket (with an optional prefix filter) if no particular file is specified. It now supports more advanced operations through the implementation of ResourcesReaderMixin and FileSystemReaderMixin. -All files are parsed with `SimpleDirectoryReader`. Hence, you may also specify a custom `file_extractor`, relying on any of the loaders in this library (or your own)! +## Features -## Usage +- Parse single files or entire buckets from GCS +- List resources in GCS buckets +- Retrieve detailed information about GCS objects +- Load specific resources from GCS +- Read file content directly +- Supports various authentication methods +- Comprehensive logging for easier debugging +- Robust error handling for improved reliability + +## Authentication + +When initializing `GCSReader`, you may pass in your [GCP Service Account Key](https://cloud.google.com/iam/docs/keys-create-delete) in several ways: -To use this loader, you need to pass in the name of your GCS Bucket. After that, if you want to just parse a single file, pass in its key. Note that if the file is nested in a subdirectory, the key should contain that, so like `subdirectory/input.txt`. +1. As a file path (`service_account_key_path`) +2. As a JSON string (`service_account_key_json`) +3. As a dictionary (`service_account_key`) -Otherwise, you may specify a prefix if you only want to parse certain files in the Bucket, or a subdirectory. GCP Service Account Key credentials may either be passed in during initialization or stored locally (see above). +If no credentials are provided, the loader will attempt to use default credentials. + +## Usage + +To use this loader, you need to pass in the name of your GCS Bucket. You can then either parse a single file by passing its key, or parse multiple files using a prefix. ```python -loader = GCSReader( +from llama_index import GCSReader +import logging + +# Set up logging (optional, but recommended) +logging.basicConfig(level=logging.INFO) + +# Initialize the reader +reader = GCSReader( bucket="scrabble-dictionary", - key="dictionary.txt", + key="dictionary.txt", # Optional: specify a single file + # prefix="subdirectory/", # Optional: specify a prefix to filter files service_account_key_json="[SERVICE_ACCOUNT_KEY_JSON]", ) -documents = loader.load_data() + +# Load data +documents = reader.load_data() + +# List resources in the bucket +resources = reader.list_resources() + +# Get information about a specific resource +resource_info = reader.get_resource_info("dictionary.txt") + +# Load a specific resource +specific_doc = reader.load_resource("dictionary.txt") + +# Read file content directly +file_content = reader.read_file_content("dictionary.txt") + +print(f"Loaded {len(documents)} documents") +print(f"Found {len(resources)} resources") +print(f"Resource info: {resource_info}") +print(f"Specific document: {specific_doc}") +print(f"File content length: {len(file_content)} bytes") +``` + +Note: If the file is nested in a subdirectory, the key should contain that, e.g., `subdirectory/input.txt`. + +## Advanced Usage + +All files are parsed with `SimpleDirectoryReader`. You may specify a custom `file_extractor`, relying on any of the loaders in the LlamaIndex library (or your own)! + +```python +from llama_index import GCSReader, SimpleMongoReader + +reader = GCSReader( + bucket="my-bucket", + file_extractor={ + ".mongo": SimpleMongoReader(), + # Add more custom extractors as needed + }, +) +``` + +## Error Handling + +The GCSReader now includes comprehensive error handling. You can catch exceptions to handle specific error cases: + +```python +from google.auth.exceptions import DefaultCredentialsError + +try: + reader = GCSReader(bucket="your-bucket-name") + documents = reader.load_data() +except DefaultCredentialsError: + print("Authentication failed. Please check your credentials.") +except Exception as e: + print(f"An error occurred: {str(e)}") +``` + +## Logging + +To get insights into the GCSReader's operations, configure logging in your application: + +```python +import logging + +logging.basicConfig(level=logging.INFO) ``` -This loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/). +This loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/). For more advanced usage, including custom file extractors, metadata extraction, and working with specific file types, please refer to the [LlamaIndex documentation](https://docs.llamaindex.ai/). diff --git a/llama-index-integrations/readers/llama-index-readers-gcs/llama_index/readers/gcs/base.py b/llama-index-integrations/readers/llama-index-readers-gcs/llama_index/readers/gcs/base.py index 3b8f6b8dc9b72..6a592379c2910 100644 --- a/llama-index-integrations/readers/llama-index-readers-gcs/llama_index/readers/gcs/base.py +++ b/llama-index-integrations/readers/llama-index-readers-gcs/llama_index/readers/gcs/base.py @@ -4,46 +4,57 @@ A loader that fetches a file or iterates through a directory on Google Cloud Storage (GCS). """ + import json +import logging from typing import Callable, Dict, List, Optional, Union +from typing_extensions import Annotated +from datetime import datetime +from pathlib import Path from google.oauth2 import service_account -from llama_index.core.readers import SimpleDirectoryReader -from llama_index.core.readers.base import BaseReader, BasePydanticReader +from google.auth.exceptions import DefaultCredentialsError +from llama_index.core.readers import SimpleDirectoryReader, FileSystemReaderMixin +from llama_index.core.readers.base import ( + BasePydanticReader, + ResourcesReaderMixin, + BaseReader, +) from llama_index.core.schema import Document -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, WithJsonSchema + +# Set up logging +logger = logging.getLogger(__name__) -# Scope for reading and downloading GCS files SCOPES = ["https://www.googleapis.com/auth/devstorage.read_only"] -class GCSReader(BasePydanticReader): +FileMetadataCallable = Annotated[ + Callable[[str], Dict], + WithJsonSchema({"type": "string"}), +] + + +class GCSReader(BasePydanticReader, ResourcesReaderMixin, FileSystemReaderMixin): """ - General reader for any GCS file or directory. - - If key is not set, the entire bucket (filtered by prefix) is parsed. - - Args: - bucket (str): the name of your GCS bucket - key (Optional[str]): the name of the specific file. If none is provided, - this loader will iterate through the entire bucket. - prefix (Optional[str]): the prefix to filter by in the case that the loader - iterates through the entire bucket. Defaults to empty string. - recursive (bool): Whether to recursively search in subdirectories. - True by default. - file_extractor (Optional[Dict[str, BaseReader]]): A mapping of file - extension to a BaseReader class that specifies how to convert that file - to text. See `SimpleDirectoryReader` for more details. - required_exts (Optional[List[str]]): List of required extensions. - Default is None. - num_files_limit (Optional[int]): Maximum number of files to read. - Default is None. - file_metadata (Optional[Callable[str, Dict]]): A function that takes - in a filename and returns a Dict of metadata for the Document. - Default is None. - service_account_key (Optional[Dict[str, str]]): provide GCP service account key directly. - service_account_key_json (Optional[str]): provide GCP service account key as a JSON string. - service_account_key_path (Optional[str]): provide path to file containing GCP service account key. + A reader for Google Cloud Storage (GCS) files and directories. + + This class allows reading files from GCS, listing resources, and retrieving resource information. + It supports authentication via service account keys and implements various reader mixins. + + Attributes: + bucket (str): The name of the GCS bucket. + key (Optional[str]): The specific file key to read. If None, the entire bucket is parsed. + prefix (Optional[str]): The prefix to filter by when iterating through the bucket. + recursive (bool): Whether to recursively search in subdirectories. + file_extractor (Optional[Dict[str, Union[str, BaseReader]]]): Custom file extractors. + required_exts (Optional[List[str]]): List of required file extensions. + filename_as_id (bool): Whether to use the filename as the document ID. + num_files_limit (Optional[int]): Maximum number of files to read. + file_metadata (Optional[Callable[[str], Dict]]): Function to extract metadata from filenames. + service_account_key (Optional[Dict[str, str]]): Service account key as a dictionary. + service_account_key_json (Optional[str]): Service account key as a JSON string. + service_account_key_path (Optional[str]): Path to the service account key file. """ is_remote: bool = True @@ -58,38 +69,66 @@ class GCSReader(BasePydanticReader): required_exts: Optional[List[str]] = None filename_as_id: bool = True num_files_limit: Optional[int] = None - file_metadata: Optional[Callable[[str], Dict]] = Field(default=None, exclude=True) + file_metadata: Optional[FileMetadataCallable] = Field(default=None, exclude=True) service_account_key: Optional[Dict[str, str]] = None service_account_key_json: Optional[str] = None service_account_key_path: Optional[str] = None @classmethod def class_name(cls) -> str: + """Return the name of the class.""" return "GCSReader" - def load_gcs_files_as_docs(self) -> List[Document]: - """Load file(s) from GCS.""" + def _get_gcsfs(self): + """ + Create and return a GCSFileSystem object. + + This method handles authentication using the provided service account credentials. + + Returns: + GCSFileSystem: An authenticated GCSFileSystem object. + + Raises: + ValueError: If no valid authentication method is provided. + DefaultCredentialsError: If there's an issue with the provided credentials. + """ from gcsfs import GCSFileSystem - if self.service_account_key is not None: - creds = service_account.Credentials.from_service_account_info( - self.service_account_key, scopes=SCOPES - ) - elif self.service_account_key_json is not None: - creds = service_account.Credentials.from_service_account_info( - json.loads(self.service_account_key_json), scopes=SCOPES - ) - elif self.service_account_key_path is not None: - creds = service_account.Credentials.from_service_account_file( - self.service_account_key_path, scopes=SCOPES - ) - else: - # Use anonymous access if none are specified - creds = "anon" + try: + if self.service_account_key is not None: + creds = service_account.Credentials.from_service_account_info( + self.service_account_key, scopes=SCOPES + ) + elif self.service_account_key_json is not None: + creds = service_account.Credentials.from_service_account_info( + json.loads(self.service_account_key_json), scopes=SCOPES + ) + elif self.service_account_key_path is not None: + creds = service_account.Credentials.from_service_account_file( + self.service_account_key_path, scopes=SCOPES + ) + else: + logger.warning( + "No explicit credentials provided. Falling back to default credentials." + ) + creds = None # This will use default credentials - gcsfs = GCSFileSystem( - token=creds, - ) + return GCSFileSystem(token=creds) + except DefaultCredentialsError as e: + logger.error(f"Failed to authenticate with GCS: {e!s}") + raise + + def _get_simple_directory_reader(self) -> SimpleDirectoryReader: + """ + Create and return a SimpleDirectoryReader for GCS. + + This method sets up a SimpleDirectoryReader with the appropriate GCS filesystem + and other configured parameters. + + Returns: + SimpleDirectoryReader: A configured SimpleDirectoryReader for GCS. + """ + gcsfs = self._get_gcsfs() input_dir = self.bucket input_files = None @@ -99,7 +138,7 @@ def load_gcs_files_as_docs(self) -> List[Document]: elif self.prefix: input_dir = f"{input_dir}/{self.prefix}" - loader = SimpleDirectoryReader( + return SimpleDirectoryReader( input_dir=input_dir, input_files=input_files, recursive=self.recursive, @@ -111,13 +150,126 @@ def load_gcs_files_as_docs(self) -> List[Document]: fs=gcsfs, ) - return loader.load_data() - def load_data(self) -> List[Document]: """ - Load the file(s) from GCS. + Load data from the specified GCS bucket or file. Returns: - List[Document]: A list of documents loaded from GCS. + List[Document]: A list of loaded documents. + + Raises: + Exception: If there's an error loading the data. + """ + try: + logger.info(f"Loading data from GCS bucket: {self.bucket}") + return self._get_simple_directory_reader().load_data() + except Exception as e: + logger.error(f"Error loading data from GCS: {e!s}") + raise + + def list_resources(self, **kwargs) -> List[str]: """ - return self.load_gcs_files_as_docs() + List resources in the specified GCS bucket or directory. + + Args: + **kwargs: Additional arguments to pass to the underlying list_resources method. + + Returns: + List[str]: A list of resource identifiers. + + Raises: + Exception: If there's an error listing the resources. + """ + try: + logger.info(f"Listing resources in GCS bucket: {self.bucket}") + return self._get_simple_directory_reader().list_resources(**kwargs) + except Exception as e: + logger.error(f"Error listing resources in GCS: {e!s}") + raise + + def get_resource_info(self, resource_id: str, **kwargs) -> Dict: + """ + Get information about a specific GCS resource. + + Args: + resource_id (str): The identifier of the resource. + **kwargs: Additional arguments to pass to the underlying info method. + + Returns: + Dict: A dictionary containing resource information. + + Raises: + Exception: If there's an error retrieving the resource information. + """ + try: + logger.info(f"Getting info for resource: {resource_id}") + gcsfs = self._get_gcsfs() + info_result = gcsfs.info(resource_id) + + info_dict = { + "file_path": info_result.get("name"), + "file_size": info_result.get("size"), + "last_modified_date": info_result.get("updated"), + "content_hash": info_result.get("md5Hash"), + "content_type": info_result.get("contentType"), + "storage_class": info_result.get("storageClass"), + "etag": info_result.get("etag"), + "generation": info_result.get("generation"), + "created_date": info_result.get("timeCreated"), + } + + # Convert datetime objects to ISO format strings + for key in ["last_modified_date", "created_date"]: + if isinstance(info_dict.get(key), datetime): + info_dict[key] = info_dict[key].isoformat() + + return {k: v for k, v in info_dict.items() if v is not None} + except Exception as e: + logger.error(f"Error getting resource info from GCS: {e!s}") + raise + + def load_resource(self, resource_id: str, **kwargs) -> List[Document]: + """ + Load a specific resource from GCS. + + Args: + resource_id (str): The identifier of the resource to load. + **kwargs: Additional arguments to pass to the underlying load_resource method. + + Returns: + List[Document]: A list containing the loaded document. + + Raises: + Exception: If there's an error loading the resource. + """ + try: + logger.info(f"Loading resource: {resource_id}") + return self._get_simple_directory_reader().load_resource( + resource_id, **kwargs + ) + except Exception as e: + logger.error(f"Error loading resource from GCS: {e!s}") + raise + + def read_file_content(self, input_file: Path, **kwargs) -> bytes: + """ + Read the content of a specific file from GCS. + + Args: + input_file (Path): The path to the file to read. + **kwargs: Additional arguments to pass to the underlying read_file_content method. + + Returns: + bytes: The content of the file. + + Raises: + Exception: If there's an error reading the file content. + """ + try: + logger.info(f"Reading file content: {input_file}") + return self._get_simple_directory_reader().read_file_content( + input_file, **kwargs + ) + except Exception as e: + logger.error(f"Error reading file content from GCS: {e!s}") + raise diff --git a/llama-index-integrations/readers/llama-index-readers-gcs/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-gcs/pyproject.toml index b1b467aec40c0..116826f1d8a27 100644 --- a/llama-index-integrations/readers/llama-index-readers-gcs/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-gcs/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["nfiacco"] name = "llama-index-readers-gcs" readme = "README.md" -version = "0.1.7" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-file = "^0.1.11" +llama-index-readers-file = "^0.2.0" gcsfs = "^2024.3.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-genius/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-genius/pyproject.toml index 8d79097ae7bd7..d6e54f1d82e5d 100644 --- a/llama-index-integrations/readers/llama-index-readers-genius/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-genius/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-genius" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" lyricsgenius = "^3.0.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml index 176cdb002d50f..9d23c07a60a35 100644 --- a/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml @@ -31,13 +31,13 @@ license = "MIT" maintainers = ["ahmetkca", "moncho", "rwood-97"] name = "llama-index-readers-github" readme = "README.md" -version = "0.1.9" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-file = "^0.1.1" +llama-index-readers-file = "^0.2.0" httpx = ">=0.26.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-gitlab/llama_index/readers/gitlab/issues/base.py b/llama-index-integrations/readers/llama-index-readers-gitlab/llama_index/readers/gitlab/issues/base.py index 1daa7558ce042..c218d3b64b1ac 100644 --- a/llama-index-integrations/readers/llama-index-readers-gitlab/llama_index/readers/gitlab/issues/base.py +++ b/llama-index-integrations/readers/llama-index-readers-gitlab/llama_index/readers/gitlab/issues/base.py @@ -84,7 +84,7 @@ def _build_document_from_issue(self, issue: GitLabIssue) -> Document: title = issue_dict["title"] description = issue_dict["description"] document = Document( - doc_id=issue_dict["iid"], + doc_id=str(issue_dict["iid"]), text=f"{title}\n{description}", ) extra_info = { diff --git a/llama-index-integrations/readers/llama-index-readers-gitlab/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-gitlab/pyproject.toml index f5f6f65959a2e..016d24c2201a8 100644 --- a/llama-index-integrations/readers/llama-index-readers-gitlab/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-gitlab/pyproject.toml @@ -32,12 +32,12 @@ maintainers = ["jiachengzhang1"] name = "llama-index-readers-gitlab" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" python-gitlab = "^4.8.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-google/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-google/CHANGELOG.md index 889252fd4e5f6..e7e6976272dbe 100644 --- a/llama-index-integrations/readers/llama-index-readers-google/CHANGELOG.md +++ b/llama-index-integrations/readers/llama-index-readers-google/CHANGELOG.md @@ -1,5 +1,12 @@ # CHANGELOG +## [0.3.1] - 2024-08-19 + +- Implemented ResourcesReaderMixin and FileSystemReaderMixin for extended functionality +- New methods: list_resources, get_resource_info, load_resource, read_file_content +- Comprehensive logging for better debugging and operational insights +- Detailed exception handling + ## [0.2.4] - 2024-04-01 - Add support for additional params when initializing GoogleDriveReader diff --git a/llama-index-integrations/readers/llama-index-readers-google/README.md b/llama-index-integrations/readers/llama-index-readers-google/README.md index 701f2979039ab..052b80a5e47c8 100644 --- a/llama-index-integrations/readers/llama-index-readers-google/README.md +++ b/llama-index-integrations/readers/llama-index-readers-google/README.md @@ -1,6 +1,7 @@ # LlamaIndex Integration: Google Readers -Effortlessly incorporate Google-based data loaders into your Python workflow using LlamaIndex. Unlock the potential of various readers to enhance your data loading capabilities, including: +Effortlessly incorporate Google-based data loaders into your Python workflow using LlamaIndex. It now supports more advanced operations through the implementation of ResourcesReaderMixin and FileSystemReaderMixin. +Unlock the potential of various readers to enhance your data loading capabilities, including: - Google Calendar - Google Chat @@ -33,6 +34,39 @@ See [this example](https://github.com/run-llama/gmail-extractor/blob/main/gmail. ## Examples +### Google Drive Reader + +```python +from llama_index.readers.google import GoogleDriveReader + +# Initialize the reader +reader = GoogleDriveReader( + folder_id="folder_id", + service_account_key="[SERVICE_ACCOUNT_KEY_JSON]", +) + +# Load data +documents = reader.load_data() + +# List resources in the drive +resources = reader.list_resources() + +# Get information about a specific resource +resource_info = reader.get_resource_info("file.txt") + +# Load a specific resource +specific_doc = reader.load_resource("file.txt") + +# Read file content directly +file_content = reader.read_file_content("path/to/file.txt") + +print(f"Loaded {len(documents)} documents") +print(f"Found {len(resources)} resources") +print(f"Resource info: {resource_info}") +print(f"Specific document: {specific_doc}") +print(f"File content length: {len(file_content)} bytes") +``` + ### Google Docs Reader ```python diff --git a/llama-index-integrations/readers/llama-index-readers-google/llama_index/readers/google/drive/base.py b/llama-index-integrations/readers/llama-index-readers-google/llama_index/readers/google/drive/base.py index 401673b909c80..5f03aea773b6e 100644 --- a/llama-index-integrations/readers/llama-index-readers-google/llama_index/readers/google/drive/base.py +++ b/llama-index-integrations/readers/llama-index-readers-google/llama_index/readers/google/drive/base.py @@ -13,8 +13,12 @@ from google_auth_oauthlib.flow import InstalledAppFlow from llama_index.core.bridge.pydantic import Field, PrivateAttr -from llama_index.core.readers import SimpleDirectoryReader -from llama_index.core.readers.base import BasePydanticReader, BaseReader +from llama_index.core.readers import SimpleDirectoryReader, FileSystemReaderMixin +from llama_index.core.readers.base import ( + BasePydanticReader, + BaseReader, + ResourcesReaderMixin, +) from llama_index.core.schema import Document logger = logging.getLogger(__name__) @@ -23,7 +27,9 @@ SCOPES = ["https://www.googleapis.com/auth/drive.readonly"] -class GoogleDriveReader(BasePydanticReader): +class GoogleDriveReader( + BasePydanticReader, ResourcesReaderMixin, FileSystemReaderMixin +): """Google Drive Reader. Reads files from Google Drive. Credentials passed directly to the constructor @@ -539,3 +545,64 @@ def load_data( else: logger.warning("Either 'folder_id' or 'file_ids' must be provided.") return [] + + def list_resources(self, **kwargs) -> List[str]: + """List resources in the specified Google Drive folder or files.""" + self._creds = self._get_credentials() + + drive_id = kwargs.get("drive_id", self.drive_id) + folder_id = kwargs.get("folder_id", self.folder_id) + file_ids = kwargs.get("file_ids", self.file_ids) + query_string = kwargs.get("query_string", self.query_string) + + if folder_id: + fileids_meta = self._get_fileids_meta( + drive_id, folder_id, query_string=query_string + ) + elif file_ids: + fileids_meta = [] + for file_id in file_ids: + fileids_meta.extend( + self._get_fileids_meta( + drive_id, file_id=file_id, query_string=query_string + ) + ) + else: + raise ValueError("Either 'folder_id' or 'file_ids' must be provided.") + + return [meta[0] for meta in fileids_meta] # Return list of file IDs + + def get_resource_info(self, resource_id: str, **kwargs) -> Dict: + """Get information about a specific Google Drive resource.""" + self._creds = self._get_credentials() + + fileids_meta = self._get_fileids_meta(file_id=resource_id) + if not fileids_meta: + raise ValueError(f"Resource with ID {resource_id} not found.") + + meta = fileids_meta[0] + return { + "file_path": meta[2], + "file_size": None, + "last_modified_date": meta[5], + "content_hash": None, + "content_type": meta[3], + "author": meta[1], + "created_date": meta[4], + } + + def load_resource(self, resource_id: str, **kwargs) -> List[Document]: + """Load a specific resource from Google Drive.""" + return self._load_from_file_ids( + self.drive_id, [resource_id], None, self.query_string + ) + + def read_file_content(self, file_path: Union[str, Path], **kwargs) -> bytes: + """Read the content of a specific file from Google Drive.""" + self._creds = self._get_credentials() + + with tempfile.TemporaryDirectory() as temp_dir: + temp_file = os.path.join(temp_dir, "temp_file") + downloaded_file = self._download_file(file_path, temp_file) + with open(downloaded_file, "rb") as file: + return file.read() diff --git a/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml index 91c59a5454633..51cc904062f76 100644 --- a/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml @@ -47,16 +47,17 @@ maintainers = [ ] name = "llama-index-readers-google" readme = "README.md" -version = "0.2.11" +version = "0.4.0" [tool.poetry.dependencies] python = ">=3.10,<4.0" -llama-index-core = "^0.10.11.post1" google-api-python-client = "^2.115.0" google-auth-httplib2 = "^0.2.0" google-auth-oauthlib = "^1.2.0" pydrive = "^1.3.1" gkeepapi = "^0.15.1" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-gpt-repo/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-gpt-repo/pyproject.toml index 579d9d3f8b06e..682f2b16dab37 100644 --- a/llama-index-integrations/readers/llama-index-readers-gpt-repo/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-gpt-repo/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["mpoon"] name = "llama-index-readers-gpt-repo" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/pyproject.toml index 6b6310ac71d16..d052b83aefc00 100644 --- a/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["jexp"] name = "llama-index-readers-graphdb-cypher" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" neo4j = "^5.16.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-graphql/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-graphql/pyproject.toml index 2b39f7e69ef58..c75e96691b7ae 100644 --- a/llama-index-integrations/readers/llama-index-readers-graphql/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-graphql/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["jexp"] name = "llama-index-readers-graphql" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" gql = "^3.5.0" requests-toolbelt = "^1.0.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-guru/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-guru/pyproject.toml index 6314b3a34c476..0d2bb66b1f528 100644 --- a/llama-index-integrations/readers/llama-index-readers-guru/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-guru/pyproject.toml @@ -29,12 +29,13 @@ license = "MIT" maintainers = ["mcclain-thiel"] name = "llama-index-readers-guru" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" bs4 = "*" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-hatena-blog/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-hatena-blog/pyproject.toml index 0d76b817d71d2..ad03896070068 100644 --- a/llama-index-integrations/readers/llama-index-readers-hatena-blog/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-hatena-blog/pyproject.toml @@ -29,14 +29,14 @@ license = "MIT" maintainers = ["Shoya SHIRAKI"] name = "llama-index-readers-hatena-blog" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" requests = "^2.31.0" beautifulsoup4 = "^4.12.3" lxml = "^5.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-hive/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-hive/pyproject.toml index 9111932f327c5..04714d6957883 100644 --- a/llama-index-integrations/readers/llama-index-readers-hive/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-hive/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["kasen"] name = "llama-index-readers-hive" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pyhive = "^0.7.0" thrift-sasl = "^0.4.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-hubspot/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-hubspot/pyproject.toml index ea233d15cde7c..92f82f8305f50 100644 --- a/llama-index-integrations/readers/llama-index-readers-hubspot/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-hubspot/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["ykhli"] name = "llama-index-readers-hubspot" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" hubspot-api-client = "^8.2.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-huggingface-fs/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-huggingface-fs/pyproject.toml index 5707b2103660d..3683ccde0a67a 100644 --- a/llama-index-integrations/readers/llama-index-readers-huggingface-fs/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-huggingface-fs/pyproject.toml @@ -29,12 +29,13 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-readers-huggingface-fs" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" huggingface-hub = "^0.20.3" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-hwp/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-hwp/pyproject.toml index 98c81f23bd130..abc2d2080b8c3 100644 --- a/llama-index-integrations/readers/llama-index-readers-hwp/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-hwp/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-hwp" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" olefile = "^0.47" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-iceberg/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-iceberg/pyproject.toml index ee02bd0fc4d3c..7dca31cb26866 100644 --- a/llama-index-integrations/readers/llama-index-readers-iceberg/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-iceberg/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-readers-iceberg" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" pyiceberg = "^0.6.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-imdb-review/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-imdb-review/pyproject.toml index 89eea5bedc12b..5768979671fb5 100644 --- a/llama-index-integrations/readers/llama-index-readers-imdb-review/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-imdb-review/pyproject.toml @@ -29,14 +29,15 @@ license = "MIT" maintainers = ["Athe-kunal"] name = "llama-index-readers-imdb-review" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" selenium = "4.8.3" webdriver-manager = "4.0.1" imdbpy = "2022.7.9" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-intercom/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-intercom/pyproject.toml index fadf8a3eef9b2..0af5dbdc2554f 100644 --- a/llama-index-integrations/readers/llama-index-readers-intercom/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-intercom/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["bbornsztein"] name = "llama-index-readers-intercom" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-jaguar/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-jaguar/pyproject.toml index 62cdb6589e649..548891551ff67 100644 --- a/llama-index-integrations/readers/llama-index-readers-jaguar/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-jaguar/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-jaguar" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" jaguardb-http-client = "^3.4.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml index c09dfc768da47..61b3e6680e90f 100644 --- a/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["bearguy"] name = "llama-index-readers-jira" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" jira = "^3.6.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-joplin/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-joplin/pyproject.toml index 81e6261ac717d..c3418d5d2417b 100644 --- a/llama-index-integrations/readers/llama-index-readers-joplin/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-joplin/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["alondmnt"] name = "llama-index-readers-joplin" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-file = "^0.1.1" +llama-index-readers-file = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-json/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-json/pyproject.toml index 10c076d8fd660..e67b646ffc32d 100644 --- a/llama-index-integrations/readers/llama-index-readers-json/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-json/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["yisding"] name = "llama-index-readers-json" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-kaltura/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-kaltura/pyproject.toml index 7d131753ebeb7..9a7f0525f115e 100644 --- a/llama-index-integrations/readers/llama-index-readers-kaltura/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-kaltura/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["kaltura"] name = "llama-index-readers-kaltura-esearch" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" kalturaapiclient = ">=19.3.0,<19.4.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-kibela/llama_index/readers/kibela/base.py b/llama-index-integrations/readers/llama-index-readers-kibela/llama_index/readers/kibela/base.py index 39a89cd468236..f199f31fce455 100644 --- a/llama-index-integrations/readers/llama-index-readers-kibela/llama_index/readers/kibela/base.py +++ b/llama-index-integrations/readers/llama-index-readers-kibela/llama_index/readers/kibela/base.py @@ -1,14 +1,15 @@ """LLama Kibela Reader.""" + from typing import Dict, Generic, List, Optional, TypeVar from llama_index.core.readers.base import BaseReader from llama_index.core.schema import Document -from llama_index.core.bridge.pydantic import BaseModel, GenericModel, parse_obj_as +from llama_index.core.bridge.pydantic import BaseModel NodeType = TypeVar("NodeType") -class Edge(GenericModel, Generic[NodeType]): +class Edge(BaseModel, Generic[NodeType]): node: Optional[NodeType] cursor: Optional[str] @@ -19,7 +20,7 @@ class PageInfo(BaseModel): hasNextPage: Optional[bool] -class Connection(GenericModel, Generic[NodeType]): +class Connection(BaseModel, Generic[NodeType]): nodes: Optional[List[NodeType]] = None edges: Optional[List[Edge[NodeType]]] pageInfo: Optional[PageInfo] @@ -94,7 +95,7 @@ def load_data(self) -> List[Document]: # See https://github.com/kibela/kibela-api-v1-document#1%E7%A7%92%E3%81%82%E3%81%9F%E3%82%8A%E3%81%AE%E3%83%AA%E3%82%AF%E3%82%A8%E3%82%B9%E3%83%88%E6%95%B0 while has_next: res = self.request(query, params) - note_conn = parse_obj_as(Connection[Note], res["notes"]) + note_conn = Connection[Note].model_validate(res["notes"]) for note in note_conn.edges: doc = ( f"---\nurl: {note.node.url}\ntitle:" diff --git a/llama-index-integrations/readers/llama-index-readers-kibela/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-kibela/pyproject.toml index 5208fccceb9c0..f75a9f9b0176b 100644 --- a/llama-index-integrations/readers/llama-index-readers-kibela/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-kibela/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["higebu"] name = "llama-index-readers-kibela" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" gql = "^3.5.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-lilac/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-lilac/pyproject.toml index 74bedbe72573c..0e4f1167c0fc8 100644 --- a/llama-index-integrations/readers/llama-index-readers-lilac/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-lilac/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["nsthorat"] name = "llama-index-readers-lilac" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" lilac = ">=0.1.5,<0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-linear/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-linear/pyproject.toml index 1489c5f3899e7..5821b86257a78 100644 --- a/llama-index-integrations/readers/llama-index-readers-linear/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-linear/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["Sushmithamallesh"] name = "llama-index-readers-linear" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" gql = "^3.5.0" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-llama-parse/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-llama-parse/pyproject.toml index 06ca849d5fd88..e367ef9afc939 100644 --- a/llama-index-integrations/readers/llama-index-readers-llama-parse/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-llama-parse/pyproject.toml @@ -28,12 +28,12 @@ keywords = ["PDF", "llama", "llama-parse", "parse"] license = "MIT" name = "llama-index-readers-llama-parse" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.7" llama-parse = ">=0.4.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/pyproject.toml index 59ac0fc24b5cc..a53b90ac1737f 100644 --- a/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["Dain Im"] name = "llama-index-readers-macrometa-gdn" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-make-com/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-make-com/pyproject.toml index afae8dbc4e45d..e85504618ba2e 100644 --- a/llama-index-integrations/readers/llama-index-readers-make-com/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-make-com/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-make-com" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-mangadex/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-mangadex/pyproject.toml index 18c686d90eb1c..ddf163e1ce53d 100644 --- a/llama-index-integrations/readers/llama-index-readers-mangadex/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-mangadex/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["choombaa"] name = "llama-index-readers-mangadex" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/pyproject.toml index 54c79a28762d8..9bfce1e907132 100644 --- a/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["mangoapps"] name = "llama-index-readers-mangoapps-guides" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" beautifulsoup4 = ">=4.11.1" requests = ">=2.28.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-maps/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-maps/pyproject.toml index bb87299b1f64e..adf3e3f477ced 100644 --- a/llama-index-integrations/readers/llama-index-readers-maps/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-maps/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["carrotpy"] name = "llama-index-readers-maps" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" osmxtract = "^0.0.1" geopy = "^2.4.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-mbox/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-mbox/pyproject.toml index 717b79c98fa29..28dafde33d3ef 100644 --- a/llama-index-integrations/readers/llama-index-readers-mbox/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-mbox/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["minosvasilias"] name = "llama-index-readers-mbox" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-file = "^0.1.1" +llama-index-readers-file = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-memos/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-memos/pyproject.toml index 3e2c636eb59b3..196cd7e5e244e 100644 --- a/llama-index-integrations/readers/llama-index-readers-memos/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-memos/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["bubu"] name = "llama-index-readers-memos" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-metal/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-metal/pyproject.toml index 2e35c4a4f6f6f..a8041c1645cb6 100644 --- a/llama-index-integrations/readers/llama-index-readers-metal/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-metal/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["getmetal"] name = "llama-index-readers-metal" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" metal-sdk = "^2.5.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/llama_index/readers/microsoft_onedrive/base.py b/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/llama_index/readers/microsoft_onedrive/base.py index b9903786b8600..7b84f32e57c17 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/llama_index/readers/microsoft_onedrive/base.py +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/llama_index/readers/microsoft_onedrive/base.py @@ -87,9 +87,6 @@ def __init__( file_extractor: Optional[Dict[str, Union[str, BaseReader]]] = None, **kwargs, ) -> None: - self._is_interactive_auth = not client_secret - self._authority = f"https://login.microsoftonline.com/{tenant_id}/" - super().__init__( client_id=client_id, client_secret=client_secret, @@ -102,6 +99,8 @@ def __init__( file_extractor=file_extractor, **kwargs, ) + self._is_interactive_auth = not client_secret + self._authority = f"https://login.microsoftonline.com/{tenant_id}/" def _authenticate_with_msal(self) -> Any: """Authenticate with MSAL. diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/poetry.lock b/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/poetry.lock index e07de0518e83a..3ef2f4171312b 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/poetry.lock +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/poetry.lock @@ -1,91 +1,118 @@ -# This file is automatically 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"jaraco.functools", "jaraco.itertools", [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "2c1a9bc3516938ede7197345b8cf727887dcc7eebc85cc841e907e92705e3016" +content-hash = "6a2ead2aa89385bb7ac50149556e66d9b64f48d7cc3687915ec4e58dcc374a38" diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/pyproject.toml index b52cf305dd22f..fc464a17f7396 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["godwin3737"] name = "llama-index-readers-microsoft-onedrive" readme = "README.md" -version = "0.1.8" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" msal = "^1.26.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/pyproject.toml index 37c1d6e267bc2..2ec71ec1b8172 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["tevslin"] name = "llama-index-readers-microsoft-outlook" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py index 789d9b78099d5..48910873fdfc4 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py @@ -47,6 +47,7 @@ class SharePointReader(BasePydanticReader, ResourcesReaderMixin, FileSystemReade client_secret: str = None tenant_id: str = None sharepoint_site_name: Optional[str] = None + sharepoint_site_id: Optional[str] = None sharepoint_folder_path: Optional[str] = None sharepoint_folder_id: Optional[str] = None file_extractor: Optional[Dict[str, Union[str, BaseReader]]] = Field( @@ -122,7 +123,9 @@ def _get_access_token(self) -> str: logger.error(response.json()["error"]) raise ValueError(response.json()["error_description"]) - def _get_site_id_with_host_name(self, access_token, sharepoint_site_name) -> str: + def _get_site_id_with_host_name( + self, access_token, sharepoint_site_name: Optional[str] + ) -> str: """ Retrieves the site ID of a SharePoint site using the provided site name. @@ -138,10 +141,16 @@ def _get_site_id_with_host_name(self, access_token, sharepoint_site_name) -> str if hasattr(self, "_site_id_with_host_name"): return self._site_id_with_host_name - site_information_endpoint = f"https://graph.microsoft.com/v1.0/sites" - self._authorization_headers = {"Authorization": f"Bearer {access_token}"} + if self.sharepoint_site_id: + return self.sharepoint_site_id + + if not (sharepoint_site_name): + raise ValueError("The SharePoint site name or ID must be provided.") + + site_information_endpoint = f"https://graph.microsoft.com/v1.0/sites" + while site_information_endpoint: response = requests.get( url=site_information_endpoint, @@ -267,6 +276,7 @@ def _download_files_and_extract_metadata( """ files_path = self.list_resources( sharepoint_site_name=self.sharepoint_site_name, + sharepoint_site_id=self.sharepoint_site_id, sharepoint_folder_id=folder_id, ) @@ -424,7 +434,7 @@ def _download_file( def _download_files_from_sharepoint( self, download_dir: str, - sharepoint_site_name: str, + sharepoint_site_name: Optional[str], sharepoint_folder_path: Optional[str], sharepoint_folder_id: Optional[str], recursive: bool, @@ -547,7 +557,7 @@ def load_data( sharepoint_folder_id = self.sharepoint_folder_id # TODO: make both of these values optional — and just default to the client ID defaults - if not sharepoint_site_name: + if not (sharepoint_site_name or self.sharepoint_site_id): raise ValueError("sharepoint_site_name must be provided.") try: @@ -641,6 +651,7 @@ def list_resources( sharepoint_site_name: Optional[str] = None, sharepoint_folder_path: Optional[str] = None, sharepoint_folder_id: Optional[str] = None, + sharepoint_site_id: Optional[str] = None, recursive: bool = True, ) -> List[Path]: """ @@ -665,8 +676,13 @@ def list_resources( if not sharepoint_folder_id: sharepoint_folder_id = self.sharepoint_folder_id - if not sharepoint_site_name: - raise ValueError("sharepoint_site_name must be provided.") + if not sharepoint_site_id: + sharepoint_site_id = self.sharepoint_site_id + + if not (sharepoint_site_name or sharepoint_site_id): + raise ValueError( + "sharepoint_site_name or sharepoint_site_id must be provided." + ) file_paths = [] try: diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml index 35a4a15264e13..b4ab9d40b29ca 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["arun-soliton"] name = "llama-index-readers-microsoft-sharepoint" readme = "README.md" -version = "0.2.7" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.37.post1" requests = "^2.31.0" -llama-index-readers-file = "^0.1.27" +llama-index-readers-file = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-milvus/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-milvus/pyproject.toml index a1fb9f697bb80..6259f1f85ef5b 100644 --- a/llama-index-integrations/readers/llama-index-readers-milvus/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-milvus/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["filip-halt"] name = "llama-index-readers-milvus" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pymilvus = "^2.3.6" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml index e50b9c837b284..1d3c095dd740c 100644 --- a/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml @@ -28,13 +28,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-minio" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" minio = "^7.2.3" boto3 = "^1.34.29" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-mondaydotcom/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-mondaydotcom/pyproject.toml index 9250089950e96..b4ecba9f115cf 100644 --- a/llama-index-integrations/readers/llama-index-readers-mondaydotcom/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-mondaydotcom/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["nadavgr"] name = "llama-index-readers-mondaydotcom" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-mongodb/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-mongodb/pyproject.toml index 962a25d20b6c4..cce58b5f84459 100644 --- a/llama-index-integrations/readers/llama-index-readers-mongodb/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-mongodb/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-readers-mongodb" readme = "README.md" -version = "0.1.9" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pymongo = "^4.6.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-myscale/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-myscale/pyproject.toml index 30854b07bf189..a5a725e6b1f93 100644 --- a/llama-index-integrations/readers/llama-index-readers-myscale/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-myscale/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-myscale" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" clickhouse-connect = "^0.7.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-notion/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-notion/pyproject.toml index 6eddfead5a356..2036ef1574382 100644 --- a/llama-index-integrations/readers/llama-index-readers-notion/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-notion/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-readers-notion" readme = "README.md" -version = "0.1.10" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-nougat-ocr/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-nougat-ocr/pyproject.toml index a55595249c22a..f1090be38623f 100644 --- a/llama-index-integrations/readers/llama-index-readers-nougat-ocr/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-nougat-ocr/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["mdarshad1000"] name = "llama-index-readers-nougat-ocr" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" nougat-ocr = "^0.1.17" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-obsidian/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-obsidian/pyproject.toml index a9ad50b5e01bd..b13e34ea8e9b7 100644 --- a/llama-index-integrations/readers/llama-index-readers-obsidian/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-obsidian/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["hursh-desai"] name = "llama-index-readers-obsidian" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-file = "^0.1.1" +llama-index-readers-file = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-openalex/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-openalex/pyproject.toml index c988619cc6581..d5f903452336f 100644 --- a/llama-index-integrations/readers/llama-index-readers-openalex/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-openalex/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["shauryr"] name = "llama-index-readers-openalex" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-openapi/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-openapi/pyproject.toml index b6fe54427df08..a099a1526e05a 100644 --- a/llama-index-integrations/readers/llama-index-readers-openapi/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-openapi/pyproject.toml @@ -27,11 +27,11 @@ license = "MIT" name = "llama-index-readers-openapi" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-opendal/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-opendal/pyproject.toml index 7ce225d991fb2..935a3d01e9d18 100644 --- a/llama-index-integrations/readers/llama-index-readers-opendal/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-opendal/pyproject.toml @@ -32,12 +32,12 @@ license = "MIT" maintainers = ["OpenDAL Contributors"] name = "llama-index-readers-opendal-reader" readme = "README.md" -version = "0.1.3" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -opendal = "0.30.3" +llama-index-core = "^0.11.0" +opendal = "*" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-opensearch/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-opensearch/pyproject.toml index 01fbabd6c7fab..0edab3350cdc5 100644 --- a/llama-index-integrations/readers/llama-index-readers-opensearch/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-opensearch/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["chnsagitchen"] name = "llama-index-readers-opensearch" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" opensearch-py = "^2.4.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-pandas-ai/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pandas-ai/pyproject.toml index 851b66616782f..faa485c5f569d 100644 --- a/llama-index-integrations/readers/llama-index-readers-pandas-ai/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-pandas-ai/pyproject.toml @@ -29,12 +29,14 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-readers-pandas-ai" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.1" pandasai = ">=2.2.12" +pandas = "*" +llama-index-core = "^0.11.0" +llama-index-readers-file = "^0.2.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml index 31ec330259ff9..f51cc44669f90 100644 --- a/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["thejessezhang"] name = "llama-index-readers-papers" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" arxiv = "^2.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-patentsview/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-patentsview/pyproject.toml index 07d315f978c07..26aa1f2331d29 100644 --- a/llama-index-integrations/readers/llama-index-readers-patentsview/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-patentsview/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["shao-shuai"] name = "llama-index-readers-patentsview" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-pathway/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pathway/pyproject.toml index 08551a8064897..6c9f9ae8248a5 100644 --- a/llama-index-integrations/readers/llama-index-readers-pathway/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-pathway/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-pathway" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-pdb/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pdb/pyproject.toml index 832c4f02e0086..1fff8a6f475c9 100644 --- a/llama-index-integrations/readers/llama-index-readers-pdb/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-pdb/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["joshuakto"] name = "llama-index-readers-pdb" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-pdf-marker/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pdf-marker/pyproject.toml index 598166575bcfd..1b5fbcf0263c0 100644 --- a/llama-index-integrations/readers/llama-index-readers-pdf-marker/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-pdf-marker/pyproject.toml @@ -30,12 +30,12 @@ license = "GPL-3.0-or-later" name = "llama-index-readers-pdf-marker" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.0" marker-pdf = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-pdf-table/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pdf-table/pyproject.toml index 634fdca77f81f..95b75c8b6e158 100644 --- a/llama-index-integrations/readers/llama-index-readers-pdf-table/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-pdf-table/pyproject.toml @@ -29,14 +29,15 @@ license = "MIT" maintainers = ["yy0867"] name = "llama-index-readers-pdf-table" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +python = ">=3.9,<4.0" +llama-index-core = "^0.11.0" camelot-py = "^0.11.0" opencv-python = "^4.9.0.80" ghostscript = "^0.7" +pandas = "*" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-pebblo/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pebblo/pyproject.toml index a3232855344b9..0a195f344b358 100644 --- a/llama-index-integrations/readers/llama-index-readers-pebblo/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-pebblo/pyproject.toml @@ -30,14 +30,15 @@ license = "MIT" name = "llama-index-readers-pebblo" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" langchain-community = ">=0.0.303" langchain = ">=0.0.303" requests = "^2" +llama-index-readers-file = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-preprocess/llama_index/readers/preprocess/base.py b/llama-index-integrations/readers/llama-index-readers-preprocess/llama_index/readers/preprocess/base.py index 5f064fea0cc16..fdf048a3acfca 100644 --- a/llama-index-integrations/readers/llama-index-readers-preprocess/llama_index/readers/preprocess/base.py +++ b/llama-index-integrations/readers/llama-index-readers-preprocess/llama_index/readers/preprocess/base.py @@ -37,18 +37,24 @@ def __init__(self, api_key: str, *args, **kwargs): if key == "filepath": self._filepath = value self._preprocess.set_filepath(value) + if key == "process_id": self._process_id = value self._preprocess.set_process_id(value) + + elif key in ["table_output_format", "table_output"]: + _info["table_output_format"] = value + + elif key in ["repeat_table_header", "table_header"]: + _info["repeat_table_header"] = value + elif key in [ "merge", - "max", - "min", - "min_min", - "table_output", "repeat_title", - "table_header", - "lamguage", + "keep_header", + "keep_footer", + "smart_header", + "image_text", ]: _info[key] = value diff --git a/llama-index-integrations/readers/llama-index-readers-preprocess/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-preprocess/pyproject.toml index 82991873e317c..2b323649bc6ce 100644 --- a/llama-index-integrations/readers/llama-index-readers-preprocess/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-preprocess/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["preprocess"] name = "llama-index-readers-preprocess" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -pypreprocess = "^1.2.0" +pypreprocess = "^1.4.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-psychic/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-psychic/pyproject.toml index 5110ac8f2fcae..2b6a70d5f733f 100644 --- a/llama-index-integrations/readers/llama-index-readers-psychic/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-psychic/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-psychic" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" psychicapi = "^0.8.4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-qdrant/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-qdrant/pyproject.toml index 841dfa67e3f3b..440484c7648d4 100644 --- a/llama-index-integrations/readers/llama-index-readers-qdrant/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-qdrant/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["kacperlukawski"] name = "llama-index-readers-qdrant" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" qdrant-client = "^1.7.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-rayyan/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-rayyan/pyproject.toml index d3b7b08cda0a8..3a47578c57a1f 100644 --- a/llama-index-integrations/readers/llama-index-readers-rayyan/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-rayyan/pyproject.toml @@ -29,14 +29,14 @@ license = "MIT" maintainers = ["hammady"] name = "llama-index-readers-rayyan" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" rayyan-sdk = "^1.0rc7" tqdm = "^4.66.1" tenacity = "^8.2.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-readme/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-readme/pyproject.toml index fdc194b4f1af1..17e264d869d00 100644 --- a/llama-index-integrations/readers/llama-index-readers-readme/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-readme/pyproject.toml @@ -28,13 +28,13 @@ license = "MIT" name = "llama-index-readers-readme" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" beautifulsoup4 = "^4.12.3" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-readwise/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-readwise/pyproject.toml index 5973f0f37b141..044bc9d73e09f 100644 --- a/llama-index-integrations/readers/llama-index-readers-readwise/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-readwise/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["alexbowe"] name = "llama-index-readers-readwise" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-reddit/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-reddit/pyproject.toml index c112dcea17024..8f2536713ebbd 100644 --- a/llama-index-integrations/readers/llama-index-readers-reddit/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-reddit/pyproject.toml @@ -29,16 +29,16 @@ license = "MIT" maintainers = ["vanessahlyan"] name = "llama-index-readers-reddit" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" praw = ">=7.6,<8.0" prawcore = ">=2.3,<3.0" requests = ">=2.28,<3.0" update-checker = ">=0.18,<1.0" websocket-client = ">=1.5,<2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-remote-depth/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-remote-depth/pyproject.toml index 488e45683018e..84877534516ba 100644 --- a/llama-index-integrations/readers/llama-index-readers-remote-depth/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-remote-depth/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["simonMoisselin"] name = "llama-index-readers-remote-depth" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.11.post1" -llama-index-readers-remote = "^0.1.1" +llama-index-readers-remote = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-remote/llama_index/readers/remote/base.py b/llama-index-integrations/readers/llama-index-readers-remote/llama_index/readers/remote/base.py index f68383f0d84b5..ed878f3297e00 100644 --- a/llama-index-integrations/readers/llama-index-readers-remote/llama_index/readers/remote/base.py +++ b/llama-index-integrations/readers/llama-index-readers-remote/llama_index/readers/remote/base.py @@ -53,6 +53,24 @@ def load_data(self, url: str) -> List[Document]: from urllib.parse import urlparse from urllib.request import Request, urlopen + # check the URL + parsed_url = urlparse(url) + + # Check if the scheme is http or https + if parsed_url.scheme not in ( + "http", + "https", + "ftp", + "ws", + "wss", + "sftp", + "ftps", + "s3", + ): + raise ValueError( + "Invalid URL scheme. Only http, https, ftp, ftps, sftp, ws, wss, and s3 are allowed." + ) + extra_info = {"Source": url} req = Request(url, headers={"User-Agent": "Magic Browser"}) diff --git a/llama-index-integrations/readers/llama-index-readers-remote/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-remote/pyproject.toml index 8a77398986d03..49653a6aa9a47 100644 --- a/llama-index-integrations/readers/llama-index-readers-remote/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-remote/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["thejessezhang"] name = "llama-index-readers-remote" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-readers-youtube-transcript = "^0.1.4" +llama-index-readers-youtube-transcript = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-s3/llama_index/readers/s3/base.py b/llama-index-integrations/readers/llama-index-readers-s3/llama_index/readers/s3/base.py index 4086d2bbe5460..f2735fa40f4c6 100644 --- a/llama-index-integrations/readers/llama-index-readers-s3/llama_index/readers/s3/base.py +++ b/llama-index-integrations/readers/llama-index-readers-s3/llama_index/readers/s3/base.py @@ -7,6 +7,7 @@ import warnings from typing import Callable, Dict, List, Optional, Union +from typing_extensions import Annotated from datetime import datetime, timezone from pathlib import Path @@ -17,7 +18,13 @@ ResourcesReaderMixin, ) from llama_index.core.schema import Document -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, WithJsonSchema + + +FileMetadataCallable = Annotated[ + Callable[[str], Dict], + WithJsonSchema({"type": "string"}), +] class S3Reader(BasePydanticReader, ResourcesReaderMixin, FileSystemReaderMixin): @@ -61,7 +68,7 @@ class S3Reader(BasePydanticReader, ResourcesReaderMixin, FileSystemReaderMixin): required_exts: Optional[List[str]] = None filename_as_id: bool = True num_files_limit: Optional[int] = None - file_metadata: Optional[Callable[[str], Dict]] = Field(default=None, exclude=True) + file_metadata: Optional[FileMetadataCallable] = Field(default=None, exclude=True) aws_access_id: Optional[str] = None aws_access_secret: Optional[str] = None aws_session_token: Optional[str] = None diff --git a/llama-index-integrations/readers/llama-index-readers-s3/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-s3/pyproject.toml index 7ad205333a8b4..f4e952d45682c 100644 --- a/llama-index-integrations/readers/llama-index-readers-s3/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-s3/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["thejessezhang"] name = "llama-index-readers-s3" readme = "README.md" -version = "0.1.10" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.50.post1" -llama-index-readers-file = "^0.1.25" +llama-index-readers-file = "^0.2.0" s3fs = ">=2024.3.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-sec-filings/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-sec-filings/pyproject.toml index 45169e16be6b6..04ed3ea8d481a 100644 --- a/llama-index-integrations/readers/llama-index-readers-sec-filings/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-sec-filings/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["Athe-kunal"] name = "llama-index-readers-sec-filings" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" faker = "*" ratelimit = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-semanticscholar/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-semanticscholar/pyproject.toml index dbbbd0c9affa4..ebceafd15709c 100644 --- a/llama-index-integrations/readers/llama-index-readers-semanticscholar/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-semanticscholar/pyproject.toml @@ -29,14 +29,14 @@ license = "MIT" maintainers = ["shauryr"] name = "llama-index-readers-semanticscholar" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" semanticscholar = "0.4.1" arxiv = "1.4.8" pypdf2 = "3.0.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-semanticscholar/tests/test.py b/llama-index-integrations/readers/llama-index-readers-semanticscholar/tests/test.py index 96925f00ba140..9e3adf9ea3bf9 100644 --- a/llama-index-integrations/readers/llama-index-readers-semanticscholar/tests/test.py +++ b/llama-index-integrations/readers/llama-index-readers-semanticscholar/tests/test.py @@ -2,12 +2,10 @@ import openai from llama_index import ( - ServiceContext, StorageContext, VectorStoreIndex, load_index_from_storage, ) -from llama_index.core.llms import OpenAI from llama_index.core.query_engine import CitationQueryEngine from llama_index.readers.semanticscholar.base import SemanticScholarReader @@ -16,9 +14,6 @@ # initialize the service context openai.api_key = os.environ["OPENAI_API_KEY"] -service_context = ServiceContext.from_defaults( - llm=OpenAI(model="gpt-3.5-turbo", temperature=0) -) query_space = "large language models" query_string = "limitations of using large language models" @@ -35,12 +30,11 @@ if not os.path.exists(persist_dir): # Load data from Semantic Scholar documents = s2reader.load_data(query_space, total_papers, full_text=full_text) - index = VectorStoreIndex.from_documents(documents, service_context=service_context) + index = VectorStoreIndex.from_documents(documents) index.storage_context.persist(persist_dir=persist_dir) else: index = load_index_from_storage( StorageContext.from_defaults(persist_dir=persist_dir), - service_context=service_context, ) # initialize the citation query engine query_engine = CitationQueryEngine.from_args( diff --git a/llama-index-integrations/readers/llama-index-readers-singlestore/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-singlestore/pyproject.toml index 780fab1efe4ba..78f4d6a5399b3 100644 --- a/llama-index-integrations/readers/llama-index-readers-singlestore/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-singlestore/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["singlestore"] name = "llama-index-readers-singlestore" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-database = "^0.1.1" +llama-index-readers-database = "^0.2.0" pymysql = "^1.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-slack/llama_index/readers/slack/base.py b/llama-index-integrations/readers/llama-index-readers-slack/llama_index/readers/slack/base.py index 999a27728ae13..7bd71596438e9 100644 --- a/llama-index-integrations/readers/llama-index-readers-slack/llama_index/readers/slack/base.py +++ b/llama-index-integrations/readers/llama-index-readers-slack/llama_index/readers/slack/base.py @@ -61,9 +61,9 @@ def __init__( "variable `SLACK_BOT_TOKEN`." ) if ssl is None: - self._client = WebClient(token=slack_token) + client = WebClient(token=slack_token) else: - self._client = WebClient(token=slack_token, ssl=ssl) + client = WebClient(token=slack_token, ssl=ssl) if latest_date is not None and earliest_date is None: raise ValueError( "Must specify `earliest_date` if `latest_date` is specified." @@ -76,7 +76,7 @@ def __init__( latest_date_timestamp = latest_date.timestamp() else: latest_date_timestamp = datetime.now().timestamp() or latest_date_timestamp - res = self._client.api_test() + res = client.api_test() if not res["ok"]: raise ValueError(f"Error initializing Slack API: {res['error']}") @@ -85,6 +85,7 @@ def __init__( earliest_date_timestamp=earliest_date_timestamp, latest_date_timestamp=latest_date_timestamp, ) + self._client = client @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/readers/llama-index-readers-slack/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-slack/pyproject.toml index 4fd4c193e4441..eb9a28b125d1d 100644 --- a/llama-index-integrations/readers/llama-index-readers-slack/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-slack/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-readers-slack" readme = "README.md" -version = "0.1.5" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" slack-sdk = "^3.26.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/pyproject.toml index 7e59cad5cd9f6..5ad807a7e92af 100644 --- a/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["ansukla"] name = "llama-index-readers-smart-pdf-loader" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" llmsherpa = "^0.1.4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-snowflake/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-snowflake/pyproject.toml index b606de360fb55..0ce1cbdb3e253 100644 --- a/llama-index-integrations/readers/llama-index-readers-snowflake/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-snowflake/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["godwin3737"] name = "llama-index-readers-snowflake" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/pyproject.toml index 1585373bf1893..6c482aa3992ab 100644 --- a/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["smyja"] name = "llama-index-readers-snscrape-twitter" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.snscrape] git = "https://github.com/JustAnotherArchivist/snscrape.git" diff --git a/llama-index-integrations/readers/llama-index-readers-spotify/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-spotify/pyproject.toml index 869d9a0fd1e05..091d61aa27932 100644 --- a/llama-index-integrations/readers/llama-index-readers-spotify/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-spotify/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["ong"] name = "llama-index-readers-spotify" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" spotipy = "^2.23.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-stackoverflow/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-stackoverflow/pyproject.toml index 326bee3cc2d6f..32f910501563e 100644 --- a/llama-index-integrations/readers/llama-index-readers-stackoverflow/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-stackoverflow/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["allen-munsch"] name = "llama-index-readers-stackoverflow" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-steamship/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-steamship/pyproject.toml index 69f684875e4fc..116fbc1d01dec 100644 --- a/llama-index-integrations/readers/llama-index-readers-steamship/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-steamship/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["douglas-reid"] name = "llama-index-readers-steamship" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-string-iterable/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-string-iterable/pyproject.toml index e8075825fd443..d2d14449126fc 100644 --- a/llama-index-integrations/readers/llama-index-readers-string-iterable/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-string-iterable/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-string-iterable" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-stripe-docs/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-stripe-docs/pyproject.toml index c1835653d1067..5f1d4561db0c1 100644 --- a/llama-index-integrations/readers/llama-index-readers-stripe-docs/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-stripe-docs/pyproject.toml @@ -29,14 +29,14 @@ license = "MIT" maintainers = ["amorriscode"] name = "llama-index-readers-stripe-docs" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" -html2text = "^2020.1.16" +html2text = "^2024.2.26" urllib3 = "^2.1.0" -llama-index-readers-web = "^0.1.6" +llama-index-readers-web = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-structured-data/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-structured-data/pyproject.toml index 0428b38f4f77e..827e00060a420 100644 --- a/llama-index-integrations/readers/llama-index-readers-structured-data/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-structured-data/pyproject.toml @@ -30,11 +30,12 @@ license = "MIT" name = "llama-index-readers-structured-data" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-telegram/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-telegram/pyproject.toml index 5607edf848fe9..b5dab6df348fe 100644 --- a/llama-index-integrations/readers/llama-index-readers-telegram/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-telegram/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["diicell"] name = "llama-index-readers-telegram" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" telethon = "^1.33.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-toggl/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-toggl/pyproject.toml index a181a0d2206d9..f4a4490c18f8f 100644 --- a/llama-index-integrations/readers/llama-index-readers-toggl/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-toggl/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-readers-toggl" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" python-toggl = "^1.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-trello/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-trello/pyproject.toml index 3a558f872c552..05ef5f1af494b 100644 --- a/llama-index-integrations/readers/llama-index-readers-trello/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-trello/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["bluzir"] name = "llama-index-readers-trello" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" py-trello = "^0.19.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-twitter/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-twitter/pyproject.toml index e6ab9f02a8bd4..29228b26e2bf2 100644 --- a/llama-index-integrations/readers/llama-index-readers-twitter/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-twitter/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["ravi03071991"] name = "llama-index-readers-twitter" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" tweepy = "^4.14.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-txtai/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-txtai/pyproject.toml index a351fef725cfb..335e395cf5cc8 100644 --- a/llama-index-integrations/readers/llama-index-readers-txtai/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-txtai/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-txtai" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-upstage/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-upstage/pyproject.toml index 051b6773e439a..0f1098b8b0577 100644 --- a/llama-index-integrations/readers/llama-index-readers-upstage/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-upstage/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-readers-upstage" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" pymupdf = "^1.23.21" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-weather/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-weather/pyproject.toml index 369a1b62585e0..be1d08bf7abbf 100644 --- a/llama-index-integrations/readers/llama-index-readers-weather/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-weather/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["iamadhee"] name = "llama-index-readers-weather" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pyowm = "^3.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-weaviate/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-weaviate/pyproject.toml index 7b76448bffe03..63bf6a916ea7c 100644 --- a/llama-index-integrations/readers/llama-index-readers-weaviate/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-weaviate/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-readers-weaviate" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" weaviate-client = "^3.26.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py index 384ee2d181476..e9144d7692af2 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py @@ -148,8 +148,8 @@ class BeautifulSoupWebReader(BasePydanticReader): _website_extractor: Dict[str, Callable] = PrivateAttr() def __init__(self, website_extractor: Optional[Dict[str, Callable]] = None) -> None: - self._website_extractor = website_extractor or DEFAULT_WEBSITE_EXTRACTOR super().__init__() + self._website_extractor = website_extractor or DEFAULT_WEBSITE_EXTRACTOR @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/firecrawl_web/base.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/firecrawl_web/base.py index ebdc4d98cf69a..7367dc57e3e5a 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/firecrawl_web/base.py +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/firecrawl_web/base.py @@ -87,6 +87,7 @@ def load_data( ) elif self.mode == "crawl": firecrawl_docs = self.firecrawl.crawl_url(url, params=self.params) + firecrawl_docs = firecrawl_docs.get("data", []) for doc in firecrawl_docs: documents.append( Document( diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/simple_web/base.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/simple_web/base.py index e19e485ce15a8..7e597ae1524bb 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/simple_web/base.py +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/simple_web/base.py @@ -38,8 +38,8 @@ def __init__( raise ImportError( "`html2text` package not found, please run `pip install html2text`" ) - self._metadata_fn = metadata_fn super().__init__(html_to_text=html_to_text) + self._metadata_fn = metadata_fn @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml index 39f1ae6cc69c2..eb38ea0f97036 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml @@ -45,11 +45,10 @@ license = "MIT" maintainers = ["HawkClaws", "Hironsan", "NA", "an-bluecat", "bborn", "jasonwcfan", "kravetsmic", "pandazki", "ruze00", "selamanse", "thejessezhang"] name = "llama-index-readers-web" readme = "README.md" -version = "0.1.23" +version = "0.2.2" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" selenium = "^4.17.2" chromedriver-autoinstaller = "^0.6.3" html2text = "^2024.2.26" @@ -60,6 +59,7 @@ urllib3 = ">=1.1.0" playwright = ">=1.30,<2.0" newspaper3k = "^0.2.8" spider-client = "^0.0.27" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-whatsapp/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-whatsapp/pyproject.toml index c78db49c75e8e..1ffaf731f126b 100644 --- a/llama-index-integrations/readers/llama-index-readers-whatsapp/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-whatsapp/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["batmanscode"] name = "llama-index-readers-whatsapp" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" pandas = "^2.2.0" chat-miner = "^0.5.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-wikipedia/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-wikipedia/pyproject.toml index ed69108be64e0..910fee4de9674 100644 --- a/llama-index-integrations/readers/llama-index-readers-wikipedia/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-wikipedia/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-readers-wikipedia" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-wordlift/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-wordlift/pyproject.toml index 527d3531d1e3f..2aa027fb14fce 100644 --- a/llama-index-integrations/readers/llama-index-readers-wordlift/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-wordlift/pyproject.toml @@ -29,14 +29,14 @@ license = "MIT" maintainers = ["msftwarelab"] name = "llama-index-readers-wordlift" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" langchain = "^0.1.4" graphql-core = "^3.2.3" bs4 = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-wordpress/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-wordpress/pyproject.toml index dd4c92f9c3572..0342f560f8155 100644 --- a/llama-index-integrations/readers/llama-index-readers-wordpress/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-wordpress/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["bbornsztein"] name = "llama-index-readers-wordpress" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-youtube-metadata/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-youtube-metadata/pyproject.toml index 32bba3d780ee8..01a66a6cb3f9e 100644 --- a/llama-index-integrations/readers/llama-index-readers-youtube-metadata/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-youtube-metadata/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-readers-youtube-metadata" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" youtube-transcript-api = "^0.6.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/readers/llama-index-readers-youtube-transcript/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-youtube-transcript/pyproject.toml index 6c6faaa0a754f..671dc3bb3d079 100644 --- a/llama-index-integrations/readers/llama-index-readers-youtube-transcript/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-youtube-transcript/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["ravi03071991"] name = "llama-index-readers-youtube-transcript" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" youtube-transcript-api = ">=0.5.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-zendesk/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-zendesk/pyproject.toml index a5204748273c3..ea653f9b777cf 100644 --- a/llama-index-integrations/readers/llama-index-readers-zendesk/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-zendesk/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["bbornsztein"] name = "llama-index-readers-zendesk" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" beautifulsoup4 = "^4.12.3" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-zep/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-zep/pyproject.toml index 9529ff1d265c5..736d59948b6ec 100644 --- a/llama-index-integrations/readers/llama-index-readers-zep/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-zep/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["zep"] name = "llama-index-readers-zep" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -zep-python = ">=1.0.0,<1.1.0" +python = ">=3.9.0,<4.0" +zep-python = "^1.5.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/readers/llama-index-readers-zulip/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-zulip/pyproject.toml index 6543c89504ee1..58e3eaf90f9c6 100644 --- a/llama-index-integrations/readers/llama-index-readers-zulip/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-zulip/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["plurigrid"] name = "llama-index-readers-zulip" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/response_synthesizers/llama-index-response-synthesizers-google/pyproject.toml b/llama-index-integrations/response_synthesizers/llama-index-response-synthesizers-google/pyproject.toml index 23fce426e2f87..958a996addc15 100644 --- a/llama-index-integrations/response_synthesizers/llama-index-response-synthesizers-google/pyproject.toml +++ b/llama-index-integrations/response_synthesizers/llama-index-response-synthesizers-google/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-response-synthesizers-google" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-vector-stores-google = "^0.1.3" +llama-index-vector-stores-google = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-bedrock/pyproject.toml b/llama-index-integrations/retrievers/llama-index-retrievers-bedrock/pyproject.toml index 177b04030bfd8..bd9afb453f2b1 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-bedrock/pyproject.toml +++ b/llama-index-integrations/retrievers/llama-index-retrievers-bedrock/pyproject.toml @@ -27,11 +27,11 @@ license = "MIT" name = "llama-index-retrievers-bedrock" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-bm25/pyproject.toml b/llama-index-integrations/retrievers/llama-index-retrievers-bm25/pyproject.toml index f26afc552cdfb..80b8155095fca 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-bm25/pyproject.toml +++ b/llama-index-integrations/retrievers/llama-index-retrievers-bm25/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-retrievers-bm25" readme = "README.md" -version = "0.2.2" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" bm25s = "^0.1.7" pystemmer = "^2.2.0.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-duckdb-retriever/pyproject.toml b/llama-index-integrations/retrievers/llama-index-retrievers-duckdb-retriever/pyproject.toml index 128f878709aab..a93a6e22264ad 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-duckdb-retriever/pyproject.toml +++ b/llama-index-integrations/retrievers/llama-index-retrievers-duckdb-retriever/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-retrievers-duckdb-retriever" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pymongo = "^4.6.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-mongodb-atlas-bm25-retriever/pyproject.toml b/llama-index-integrations/retrievers/llama-index-retrievers-mongodb-atlas-bm25-retriever/pyproject.toml index e3f7c298c7a0d..e0db2bb62d10d 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-mongodb-atlas-bm25-retriever/pyproject.toml +++ b/llama-index-integrations/retrievers/llama-index-retrievers-mongodb-atlas-bm25-retriever/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-retrievers-mongodb-atlas-bm25-retriever" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pymongo = "^4.6.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-pathway/pyproject.toml b/llama-index-integrations/retrievers/llama-index-retrievers-pathway/pyproject.toml index 51585cda900ad..275b92726f5f7 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-pathway/pyproject.toml +++ b/llama-index-integrations/retrievers/llama-index-retrievers-pathway/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-retrievers-pathway" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-vertexai-search/pyproject.toml b/llama-index-integrations/retrievers/llama-index-retrievers-vertexai-search/pyproject.toml index 5d03228953d4d..e2ec92833370b 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-vertexai-search/pyproject.toml +++ b/llama-index-integrations/retrievers/llama-index-retrievers-vertexai-search/pyproject.toml @@ -26,15 +26,15 @@ description = "llama-index retrievers vertex ai search integration" license = "MIT" name = "llama-index-retrievers-vertexai-search" readme = "README.md" -version = "0.0.1" +version = "0.1.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.0" google-cloud-aiplatform = "^1.53.0" google-cloud-discoveryengine = "^0.11.13" google-auth-httplib2 = "^0.2.0" google-auth-oauthlib = "^1.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-videodb/llama_index/retrievers/videodb/base.py b/llama-index-integrations/retrievers/llama-index-retrievers-videodb/llama_index/retrievers/videodb/base.py index df317dac1486a..6ec483a987c08 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-videodb/llama_index/retrievers/videodb/base.py +++ b/llama-index-integrations/retrievers/llama-index-retrievers-videodb/llama_index/retrievers/videodb/base.py @@ -7,16 +7,12 @@ from typing import List, Optional from videodb import connect +from videodb import SearchType, IndexType logger = logging.getLogger(__name__) -class SearchType: - keyword = "keyword" - semantic = "semantic" - - class VideoDBRetriever(BaseRetriever): def __init__( self, @@ -26,6 +22,8 @@ def __init__( score_threshold: Optional[float] = 0.2, result_threshold: Optional[int] = 5, search_type: Optional[str] = SearchType.semantic, + index_type: Optional[str] = IndexType.spoken_word, + scene_index_id: Optional[str] = None, base_url: Optional[str] = None, callback_manager: Optional[CallbackManager] = None, ) -> None: @@ -43,6 +41,8 @@ def __init__( self.score_threshold = score_threshold self.result_threshold = result_threshold self.search_type = search_type + self.scene_index_id = scene_index_id + self.index_type = index_type super().__init__(callback_manager) def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: @@ -52,19 +52,24 @@ def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: kwargs["base_url"] = self._base_url conn = connect(**kwargs) if self.video: + search_args = { + "query": query_bundle.query_str, + "search_type": self.search_type, + "index_type": self.index_type, + "score_threshold": self.score_threshold, + "result_threshold": self.result_threshold, + } + if self.index_type == IndexType.scene and self.scene_index_id: + search_args["index_id"] = self.scene_index_id coll = conn.get_collection(self.collection) video = coll.get_video(self.video) - search_res = video.search( - query_bundle.query_str, - search_type=self.search_type, - score_threshold=self.score_threshold, - result_threshold=self.result_threshold, - ) + search_res = video.search(**search_args) else: coll = conn.get_collection(self.collection) search_res = coll.search( query_bundle.query_str, search_type=self.search_type, + index_type=self.index_type, score_threshold=self.score_threshold, result_threshold=self.result_threshold, ) @@ -82,6 +87,7 @@ def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: "title": shot.video_title, "start": shot.start, "end": shot.end, + "type": self.index_type, }, ) nodes.append(NodeWithScore(node=textnode, score=score)) diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-videodb/pyproject.toml b/llama-index-integrations/retrievers/llama-index-retrievers-videodb/pyproject.toml index b0b5c24878516..7537ee1e739de 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-videodb/pyproject.toml +++ b/llama-index-integrations/retrievers/llama-index-retrievers-videodb/pyproject.toml @@ -32,12 +32,12 @@ maintainers = ["Rohit Garg "] name = "llama-index-retrievers-videodb" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -videodb = "^0.0" +videodb = ">=0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-you/pyproject.toml b/llama-index-integrations/retrievers/llama-index-retrievers-you/pyproject.toml index 69d7e920dcb1f..fc57a9e62bd71 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-you/pyproject.toml +++ b/llama-index-integrations/retrievers/llama-index-retrievers-you/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-retrievers-you" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/.gitignore b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/BUILD b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/Makefile b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/README.md b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/README.md new file mode 100644 index 0000000000000..50ad75f53b45b --- /dev/null +++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/README.md @@ -0,0 +1,99 @@ +# LlamaIndex Selectors Integration: NotDiamond + +[Not Diamond](https://notdiamond.ai) offers an AI-powered model router that automatically determines which LLM is best suited to respond to any query, improving LLM output quality by combining multiple LLMs into a **meta-model** that learns when to call each LLM. + +Not Diamond supports cost and latency tradeoffs, customized router training, and real-time router personalization. Learn more via [the documentation](https://notdiamond.readme.io/). + +## Installation + +```shell +pip install llama-index-selectors-notdiamond +``` + +## Configuration + +- API keys: You need API keys from [Not Diamond](https://app.notdiamond.ai/keys) and any LLM you want to use. + +## Quick Start + +```python +import os +from typing import List + +from llama_index.core import ( + SimpleDirectoryReader, + VectorStoreIndex, + SummaryIndex, + Settings, +) +from llama_index.core.query_engine import RouterQueryEngine +from llama_index.core.tools import QueryEngineTool +from llama_index.selectors.notdiamond.base import NotDiamondSelector + +from notdiamond import NotDiamond + + +# Set up your API keys +os.environ["OPENAI_API_KEY"] = "sk-..." +os.environ["ANTHROPIC_API_KEY"] = "sk-ant-..." +os.environ["NOTDIAMOND_API_KEY"] = "sk-..." + + +# Create indexes +documents = SimpleDirectoryReader("data/paul_graham").load_data() +nodes = Settings.node_parser.get_nodes_from_documents(documents) + +vector_index = VectorStoreIndex.from_documents(documents) +summary_index = SummaryIndex.from_documents(documents) +query_text = "What was Paul Graham's role at Yahoo?" + + +# Set up Tools for the QueryEngine +list_query_engine = summary_index.as_query_engine( + response_mode="tree_summarize", + use_async=True, +) +vector_query_engine = vector_index.as_query_engine() + +list_tool = QueryEngineTool.from_defaults( + query_engine=list_query_engine, + description=( + "Useful for summarization questions related to Paul Graham eassy on" + " What I Worked On." + ), +) + +vector_tool = QueryEngineTool.from_defaults( + query_engine=vector_query_engine, + description=( + "Useful for retrieving specific context from Paul Graham essay on What" + " I Worked On." + ), +) + + +# Create a NotDiamondSelector and RouterQueryEngine +client = NotDiamond( + api_key=os.environ["NOTDIAMOND_API_KEY"], + llm_configs=["openai/gpt-4o", "anthropic/claude-3-5-sonnet-20240620"], +) +preference_id = client.create_preference_id() +client.preference_id = preference_id + +nd_selector = NotDiamondSelector(client=client) + +query_engine = RouterQueryEngine( + selector=nd_selector, + query_engine_tools=[ + list_tool, + vector_tool, + ], +) + + +# Use Not Diamond to Query Indexes +response = query_engine.query( + "Please summarize Paul Graham's working experience." +) +print(str(response)) +``` diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/BUILD b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/__init__.py b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/__init__.py new file mode 100644 index 0000000000000..6f2a506585d89 --- /dev/null +++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/__init__.py @@ -0,0 +1,4 @@ +from llama_index.selectors.notdiamond.base import NotDiamondSelector + + +__all__ = ["NotDiamondSelector"] diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/base.py b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/base.py new file mode 100644 index 0000000000000..083858c54baeb --- /dev/null +++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/llama_index/selectors/notdiamond/base.py @@ -0,0 +1,178 @@ +import logging +import os +from typing import Sequence + +from llama_index.core.llms.llm import LLM +from llama_index.core.schema import QueryBundle +from llama_index.core.tools.types import ToolMetadata +from llama_index.core.base.base_selector import SelectorResult +from llama_index.core.selectors import LLMSingleSelector +from llama_index.core.selectors.llm_selectors import _build_choices_text + +from notdiamond import NotDiamond, LLMConfig, Metric + +LOGGER = logging.getLogger(__name__) +LOGGER.setLevel(logging.WARNING) + + +class NotDiamondSelectorResult(SelectorResult): + """A single selection of a choice provided by Not Diamond.""" + + class Config: + arbitrary_types_allowed = True + + session_id: str + llm: LLMConfig + + @classmethod + def from_selector_result( + cls, selector_result: SelectorResult, session_id: str, best_llm: LLMConfig + ) -> "NotDiamondSelectorResult": + return cls(session_id=session_id, llm=best_llm, **selector_result.dict()) + + +class NotDiamondSelector(LLMSingleSelector): + def __init__( + self, + client: NotDiamond, + metric: Metric = None, + timeout: int = 10, + api_key: str = None, + *args, + **kwargs, + ): + """ + Initialize a NotDiamondSelector. Users should instantiate and configure a NotDiamond client as needed before + creating this selector. The constructor will raise errors re: required client fields. + """ + # Not needed - we will route using our own client based on the query prompt + # Add @property for _llm here + _encap_selector = LLMSingleSelector.from_defaults() + self._llm = None + self._prompt = _encap_selector._prompt + + if not getattr(client, "llm_configs", None): + raise ValueError( + "NotDiamond client must have llm_configs before creating a NotDiamondSelector." + ) + + if metric and not isinstance(metric, Metric): + raise ValueError(f"Invalid metric - needed type Metric but got {metric}") + self._metric = metric or Metric("accuracy") + + self._client = client + self._llms = [ + self._llm_config_to_client(llm_config) + for llm_config in self._client.llm_configs + ] + self._timeout = timeout + super().__init__(_encap_selector._llm, _encap_selector._prompt, *args, **kwargs) + + def _llm_config_to_client(self, llm_config: LLMConfig | str) -> LLM: + """ + For the selected LLMConfig dynamically create an LLM instance. NotDiamondSelector will + assign this to self._llm to help select the best index. + """ + if isinstance(llm_config, str): + llm_config = LLMConfig.from_string(llm_config) + provider, model = llm_config.provider, llm_config.model + + output = None + if provider == "openai": + from llama_index.llms.openai import OpenAI + + output = OpenAI(model=model, api_key=os.getenv("OPENAI_API_KEY")) + elif provider == "anthropic": + from llama_index.llms.anthropic import Anthropic + + output = Anthropic(model=model, api_key=os.getenv("ANTHROPIC_API_KEY")) + elif provider == "cohere": + from llama_index.llms.cohere import Cohere + + output = Cohere(model=model, api_key=os.getenv("COHERE_API_KEY")) + elif provider == "mistral": + from llama_index.llms.mistralai import MistralAI + + output = MistralAI(model=model, api_key=os.getenv("MISTRALAI_API_KEY")) + elif provider == "togetherai": + from llama_index.llms.together import TogetherLLM + + output = TogetherLLM(model=model, api_key=os.getenv("TOGETHERAI_API_KEY")) + else: + raise ValueError(f"Unsupported provider for NotDiamondSelector: {provider}") + + return output + + def _select( + self, choices: Sequence[ToolMetadata], query: QueryBundle, timeout: int = None + ) -> SelectorResult: + """ + Call Not Diamond to select the best LLM for the given prompt, then have the LLM select the best tool. + """ + messages = [ + {"role": "system", "content": self._format_prompt(choices, query)}, + {"role": "user", "content": query.query_str}, + ] + + session_id, best_llm = self._client.model_select( + messages=messages, + llm_configs=self._client.llm_configs, + metric=self._metric, + notdiamond_api_key=self._client.api_key, + max_model_depth=self._client.max_model_depth, + hash_content=self._client.hash_content, + tradeoff=self._client.tradeoff, + preference_id=self._client.preference_id, + tools=self._client.tools, + timeout=timeout or self._timeout, + ) + + self._llm = self._llm_config_to_client(best_llm) + + return NotDiamondSelectorResult.from_selector_result( + super()._select(choices, query), session_id, best_llm + ) + + async def _aselect( + self, choices: Sequence[ToolMetadata], query: QueryBundle, timeout: int = None + ) -> SelectorResult: + """ + Call Not Diamond asynchronously to select the best LLM for the given prompt, then have the LLM select the best tool. + """ + messages = [ + {"role": "system", "content": self._format_prompt(choices, query)}, + {"role": "user", "content": query.query_str}, + ] + + session_id, best_llm = await self._client.amodel_select( + messages=messages, + llm_configs=self._client.llm_configs, + metric=self._metric, + notdiamond_api_key=self._client.api_key, + max_model_depth=self._client.max_model_depth, + hash_content=self._client.hash_content, + tradeoff=self._client.tradeoff, + preference_id=self._client.preference_id, + tools=self._client.tools, + timeout=timeout or self._timeout, + ) + + self._llm = self._llm_config_to_client(best_llm) + + return NotDiamondSelectorResult.from_selector_result( + await super()._aselect(choices, query), session_id, best_llm + ) + + def _format_prompt( + self, choices: Sequence[ToolMetadata], query: QueryBundle + ) -> str: + """ + A system prompt for selection is created when instantiating the parent LLMSingleSelector class. + This method formats the prompt into a str so that it can be serialized for the NotDiamond API. + """ + context_list = _build_choices_text(choices) + return self._prompt.format( + num_choices=len(choices), + context_list=context_list, + query_str=query.query_str, + ) diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/pyproject.toml b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/pyproject.toml new file mode 100644 index 0000000000000..7cfb12f5b3205 --- /dev/null +++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/pyproject.toml @@ -0,0 +1,56 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = true +import_path = "llama_index.selectors.notdiamond" + +[tool.llamahub.class_authors] +NotDiamondSelector = "acompa" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.11" + +[tool.poetry] +authors = ["Not Diamond "] +description = "llama-index selectors Not Diamond integration" +name = "llama-index-selectors-notdiamond" +packages = [{include = "llama_index/"}] +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.10.0,<3.12" +llama-index-core = "^0.10.0" +notdiamond = "^0.3.5" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} +codespell = {extras = ["toml"], version = ">=v2.2.6"} +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/tests/BUILD b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/tests/BUILD new file mode 100644 index 0000000000000..db53f45e8d68e --- /dev/null +++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/tests/BUILD @@ -0,0 +1,3 @@ +python_tests( + interpreter_constraints=["==3.10.*"] +) diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/tests/__init__.py b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/selectors/llama-index-selectors-notdiamond/tests/test_selectors_notdiamond.py b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/tests/test_selectors_notdiamond.py new file mode 100644 index 0000000000000..754f96a686f8f --- /dev/null +++ b/llama-index-integrations/selectors/llama-index-selectors-notdiamond/tests/test_selectors_notdiamond.py @@ -0,0 +1,95 @@ +import os +import pytest +from typing import List +from unittest.mock import MagicMock, patch, AsyncMock +import uuid + +from llama_index.core.base.base_selector import ( + SelectorResult, + SingleSelection, +) +from llama_index.core.schema import QueryBundle +from llama_index.core.tools import ToolMetadata +from llama_index.selectors.notdiamond.base import NotDiamondSelector, LLMSingleSelector + +from notdiamond import LLMConfig + + +@pytest.fixture() +def session_id() -> str: + return str(uuid.uuid4()) + + +@pytest.fixture() +def choices() -> List[ToolMetadata]: + return [ + ToolMetadata( + name="vector_index", description="Great for asking questions about recipes." + ), + ToolMetadata(name="list_index", description="Great for summarizing recipes."), + ] + + +@pytest.fixture() +def nd_selector(session_id): + from notdiamond import NotDiamond + + os.environ["OPENAI_API_KEY"] = "test" + os.environ["ANTHROPIC_API_KEY"] = "test" + + llm_configs = [ + LLMConfig(provider="openai", model="gpt-4o"), + LLMConfig(provider="anthropic", model="claude-3-opus-20240229"), + ] + + # mocking out model_select calls on client + _client = MagicMock(stub=NotDiamond, api_key="test", llm_configs=llm_configs) + _client.model_select.return_value = (session_id, llm_configs[0]) + + async def aselect(*args, **kwargs): + return (session_id, llm_configs[0]) + + _client.amodel_select = aselect + selector = NotDiamondSelector(client=_client) + + # monkeypatch the _select and _aselect methods on parent class of NDSelector + LLMSingleSelector._select = MagicMock( + return_value=SelectorResult( + selections=[SingleSelection(index=0, reason="test")] + ) + ) + LLMSingleSelector._aselect = AsyncMock( + return_value=SelectorResult( + selections=[SingleSelection(index=1, reason="test")] + ) + ) + + return selector + + +class TestNotDiamondSelector: + @patch("llama_index.llms.openai.OpenAI") + def test_select(self, openai_mock, nd_selector, choices, session_id): + """_select should call openai, as mocked.""" + openai_mock.return_value = MagicMock() + openai_mock.return_value.chat.return_value.message.content = "vector_index" + query = "Please describe the llama_index framework in 280 characters or less." + result = nd_selector._select(choices, QueryBundle(query_str=query)) + assert result.session_id == session_id + assert str(result.llm) == "openai/gpt-4o" + assert result.selections[0].index == 0 + assert openai_mock.is_called + + @pytest.mark.asyncio() + @patch("llama_index.llms.anthropic.Anthropic") + async def test_aselect(self, anthropic_mock, nd_selector, choices, session_id): + """_aselect should call anthropic, as mocked.""" + anthropic_mock.return_value = MagicMock() + anthropic_mock.return_value.chat.return_value.message.content = "vector_index" + + query = "How can I cook a vegan variant of deviled eggs?" + result = await nd_selector._aselect(choices, QueryBundle(query_str=query)) + assert result.session_id == session_id + assert str(result.llm) == "anthropic/claude-3-opus-20240229" + assert result.selections[0].index == 1 + assert anthropic_mock.is_called diff --git a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-azure/pyproject.toml b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-azure/pyproject.toml index 9ec4d35ebcde7..1390541d681df 100644 --- a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-azure/pyproject.toml +++ b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-azure/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-chat-store-azure" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" azure-data-tables = "^12.5.0" -llama-index-utils-azure = "^0.1.0" +llama-index-utils-azure = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml index 218d13ff797b5..50429344bbb7f 100644 --- a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml +++ b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-chat-store-redis" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.11.post1" redis = ">=4.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-azure/pyproject.toml b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-azure/pyproject.toml index c1e7c4fe7e9f0..afd60d4b73e1f 100644 --- a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-azure/pyproject.toml +++ b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-azure/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-docstore-azure" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-storage-kvstore-azure = "^0.1.0" +llama-index-storage-kvstore-azure = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-dynamodb/pyproject.toml b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-dynamodb/pyproject.toml index fcfcebcad919f..61d6711a9a2da 100644 --- a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-dynamodb/pyproject.toml +++ b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-dynamodb/pyproject.toml @@ -27,12 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-docstore-dynamodb" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-storage-kvstore-dynamodb = "^0.1.1" +llama-index-storage-kvstore-dynamodb = "^0.2.0" +boto3 = "^1.35.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-elasticsearch/pyproject.toml b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-elasticsearch/pyproject.toml index f2b496ecfb41c..fd0f6423192cc 100644 --- a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-elasticsearch/pyproject.toml +++ b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-elasticsearch/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-docstore-elasticsearch" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-storage-kvstore-elasticsearch = "^0.1.1" +llama-index-storage-kvstore-elasticsearch = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-firestore/pyproject.toml b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-firestore/pyproject.toml index 14cd09533c9ed..c3524a7a9a4ef 100644 --- a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-firestore/pyproject.toml +++ b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-firestore/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-docstore-firestore" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.1" -llama-index-storage-kvstore-firestore = ">=0.1.1" +llama-index-storage-kvstore-firestore = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-mongodb/pyproject.toml b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-mongodb/pyproject.toml index 884c985b3bfd8..530c4056095a2 100644 --- a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-mongodb/pyproject.toml +++ b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-mongodb/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-docstore-mongodb" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-storage-kvstore-mongodb = "^0.1.1" +llama-index-storage-kvstore-mongodb = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-postgres/pyproject.toml b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-postgres/pyproject.toml index 3e6d29dd8df3a..fd1bfebd86e80 100644 --- a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-postgres/pyproject.toml +++ b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-postgres/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-docstore-postgres" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-storage-kvstore-postgres = "^0.1.2" +llama-index-storage-kvstore-postgres = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-redis/pyproject.toml b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-redis/pyproject.toml index 96259820f9197..274eca9f4cd32 100644 --- a/llama-index-integrations/storage/docstore/llama-index-storage-docstore-redis/pyproject.toml +++ b/llama-index-integrations/storage/docstore/llama-index-storage-docstore-redis/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-docstore-redis" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-storage-kvstore-redis = "^0.1.1" +llama-index-storage-kvstore-redis = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-azure/pyproject.toml b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-azure/pyproject.toml index 74be16574ee4a..819e47cd9d4c7 100644 --- a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-azure/pyproject.toml +++ b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-azure/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-index-store-azure" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.61" -llama-index-storage-kvstore-azure = "^0.1.0" +llama-index-storage-kvstore-azure = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-dynamodb/pyproject.toml b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-dynamodb/pyproject.toml index 5a77d17c42962..6bb24625891ed 100644 --- a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-dynamodb/pyproject.toml +++ b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-dynamodb/pyproject.toml @@ -27,12 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-index-store-dynamodb-store" readme = "README.md" -version = "0.2.0" +version = "0.3.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.61" -llama-index-storage-kvstore-dynamodb = "^0.1.1" +llama-index-storage-kvstore-dynamodb = "^0.2.0" +llama-index-core = "^0.11.0" +boto3 = "^1.35.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-elasticsearch/pyproject.toml b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-elasticsearch/pyproject.toml index ddd6acd2d5544..2c7e0ca8c4bcc 100644 --- a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-elasticsearch/pyproject.toml +++ b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-elasticsearch/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-index-store-elasticsearch" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.61" -llama-index-storage-kvstore-elasticsearch = "^0.1.1" +llama-index-storage-kvstore-elasticsearch = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-firestore/pyproject.toml b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-firestore/pyproject.toml index 3ca0859529b92..6c1e7d8656f5d 100644 --- a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-firestore/pyproject.toml +++ b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-firestore/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-index-store-firestore" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.61" -llama-index-storage-kvstore-firestore = ">=0.1.1" +llama-index-storage-kvstore-firestore = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-mongodb/pyproject.toml b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-mongodb/pyproject.toml index ca2ce7096cfca..3daee5e25b788 100644 --- a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-mongodb/pyproject.toml +++ b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-mongodb/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-index-store-mongodb" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.61" -llama-index-storage-kvstore-mongodb = "^0.1.1" +llama-index-storage-kvstore-mongodb = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-postgres/pyproject.toml b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-postgres/pyproject.toml index 11a5e8fdb4431..127af1dd9de9d 100644 --- a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-postgres/pyproject.toml +++ b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-postgres/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-index-store-postgres" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.61" -llama-index-storage-kvstore-postgres = "^0.1.2" +llama-index-storage-kvstore-postgres = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-redis/pyproject.toml b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-redis/pyproject.toml index bcb7352bc7d99..1d731eef40c66 100644 --- a/llama-index-integrations/storage/index_store/llama-index-storage-index-store-redis/pyproject.toml +++ b/llama-index-integrations/storage/index_store/llama-index-storage-index-store-redis/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-index-store-redis" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.61" -llama-index-storage-kvstore-redis = "^0.1.1" +llama-index-storage-kvstore-redis = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-azure/pyproject.toml b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-azure/pyproject.toml index f92884f8e0515..2e0ae7a51b35c 100644 --- a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-azure/pyproject.toml +++ b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-azure/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-kvstore-azure" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" azure-data-tables = "^12.5.0" -llama-index-utils-azure = "^0.1.0" +llama-index-utils-azure = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-dynamodb/pyproject.toml b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-dynamodb/pyproject.toml index 5221a8f5a24fa..b40d9a45beb36 100644 --- a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-dynamodb/pyproject.toml +++ b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-dynamodb/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-kvstore-dynamodb" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" boto3 = "^1.34.27" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-elasticsearch/pyproject.toml b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-elasticsearch/pyproject.toml index 085f0f6125d5d..656b296628407 100644 --- a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-elasticsearch/pyproject.toml +++ b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-elasticsearch/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-kvstore-elasticsearch" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" elasticsearch = "^8.12.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-firestore/pyproject.toml b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-firestore/pyproject.toml index 46f0a8c4898ab..f98462b3c8528 100644 --- a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-firestore/pyproject.toml +++ b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-firestore/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-kvstore-firestore" readme = "README.md" -version = "0.2.1" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.1" google-cloud-firestore = "^2.14.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-mongodb/pyproject.toml b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-mongodb/pyproject.toml index 88cc3b7c6fae0..4e5cb9729def2 100644 --- a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-mongodb/pyproject.toml +++ b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-mongodb/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-kvstore-mongodb" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pymongo = "^4.6.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-postgres/pyproject.toml b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-postgres/pyproject.toml index 12409a6dde9f1..9cdec6f553391 100644 --- a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-postgres/pyproject.toml +++ b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-postgres/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-kvstore-postgres" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] docker = "^7.0.0" diff --git a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-redis/pyproject.toml b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-redis/pyproject.toml index 5ac92ef3fd6ae..98a858cd8910a 100644 --- a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-redis/pyproject.toml +++ b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-redis/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-kvstore-redis" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" redis = "^5.0.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-s3/pyproject.toml b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-s3/pyproject.toml index 6027a410fd798..df0f97767fb6a 100644 --- a/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-s3/pyproject.toml +++ b/llama-index-integrations/storage/kvstore/llama-index-storage-kvstore-s3/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-kvstore-s3" readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" boto3 = "^1.34.27" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-arxiv/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-arxiv/pyproject.toml index d9d83bd5a3d20..e7f2725b5467d 100644 --- a/llama-index-integrations/tools/llama-index-tools-arxiv/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-arxiv/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-arxiv" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" arxiv = "^2.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-azure-code-interpreter/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-azure-code-interpreter/pyproject.toml index a2a1c150f4d2e..7debe687cc873 100644 --- a/llama-index-integrations/tools/llama-index-tools-azure-code-interpreter/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-azure-code-interpreter/pyproject.toml @@ -28,13 +28,13 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-azure-code-interpreter" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" azure-identity = "^1.16.0" requests = "^2.31.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-azure-cv/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-azure-cv/pyproject.toml index 41ee0901ee965..738d6d07e1a73 100644 --- a/llama-index-integrations/tools/llama-index-tools-azure-cv/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-azure-cv/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-azure-cv" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-azure-speech/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-azure-speech/pyproject.toml index 620cc29b6e3d9..e20349166ea7a 100644 --- a/llama-index-integrations/tools/llama-index-tools-azure-speech/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-azure-speech/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-azure-speech" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-azure-translate/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-azure-translate/pyproject.toml index cef3094efcf81..60e67b6b46e93 100644 --- a/llama-index-integrations/tools/llama-index-tools-azure-translate/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-azure-translate/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-azure-translate" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-bing-search/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-bing-search/pyproject.toml index 3cc4bf5e19e10..767a1b84358b1 100644 --- a/llama-index-integrations/tools/llama-index-tools-bing-search/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-bing-search/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-bing-search" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-box/.gitignore b/llama-index-integrations/tools/llama-index-tools-box/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/tools/llama-index-tools-box/BUILD b/llama-index-integrations/tools/llama-index-tools-box/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/tools/llama-index-tools-box/Makefile b/llama-index-integrations/tools/llama-index-tools-box/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/tools/llama-index-tools-box/README.md b/llama-index-integrations/tools/llama-index-tools-box/README.md new file mode 100644 index 0000000000000..616f1fe69eecc --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/README.md @@ -0,0 +1,15 @@ +# Box Tools for Llama-Index + +This repository provides a suite of Python tools for seamlessly integrating Box content with Llama Index. These tools offer an agnostic interface, allowing them to be used within any agent built within the Llama Index framework. + +## Available tools include: + +- **[Box Search](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/tools/llama-index-tools-box/examples/box_search.ipynb)**: Search for Box resources based on keywords and various filtering options. +- **[Search by Metadata](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/tools/llama-index-tools-box/examples/box_search_by_metadata.ipynb)**: Search for Box resources based on specific metadata fields (e.g., owner, creation date). +- **[Text Extraction](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/tools/llama-index-tools-box/examples/box_extract.ipynb)**: Extract plain text content from supported Box file formats. +- **[Box AI Prompt](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/tools/llama-index-tools-box/examples/box_ai_prompt.ipynb)**: Utilize Box AI features to analyze Box content based on user-defined prompts. +- **[Box AI Extract](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/tools/llama-index-tools-box/examples/box_ai_extract.ipynb)**: Extract structured data from Box content using pre-trained Box AI models. + +By leveraging these tools, developers can empower their Llama Index agents to access, understand, and utilize valuable information stored within Box. + +Check the links above for detailed usage instructions and examples for each tool. diff --git a/llama-index-integrations/tools/llama-index-tools-box/examples/box_ai_extract.ipynb b/llama-index-integrations/tools/llama-index-tools-box/examples/box_ai_extract.ipynb new file mode 100644 index 0000000000000..8f1bebdd3233e --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/examples/box_ai_extract.ipynb @@ -0,0 +1,92 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup Box Client\n", + "from box_sdk_gen import DeveloperTokenConfig, BoxDeveloperTokenAuth, BoxClient\n", + "\n", + "BOX_DEV_TOKEN = \"your_box_dev_token\"\n", + "\n", + "config = DeveloperTokenConfig(BOX_DEV_TOKEN)\n", + "auth = BoxDeveloperTokenAuth(config)\n", + "box_client = BoxClient(auth)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up OpenAI Client and Agent\n", + "import openai\n", + "\n", + "openai.api_key = \"your_openai_api_key\"\n", + "\n", + "from llama_index.agent.openai import OpenAIAgent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.tools.box import BoxAIExtractToolSpec\n", + "\n", + "\n", + "document_id = \"test_txt_invoice_id\"\n", + "openai_api_key = \"openai_api_key\"\n", + "ai_prompt = (\n", + " '{\"doc_type\",\"date\",\"total\",\"vendor\",\"invoice_number\",\"purchase_order_number\"}'\n", + ")\n", + "\n", + "box_tool = BoxAIExtractToolSpec(box_client=box_client)\n", + "\n", + "agent = OpenAIAgent.from_tools(\n", + " box_tool.to_tool_list(),\n", + " verbose=True,\n", + ")\n", + "\n", + "answer = agent.chat(f\"{ai_prompt} for {document_id}\")\n", + "print(answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "tests/test_tools_box_ai_extract.py Added user message to memory: {\"doc_type\",\"date\",\"total\",\"vendor\",\"invoice_number\",\"purchase_order_number\"} for 1517629086517\n", + "=== Calling Function ===\n", + "Calling function: ai_extract with args: {\"file_id\":\"1517629086517\",\"ai_prompt\":\"Extract the document type, date, total amount, vendor, invoice number, and purchase order number.\"}\n", + "Got output: Doc ID: edc0036b-4228-4717-9e43-23d86089f4bb\n", + "Text: {\"Vendor\": \"Quasar Innovations\", \"Invoice Number\": \"Q2468\",\n", + "\"Purchase Order Number\": \"003\", \"Total\": \"$1,050\", \"Date\": \"August 8,\n", + "2024\"}\n", + "========================\n", + "\n", + "The extracted information from the document with ID 1517629086517 is as follows:\n", + "\n", + "- Document Type: Not specified\n", + "- Date: August 8, 2024\n", + "- Total Amount: $1,050\n", + "- Vendor: Quasar Innovations\n", + "- Invoice Number: Q2468\n", + "- Purchase Order Number: 003\n", + "```" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/llama-index-integrations/tools/llama-index-tools-box/examples/box_ai_prompt.ipynb b/llama-index-integrations/tools/llama-index-tools-box/examples/box_ai_prompt.ipynb new file mode 100644 index 0000000000000..c8a9d37b659d6 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/examples/box_ai_prompt.ipynb @@ -0,0 +1,88 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup Box Client\n", + "from box_sdk_gen import DeveloperTokenConfig, BoxDeveloperTokenAuth, BoxClient\n", + "\n", + "BOX_DEV_TOKEN = \"your_box_dev_token\"\n", + "\n", + "config = DeveloperTokenConfig(BOX_DEV_TOKEN)\n", + "auth = BoxDeveloperTokenAuth(config)\n", + "box_client = BoxClient(auth)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up OpenAI Client and Agent\n", + "import openai\n", + "\n", + "openai.api_key = \"your_openai_api_key\"\n", + "\n", + "from llama_index.agent.openai import OpenAIAgent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.tools.box import BoxAIPromptToolSpec\n", + "\n", + "\n", + "document_id = \"your_document_id\"\n", + "ai_prompt = \"Summarize the document\"\n", + "\n", + "\n", + "box_tool = BoxAIPromptToolSpec(box_client=box_client)\n", + "\n", + "agent = OpenAIAgent.from_tools(\n", + " box_tool.to_tool_list(),\n", + " verbose=True,\n", + ")\n", + "\n", + "answer = agent.chat(f\"{ai_prompt} for {document_id}\")\n", + "print(answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "Added user message to memory: Summarize the document for 1584056661506\n", + "=== Calling Function ===\n", + "Calling function: ai_prompt with args: {\"file_id\":\"1584056661506\",\"ai_prompt\":\"Please summarize this document.\"}\n", + "Got output: Doc ID: cddd223a-fce8-4798-9c23-b7066887ec66\n", + "Text: The document is an action plan for teaching economics, with a\n", + "focus on incorporating 21st-century approaches and skills. The long-\n", + "term goal is to teach economics while considering these modern\n", + "approaches. The short-term goals include working on the creativity and\n", + "intellectual curiosity requirements of students, as well as developing\n", + "their interp...\n", + "========================\n", + "\n", + "The document is an action plan for teaching economics, emphasizing the integration of 21st-century approaches and skills. \n", + "The long-term goal is to teach economics while incorporating modern methods. Short-term goals include enhancing students' \n", + "creativity, intellectual curiosity, and developing their...\n", + "```" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/llama-index-integrations/tools/llama-index-tools-box/examples/box_extract.ipynb b/llama-index-integrations/tools/llama-index-tools-box/examples/box_extract.ipynb new file mode 100644 index 0000000000000..21d63f210c247 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/examples/box_extract.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup Box Client\n", + "from box_sdk_gen import DeveloperTokenConfig, BoxDeveloperTokenAuth, BoxClient\n", + "\n", + "BOX_DEV_TOKEN = \"your_box_dev_token\"\n", + "\n", + "config = DeveloperTokenConfig(BOX_DEV_TOKEN)\n", + "auth = BoxDeveloperTokenAuth(config)\n", + "box_client = BoxClient(auth)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up OpenAI Client and Agent\n", + "import openai\n", + "\n", + "openai.api_key = \"your_openai_api_key\"\n", + "\n", + "from llama_index.agent.openai import OpenAIAgent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.tools.box import BoxTextExtractToolSpec\n", + "\n", + "document_id = \"your_document_id\"\n", + "\n", + "box_tool = BoxTextExtractToolSpec(box_client=box_client)\n", + "\n", + "agent = OpenAIAgent.from_tools(\n", + " box_tool.to_tool_list(),\n", + " verbose=True,\n", + ")\n", + "\n", + "answer = agent.chat(f\"read document {document_id}\")\n", + "print(answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "=== Calling Function ===\n", + "Calling function: extract with args: {\"file_id\":\"1584056661506\"}\n", + "Got output: Doc ID: da890d37-396a-4f0b-ad5a-1fb40f99782b\n", + "Text: Action Plan For Teaching Economics 1 Long term Goal To\n", + "teach Economics by keeping in mind the 21st Century approaches and\n", + "skills 2 Short term Goal To work on creativity and intellectual\n", + "curiosity requirements of the students To develop interpersonal and\n", + "collaborative skills of the students 3\n", + "========================\n", + "\n", + "I have extracted the text content from the document with ID 1584056661506. Here is a snippet of the text:\n", + "\n", + "\"Action Plan For Teaching Economics\n", + "1 Long term Goal\n", + "To teach Economics by keeping in mind the 21st Century approaches and skills\n", + "2 Short term Goal\n", + "To work on creativity and intellectual curiosity requirements of the students\n", + "To develop interpersonal and collaborative skills of the students\"\n", + "\n", + "Let me know if you need more information or assistance with this document.\n", + "```" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/llama-index-integrations/tools/llama-index-tools-box/examples/box_search.ipynb b/llama-index-integrations/tools/llama-index-tools-box/examples/box_search.ipynb new file mode 100644 index 0000000000000..89bab88fb36a9 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/examples/box_search.ipynb @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup Box Client\n", + "from box_sdk_gen import DeveloperTokenConfig, BoxDeveloperTokenAuth, BoxClient\n", + "\n", + "BOX_DEV_TOKEN = \"your_box_dev_token\"\n", + "\n", + "config = DeveloperTokenConfig(BOX_DEV_TOKEN)\n", + "auth = BoxDeveloperTokenAuth(config)\n", + "box_client = BoxClient(auth)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up OpenAI Client and Agent\n", + "import openai\n", + "\n", + "openai.api_key = \"your-openai-api-key\"\n", + "\n", + "from llama_index.agent.openai import OpenAIAgent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import and initialize Box tool spec\n", + "\n", + "from llama_index.tools.box import BoxSearchToolSpec\n", + "\n", + "box_tool_spec = BoxSearchToolSpec(box_client)\n", + "\n", + "# Create an agent with the Box tool spec\n", + "agent = OpenAIAgent.from_tools(\n", + " box_tool_spec.to_tool_list(),\n", + " verbose=True,\n", + ")\n", + "\n", + "answer = agent.chat(\"search all invoices\")\n", + "print(answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "I found the following invoices:\n", + "\n", + "1. **Invoice-A5555.txt**\n", + " - Size: 150 bytes\n", + " - Created By: RB Admin\n", + " - Created At: 2024-04-30 06:22:18\n", + "\n", + "2. **Invoice-Q2468.txt**\n", + " - Size: 176 bytes\n", + " - Created By: RB Admin\n", + " - Created At: 2024-04-30 06:22:19\n", + "\n", + "3. **Invoice-B1234.txt**\n", + " - Size: 168 bytes\n", + " - Created By: RB Admin\n", + " - Created At: 2024-04-30 06:22:15\n", + "\n", + "4. **Invoice-Q8888.txt**\n", + " - Size: 164 bytes\n", + " - Created By: RB Admin\n", + " - Created At: 2024-04-30 06:22:14\n", + "\n", + "5. **Invoice-C9876.txt**\n", + " - Size: 189 bytes\n", + " - Created By: RB Admin\n", + " - Created At: 2024-04-30 06:22:17\n", + "\n", + "These are the invoices found in the search. Let me know if you need more information or assistance with these invoices.\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llama-index-readers-box-9HYYEbiN-py3.12", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/llama-index-integrations/tools/llama-index-tools-box/examples/box_search_by_metadata.ipynb b/llama-index-integrations/tools/llama-index-tools-box/examples/box_search_by_metadata.ipynb new file mode 100644 index 0000000000000..54b54424658e4 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/examples/box_search_by_metadata.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup Box Client\n", + "from box_sdk_gen import DeveloperTokenConfig, BoxDeveloperTokenAuth, BoxClient\n", + "\n", + "BOX_DEV_TOKEN = \"your_box_dev_token\"\n", + "\n", + "config = DeveloperTokenConfig(BOX_DEV_TOKEN)\n", + "auth = BoxDeveloperTokenAuth(config)\n", + "box_client = BoxClient(auth)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up OpenAI Client and Agent\n", + "import openai\n", + "\n", + "openai.api_key = \"your_openai_api_key\"\n", + "\n", + "from llama_index.agent.openai import OpenAIAgent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.tools.box import (\n", + " BoxSearchByMetadataToolSpec,\n", + " BoxSearchByMetadataOptions,\n", + ")\n", + "\n", + "# Define your metadata search query\n", + "# This must be done when instantiating the tool because the AI agent wont have a clue how to execute the search\n", + "\n", + "# Parameters\n", + "from_ = \"enterprise_\" + \"your_box_enterprise_id\" + \".\" + \"your_metadata_template_key\"\n", + "ancestor_folder_id = \"your_starting_folder_id\"\n", + "query = \"documentType = :docType \" # Your metadata query string\n", + "query_params = '{\"docType\": \"Invoice\"}' # Your metadata query parameters\n", + "\n", + "# Search options\n", + "options = BoxSearchByMetadataOptions(\n", + " from_=from_,\n", + " ancestor_folder_id=ancestor_folder_id,\n", + " query=query,\n", + ")\n", + "\n", + "box_tool = BoxSearchByMetadataToolSpec(box_client=box_client, options=options)\n", + "\n", + "agent = OpenAIAgent.from_tools(\n", + " box_tool.to_tool_list(),\n", + " verbose=True,\n", + ")\n", + "\n", + "answer = agent.chat(\n", + " f\"search all documents using the query_params as the key value pair of {query_params} \"\n", + ")\n", + "print(answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "tests/test_tools_box_search_by_metadata.py Added user message to memory: search all documents using the query_params as the key value pair of {\"docType\": \"Invoice\"} \n", + "=== Calling Function ===\n", + "Calling function: search with args: {\"query_params\":\"{\\\"docType\\\": \\\"Invoice\\\"}\"}\n", + "========================\n", + "I found the following documents with the query parameter \"docType: Invoice\":\n", + "\n", + "1. Document ID: ee053400-e501-49d0-9e9f-e021bd86d9c6\n", + " - Name: Invoice-B1234.txt\n", + " - Size: 168 bytes\n", + " - Created By: RB Admin\n", + " - Created At: 2024-04-30 06:22:15\n", + "\n", + "2. Document ID: 475cfee1-557a-4a3b-bc7c-18634d3a5d99\n", + " - Name: Invoice-C9876.txt\n", + " - Size: 189 bytes\n", + " - Created By: RB Admin\n", + " - Created At: 2024-04-30 06:22:17\n", + "\n", + "3. Document ID: 9dbfd4ec-639f-4c7e-8045-645866e780a3\n", + " - Name: Invoice-Q2468.txt\n", + " - Size: 176 bytes\n", + " - Created By: RB Admin\n", + " - Created At: 2024-04-30 06:22:19\n", + "\n", + "These are the documents matching the query parameter. Let me know if you need more information or assistance.\n", + "\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llama-index-readers-box-9HYYEbiN-py3.12", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/BUILD b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/__init__.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/__init__.py new file mode 100644 index 0000000000000..df3b98752dbf4 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/__init__.py @@ -0,0 +1,18 @@ +from llama_index.tools.box.search.base import BoxSearchToolSpec, BoxSearchOptions +from llama_index.tools.box.search_by_metadata.base import ( + BoxSearchByMetadataToolSpec, + BoxSearchByMetadataOptions, +) +from llama_index.tools.box.text_extract.base import BoxTextExtractToolSpec +from llama_index.tools.box.ai_prompt.base import BoxAIPromptToolSpec +from llama_index.tools.box.ai_extract.base import BoxAIExtractToolSpec + +__all__ = [ + "BoxSearchToolSpec", + "BoxSearchOptions", + "BoxSearchByMetadataToolSpec", + "BoxSearchByMetadataOptions", + "BoxTextExtractToolSpec", + "BoxAIPromptToolSpec", + "BoxAIExtractToolSpec", +] diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_extract/BUILD b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_extract/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_extract/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_extract/__init__.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_extract/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_extract/base.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_extract/base.py new file mode 100644 index 0000000000000..e37f3cc7b1594 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_extract/base.py @@ -0,0 +1,86 @@ +from llama_index.core.schema import Document +from llama_index.core.tools.tool_spec.base import BaseToolSpec + +from box_sdk_gen import BoxClient + +from llama_index.readers.box.BoxAPI.box_api import ( + box_check_connection, + get_box_files_details, + get_files_ai_extract_data, + add_extra_header_to_box_client, +) + +from llama_index.readers.box.BoxAPI.box_llama_adaptors import box_file_to_llama_document + + +class BoxAIExtractToolSpec(BaseToolSpec): + """ + Extracts AI generated content from a Box file. + + Args: + box_client (BoxClient): A BoxClient instance for interacting with Box API. + + Attributes: + spec_functions (list): A list of supported functions. + _box_client (BoxClient): An instance of BoxClient for interacting with Box API. + + Methods: + ai_extract(file_id, ai_prompt): Extracts AI generated content from a Box file. + + Args: + file_id (str): The ID of the Box file. + ai_prompt (str): The AI prompt to use for extraction. + + Returns: + Document: A Document object containing the extracted AI content. + """ + + spec_functions = ["ai_extract"] + + _box_client: BoxClient + + def __init__(self, box_client: BoxClient) -> None: + """ + Initializes the BoxAIExtractToolSpec with a BoxClient instance. + + Args: + box_client (BoxClient): The BoxClient instance to use for interacting with the Box API. + """ + self._box_client = add_extra_header_to_box_client(box_client) + + def ai_extract( + self, + file_id: str, + ai_prompt: str, + ) -> Document: + """ + Extracts AI generated content from a Box file using the provided AI prompt. + + Args: + file_id (str): The ID of the Box file to process. + ai_prompt (str): The AI prompt to use for content extraction. + + Returns: + Document: A Document object containing the extracted AI content, + including metadata about the original Box file. + """ + # Connect to Box + box_check_connection(self._box_client) + + # get payload information + box_file = get_box_files_details( + box_client=self._box_client, file_ids=[file_id] + )[0] + + box_file = get_files_ai_extract_data( + box_client=self._box_client, + box_files=[box_file], + ai_prompt=ai_prompt, + )[0] + + doc = box_file_to_llama_document(box_file) + doc.text = box_file.ai_response if box_file.ai_response else "" + doc.metadata["ai_prompt"] = box_file.ai_prompt + doc.metadata["ai_response"] = box_file.ai_response + + return doc diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_prompt/BUILD b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_prompt/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_prompt/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_prompt/__init__.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_prompt/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_prompt/base.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_prompt/base.py new file mode 100644 index 0000000000000..3e33593f0c6a0 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/ai_prompt/base.py @@ -0,0 +1,92 @@ +from llama_index.core.schema import Document +from llama_index.core.tools.tool_spec.base import BaseToolSpec + +from box_sdk_gen import ( + BoxClient, +) + +from llama_index.readers.box.BoxAPI.box_api import ( + box_check_connection, + get_box_files_details, + get_ai_response_from_box_files, + add_extra_header_to_box_client, +) + +from llama_index.readers.box.BoxAPI.box_llama_adaptors import box_file_to_llama_document + + +class BoxAIPromptToolSpec(BaseToolSpec): + """ + Generates AI prompts based on a Box file. + + Args: + box_client (BoxClient): A BoxClient instance for interacting with Box API. + + Attributes: + spec_functions (list): A list of supported functions. + _box_client (BoxClient): An instance of BoxClient for interacting with Box API. + + Methods: + ai_prompt(file_id, ai_prompt): Generates an AI prompt based on a Box file. + + Args: + file_id (str): The ID of the Box file. + ai_prompt (str): The base AI prompt to use. + + Returns: + Document: A Document object containing the generated AI prompt. + """ + + spec_functions = ["ai_prompt"] + + _box_client: BoxClient + + def __init__(self, box_client: BoxClient) -> None: + """ + Initializes the BoxAIPromptToolSpec with a BoxClient instance. + + Args: + box_client (BoxClient): The BoxClient instance to use for interacting with the Box API. + """ + self._box_client = add_extra_header_to_box_client(box_client) + + def ai_prompt( + self, + file_id: str, + ai_prompt: str, + ) -> Document: + """ + Generates an AI prompt based on a Box file. + + Retrieves the specified Box file, constructs an AI prompt using the provided base prompt, + and returns a Document object containing the generated prompt and file metadata. + + Args: + file_id (str): The ID of the Box file to process. + ai_prompt (str): The base AI prompt to use as a template. + + Returns: + Document: A Document object containing the generated AI prompt and file metadata. + """ + # Connect to Box + box_check_connection(self._box_client) + + # get box files information + box_file = get_box_files_details( + box_client=self._box_client, file_ids=[file_id] + )[0] + + box_file = get_ai_response_from_box_files( + box_client=self._box_client, + box_files=[box_file], + ai_prompt=ai_prompt, + )[0] + + doc = box_file_to_llama_document(box_file) + doc.text = box_file.ai_response if box_file.ai_response else "" + doc.metadata["ai_prompt"] = box_file.ai_prompt + doc.metadata["ai_response"] = ( + box_file.ai_response if box_file.ai_response else "" + ) + + return doc diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search/BUILD b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search/__init__.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search/base.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search/base.py new file mode 100644 index 0000000000000..5cfe7d3d0c3cd --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search/base.py @@ -0,0 +1,146 @@ +from typing import List, Optional +from llama_index.core.schema import Document +from llama_index.core.tools.tool_spec.base import BaseToolSpec + +from box_sdk_gen import ( + BoxClient, + SearchForContentScope, + SearchForContentContentTypes, +) + +from llama_index.readers.box.BoxAPI.box_api import ( + box_check_connection, + search_files, + get_box_files_details, + add_extra_header_to_box_client, +) + +from llama_index.readers.box.BoxAPI.box_llama_adaptors import box_file_to_llama_document + + +class BoxSearchOptions: + """ + Represents options for searching Box resources. + + This class provides a way to specify various criteria for filtering search results + when using the `BoxSearchToolSpec` class. You can define parameters like search + scope, file extensions, date ranges (created/updated at), size range, owner IDs, + and more to refine your search. + + Attributes: + scope (Optional[SearchForContentScope]): The scope of the search (e.g., all + content, trashed content). + file_extensions (Optional[List[str]]): A list of file extensions to filter by. + created_at_range (Optional[List[str]]): A list representing a date range for + file creation time (format: YYYY-MM-DD). + updated_at_range (Optional[List[str]]): A list representing a date range for + file update time (format: YYYY-MM-DD). + size_range (Optional[List[int]]): A list representing a range for file size (in bytes). + owner_user_ids (Optional[List[str]]): A list of user IDs to filter by owner. + recent_updater_user_ids (Optional[List[str]]): A list of user IDs to filter by + recent updater. + ancestor_folder_ids (Optional[List[str]]): A list of folder IDs to search within. + content_types (Optional[List[SearchForContentContentTypes]]): A list of content + types to filter by. + limit (Optional[int]): The maximum number of search results to return. + offset (Optional[int]): The offset to start results from (for pagination). + """ + + scope: Optional[SearchForContentScope] = None + file_extensions: Optional[List[str]] = None + created_at_range: Optional[List[str]] = None + updated_at_range: Optional[List[str]] = None + size_range: Optional[List[int]] = None + owner_user_ids: Optional[List[str]] = None + recent_updater_user_ids: Optional[List[str]] = None + ancestor_folder_ids: Optional[List[str]] = None + content_types: Optional[List[SearchForContentContentTypes]] = None + limit: Optional[int] = None + offset: Optional[int] = None + + +class BoxSearchToolSpec(BaseToolSpec): + """ + Provides functionalities for searching Box resources. + + This class allows you to search for Box resources based on various criteria + specified using the `BoxSearchOptions` class. It utilizes the Box API search + functionality and returns a list of `Document` objects containing information + about the found resources. + + Attributes: + spec_functions (list): A list of supported functions (always "box_search"). + _box_client (BoxClient): An instance of BoxClient for interacting with Box API. + _options (BoxSearchOptions): An instance of BoxSearchOptions containing search options. + + Methods: + box_search(query: str) -> List[Document]: + Performs a search for Box resources based on the provided query and configured + search options. Returns a list of `Document` objects representing the found resources. + """ + + spec_functions = ["box_search"] + + _box_client: BoxClient + _options: BoxSearchOptions + + def __init__( + self, box_client: BoxClient, options: BoxSearchOptions = BoxSearchOptions() + ) -> None: + """ + Initializes a `BoxSearchToolSpec` instance. + + Args: + box_client (BoxClient): An authenticated Box API client. + options (BoxSearchOptions, optional): An instance of `BoxSearchOptions` containing search options. + Defaults to `BoxSearchOptions()`. + """ + self._box_client = add_extra_header_to_box_client(box_client) + self._options = options + + def box_search( + self, + query: str, + ) -> List[Document]: + """ + Searches for Box resources based on the provided query and configured search options. + + This method utilizes the Box API search functionality to find resources matching the provided + query and search options specified in the `BoxSearchOptions` object. It returns a list of + `Document` objects containing information about the found resources. + + Args: + query (str): The search query to use for searching Box resources. + + Returns: + List[Document]: A list of `Document` objects representing the found Box resources. + """ + box_check_connection(self._box_client) + + box_files = search_files( + box_client=self._box_client, + query=query, + scope=self._options.scope, + file_extensions=self._options.file_extensions, + created_at_range=self._options.created_at_range, + updated_at_range=self._options.updated_at_range, + size_range=self._options.size_range, + owner_user_ids=self._options.owner_user_ids, + recent_updater_user_ids=self._options.recent_updater_user_ids, + ancestor_folder_ids=self._options.ancestor_folder_ids, + content_types=self._options.content_types, + limit=self._options.limit, + offset=self._options.offset, + ) + + box_files = get_box_files_details( + box_client=self._box_client, file_ids=[file.id for file in box_files] + ) + + docs: List[Document] = [] + + for file in box_files: + doc = box_file_to_llama_document(file) + docs.append(doc) + + return docs diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search_by_metadata/BUILD b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search_by_metadata/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search_by_metadata/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search_by_metadata/__init__.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search_by_metadata/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search_by_metadata/base.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search_by_metadata/base.py new file mode 100644 index 0000000000000..0b01d508a4bb8 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/search_by_metadata/base.py @@ -0,0 +1,143 @@ +from typing import List, Optional +import json +from llama_index.core.schema import Document +from llama_index.core.tools.tool_spec.base import BaseToolSpec + +from box_sdk_gen import ( + BoxClient, +) + +from llama_index.readers.box.BoxAPI.box_api import ( + box_check_connection, + search_files_by_metadata, + get_box_files_details, + add_extra_header_to_box_client, +) + +from llama_index.readers.box.BoxAPI.box_llama_adaptors import ( + box_file_to_llama_document, +) + + +class BoxSearchByMetadataOptions: + """ + Represents options for searching Box resources based on metadata. + + This class provides a way to specify parameters for searching Box resources + using metadata. You can define the starting point for the search (`from_`), the + ancestor folder ID to search within (`ancestor_folder_id`), an optional search + query (`query`), and a limit on the number of returned results (`limit`). + + Attributes: + from_ (str): The starting point for the search, such as "folder" or "file". + ancestor_folder_id (str): The ID of the ancestor folder to search within. + query (Optional[str]): An optional search query string to refine the search + based on metadata. + limit (Optional[int]): The maximum number of search results to return. + """ + + from_: str + ancestor_folder_id: str + query: Optional[str] = (None,) + limit: Optional[int] = None + + def __init__( + self, + from_: str, + ancestor_folder_id: str, + query: Optional[str] = None, + limit: Optional[int] = None, + ) -> None: + self.from_ = from_ + self.ancestor_folder_id = ancestor_folder_id + self.query = query + self.limit = limit + + +class BoxSearchByMetadataToolSpec(BaseToolSpec): + """ + Provides functionalities for searching Box resources based on metadata. + + This class allows you to search for Box resources based on metadata specified + using the `BoxSearchByMetadataOptions` class. It utilizes the Box API search + functionality and returns a list of `Document` objects containing information + about the found resources. + + Attributes: + spec_functions (list): A list of supported functions (always "search"). + _box_client (BoxClient): An instance of BoxClient for interacting with Box API. + _options (BoxSearchByMetadataOptions): An instance of BoxSearchByMetadataOptions + containing search options. + + Methods: + search(query_params: Optional[str] = None) -> List[Document]: + Performs a search for Box resources based on the configured metadata options + and optional query parameters. Returns a list of `Document` objects representing + the found resources. + """ + + spec_functions = ["search"] + + _box_client: BoxClient + _options: BoxSearchByMetadataOptions + + def __init__( + self, box_client: BoxClient, options: BoxSearchByMetadataOptions + ) -> None: + """ + Initializes a `BoxSearchByMetadataToolSpec` instance. + + Args: + box_client (BoxClient): An authenticated Box API client. + options (BoxSearchByMetadataToolSpec, optional): An instance of `BoxSearchByMetadataToolSpec` containing search options. + Defaults to `BoxSearchByMetadataToolSpec()`. + """ + self._box_client = add_extra_header_to_box_client(box_client) + self._options = options + + def search( + self, + query_params: Optional[str] = None, + ) -> List[Document]: + """ + Searches for Box resources based on metadata and returns a list of documents. + + This method leverages the configured metadata options (`self._options`) to + search for Box resources. It converts the provided JSON string (`query_params`) + into a dictionary and uses it to refine the search based on additional + metadata criteria. It retrieves matching Box files and then converts them + into `Document` objects containing relevant information. + + Args: + query_params (Optional[str]): An optional JSON string representing additional + query parameters for filtering by metadata. + + Returns: + List[Document]: A list of `Document` objects representing the found Box resources. + """ + box_check_connection(self._box_client) + + # Box API accepts a dictionary of query parameters as a string, so we need to + # convert the provided JSON string to a dictionary. + params_dict = json.loads(query_params) + + box_files = search_files_by_metadata( + box_client=self._box_client, + from_=self._options.from_, + ancestor_folder_id=self._options.ancestor_folder_id, + query=self._options.query, + query_params=params_dict, + limit=self._options.limit, + ) + + box_files = get_box_files_details( + box_client=self._box_client, file_ids=[file.id for file in box_files] + ) + + docs: List[Document] = [] + + for file in box_files: + doc = box_file_to_llama_document(file) + docs.append(doc) + + return docs diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/text_extract/BUILD b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/text_extract/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/text_extract/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/text_extract/__init__.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/text_extract/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/text_extract/base.py b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/text_extract/base.py new file mode 100644 index 0000000000000..66cb5f351046f --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/llama_index/tools/box/text_extract/base.py @@ -0,0 +1,77 @@ +from llama_index.core.schema import Document +from llama_index.core.tools.tool_spec.base import BaseToolSpec + +from box_sdk_gen import ( + BoxClient, +) + +from llama_index.readers.box.BoxAPI.box_api import ( + box_check_connection, + get_box_files_details, + get_text_representation, + add_extra_header_to_box_client, +) + +from llama_index.readers.box.BoxAPI.box_llama_adaptors import box_file_to_llama_document + + +class BoxTextExtractToolSpec(BaseToolSpec): + """Box Text Extraction Tool Specification. + + This class provides a specification for extracting text content from Box files + and creating Document objects. It leverages the Box API to retrieve the + text representation (if available) of specified Box files. + + Attributes: + _box_client (BoxClient): An instance of the Box client for interacting + with the Box API. + """ + + spec_functions = ["extract"] + _box_client: BoxClient + + def __init__(self, box_client: BoxClient) -> None: + """ + Initializes the Box Text Extraction Tool Specification with the + provided Box client instance. + + Args: + box_client (BoxClient): The Box client instance. + """ + self._box_client = add_extra_header_to_box_client(box_client) + + def extract( + self, + file_id: str, + ) -> Document: + """ + Extracts text content from Box files and creates Document objects. + + This method utilizes the Box API to retrieve the text representation + (if available) of the specified Box files. It then creates Document + objects containing the extracted text and file metadata. + + Args: + file_id (str): A of Box file ID + to extract text from. + + Returns: + List[Document]: A list of Document objects containing the extracted + text content and file metadata. + """ + # Connect to Box + box_check_connection(self._box_client) + + # get payload information + box_file = get_box_files_details( + box_client=self._box_client, file_ids=[file_id] + )[0] + + box_file = get_text_representation( + box_client=self._box_client, + box_files=[box_file], + )[0] + + doc = box_file_to_llama_document(box_file) + doc.text = box_file.text_representation if box_file.text_representation else "" + return doc diff --git a/llama-index-integrations/tools/llama-index-tools-box/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-box/pyproject.toml new file mode 100644 index 0000000000000..6f226d1cf64cb --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/pyproject.toml @@ -0,0 +1,64 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = true +import_path = "llama_index.tools.box" + +[tool.llamahub.class_authors] +BoxAIExtractToolSpec = "barduinor" +BoxAIPromptToolSpec = "barduinor" +BoxSearchByMetadataToolSpec = "barduinor" +BoxSearchToolSpec = "barduinor" +BoxTextExtractToolSpec = "barduinor" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Rui Barbosa "] +description = "llama-index tools box integration" +license = "MIT" +name = "llama-index-tools-box" +packages = [{include = "llama_index/"}] +readme = "README.md" +version = "0.2.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +box-sdk-gen = "^1.1.0" +llama-index-readers-box = "^0.2.0" +llama-index-agent-openai = "^0.3.0" +llama-index-core = "^0.11.0" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} +codespell = {extras = ["toml"], version = ">=v2.2.6"} +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +python-dotenv = "^1.0.0" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/BUILD b/llama-index-integrations/tools/llama-index-tools-box/tests/BUILD new file mode 100644 index 0000000000000..f04e4a70e5128 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/tests/BUILD @@ -0,0 +1,9 @@ +python_tests( + name="tests", + +) + +python_test_utils( + name="test_utils", + +) diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/__init__.py b/llama-index-integrations/tools/llama-index-tools-box/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/conftest.py b/llama-index-integrations/tools/llama-index-tools-box/tests/conftest.py new file mode 100644 index 0000000000000..a0c86328f7695 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/tests/conftest.py @@ -0,0 +1,139 @@ +import os +import dotenv +import pytest +from box_sdk_gen import CCGConfig, BoxCCGAuth, BoxClient, JWTConfig, BoxJWTAuth + + +@pytest.fixture(scope="module") +def box_environment_ccg(): + dotenv.load_dotenv() + + # Common configurations + client_id = os.getenv("BOX_CLIENT_ID", "YOUR_BOX_CLIENT_ID") + client_secret = os.getenv("BOX_CLIENT_SECRET", "YOUR_BOX_CLIENT_SECRET") + + # CCG configurations + enterprise_id = os.getenv("BOX_ENTERPRISE_ID", "YOUR_BOX_ENTERPRISE_ID") + ccg_user_id = os.getenv("BOX_USER_ID") + + return { + "client_id": client_id, + "client_secret": client_secret, + "enterprise_id": enterprise_id, + "ccg_user_id": ccg_user_id, + } + + +@pytest.fixture(scope="module") +def box_client_ccg_unit_testing(box_environment_ccg): + config = CCGConfig( + client_id=box_environment_ccg["client_id"], + client_secret=box_environment_ccg["client_secret"], + enterprise_id=box_environment_ccg["enterprise_id"], + user_id=box_environment_ccg["ccg_user_id"], + ) + auth = BoxCCGAuth(config) + if config.user_id: + auth.with_user_subject(config.user_id) + return BoxClient(auth) + + +@pytest.fixture(scope="module") +def box_client_ccg_integration_testing(box_environment_ccg): + config = CCGConfig( + client_id=box_environment_ccg["client_id"], + client_secret=box_environment_ccg["client_secret"], + enterprise_id=box_environment_ccg["enterprise_id"], + user_id=box_environment_ccg["ccg_user_id"], + ) + if config.client_id == "YOUR_BOX_CLIENT_ID": + raise pytest.skip( + f"Create a .env file with the Box credentials to run integration tests." + ) + auth = BoxCCGAuth(config) + if config.user_id: + auth.with_user_subject(config.user_id) + return BoxClient(auth) + + +@pytest.fixture(scope="module") +def box_environment_jwt(): + dotenv.load_dotenv() + + # JWT configurations + jwt_config_path = os.getenv("JWT_CONFIG_PATH", ".jwt.config.json") + jwt_user_id = os.getenv("BOX_USER_ID") + + return { + "jwt_config_path": jwt_config_path, # Path to the JWT config file + "jwt_user_id": jwt_user_id, + } + + +@pytest.fixture(scope="module") +def box_client_jwt_unit_testing(box_environment_jwt): + # check if .env file is configured + jwt_config_path = box_environment_jwt["jwt_config_path"] + if not os.path.exists(jwt_config_path): + config = JWTConfig( + client_id="YOUR_BOX_CLIENT_ID", + client_secret="YOUR_BOX_CLIENT_SECRET", + jwt_key_id="YOUR_BOX_JWT_KEY_ID", + private_key="YOUR_BOX_PRIVATE_KEY", + private_key_passphrase="YOUR_BOX_PRIVATE_KEY_PASSPHRASE", + enterprise_id="YOUR_BOX_ENTERPRISE_ID", + ) + else: + config = JWTConfig.from_config_file(jwt_config_path) + user_id = box_environment_jwt["jwt_user_id"] + if user_id: + config.user_id = user_id + config.enterprise_id = None + auth = BoxJWTAuth(config) + return BoxClient(auth) + + +@pytest.fixture(scope="module") +def box_client_jwt_integration_testing(box_environment_jwt): + jwt_config_path = box_environment_jwt["jwt_config_path"] + if not os.path.exists(jwt_config_path): + config = JWTConfig( + client_id="YOUR_BOX_CLIENT_ID", + client_secret="YOUR_BOX_CLIENT_SECRET", + jwt_key_id="YOUR_BOX_JWT_KEY_ID", + private_key="YOUR_BOX_PRIVATE_KEY", + private_key_passphrase="YOUR_BOX_PRIVATE_KEY_PASSPHRASE", + ) + else: + config = JWTConfig.from_config_file(jwt_config_path) + user_id = box_environment_jwt["jwt_user_id"] + if user_id: + config.user_id = user_id + config.enterprise_id = None + + if config.client_id == "YOUR_BOX_CLIENT_ID": + raise pytest.skip( + f"Create a .env file with the Box credentials to run integration tests." + ) + auth = BoxJWTAuth(config) + return BoxClient(auth) + + +def get_testing_data() -> dict: + return { + "disable_folder_tests": True, + "test_folder_id": "273980493541", + "test_doc_id": "1584054722303", + "test_ppt_id": "1584056661506", + "test_xls_id": "1584048916472", + "test_pdf_id": "1584049890463", + "test_json_id": "1584058432468", + "test_csv_id": "1584054196674", + "test_txt_waiver_id": "1514587167701", + "test_folder_invoice_po_id": "261452450320", + "test_txt_invoice_id": "1517629086517", + "test_txt_po_id": "1517628697289", + "metadata_template_key": "rbInvoicePO", + "metadata_enterprise_scope": "enterprise_" + os.getenv("BOX_ENTERPRISE_ID"), + "openai_api_key": os.getenv("OPENAI_API_KEY"), + } diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box.py b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box.py new file mode 100644 index 0000000000000..b9cb75cfffd1d --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box.py @@ -0,0 +1,18 @@ +from llama_index.core.tools.tool_spec.base import BaseToolSpec + +from llama_index.tools.box import ( + BoxSearchToolSpec, + BoxSearchByMetadataToolSpec, + BoxTextExtractToolSpec, +) + + +def test_box_class(): + names_of_base_classes = [b.__name__ for b in BoxSearchToolSpec.__mro__] + assert BaseToolSpec.__name__ in names_of_base_classes + + names_of_base_classes = [b.__name__ for b in BoxSearchByMetadataToolSpec.__mro__] + assert BaseToolSpec.__name__ in names_of_base_classes + + names_of_base_classes = [b.__name__ for b in BoxTextExtractToolSpec.__mro__] + assert BaseToolSpec.__name__ in names_of_base_classes diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_extract.py b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_extract.py new file mode 100644 index 0000000000000..dab8aadd64852 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_extract.py @@ -0,0 +1,50 @@ +import pytest +import openai +from box_sdk_gen import BoxClient + +from llama_index.tools.box import BoxAIExtractToolSpec +from llama_index.agent.openai import OpenAIAgent + +from tests.conftest import get_testing_data + +AI_PROMPT = ( + '{"doc_type","date","total","vendor","invoice_number","purchase_order_number"}' +) + + +def test_box_tool_ai_extract(box_client_ccg_integration_testing: BoxClient): + test_data = get_testing_data() + + box_tool = BoxAIExtractToolSpec(box_client=box_client_ccg_integration_testing) + + doc = box_tool.ai_extract( + file_id=test_data["test_txt_invoice_id"], ai_prompt=AI_PROMPT + ) + + assert doc.text is not None + + +def test_box_tool_ai_extract_agent(box_client_ccg_integration_testing: BoxClient): + test_data = get_testing_data() + + document_id = test_data["test_txt_invoice_id"] + openai_api_key = test_data["openai_api_key"] + ai_prompt = ( + '{"doc_type","date","total","vendor","invoice_number","purchase_order_number"}' + ) + + if openai_api_key is None: + raise pytest.skip("OpenAI API key is not provided.") + + openai.api_key = openai_api_key + + box_tool = BoxAIExtractToolSpec(box_client=box_client_ccg_integration_testing) + + agent = OpenAIAgent.from_tools( + box_tool.to_tool_list(), + verbose=True, + ) + + answer = agent.chat(f"{ai_prompt} for {document_id}") + # print(answer) + assert answer is not None diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_prompt.py b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_prompt.py new file mode 100644 index 0000000000000..67d8cac469320 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_prompt.py @@ -0,0 +1,41 @@ +import pytest +from box_sdk_gen import BoxClient + +from llama_index.tools.box import BoxAIPromptToolSpec +from llama_index.agent.openai import OpenAIAgent + +from tests.conftest import get_testing_data + + +def test_box_tool_ai_prompt(box_client_ccg_integration_testing: BoxClient): + test_data = get_testing_data() + + box_tool = BoxAIPromptToolSpec(box_client=box_client_ccg_integration_testing) + + doc = box_tool.ai_prompt( + file_id=test_data["test_ppt_id"], ai_prompt="Summarize the document" + ) + + assert doc.text is not None + + +def test_box_tool_ai_prompt_agent(box_client_ccg_integration_testing: BoxClient): + test_data = get_testing_data() + + document_id = test_data["test_ppt_id"] + openai_api_key = test_data["openai_api_key"] + ai_prompt = "Summarize the document" + + if openai_api_key is None: + raise pytest.skip("OpenAI API key is not provided.") + + box_tool = BoxAIPromptToolSpec(box_client=box_client_ccg_integration_testing) + + agent = OpenAIAgent.from_tools( + box_tool.to_tool_list(), + verbose=True, + ) + + answer = agent.chat(f"{ai_prompt} for {document_id}") + # print(answer) + assert answer is not None diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_search.py b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_search.py new file mode 100644 index 0000000000000..8f46cb31ef582 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_search.py @@ -0,0 +1,45 @@ +from box_sdk_gen import BoxClient +import openai +import pytest + +from llama_index.agent.openai import OpenAIAgent +from llama_index.tools.box import BoxSearchToolSpec, BoxSearchOptions + +from tests.conftest import get_testing_data + + +def test_box_tool_search(box_client_ccg_integration_testing: BoxClient): + options = BoxSearchOptions() + options.limit = 5 + + box_tool = BoxSearchToolSpec(box_client_ccg_integration_testing, options=options) + + query = "invoice" + docs = box_tool.box_search(query=query) + assert len(docs) > 0 + + +def test_box_tool_search_agent(box_client_ccg_integration_testing: BoxClient): + test_data = get_testing_data() + openai_api_key = test_data["openai_api_key"] + + if openai_api_key is None: + raise pytest.skip("OpenAI API key is not provided.") + + options = BoxSearchOptions() + options.limit = 5 + + box_tool_spec = BoxSearchToolSpec( + box_client_ccg_integration_testing, options=options + ) + + openai.api_key = openai_api_key + + agent = OpenAIAgent.from_tools( + box_tool_spec.to_tool_list(), + verbose=True, + ) + + answer = agent.chat("search all invoices") + # print(answer) + assert answer is not None diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_search_by_metadata.py b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_search_by_metadata.py new file mode 100644 index 0000000000000..fc448e02daa83 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_search_by_metadata.py @@ -0,0 +1,85 @@ +import pytest +import openai +from box_sdk_gen import BoxClient + +from llama_index.tools.box import ( + BoxSearchByMetadataToolSpec, + BoxSearchByMetadataOptions, +) +from llama_index.agent.openai import OpenAIAgent + +from tests.conftest import get_testing_data + + +def test_box_tool_search_by_metadata(box_client_ccg_integration_testing: BoxClient): + test_data = get_testing_data() + + # Parameters + from_ = ( + test_data["metadata_enterprise_scope"] + + "." + + test_data["metadata_template_key"] + ) + ancestor_folder_id = test_data["test_folder_invoice_po_id"] + query = "documentType = :docType " + query_params = '{"docType": "Invoice"}' + + # Search options + options = BoxSearchByMetadataOptions( + from_=from_, + ancestor_folder_id=ancestor_folder_id, + query=query, + ) + + box_tool = BoxSearchByMetadataToolSpec( + box_client=box_client_ccg_integration_testing, options=options + ) + + docs = box_tool.search( + query_params=query_params, + ) + assert len(docs) > 0 + + +def test_box_tool_search_by_metadata_agent( + box_client_ccg_integration_testing: BoxClient, +): + test_data = get_testing_data() + openai_api_key = test_data["openai_api_key"] + + if openai_api_key is None: + raise pytest.skip("OpenAI API key is not provided.") + + # Parameters + from_ = ( + test_data["metadata_enterprise_scope"] + + "." + + test_data["metadata_template_key"] + ) + ancestor_folder_id = test_data["test_folder_invoice_po_id"] + query = "documentType = :docType " + query_params = '{"docType": "Invoice"}' + + # Search options + options = BoxSearchByMetadataOptions( + from_=from_, + ancestor_folder_id=ancestor_folder_id, + query=query, + ) + + box_tool = BoxSearchByMetadataToolSpec( + box_client=box_client_ccg_integration_testing, options=options + ) + + openai.api_key = openai_api_key + + agent = OpenAIAgent.from_tools( + box_tool.to_tool_list(), + verbose=True, + ) + + answer = agent.chat( + f"search all documents using the query_params as the key value pair of {query_params} " + ) + print(answer) + assert answer is not None diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_text_extract.py b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_text_extract.py new file mode 100644 index 0000000000000..3ce0dda1c86a2 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_text_extract.py @@ -0,0 +1,38 @@ +import pytest +from box_sdk_gen import BoxClient + +from llama_index.tools.box import BoxTextExtractToolSpec +from llama_index.agent.openai import OpenAIAgent + +from tests.conftest import get_testing_data + + +def test_box_tool_extract(box_client_ccg_integration_testing: BoxClient): + test_data = get_testing_data() + + box_tool = BoxTextExtractToolSpec(box_client=box_client_ccg_integration_testing) + + doc = box_tool.extract(test_data["test_ppt_id"]) + + assert doc.text is not None + + +def test_box_tool_extract_agent(box_client_ccg_integration_testing: BoxClient): + test_data = get_testing_data() + + document_id = test_data["test_ppt_id"] + openai_api_key = test_data["openai_api_key"] + + if openai_api_key is None: + raise pytest.skip("OpenAI API key is not provided.") + + box_tool = BoxTextExtractToolSpec(box_client=box_client_ccg_integration_testing) + + agent = OpenAIAgent.from_tools( + box_tool.to_tool_list(), + verbose=True, + ) + + answer = agent.chat(f"read document {document_id}") + # print(answer) + assert answer is not None diff --git a/llama-index-integrations/tools/llama-index-tools-brave-search/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-brave-search/pyproject.toml index fb8e44f548c73..c87ad603418ad 100644 --- a/llama-index-integrations/tools/llama-index-tools-brave-search/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-brave-search/pyproject.toml @@ -31,11 +31,11 @@ license = "MIT" name = "llama-index-tools-brave-search" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/tools/llama-index-tools-cassandra/llama_index/tools/cassandra/cassandra_database_wrapper.py b/llama-index-integrations/tools/llama-index-tools-cassandra/llama_index/tools/cassandra/cassandra_database_wrapper.py index bac6c207620e6..21940416b1218 100644 --- a/llama-index-integrations/tools/llama-index-tools-cassandra/llama_index/tools/cassandra/cassandra_database_wrapper.py +++ b/llama-index-integrations/tools/llama-index-tools-cassandra/llama_index/tools/cassandra/cassandra_database_wrapper.py @@ -1,4 +1,5 @@ """Apache Cassandra database wrapper.""" + from __future__ import annotations import re @@ -6,7 +7,7 @@ from typing import Any, Dict, List, Optional, Sequence, Tuple, Union from cassandra.cluster import ResultSet, Session -from llama_index.core.bridge.pydantic import BaseModel, Field, root_validator +from llama_index.core.bridge.pydantic import BaseModel, Field, model_validator IGNORED_KEYSPACES = [ "system", @@ -488,7 +489,7 @@ class Table(BaseModel): class Config: frozen = True - @root_validator() + @model_validator(mode="before") def check_required_fields(cls, class_values: dict) -> dict: if not class_values["columns"]: raise ValueError("non-empty column list for must be provided") diff --git a/llama-index-integrations/tools/llama-index-tools-cassandra/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-cassandra/pyproject.toml index 6d3a446c127c8..3534b03f1171b 100644 --- a/llama-index-integrations/tools/llama-index-tools-cassandra/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-cassandra/pyproject.toml @@ -27,12 +27,12 @@ license = "MIT" name = "llama-index-tools-cassandra" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" cassio = "^0.1.7" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/pyproject.toml index 06cc42959d364..8593eb25b403d 100644 --- a/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-chatgpt-plugin" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-tools-openapi = "^0.1.3" +llama-index-tools-openapi = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-code-interpreter/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-code-interpreter/pyproject.toml index 463f634a8ea65..cae7554358682 100644 --- a/llama-index-integrations/tools/llama-index-tools-code-interpreter/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-code-interpreter/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-code-interpreter" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-cogniswitch/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-cogniswitch/pyproject.toml index f81bda93ea49d..5a5d07dd3e128 100644 --- a/llama-index-integrations/tools/llama-index-tools-cogniswitch/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-cogniswitch/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["cogniswitch"] name = "llama-index-tools-cogniswitch" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-database/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-database/pyproject.toml index 3b54163cbfb4c..454c229194bc1 100644 --- a/llama-index-integrations/tools/llama-index-tools-database/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-database/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-database" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-duckduckgo/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-duckduckgo/pyproject.toml index 0defe0d8cdab9..19b0272c58756 100644 --- a/llama-index-integrations/tools/llama-index-tools-duckduckgo/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-duckduckgo/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["leehuwuj"] name = "llama-index-tools-duckduckgo" readme = "README.md" -version = "0.1.1" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index = "^0.10.1" +llama-index = "^0.11.2" duckduckgo-search = "^6.1.0" [tool.poetry.group.dev.dependencies] diff --git a/llama-index-integrations/tools/llama-index-tools-exa/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-exa/pyproject.toml index 67510085e8776..f12f678777f71 100644 --- a/llama-index-integrations/tools/llama-index-tools-exa/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-exa/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["jeffzwang"] name = "llama-index-tools-exa" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" exa-py = "^1.0.8" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-finance/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-finance/pyproject.toml index ac113fc501e84..62c6bfdd9c7f1 100644 --- a/llama-index-integrations/tools/llama-index-tools-finance/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-finance/pyproject.toml @@ -28,14 +28,15 @@ license = "MIT" name = "llama-index-tools-finance" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.0" yfinance = "^0.2.36" newsapi-python = "^0.2.7" pytrends = "^4.9.2" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = ">=23.7.0,<=24.3.0"} diff --git a/llama-index-integrations/tools/llama-index-tools-google/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-google/pyproject.toml index d1281a6659af7..da0d642a43b08 100644 --- a/llama-index-integrations/tools/llama-index-tools-google/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-google/pyproject.toml @@ -31,15 +31,15 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-google" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" google-api-python-client = "^2.115.0" google-auth-httplib2 = "^0.2.0" google-auth-oauthlib = "^1.2.0" beautifulsoup4 = "^4.12.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-graphql/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-graphql/pyproject.toml index 22f1af9aa05b1..5b69c97e21337 100644 --- a/llama-index-integrations/tools/llama-index-tools-graphql/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-graphql/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-graphql" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-ionic-shopping/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-ionic-shopping/pyproject.toml index 4ce55d27c6c7c..0419defb3c934 100644 --- a/llama-index-integrations/tools/llama-index-tools-ionic-shopping/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-ionic-shopping/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["stewartjarod"] name = "llama-index-tools-ionic-shopping" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" ionic-api-sdk = "^0.9.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-jina/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-jina/pyproject.toml index 40f34f51f077a..244119de1b9f3 100644 --- a/llama-index-integrations/tools/llama-index-tools-jina/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-jina/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-tools-jina" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" yarl = "^1.9.4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/tools/llama-index-tools-metaphor/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-metaphor/pyproject.toml index 31981034ea0b9..9f63b9f72be2e 100644 --- a/llama-index-integrations/tools/llama-index-tools-metaphor/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-metaphor/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-metaphor" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" metaphor-python = "^0.1.23" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml index 8629199bac927..b17cb5b66a792 100644 --- a/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml @@ -28,14 +28,14 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-multion" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" multion = "^0.3.11" pytesseract = "^0.3.10" pillow = "^10.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-neo4j/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-neo4j/pyproject.toml index fcb9b9b0b356b..2c53380b244d5 100644 --- a/llama-index-integrations/tools/llama-index-tools-neo4j/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-neo4j/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["shahafp"] name = "llama-index-tools-neo4j" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-graph-stores-neo4j = "^0.1.1" +llama-index-graph-stores-neo4j = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-notion/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-notion/pyproject.toml index d4615d118c7da..046e90ead4bed 100644 --- a/llama-index-integrations/tools/llama-index-tools-notion/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-notion/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-tools-notion" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-notion = "^0.1.1" +llama-index-readers-notion = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-openai/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-openai/pyproject.toml index b06fbc440c69f..a4e09ace5b20f 100644 --- a/llama-index-integrations/tools/llama-index-tools-openai/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-openai/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["manelferreira_"] name = "llama-index-tools-openai-image-generation" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-openapi/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-openapi/pyproject.toml index 4954982b19ead..34fb914225f05 100644 --- a/llama-index-integrations/tools/llama-index-tools-openapi/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-openapi/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-openapi" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-passio-nutrition-ai/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-passio-nutrition-ai/pyproject.toml index 65ec0189457c8..3328555ddd638 100644 --- a/llama-index-integrations/tools/llama-index-tools-passio-nutrition-ai/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-passio-nutrition-ai/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["ivyas21"] name = "llama-index-tools-passio-nutrition-ai" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-playgrounds/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-playgrounds/pyproject.toml index a27756897732d..79cd231d7d5aa 100644 --- a/llama-index-integrations/tools/llama-index-tools-playgrounds/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-playgrounds/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" maintainers = ["tachi"] name = "llama-index-tools-playgrounds" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-tools-graphql = "^0.1.1" +llama-index-tools-graphql = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-python-file/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-python-file/pyproject.toml index 0a5ee7f85ed15..86fb6737715a1 100644 --- a/llama-index-integrations/tools/llama-index-tools-python-file/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-python-file/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-python-file" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-requests/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-requests/pyproject.toml index e95b74d852400..b8fbe9a27a3f0 100644 --- a/llama-index-integrations/tools/llama-index-tools-requests/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-requests/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-requests" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-salesforce/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-salesforce/pyproject.toml index 50b9b77a273d3..1172356bd734d 100644 --- a/llama-index-integrations/tools/llama-index-tools-salesforce/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-salesforce/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["chrispangg"] name = "llama-index-tools-salesforce" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" simple-salesforce = "^1.12.5" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-shopify/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-shopify/pyproject.toml index d3af3bbd0b54b..80327a89b8624 100644 --- a/llama-index-integrations/tools/llama-index-tools-shopify/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-shopify/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-shopify" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" shopifyapi = "^12.4.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-slack/llama_index/tools/slack/base.py b/llama-index-integrations/tools/llama-index-tools-slack/llama_index/tools/slack/base.py index 47f99062249d1..53c35467ade19 100644 --- a/llama-index-integrations/tools/llama-index-tools-slack/llama_index/tools/slack/base.py +++ b/llama-index-integrations/tools/llama-index-tools-slack/llama_index/tools/slack/base.py @@ -49,7 +49,7 @@ def send_message( message: str, ) -> None: """Send a message to a channel given the channel ID.""" - slack_client = self.reader.client + slack_client = self.reader._client try: msg_result = slack_client.chat_postMessage( channel=channel_id, @@ -64,7 +64,7 @@ def fetch_channels( self, ) -> List[str]: """Fetch a list of relevant channels.""" - slack_client = self.reader.client + slack_client = self.reader._client try: msg_result = slack_client.conversations_list() logger.info(msg_result) diff --git a/llama-index-integrations/tools/llama-index-tools-slack/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-slack/pyproject.toml index d1981fb17c6b5..e1cfaa70cf9ec 100644 --- a/llama-index-integrations/tools/llama-index-tools-slack/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-slack/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-tools-slack" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-slack = "^0.1.1" +llama-index-readers-slack = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-tavily-research/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-tavily-research/pyproject.toml index 6a726b3afcbf9..02f145f6a4c8d 100644 --- a/llama-index-integrations/tools/llama-index-tools-tavily-research/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-tavily-research/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["rotemweiss57"] name = "llama-index-tools-tavily-research" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" tavily-python = ">=0.2.4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-text-to-image/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-text-to-image/pyproject.toml index b988c7675d07f..0fa5d75fb4dbb 100644 --- a/llama-index-integrations/tools/llama-index-tools-text-to-image/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-text-to-image/pyproject.toml @@ -28,13 +28,14 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-text-to-image" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" pillow = "^10.2.0" matplotlib = "^3.8.2" +openai = ">=1.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-vector-db/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-vector-db/pyproject.toml index 247206d735314..26ec948d47ae6 100644 --- a/llama-index-integrations/tools/llama-index-tools-vector-db/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-vector-db/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-tools-vector-db" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-waii/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-waii/pyproject.toml index aaff7bfb73964..5887a72632395 100644 --- a/llama-index-integrations/tools/llama-index-tools-waii/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-waii/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["wangdatan"] name = "llama-index-tools-waii" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" waii-sdk-py = "^1.9.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-weather/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-weather/pyproject.toml index 14432a921da06..50fa1ba536780 100644 --- a/llama-index-integrations/tools/llama-index-tools-weather/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-weather/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["logan-markewich"] name = "llama-index-tools-weather" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-wikipedia/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-wikipedia/pyproject.toml index bdc6d79bd2f82..6449abe95bba1 100644 --- a/llama-index-integrations/tools/llama-index-tools-wikipedia/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-wikipedia/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-wikipedia" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" wikipedia = ">=1.4,<2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/pyproject.toml index 0ab126fedaf09..3c86e0dae5295 100644 --- a/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-wolfram-alpha" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-yahoo-finance/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/pyproject.toml index 884dcc3c2c1f0..3b26d885c2c93 100644 --- a/llama-index-integrations/tools/llama-index-tools-yahoo-finance/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/pyproject.toml @@ -27,12 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-tools-yahoo-finance" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +pandas = "*" yfinance = "^0.2.36" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-yelp/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-yelp/pyproject.toml index d5ccc33e57b45..e2f48936984b1 100644 --- a/llama-index-integrations/tools/llama-index-tools-yelp/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-yelp/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-yelp" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" yelpapi = "^2.5.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/tools/llama-index-tools-zapier/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-zapier/pyproject.toml index 8bc4a661605c8..3876b50780512 100644 --- a/llama-index-integrations/tools/llama-index-tools-zapier/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-zapier/pyproject.toml @@ -28,11 +28,11 @@ license = "MIT" maintainers = ["ajhofmann"] name = "llama-index-tools-zapier" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/base.py index 34a0ca0443cfb..51d07da92eb5d 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/llama_index/vector_stores/alibabacloud_opensearch/base.py @@ -131,8 +131,9 @@ def __init__( self.embedding_field = embedding_field self.text_field = text_field self.search_config = search_config + self.output_fields = output_fields - if output_fields is None: + if self.output_fields is None: self.output_fields = ( list(self.field_mapping.values()) if self.field_mapping else [] ) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/pyproject.toml index 18a3f1083d8f2..30bbdb4453bdf 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-alibabacloud-opensearch/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-alibabacloud-opensearch" readme = "README.md" -version = "0.1.1" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -alibabacloud_ha3engine_vector = "^1.1.3" +alibabacloud_ha3engine_vector = "^1.1.8" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-analyticdb/llama_index/vector_stores/analyticdb/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-analyticdb/llama_index/vector_stores/analyticdb/base.py index c170a33977632..a3b04709bf5ba 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-analyticdb/llama_index/vector_stores/analyticdb/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-analyticdb/llama_index/vector_stores/analyticdb/base.py @@ -57,9 +57,11 @@ def _recursively_parse_adb_filter(filters: MetadataFilters) -> Union[str, None]: return None return f" {filters.condition} ".join( [ - _build_filter_clause(filter_) - if isinstance(filter_, MetadataFilter) - else f"({_recursively_parse_adb_filter(filter_)})" + ( + _build_filter_clause(filter_) + if isinstance(filter_, MetadataFilter) + else f"({_recursively_parse_adb_filter(filter_)})" + ) for filter_ in filters.filters ] ) @@ -87,7 +89,7 @@ class AnalyticDBVectorStore(BasePydanticVectorStore): """ stores_text: bool = True - flat_metadata = False + flat_metadata: bool = False region_id: str instance_id: str @@ -129,7 +131,6 @@ def __init__( raise ValueError("client not specified") if not namespace_password: namespace_password = account_password - self._client = client super().__init__( region_id=region_id, instance_id=instance_id, @@ -141,6 +142,7 @@ def __init__( embedding_dimension=embedding_dimension, metrics=metrics, ) + self._client = client @classmethod def _initialize_client( diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-analyticdb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-analyticdb/pyproject.toml index 23f3a8815e730..59fe07e051f3f 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-analyticdb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-analyticdb/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-analyticdb" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" alibabacloud_gpdb20160503 = "^3.5.0" alibabacloud_tea_openapi = "^0.3.8" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/pyproject.toml index 5bbdbe4517b48..bf0b7b79149ae 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-astra-db" readme = "README.md" -version = "0.1.8" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" astrapy = "^1.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/llama_index/vector_stores/awadb/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/llama_index/vector_stores/awadb/base.py index bacb965aec2b6..3f8f7cc4e1b44 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/llama_index/vector_stores/awadb/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/llama_index/vector_stores/awadb/base.py @@ -45,7 +45,7 @@ class AwaDBVectorStore(BasePydanticVectorStore): flat_metadata: bool = True stores_text: bool = True - DEFAULT_TABLE_NAME = "llamaindex_awadb" + DEFAULT_TABLE_NAME: str = "llamaindex_awadb" _awadb_client: Any = PrivateAttr() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/pyproject.toml index 80c65f07fb45e..178832ae1b8e8 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-awadb/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-awadb" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-awsdocdb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-awsdocdb/pyproject.toml index 84f3bae966c10..37a2826ee135a 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-awsdocdb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-awsdocdb/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-awsdocdb" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pymongo = "^4.6.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/llama_index/vector_stores/azureaisearch/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/llama_index/vector_stores/azureaisearch/base.py index a53c1dc8f57bc..0976e8d1d1e6c 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/llama_index/vector_stores/azureaisearch/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/llama_index/vector_stores/azureaisearch/base.py @@ -76,7 +76,7 @@ class AzureAISearchVectorStore(BasePydanticVectorStore): # Azure AI Search setup search_service_api_key = "YOUR-AZURE-SEARCH-SERVICE-ADMIN-KEY" search_service_endpoint = "YOUR-AZURE-SEARCH-SERVICE-ENDPOINT" - search_service_api_version = "2023-11-01" + search_service_api_version = "2024-07-01" credential = AzureKeyCredential(search_service_api_key) # Index name to use @@ -131,6 +131,7 @@ class AzureAISearchVectorStore(BasePydanticVectorStore): str, Tuple[str, MetadataIndexFieldType] ] = PrivateAttr() _vector_profile_name: str = PrivateAttr() + _compression_type: str = PrivateAttr() def _normalise_metadata_to_index_fields( self, @@ -205,6 +206,10 @@ def _create_metadata_index_fields(self) -> List[Any]: for v in self._metadata_to_index_field_map.values(): field_name, field_type = v + # Skip if the field is already mapped + if field_name in self._field_mapping.values(): + continue + if field_type == MetadataIndexFieldType.STRING: index_field_type = "Edm.String" elif field_type == MetadataIndexFieldType.INT32: @@ -223,6 +228,24 @@ def _create_metadata_index_fields(self) -> List[Any]: return index_fields + def _get_compressions(self) -> List[Any]: + """Get the compressions for the vector search.""" + from azure.search.documents.indexes.models import ( + BinaryQuantizationCompression, + ScalarQuantizationCompression, + ) + + compressions = [] + if self._compression_type == "binary": + compressions.append( + BinaryQuantizationCompression(compression_name="myBinaryCompression") + ) + elif self._compression_type == "scalar": + compressions.append( + ScalarQuantizationCompression(compression_name="myScalarCompression") + ) + return compressions + def _create_index(self, index_name: Optional[str]) -> None: """ Creates a default index based on the supplied index name, key field names and @@ -272,13 +295,18 @@ def _create_index(self, index_name: Optional[str]) -> None: metadata_index_fields = self._create_metadata_index_fields() fields.extend(metadata_index_fields) logger.info(f"Configuring {index_name} vector search") + # Determine the compression type + compressions = self._get_compressions() + + logger.info( + f"Configuring {index_name} vector search with {self._compression_type} compression" + ) # Configure the vector search algorithms and profiles vector_search = VectorSearch( algorithms=[ HnswAlgorithmConfiguration( name="myHnsw", kind=VectorSearchAlgorithmKind.HNSW, - # For more information on HNSw parameters, visit https://learn.microsoft.com//azure/search/vector-search-ranking#creating-the-hnsw-graph parameters=HnswParameters( m=4, ef_construction=400, @@ -294,17 +322,20 @@ def _create_index(self, index_name: Optional[str]) -> None: ), ), ], + compressions=compressions, profiles=[ VectorSearchProfile( name="myHnswProfile", algorithm_configuration_name="myHnsw", + compression_name=( + compressions[0].compression_name if compressions else None + ), ), - # Add more profiles if needed VectorSearchProfile( name="myExhaustiveKnnProfile", algorithm_configuration_name="myExhaustiveKnn", + compression_name=None, # Exhaustive KNN doesn't support compression at the moment ), - # Add more profiles if needed ], ) logger.info(f"Configuring {index_name} semantic search") @@ -329,18 +360,19 @@ def _create_index(self, index_name: Optional[str]) -> None: async def _acreate_index(self, index_name: Optional[str]) -> None: """ - Creates a default index based on the supplied index name, key field names and - metadata filtering keys. + Asynchronous version of index creation with optional compression. + + Creates a default index based on the supplied index name, key field names, and metadata filtering keys. """ from azure.search.documents.indexes.models import ( ExhaustiveKnnAlgorithmConfiguration, ExhaustiveKnnParameters, HnswAlgorithmConfiguration, HnswParameters, - SearchableField, SearchField, SearchFieldDataType, SearchIndex, + SearchableField, SemanticConfiguration, SemanticField, SemanticPrioritizedFields, @@ -375,7 +407,12 @@ async def _acreate_index(self, index_name: Optional[str]) -> None: logger.info(f"Configuring {index_name} metadata fields") metadata_index_fields = self._create_metadata_index_fields() fields.extend(metadata_index_fields) - logger.info(f"Configuring {index_name} vector search") + # Determine the compression type + compressions = self._get_compressions() + + logger.info( + f"Configuring {index_name} vector search with {self._compression_type} compression" + ) # Configure the vector search algorithms and profiles vector_search = VectorSearch( algorithms=[ @@ -398,17 +435,20 @@ async def _acreate_index(self, index_name: Optional[str]) -> None: ), ), ], + compressions=compressions, profiles=[ VectorSearchProfile( name="myHnswProfile", algorithm_configuration_name="myHnsw", + compression_name=( + compressions[0].compression_name if compressions else None + ), ), - # Add more profiles if needed VectorSearchProfile( name="myExhaustiveKnnProfile", algorithm_configuration_name="myExhaustiveKnn", + compression_name=None, # Exhaustive KNN doesn't support compression at the moment ), - # Add more profiles if needed ], ) logger.info(f"Configuring {index_name} semantic search") @@ -467,6 +507,7 @@ def __init__( # If we have content in other languages, it is better to enable the language analyzer to be adjusted in searchable fields. # https://learn.microsoft.com/en-us/azure/search/index-add-language-analyzers language_analyzer: str = "en.lucene", + compression_type: str = "none", **kwargs: Any, ) -> None: # ruff: noqa: E501 @@ -526,6 +567,8 @@ def __init__( except ImportError: raise ImportError(import_err_msg) + super().__init__() + self._index_client: SearchIndexClient = cast(SearchIndexClient, None) self._async_index_client: AsyncSearchIndexClient = cast( AsyncSearchIndexClient, None @@ -545,6 +588,7 @@ def __init__( ) self._language_analyzer = language_analyzer + self._compression_type = compression_type.lower() # Validate search_or_index_client if search_or_index_client is not None: @@ -658,8 +702,6 @@ def __init__( if self._index_management == IndexManagement.VALIDATE_INDEX: self._validate_index(index_name) - super().__init__() - @property def client(self) -> Any: """Get client.""" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/pyproject.toml index 2f5df9be22184..f48d733f75acc 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-azureaisearch" readme = "README.md" -version = "0.1.13" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -azure-search-documents = "^11.4.0" +azure-search-documents = "^11.5.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/pyproject.toml index ed34b87db49fe..aaa40949fb4bc 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-azurecosmosmongo" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pymongo = "^4.6.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-bagel/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-bagel/pyproject.toml index 7be8b046e1e95..099d31028a1b9 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-bagel/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-bagel/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-bagel" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-baiduvectordb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-baiduvectordb/pyproject.toml index f8002baa1125b..1e6d97ffe686f 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-baiduvectordb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-baiduvectordb/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-vector-stores-baiduvectordb" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" pymochow = "^1.0.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-cassandra/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-cassandra/pyproject.toml index ba96aa2bb9a69..8cc090aeaa139 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-cassandra/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-cassandra/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-cassandra" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" cassio = "^0.1.4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-chatgpt-plugin/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-chatgpt-plugin/pyproject.toml index c0feb7ec5cd3b..e8b7dd4b1bd54 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-chatgpt-plugin/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-chatgpt-plugin/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-chatgpt-plugin" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/llama_index/vector_stores/chroma/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/llama_index/vector_stores/chroma/base.py index b70f0e4213598..d871c1652edb9 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/llama_index/vector_stores/chroma/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/llama_index/vector_stores/chroma/base.py @@ -164,13 +164,6 @@ def __init__( ) -> None: """Init params.""" collection_kwargs = collection_kwargs or {} - if chroma_collection is None: - client = chromadb.HttpClient(host=host, port=port, ssl=ssl, headers=headers) - self._collection = client.get_or_create_collection( - name=collection_name, **collection_kwargs - ) - else: - self._collection = cast(Collection, chroma_collection) super().__init__( host=host, @@ -181,6 +174,13 @@ def __init__( persist_dir=persist_dir, collection_kwargs=collection_kwargs or {}, ) + if chroma_collection is None: + client = chromadb.HttpClient(host=host, port=port, ssl=ssl, headers=headers) + self._collection = client.get_or_create_collection( + name=collection_name, **collection_kwargs + ) + else: + self._collection = cast(Collection, chroma_collection) @classmethod def from_collection(cls, collection: Any) -> "ChromaVectorStore": diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/pyproject.toml index 5cadfface5875..d6f4c031ca981 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-chroma" readme = "README.md" -version = "0.1.10" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" chromadb = ">=0.4.0,<0.6.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/base.py index bbd31593577b1..d539c53876673 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/llama_index/vector_stores/clickhouse/base.py @@ -10,7 +10,6 @@ import re from typing import Any, Dict, List, Optional, cast -from llama_index.core import ServiceContext from llama_index.core.bridge.pydantic import PrivateAttr from llama_index.core.schema import ( BaseNode, @@ -26,6 +25,8 @@ VectorStoreQueryResult, BasePydanticVectorStore, ) +from llama_index.core import Settings + logger = logging.getLogger(__name__) @@ -136,8 +137,6 @@ class ClickHouseVectorStore(BasePydanticVectorStore): Defaults to None. search_params (dict, optional): The search parameters for a ClickHouse query. Defaults to None. - service_context (ServiceContext, optional): Vector store service context. - Defaults to None Examples: `pip install llama-index-vector-stores-clickhouse` @@ -158,8 +157,8 @@ class ClickHouseVectorStore(BasePydanticVectorStore): ``` """ - stores_text = True - flat_metadata = False + stores_text: bool = True + flat_metadata: bool = False _table_existed: bool = PrivateAttr(default=False) _client: Any = PrivateAttr() _config: Any = PrivateAttr() @@ -168,9 +167,9 @@ class ClickHouseVectorStore(BasePydanticVectorStore): _column_names: List[str] = PrivateAttr() _column_type_names: List[str] = PrivateAttr() metadata_column: str = "metadata" - AMPLIFY_RATIO_LE5 = 100 - AMPLIFY_RATIO_GT5 = 20 - AMPLIFY_RATIO_GT50 = 10 + AMPLIFY_RATIO_LE5: int = 100 + AMPLIFY_RATIO_GT5: int = 20 + AMPLIFY_RATIO_GT50: int = 10 def __init__( self, @@ -183,7 +182,6 @@ def __init__( batch_size: int = 1000, index_params: Optional[dict] = None, search_params: Optional[dict] = None, - service_context: Optional[ServiceContext] = None, **kwargs: Any, ) -> None: """Initialize params.""" @@ -199,8 +197,8 @@ def __init__( if clickhouse_client is None: raise ValueError("Missing ClickHouse client!") - self._client = clickhouse_client - self._config = ClickHouseSettings( + client = clickhouse_client + config = ClickHouseSettings( table=table, database=database, engine=engine, @@ -213,7 +211,7 @@ def __init__( ) # schema column name, type, and construct format method - self._column_config: Dict = { + column_config: Dict = { "id": {"type": "String", "extract_func": lambda x: x.node_id}, "doc_id": {"type": "String", "extract_func": lambda x: x.ref_doc_id}, "text": { @@ -235,18 +233,11 @@ def __init__( "extract_func": lambda x: json.dumps(x.metadata), }, } - self._column_names = list(self._column_config.keys()) - self._column_type_names = [ - self._column_config[column_name]["type"] - for column_name in self._column_names + column_names = list(self._column_config.keys()) + column_type_names = [ + column_config[column_name]["type"] for column_name in column_names ] - if service_context is not None: - service_context = cast(ServiceContext, service_context) - dimension = len( - service_context.embed_model.get_query_embedding("try this out") - ) - self.create_table(dimension) super().__init__( clickhouse_client=clickhouse_client, table=table, @@ -257,8 +248,14 @@ def __init__( batch_size=batch_size, index_params=index_params, search_params=search_params, - service_context=service_context, ) + self._client = client + self._config = config + self._column_config = column_config + self._column_names = column_names + self._column_type_names = column_type_names + dimension = len(Settings.embed_model.get_query_embedding("try this out")) + self.create_table(dimension) @property def client(self) -> Any: diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/pyproject.toml index a43bfd3335635..03ff0734b8a25 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-clickhouse/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-clickhouse" readme = "README.md" -version = "0.1.3" +version = "0.3.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.5" clickhouse-connect = "^0.7.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-couchbase/llama_index/vector_stores/couchbase/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-couchbase/llama_index/vector_stores/couchbase/base.py index c7de71dbb62ea..4135d59d6e3c6 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-couchbase/llama_index/vector_stores/couchbase/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-couchbase/llama_index/vector_stores/couchbase/base.py @@ -186,6 +186,7 @@ def __init__( f"got {type(cluster)}" ) + super().__init__() self._cluster = cluster if not bucket_name: @@ -242,8 +243,6 @@ def __init__( self._scope = self._bucket.scope(self._scope_name) self._collection = self._scope.collection(self._collection_name) - super().__init__() - def add(self, nodes: List[BaseNode], **kwargs: Any) -> List[str]: """ Add nodes to the collection and return their document IDs. diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-couchbase/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-couchbase/pyproject.toml index 3a6d90abee5b3..f97e7799867b8 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-couchbase/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-couchbase/pyproject.toml @@ -30,12 +30,13 @@ license = "MIT" name = "llama-index-vector-stores-couchbase" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = ">=0.10.1" couchbase = "^4.2.0" +llama-index-embeddings-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-dashvector/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-dashvector/pyproject.toml index 05ad6406a5b17..66d8aa14ff738 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-dashvector/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-dashvector/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-dashvector" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" dashvector = "^1.0.9" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/llama_index/vector_stores/databricks/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/llama_index/vector_stores/databricks/base.py index e5cb92b4abf49..64201ccfdd529 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/llama_index/vector_stores/databricks/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/llama_index/vector_stores/databricks/base.py @@ -126,6 +126,8 @@ def __init__( text_column: Optional[str] = None, columns: Optional[List[str]] = None, ) -> None: + super().__init__(text_column=text_column, columns=columns) + try: from databricks.vector_search.client import VectorSearchIndex except ImportError: @@ -155,10 +157,6 @@ def __init__( columns = [] if "doc_id" not in columns: columns = columns[:19] + ["doc_id"] - super().__init__( - text_column=text_column, - columns=columns, - ) # initialize the column name for the text column in the delta table if self._is_databricks_managed_embeddings(): diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/pyproject.toml index 97c3b4aaf0cd7..0df5606ec2f25 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/pyproject.toml @@ -26,12 +26,12 @@ description = "llama-index vector_stores databricks vector search integration" license = "MIT" name = "llama-index-vector-stores-databricks" readme = "README.md" -version = "0.1.2" +version = "0.2.3" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.1" databricks-vectorsearch = "^0.21" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/tests/test_vector_stores_databricks_vector_search.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/tests/test_vector_stores_databricks_vector_search.py deleted file mode 100644 index e8555354df2da..0000000000000 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/tests/test_vector_stores_databricks_vector_search.py +++ /dev/null @@ -1,7 +0,0 @@ -from llama_index.core.vector_stores.types import BasePydanticVectorStore -from llama_index.vector_stores.databricks import DatabricksVectorSearch - - -def test_class(): - names_of_base_classes = [b.__name__ for b in DatabricksVectorSearch.__mro__] - assert BasePydanticVectorStore.__name__ in names_of_base_classes diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/pyproject.toml index 3258578a395fb..1694d686deb51 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-deeplake" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.1" deeplake = ">=3.9.12" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-docarray/llama_index/vector_stores/txtai.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-docarray/llama_index/vector_stores/txtai.py deleted file mode 100644 index 5c0322167fb32..0000000000000 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-docarray/llama_index/vector_stores/txtai.py +++ /dev/null @@ -1,231 +0,0 @@ -"""txtai Vector store index. - -An index that is built on top of an existing vector store. - -""" - -import json -import logging -import os -import pickle -from pathlib import Path -from typing import Any, List, Optional, cast - -import fsspec -import numpy as np -from fsspec.implementations.local import LocalFileSystem -from llama_index.bridge.pydantic import PrivateAttr -from llama_index.schema import BaseNode -from llama_index.vector_stores.simple import DEFAULT_VECTOR_STORE, NAMESPACE_SEP -from llama_index.vector_stores.types import ( - DEFAULT_PERSIST_DIR, - DEFAULT_PERSIST_FNAME, - BasePydanticVectorStore, - VectorStoreQuery, - VectorStoreQueryResult, -) - -logger = logging.getLogger() - -DEFAULT_PERSIST_PATH = os.path.join( - DEFAULT_PERSIST_DIR, f"{DEFAULT_VECTOR_STORE}{NAMESPACE_SEP}{DEFAULT_PERSIST_FNAME}" -) -IMPORT_ERROR_MSG = """ - `txtai` package not found. For instructions on - how to install `txtai` please visit - https://neuml.github.io/txtai/install/ -""" - - -class TxtaiVectorStore(BasePydanticVectorStore): - """txtai Vector Store. - - Embeddings are stored within a txtai index. - - During query time, the index uses txtai to query for the top - k embeddings, and returns the corresponding indices. - - Args: - txtai_index (txtai.ann.ANN): txtai index instance - - """ - - stores_text: bool = False - - _txtai_index = PrivateAttr() - - def __init__( - self, - txtai_index: Any, - ) -> None: - """Initialize params.""" - try: - import txtai - except ImportError: - raise ImportError(IMPORT_ERROR_MSG) - - self._txtai_index = cast(txtai.ann.ANN, txtai_index) - - super().__init__() - - @classmethod - def from_persist_dir( - cls, - persist_dir: str = DEFAULT_PERSIST_DIR, - fs: Optional[fsspec.AbstractFileSystem] = None, - ) -> "TxtaiVectorStore": - persist_path = os.path.join( - persist_dir, - f"{DEFAULT_VECTOR_STORE}{NAMESPACE_SEP}{DEFAULT_PERSIST_FNAME}", - ) - # only support local storage for now - if fs and not isinstance(fs, LocalFileSystem): - raise NotImplementedError("txtai only supports local storage for now.") - return cls.from_persist_path(persist_path=persist_path, fs=None) - - @classmethod - def from_persist_path( - cls, - persist_path: str, - fs: Optional[fsspec.AbstractFileSystem] = None, - ) -> "TxtaiVectorStore": - try: - import txtai - except ImportError: - raise ImportError(IMPORT_ERROR_MSG) - - if fs and not isinstance(fs, LocalFileSystem): - raise NotImplementedError("txtai only supports local storage for now.") - - if not os.path.exists(persist_path): - raise ValueError(f"No existing {__name__} found at {persist_path}.") - - logger.info(f"Loading {__name__} config from {persist_path}.") - parent_directory = Path(persist_path).parent - config_path = parent_directory / "config.json" - jsonconfig = config_path.exists() - # Determine if config is json or pickle - config_path = config_path if jsonconfig else parent_directory / "config" - # Load configuration - with open(config_path, "r" if jsonconfig else "rb") as f: - config = json.load(f) if jsonconfig else pickle.load(f) - - logger.info(f"Loading {__name__} from {persist_path}.") - txtai_index = txtai.ann.ANNFactory.create(config) - txtai_index.load(persist_path) - return cls(txtai_index=txtai_index) - - def add( - self, - nodes: List[BaseNode], - **add_kwargs: Any, - ) -> List[str]: - """Add nodes to index. - - Args: - nodes: List[BaseNode]: list of nodes with embeddings - - """ - text_embedding_np = np.array( - [node.get_embedding() for node in nodes], dtype="float32" - ) - - # Check if the ann index is already created - # If not create the index with node embeddings - if self._txtai_index.backend is None: - self._txtai_index.index(text_embedding_np) - else: - self._txtai_index.append(text_embedding_np) - - indx_size = self._txtai_index.count() - return [str(idx) for idx in range(indx_size - len(nodes) + 1, indx_size + 1)] - - @property - def client(self) -> Any: - """Return the txtai index.""" - return self._txtai_index - - def persist( - self, - persist_path: str = DEFAULT_PERSIST_PATH, - fs: Optional[fsspec.AbstractFileSystem] = None, - ) -> None: - """Save to file. - - This method saves the vector store to disk. - - Args: - persist_path (str): The save_path of the file. - - """ - if fs and not isinstance(fs, LocalFileSystem): - raise NotImplementedError("txtai only supports local storage for now.") - - dirpath = Path(persist_path).parent - dirpath.mkdir(exist_ok=True) - - jsonconfig = self._txtai_index.config.get("format", "pickle") == "json" - # Determine if config is json or pickle - config_path = dirpath / "config.json" if jsonconfig else dirpath / "config" - - # Write configuration - with open( - config_path, - "w" if jsonconfig else "wb", - encoding="utf-8" if jsonconfig else None, - ) as f: - if jsonconfig: - # Write config as JSON - json.dump(self._txtai_index.config, f, default=str) - else: - from txtai.version import __pickle__ - - # Write config as pickle format - pickle.dump(self._txtai_index.config, f, protocol=__pickle__) - - self._txtai_index.save(persist_path) - - def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: - """ - Delete nodes using with ref_doc_id. - - Args: - ref_doc_id (str): The doc_id of the document to delete. - - """ - self._txtai_index.delete([int(ref_doc_id)]) - - def query( - self, - query: VectorStoreQuery, - **kwargs: Any, - ) -> VectorStoreQueryResult: - """Query index for top k most similar nodes. - - Args: - query (VectorStoreQuery): query to search for in the index - - """ - if query.filters is not None: - raise ValueError("Metadata filters not implemented for txtai yet.") - - query_embedding = cast(List[float], query.query_embedding) - query_embedding_np = np.array(query_embedding, dtype="float32")[np.newaxis, :] - search_result = self._txtai_index.search( - query_embedding_np, query.similarity_top_k - )[0] - # if empty, then return an empty response - if len(search_result) == 0: - return VectorStoreQueryResult(similarities=[], ids=[]) - - filtered_dists = [] - filtered_node_idxs = [] - for dist, idx in search_result: - if idx < 0: - continue - filtered_dists.append(dist) - filtered_node_idxs.append(str(idx)) - - return VectorStoreQueryResult( - similarities=filtered_dists, ids=filtered_node_idxs - ) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-docarray/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-docarray/pyproject.toml index 80ca96de89d55..3d2ffe6cc234a 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-docarray/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-docarray/pyproject.toml @@ -28,12 +28,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-docarray" readme = "README.md" -version = "0.1.2" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" docarray = "^0.40.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/llama_index/vector_stores/duckdb/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/llama_index/vector_stores/duckdb/base.py index 231b75d90f584..5ce76c34cf3ef 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/llama_index/vector_stores/duckdb/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/llama_index/vector_stores/duckdb/base.py @@ -119,27 +119,26 @@ def __init__( except ImportError: raise ImportError(import_err_msg) - self._is_initialized = False - + database_path = None if database_name == ":memory:": _home_dir = os.path.expanduser("~") - self._conn = duckdb.connect(database_name) - self._conn.execute(f"SET home_directory='{_home_dir}';") - self._conn.install_extension("json") - self._conn.load_extension("json") - self._conn.install_extension("fts") - self._conn.load_extension("fts") + conn = duckdb.connect(database_name) + conn.execute(f"SET home_directory='{_home_dir}';") + conn.install_extension("json") + conn.load_extension("json") + conn.install_extension("fts") + conn.load_extension("fts") else: # check if persist dir exists if not os.path.exists(persist_dir): os.makedirs(persist_dir) - self._database_path = os.path.join(persist_dir, database_name) + database_path = os.path.join(persist_dir, database_name) - with DuckDBLocalContext(self._database_path) as _conn: + with DuckDBLocalContext(database_path) as _conn: pass - self._conn = None + conn = None super().__init__( database_name=database_name, @@ -150,6 +149,9 @@ def __init__( text_search_config=text_search_config, persist_dir=persist_dir, ) + self._is_initialized = False + self._conn = conn + self._database_path = database_path @classmethod def from_local( diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/pyproject.toml index 65dff6b8ab92a..7c9a021eb8aee 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/pyproject.toml @@ -28,12 +28,12 @@ license = "MIT" maintainers = ["krish-adi"] name = "llama-index-vector-stores-duckdb" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" duckdb = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-dynamodb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-dynamodb/pyproject.toml index 676a01377bdb1..216d2d756a6e5 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-dynamodb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-dynamodb/pyproject.toml @@ -27,12 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-dynamodb" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-storage-kvstore-dynamodb = "^0.1.1" +boto3 = "^1.35.0" +llama-index-storage-kvstore-dynamodb = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-elasticsearch/llama_index/vector_stores/elasticsearch/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-elasticsearch/llama_index/vector_stores/elasticsearch/base.py index 15f97f6e63edf..56b837abcd37a 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-elasticsearch/llama_index/vector_stores/elasticsearch/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-elasticsearch/llama_index/vector_stores/elasticsearch/base.py @@ -243,16 +243,6 @@ def __init__( metadata_mappings = metadata_mappings or {} metadata_mappings.update(base_metadata_mappings) - self._store = AsyncVectorStore( - user_agent=get_user_agent(), - client=es_client, - index=index_name, - retrieval_strategy=retrieval_strategy, - text_field=text_field, - vector_field=vector_field, - metadata_mappings=metadata_mappings, - ) - super().__init__( index_name=index_name, es_client=es_client, @@ -268,6 +258,16 @@ def __init__( retrieval_strategy=retrieval_strategy, ) + self._store = AsyncVectorStore( + user_agent=get_user_agent(), + client=es_client, + index=index_name, + retrieval_strategy=retrieval_strategy, + text_field=text_field, + vector_field=vector_field, + metadata_mappings=metadata_mappings, + ) + # Disable query embeddings when using Sparse vectors or BM25. # ELSER generates its own embeddings server-side if not isinstance(retrieval_strategy, AsyncDenseVectorStrategy): @@ -483,7 +483,7 @@ async def aquery( top_k_scores = [] for hit in hits: source = hit["_source"] - metadata = source.get("metadata", None) + metadata = source.get("metadata", {}) text = source.get(self.text_field, None) node_id = hit["_id"] @@ -493,7 +493,7 @@ async def aquery( except Exception: # Legacy support for old metadata format logger.warning( - f"Could not parse metadata from hit {hit['_source']['metadata']}" + f"Could not parse metadata from hit {hit['_source'].get('metadata')}" ) node_info = source.get("node_info") relationships = source.get("relationships", {}) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-elasticsearch/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-elasticsearch/pyproject.toml index 654e2e86411c1..5c031a1da59b3 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-elasticsearch/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-elasticsearch/pyproject.toml @@ -27,18 +27,19 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-elasticsearch" readme = "README.md" -version = "0.2.5" +version = "0.3.2" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" elasticsearch = "^8.13.1" aiohttp = "^3.9.5" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" jupyter = "^1.0.0" mypy = "0.991" +pandas = "*" pre-commit = "3.2.0" pylint = "2.15.10" pytest = "7.2.1" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-epsilla/llama_index/vector_stores/epsilla/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-epsilla/llama_index/vector_stores/epsilla/base.py index c7511c1d59289..77f05be02d320 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-epsilla/llama_index/vector_stores/epsilla/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-epsilla/llama_index/vector_stores/epsilla/base.py @@ -67,7 +67,7 @@ class EpsillaVectorStore(BasePydanticVectorStore): ``` """ - stores_text = True + stores_text: bool = True flat_metadata: bool = False _client: vectordb.Client = PrivateAttr() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-epsilla/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-epsilla/pyproject.toml index 9147cc16b9245..6269f1f351f75 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-epsilla/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-epsilla/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-epsilla" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pyepsilla = "^0.3.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-faiss/llama_index/vector_stores/faiss/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-faiss/llama_index/vector_stores/faiss/base.py index b43f53cdad9d2..c3fc1f0ae02ab 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-faiss/llama_index/vector_stores/faiss/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-faiss/llama_index/vector_stores/faiss/base.py @@ -74,10 +74,10 @@ def __init__( except ImportError: raise ImportError(import_err_msg) - self._faiss_index = cast(faiss.Index, faiss_index) - super().__init__() + self._faiss_index = cast(faiss.Index, faiss_index) + @classmethod def from_persist_dir( cls, diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-faiss/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-faiss/pyproject.toml index b21e810e50a15..a42f05418ceb4 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-faiss/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-faiss/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-faiss" readme = "README.md" -version = "0.1.2" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-firestore/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-firestore/pyproject.toml index 124563ab64bf8..9ff793db1100e 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-firestore/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-firestore/pyproject.toml @@ -30,13 +30,13 @@ license = "MIT" name = "llama-index-vector-store-firestore" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" google-cloud-firestore = ">=2.16.0,<3.0.0" more_itertools = ">=10.2.0,<11.0.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-google/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-google/pyproject.toml index ceaaa04b15e68..787734b81273d 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-google/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-google/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-google" readme = "README.md" -version = "0.1.7" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" google-generativeai = "^0.5.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-hologres/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-hologres/pyproject.toml index ceacbe3a6372e..9dc94069178a8 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-hologres/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-hologres/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-hologres" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" hologres-vector = "0.0.10" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/pyproject.toml index b64e33daa8de5..3ddd145585932 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-jaguar/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-jaguar" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" jaguardb-http-client = "^3.4.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/llama_index/vector_stores/kdbai/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/llama_index/vector_stores/kdbai/base.py index ca1c208b39a11..f4217000be3ff 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/llama_index/vector_stores/kdbai/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/llama_index/vector_stores/kdbai/base.py @@ -76,6 +76,8 @@ def __init__( "Please add it to the dependencies." ) + super().__init__(batch_size=batch_size, hybrid_search=hybrid_search) + if table is None: raise ValueError("Must provide an existing KDB.AI table.") else: @@ -87,8 +89,6 @@ def __init__( else: self._sparse_encoder = sparse_encoder - super().__init__(batch_size=batch_size, hybrid_search=hybrid_search) - @property def client(self) -> Any: """Return KDB.AI client.""" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/pyproject.toml index 276507aa9064d..df1e4d78a1877 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-kdbai/pyproject.toml @@ -30,13 +30,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-kdbai" readme = "README.md" -version = "0.2.0" +version = "0.3.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" pykx = "^2.1.1" kdbai-client = ">=1.1.0" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-lancedb/llama_index/vector_stores/lancedb/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-lancedb/llama_index/vector_stores/lancedb/base.py index 84952ecd9a05a..37ab0ab617e40 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-lancedb/llama_index/vector_stores/lancedb/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-lancedb/llama_index/vector_stores/lancedb/base.py @@ -141,7 +141,7 @@ class LanceDBVectorStore(BasePydanticVectorStore): ``` """ - stores_text = True + stores_text: bool = True flat_metadata: bool = True uri: Optional[str] vector_column_name: Optional[str] @@ -182,6 +182,22 @@ def __init__( **kwargs: Any, ) -> None: """Init params.""" + super().__init__( + uri=uri, + table_name=table_name, + vector_column_name=vector_column_name, + nprobes=nprobes, + refine_factor=refine_factor, + text_key=text_key, + doc_id_key=doc_id_key, + mode=mode, + query_type=query_type, + overfetch_factor=overfetch_factor, + api_key=api_key, + region=region, + **kwargs, + ) + self._table_name = table_name self._metadata_keys = None self._fts_index = None @@ -236,20 +252,6 @@ def __init__( else: self._table = None - super().__init__( - uri=uri, - table_name=table_name, - vector_column_name=vector_column_name, - nprobes=nprobes, - refine_factor=refine_factor, - text_key=text_key, - doc_id_key=doc_id_key, - mode=mode, - query_type=query_type, - overfetch_factor=overfetch_factor, - **kwargs, - ) - @property def client(self) -> None: """Get client.""" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-lancedb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-lancedb/pyproject.toml index de22ca841e6f0..9f302a2037a9b 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-lancedb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-lancedb/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-lancedb" readme = "README.md" -version = "0.1.7" +version = "0.2.2" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.1" lancedb = ">=0.8.0" tantivy = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-lantern/llama_index/vector_stores/lantern/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-lantern/llama_index/vector_stores/lantern/base.py index 7580ad76b02ab..aa2c42a926575 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-lantern/llama_index/vector_stores/lantern/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-lantern/llama_index/vector_stores/lantern/base.py @@ -1,5 +1,5 @@ import logging -from typing import Any, List, NamedTuple, Optional, Type +from typing import Any, List, NamedTuple, Optional, Type, TYPE_CHECKING import asyncpg # noqa import psycopg2 # noqa @@ -19,6 +19,9 @@ node_to_metadata_dict, ) +if TYPE_CHECKING: + from sqlalchemy.sql.selectable import Select + class DBEmbeddingRow(NamedTuple): node_id: str # FIXME: verify this type hint @@ -155,10 +158,8 @@ class LanternVectorStore(BasePydanticVectorStore): """ - from sqlalchemy.sql.selectable import Select - - stores_text = True - flat_metadata = False + stores_text: bool = True + flat_metadata: bool = False connection_string: str async_connection_string: str @@ -206,6 +207,19 @@ def __init__( from sqlalchemy.orm import declarative_base + super().__init__( + connection_string=connection_string, + async_connection_string=async_connection_string, + table_name=table_name, + schema_name=schema_name, + hybrid_search=hybrid_search, + text_search_config=text_search_config, + embed_dim=embed_dim, + cache_ok=cache_ok, + perform_setup=perform_setup, + debug=debug, + ) + # sqlalchemy model self._base = declarative_base() self._table_class = get_data_model( @@ -221,19 +235,6 @@ def __init__( ef=ef, ) - super().__init__( - connection_string=connection_string, - async_connection_string=async_connection_string, - table_name=table_name, - schema_name=schema_name, - hybrid_search=hybrid_search, - text_search_config=text_search_config, - embed_dim=embed_dim, - cache_ok=cache_ok, - perform_setup=perform_setup, - debug=debug, - ) - async def close(self) -> None: if not self._is_initialized: return @@ -375,7 +376,7 @@ async def async_add(self, nodes: List[BaseNode], **kwargs: Any) -> List[str]: def _apply_filters_and_limit( self, - stmt: Select, + stmt: "Select", limit: int, metadata_filters: Optional[MetadataFilters] = None, ) -> Any: diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-lantern/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-lantern/pyproject.toml index 5c78b65cdb8ae..7d2249ed4c033 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-lantern/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-lantern/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-lantern" readme = "README.md" -version = "0.1.4" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" psycopg2-binary = "^2.9.9" asyncpg = "^0.29.0" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.sqlalchemy] extras = ["asyncio"] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-lindorm/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-lindorm/pyproject.toml index 2ae6bba69040a..5bf4d40663261 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-lindorm/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-lindorm/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-lindorm" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" opensearch-py = {extras = ["async"], version = "^2.4.2"} +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-metal/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-metal/pyproject.toml index 0542e39ac0007..699e6d944ae5d 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-metal/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-metal/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-metal" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" metal-sdk = "^2.5.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/CHANGELOG.md b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/CHANGELOG.md index c99f386f51a19..45da932eda84a 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/CHANGELOG.md +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/CHANGELOG.md @@ -1,5 +1,17 @@ # Changelog +## [v0.2.1] + +### Changed + +- Changed the default consistency level from "Strong" to "Session". + +## [v0.1.23] + +### Added + +- Added a new parameter `collection_properties` to set collection properties. + ## [v0.1.22] ### Added diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py index ec4d651f8d588..a8cba8db5706a 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py @@ -6,11 +6,12 @@ import logging from typing import Any, Dict, List, Optional, Union +from copy import deepcopy from enum import Enum -import pymilvus # noqa from llama_index.core.bridge.pydantic import Field, PrivateAttr +from llama_index.core.indices.query.embedding_utils import get_top_k_mmr_embeddings from llama_index.core.schema import BaseNode, TextNode from llama_index.core.utils import iter_batch from llama_index.vector_stores.milvus.utils import ( @@ -36,11 +37,13 @@ node_to_metadata_dict, ) from pymilvus import Collection, MilvusClient, DataType, AnnSearchRequest +from pymilvus.client.types import LoadState logger = logging.getLogger(__name__) DEFAULT_BATCH_SIZE = 100 MILVUS_ID_FIELD = "id" +DEFAULT_MMR_PREFETCH_FACTOR = 4.0 try: from pymilvus import WeightedRanker, RRFRanker @@ -111,7 +114,7 @@ class MilvusVectorStore(BasePydanticVectorStore): similarity_metric (str, optional): The similarity metric to use, currently supports IP and L2. consistency_level (str, optional): Which consistency level to use for a newly - created collection. Defaults to "Strong". + created collection. Defaults to "Session". overwrite (bool, optional): Whether to overwrite existing collection with same name. Defaults to False. text_key (str, optional): What key text is stored in in the passed collection. @@ -121,6 +124,13 @@ class MilvusVectorStore(BasePydanticVectorStore): search_config (dict, optional): The configuration used for searching the Milvus index. Note that this must be compatible with the index type specified by `index_config`. Defaults to None. + collection_properties (dict, optional): The collection properties such as TTL + (Time-To-Live) and MMAP (memory mapping). Defaults to None. + It could include: + - 'collection.ttl.seconds' (int): Once this property is set, data in the + current collection expires in the specified time. Expired data in the + collection will be cleaned up and will not be involved in searches or queries. + - 'mmap.enabled' (bool): Whether to enable memory-mapped storage at the collection level. batch_size (int): Configures the number of documents processed in one batch when inserting data into Milvus. Defaults to DEFAULT_BATCH_SIZE. enable_sparse (bool): A boolean flag indicating whether to enable support @@ -179,12 +189,13 @@ class MilvusVectorStore(BasePydanticVectorStore): embedding_field: str = DEFAULT_EMBEDDING_KEY doc_id_field: str = DEFAULT_DOC_ID_KEY similarity_metric: str = "IP" - consistency_level: str = "Strong" + consistency_level: str = "Session" overwrite: bool = False text_key: Optional[str] output_fields: List[str] = Field(default_factory=list) index_config: Optional[dict] search_config: Optional[dict] + collection_properties: Optional[dict] batch_size: int = DEFAULT_BATCH_SIZE enable_sparse: bool = False sparse_embedding_field: str = "sparse_embedding" @@ -205,12 +216,13 @@ def __init__( embedding_field: str = DEFAULT_EMBEDDING_KEY, doc_id_field: str = DEFAULT_DOC_ID_KEY, similarity_metric: str = "IP", - consistency_level: str = "Strong", + consistency_level: str = "Session", overwrite: bool = False, text_key: Optional[str] = None, output_fields: Optional[List[str]] = None, index_config: Optional[dict] = None, search_config: Optional[dict] = None, + collection_properties: Optional[dict] = None, batch_size: int = DEFAULT_BATCH_SIZE, enable_sparse: bool = False, sparse_embedding_function: Optional[BaseSparseEmbeddingFunction] = None, @@ -231,6 +243,7 @@ def __init__( output_fields=output_fields or [], index_config=index_config if index_config else {}, search_config=search_config if search_config else {}, + collection_properties=collection_properties, batch_size=batch_size, enable_sparse=enable_sparse, sparse_embedding_function=sparse_embedding_function, @@ -289,6 +302,15 @@ def __init__( self._collection = Collection(collection_name, using=self._milvusclient._using) self._create_index_if_required() + # Set properties + if collection_properties: + if self._milvusclient.get_load_state(collection_name) == LoadState.Loaded: + self._collection.release() + self._collection.set_properties(properties=collection_properties) + self._collection.load() + else: + self._collection.set_properties(properties=collection_properties) + self.enable_sparse = enable_sparse if self.enable_sparse is True and sparse_embedding_function is None: logger.warning("Sparse embedding function is not provided, using default.") @@ -392,8 +414,6 @@ def delete_nodes( node_ids (Optional[List[str]], optional): IDs of nodes to delete. Defaults to None. filters (Optional[MetadataFilters], optional): Metadata filters. Defaults to None. """ - from copy import deepcopy - filters_cpy = deepcopy(filters) or MetadataFilters(filters=[]) if node_ids: @@ -417,6 +437,58 @@ def clear(self) -> None: """Clears db.""" self._milvusclient.drop_collection(self.collection_name) + def get_nodes( + self, + node_ids: Optional[List[str]] = None, + filters: Optional[MetadataFilters] = None, + ) -> List[BaseNode]: + """Get nodes by node ids or metadata filters. + + Args: + node_ids (Optional[List[str]], optional): IDs of nodes to retrieve. Defaults to None. + filters (Optional[MetadataFilters], optional): Metadata filters. Defaults to None. + + Raises: + ValueError: Neither or both of node_ids and filters are provided. + + Returns: + List[BaseNode]: + """ + if node_ids is None and filters is None: + raise ValueError("Either node_ids or filters must be provided.") + + filters_cpy = deepcopy(filters) or MetadataFilters(filters=[]) + milvus_filter = _to_milvus_filter(filters_cpy) + + if node_ids is not None and milvus_filter: + raise ValueError("Only one of node_ids or filters can be provided.") + + res = self.client.query( + ids=node_ids, collection_name=self.collection_name, filter=milvus_filter + ) + + nodes = [] + for item in res: + if not self.text_key: + node = metadata_dict_to_node(item) + node.embedding = item.get(self.embedding_field, None) + else: + try: + text = item.pop(self.text_key) + except Exception: + raise ValueError( + "The passed in text_key value does not exist " + "in the retrieved entity." + ) from None + embedding = item.pop(self.embedding_field, None) + node = TextNode( + text=text, + embedding=embedding, + metadata=item, + ) + nodes.append(node) + return nodes + def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult: """Query index for top k most similar nodes. @@ -433,6 +505,8 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul elif query.mode == VectorStoreQueryMode.HYBRID: if self.enable_sparse is False: raise ValueError(f"QueryMode is HYBRID, but enable_sparse is False.") + elif query.mode == VectorStoreQueryMode.MMR: + pass else: raise ValueError(f"Milvus does not support {query.mode} yet.") @@ -528,6 +602,88 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul similarities.append(hit["distance"]) ids.append(hit["id"]) + elif query.mode == VectorStoreQueryMode.MMR: + # Perform MMR search + mmr_threshold = kwargs.get("mmr_threshold", None) + + if ( + kwargs.get("mmr_prefetch_factor") is not None + and kwargs.get("mmr_prefetch_k") is not None + ): + raise ValueError( + "'mmr_prefetch_factor' and 'mmr_prefetch_k' " + "cannot coexist in a call to query()" + ) + else: + if kwargs.get("mmr_prefetch_k") is not None: + prefetch_k0 = int(kwargs["mmr_prefetch_k"]) + else: + prefetch_k0 = int( + query.similarity_top_k + * kwargs.get("mmr_prefetch_factor", DEFAULT_MMR_PREFETCH_FACTOR) + ) + + res = self._milvusclient.search( + collection_name=self.collection_name, + data=[query.query_embedding], + filter=string_expr, + limit=prefetch_k0, + output_fields=output_fields, + search_params=self.search_config, + anns_field=self.embedding_field, + ) + + nodes = res[0] + node_embeddings = [] + node_ids = [] + for node in nodes: + node_embeddings.append(node["entity"]["embedding"]) + node_ids.append(node["id"]) + + mmr_similarities, mmr_ids = get_top_k_mmr_embeddings( + query_embedding=query.query_embedding, + embeddings=node_embeddings, + similarity_top_k=query.similarity_top_k, + embedding_ids=node_ids, + mmr_threshold=mmr_threshold, + ) + + node_dict = dict(list(zip(node_ids, nodes))) + selected_nodes = [node_dict[id] for id in mmr_ids if id in node_dict] + + nodes = [] + # Parse the results + for hit in selected_nodes: + if not self.text_key: + node = metadata_dict_to_node( + { + "_node_content": hit["entity"].get("_node_content", None), + "_node_type": hit["entity"].get("_node_type", None), + } + ) + else: + try: + text = hit["entity"].get(self.text_key) + except Exception: + raise ValueError( + "The passed in text_key value does not exist " + "in the retrieved entity." + ) + + metadata = { + key: hit["entity"].get(key) for key in self.output_fields + } + node = TextNode(text=text, metadata=metadata) + + nodes.append(node) + + similarities = mmr_similarities # Passing the MMR similarities instead of the original similarities + ids = mmr_ids + + logger.debug( + f"Successfully performed MMR on embeddings in collection: {self.collection_name}" + ) + else: # Perform hybrid search sparse_emb = self.sparse_embedding_function.encode_queries( diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml index dd4242984c8c2..f5b2d12769908 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-milvus" readme = "README.md" -version = "0.1.22" +version = "0.2.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pymilvus = "^2.3.6" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py index 5a3c55e08bdab..3cfe7606d1ed6 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py @@ -140,6 +140,8 @@ def __init__( index_name: DEPRECATED: Please use vector_index_name. """ + super().__init__() + if mongodb_client is not None: self._mongodb_client = cast(MongoClient, mongodb_client) else: @@ -171,7 +173,6 @@ def __init__( self._fulltext_index_name = fulltext_index_name self._insert_kwargs = insert_kwargs or {} self._oversampling_factor = oversampling_factor - super().__init__() def add( self, diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml index 576fe9566bd02..817f918190493 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml @@ -29,11 +29,10 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-mongodb" readme = "README.md" -version = "0.1.8" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pymongo = "^4.6.1" [tool.poetry.group.dev.dependencies] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-myscale/llama_index/vector_stores/myscale/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-myscale/llama_index/vector_stores/myscale/base.py index 8956042c2c516..08365608864ab 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-myscale/llama_index/vector_stores/myscale/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-myscale/llama_index/vector_stores/myscale/base.py @@ -81,9 +81,9 @@ class MyScaleVectorStore(BasePydanticVectorStore): stores_text: bool = True metadata_column: str = "metadata" - AMPLIFY_RATIO_LE5 = 100 - AMPLIFY_RATIO_GT5 = 20 - AMPLIFY_RATIO_GT50 = 10 + AMPLIFY_RATIO_LE5: int = 100 + AMPLIFY_RATIO_GT5: int = 20 + AMPLIFY_RATIO_GT50: int = 10 _index_existed: bool = PrivateAttr(False) _client: Any = PrivateAttr() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-myscale/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-myscale/pyproject.toml index 1b57431468ae3..8f046b5498611 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-myscale/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-myscale/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-myscale" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-myscale = "^0.1.1" +llama-index-readers-myscale = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-neo4jvector/llama_index/vector_stores/neo4jvector/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-neo4jvector/llama_index/vector_stores/neo4jvector/base.py index 3386b6f8ec3d8..75d10891a5d20 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-neo4jvector/llama_index/vector_stores/neo4jvector/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-neo4jvector/llama_index/vector_stores/neo4jvector/base.py @@ -200,7 +200,7 @@ class Neo4jVectorStore(BasePydanticVectorStore): """ stores_text: bool = True - flat_metadata = True + flat_metadata: bool = True distance_strategy: str index_name: str @@ -384,9 +384,9 @@ def retrieve_existing_index(self) -> bool: self.index_name = index_information[0]["name"] self.node_label = index_information[0]["labelsOrTypes"][0] self.embedding_node_property = index_information[0]["properties"][0] - self.embedding_dimension = index_information[0]["options"]["indexConfig"][ - "vector.dimensions" - ] + index_config = index_information[0]["options"]["indexConfig"] + if "vector.dimensions" in index_config: + self.embedding_dimension = index_config["vector.dimensions"] return True except IndexError: @@ -500,9 +500,12 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul base_index_query = parallel_query + ( f"MATCH (n:`{self.node_label}`) WHERE " f"n.`{self.embedding_node_property}` IS NOT NULL AND " - f"size(n.`{self.embedding_node_property}`) = " - f"toInteger({self.embedding_dimension}) AND " ) + if self.embedding_dimension: + base_index_query += ( + f"size(n.`{self.embedding_node_property}`) = " + f"toInteger({self.embedding_dimension}) AND " + ) base_cosine_query = ( " WITH n as node, vector.similarity.cosine(" f"n.`{self.embedding_node_property}`, " diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-neo4jvector/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-neo4jvector/pyproject.toml index e24e79ce78106..24b2da7a8f58f 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-neo4jvector/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-neo4jvector/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-neo4jvector" readme = "README.md" -version = "0.1.6" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" neo4j = "^5.16.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-neptune/llama_index/vector_stores/neptune/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-neptune/llama_index/vector_stores/neptune/base.py index 0b2d18e5d3fb4..6ee4c2d517ac0 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-neptune/llama_index/vector_stores/neptune/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-neptune/llama_index/vector_stores/neptune/base.py @@ -40,7 +40,7 @@ def get_details(self) -> Any: class NeptuneAnalyticsVectorStore(BasePydanticVectorStore): stores_text: bool = True - flat_metadata = True + flat_metadata: bool = True node_label: str graph_identifier: str diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-neptune/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-neptune/pyproject.toml index b4ea1e16a9729..c3d948025890a 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-neptune/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-neptune/pyproject.toml @@ -30,12 +30,12 @@ license = "MIT" name = "llama-index-vector-stores-neptune" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" boto3 = "^1.34.40" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/CHANGELOG.md b/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/CHANGELOG.md index 633b40f215c09..bcbd5d3ab7ff8 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/CHANGELOG.md +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/CHANGELOG.md @@ -1,5 +1,10 @@ # CHANGELOG — llama-index-vector-stores-opensearch +## [0.1.14] + +- Adds support for full MetadataFilters (all operators and nested filters) +- Removes necessity to prefix filter keys with "metadata." + ## [0.1.2] - Adds OpensearchVectorClient as top-level import diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/llama_index/vector_stores/opensearch/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/llama_index/vector_stores/opensearch/base.py index 439a851fc89ce..662cc15f5eda6 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/llama_index/vector_stores/opensearch/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/llama_index/vector_stores/opensearch/base.py @@ -1,14 +1,17 @@ """Elasticsearch/Opensearch vector store.""" import asyncio -import json import uuid +from datetime import datetime from typing import Any, Dict, Iterable, List, Optional, Union, cast from llama_index.core.bridge.pydantic import PrivateAttr from llama_index.core.schema import BaseNode, MetadataMode, TextNode from llama_index.core.vector_stores.types import ( + FilterCondition, + FilterOperator, + MetadataFilter, MetadataFilters, BasePydanticVectorStore, VectorStoreQuery, @@ -210,16 +213,89 @@ def _default_approximate_search_query( "query": {"knn": {vector_field: {"vector": query_vector, "k": k}}}, } - def _parse_filters(self, filters: Optional[MetadataFilters]) -> Any: - pre_filter = [] - if filters is not None: - for f in filters.legacy_filters(): - if isinstance(f.value, str): - pre_filter.append({f.key: f.value}) - else: - pre_filter.append({f.key: json.loads(str(f.value))}) + def _is_text_field(self, value: Any) -> bool: + """Check if value is a string and keyword filtering needs to be performed. - return pre_filter + Not applied to datetime strings. + """ + if isinstance(value, str): + try: + datetime.fromisoformat(value) + return False + except ValueError as e: + return True + else: + return False + + def _parse_filter(self, filter: MetadataFilter) -> dict: + """Parse a single MetadataFilter to equivalent OpenSearch expression. + + As Opensearch does not differentiate between scalar/array keyword fields, IN and ANY are equivalent. + """ + key = f"metadata.{filter.key}" + op = filter.operator + + equality_postfix = ".keyword" if self._is_text_field(value=filter.value) else "" + + if op == FilterOperator.EQ: + return {"term": {f"{key}{equality_postfix}": filter.value}} + elif op in [ + FilterOperator.GT, + FilterOperator.GTE, + FilterOperator.LT, + FilterOperator.LTE, + ]: + return {"range": {key: {filter.operator.name.lower(): filter.value}}} + elif op == FilterOperator.NE: + return { + "bool": { + "must_not": {"term": {f"{key}{equality_postfix}": filter.value}} + } + } + elif op in [FilterOperator.IN, FilterOperator.ANY]: + return {"terms": {key: filter.value}} + elif op == FilterOperator.NIN: + return {"bool": {"must_not": {"terms": {key: filter.value}}}} + elif op == FilterOperator.ALL: + return { + "terms_set": { + key: { + "terms": filter.value, + "minimum_should_match_script": {"source": "params.num_terms"}, + } + } + } + elif op == FilterOperator.TEXT_MATCH: + return {"match": {key: {"query": filter.value, "fuzziness": "AUTO"}}} + elif op == FilterOperator.CONTAINS: + return {"wildcard": {key: f"*{filter.value}*"}} + else: + raise ValueError(f"Unsupported filter operator: {filter.operator}") + + def _parse_filters_recursively(self, filters: MetadataFilters) -> dict: + """Parse (possibly nested) MetadataFilters to equivalent OpenSearch expression.""" + condition_map = {FilterCondition.AND: "must", FilterCondition.OR: "should"} + + bool_clause = condition_map[filters.condition] + bool_query: dict[str, dict[str, list[dict]]] = {"bool": {bool_clause: []}} + + for filter_item in filters.filters: + if isinstance(filter_item, MetadataFilter): + bool_query["bool"][bool_clause].append(self._parse_filter(filter_item)) + elif isinstance(filter_item, MetadataFilters): + bool_query["bool"][bool_clause].append( + self._parse_filters_recursively(filter_item) + ) + else: + raise ValueError(f"Unsupported filter type: {type(filter_item)}") + + return bool_query + + def _parse_filters(self, filters: Optional[MetadataFilters]) -> List[dict]: + """Parse MetadataFilters to equivalent OpenSearch expression.""" + if filters is None: + return [] + return [self._parse_filters_recursively(filters=filters)] def _knn_search_query( self, @@ -412,13 +488,7 @@ async def delete_nodes( query["query"]["bool"]["filter"].append({"terms": {"_id": node_ids or []}}) if filters: - for filter in self._parse_filters(filters): - newfilter = {} - - for key in filter: - newfilter[f"metadata.{key}.keyword"] = filter[key] - - query["query"]["bool"]["filter"].append({"term": newfilter}) + query["query"]["bool"]["filter"].extend(self._parse_filters(filters)) await self._os_client.delete_by_query(index=self._index, body=query) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/pyproject.toml index b63e4e60b9eeb..dc78447bcd99d 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-opensearch" readme = "README.md" -version = "0.1.13" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.opensearch-py] extras = ["async"] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/tests/test_opensearch_client.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/tests/test_opensearch_client.py index 385260eb714b2..08eec1bf76743 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/tests/test_opensearch_client.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-opensearch/tests/test_opensearch_client.py @@ -1,9 +1,10 @@ import asyncio import logging import pytest -import uuid -from typing import List, Generator import time +import uuid +from datetime import datetime +from typing import List, Generator, Set from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode from llama_index.vector_stores.opensearch import ( @@ -11,12 +12,12 @@ OpensearchVectorStore, ) from llama_index.core.vector_stores.types import ( + FilterCondition, FilterOperator, MetadataFilter, MetadataFilters, VectorStoreQuery, ) -from llama_index.core.vector_stores.types import VectorStoreQuery ## # Start Opensearch locally @@ -51,6 +52,12 @@ def _get_sample_vector(num: float) -> List[float]: return [num] + [1.0] * (TEST_EMBED_DIM - 1) +def _get_sample_vector_store_query(filters: MetadataFilters) -> VectorStoreQuery: + return VectorStoreQuery( + query_embedding=[0.1] * TEST_EMBED_DIM, similarity_top_k=100, filters=filters + ) + + @pytest.mark.skipif(opensearch_not_available, reason="opensearch is not available") def test_connection() -> None: assert True @@ -327,3 +334,333 @@ def test_clear( res = os_store.query(q) assert all(i not in res.ids for i in ["bbb", "aaa", "ddd", "ccc"]) assert len(res.ids) == 0 + + +@pytest.fixture() +def insert_document(os_store: OpensearchVectorStore): + """Factory to insert a document with custom metadata into the OpensearchVectorStore.""" + + def _insert_document(doc_id: str, metadata: dict): + """Helper function to insert a document with custom metadata.""" + os_store.add( + [ + TextNode( + id_=doc_id, + text="Lorem Ipsum", + metadata=metadata, + embedding=[0.1, 0.2, 0.3], + ) + ] + ) + + return _insert_document + + +@pytest.mark.skipif(opensearch_not_available, reason="opensearch is not available") +@pytest.mark.parametrize("operator", [FilterOperator.EQ, FilterOperator.NE]) +@pytest.mark.parametrize( + ("key", "value", "false_value"), + [ + ("author", "John Doe", "Doe John"), + ("created_at", "2019-03-23T21:34:46+00:00", "2020-03-23T21:34:46+00:00"), + ("directory", "parent/sub_dir", "parent"), + ("page", 42, 43), + ], +) +def test_filter_eq( + os_store: OpensearchVectorStore, + insert_document, + operator: FilterOperator, + key: str, + value, + false_value, +): + """Test that OpensearchVectorStore correctly applies FilterOperator.EQ/NE in filters.""" + for meta, id_ in [ + ({key: value}, "match"), + ({key: false_value}, "nomatch1"), + ({}, "nomatch2"), + ]: + insert_document(doc_id=id_, metadata=meta) + + query = _get_sample_vector_store_query( + filters=MetadataFilters( + filters=[MetadataFilter(key=key, value=value, operator=operator)] + ) + ) + query_result = os_store.query(query) + + doc_ids = {node.id_ for node in query_result.nodes} + if operator == FilterOperator.EQ: + assert doc_ids == {"match"} + else: # FilterOperator.NE + assert doc_ids == {"nomatch1", "nomatch2"} + + +@pytest.mark.skipif(opensearch_not_available, reason="opensearch is not available") +@pytest.mark.parametrize( + ("operator", "exp_doc_ids"), + [ + (FilterOperator.GT, {"page3", "page4"}), + (FilterOperator.GTE, {"page2", "page3", "page4"}), + (FilterOperator.LT, {"page1"}), + (FilterOperator.LTE, {"page1", "page2"}), + ], +) +def test_filter_range_number( + os_store: OpensearchVectorStore, + insert_document, + operator: FilterOperator, + exp_doc_ids: set, +): + """Test that OpensearchVectorStore correctly applies FilterOperator.GT/GTE/LT/LTE in filters for numbers.""" + for i in range(1, 5): + insert_document(doc_id=f"page{i}", metadata={"page": i}) + insert_document(doc_id="nomatch", metadata={}) + + query = _get_sample_vector_store_query( + filters=MetadataFilters( + filters=[MetadataFilter(key="page", value=2, operator=operator)] + ) + ) + query_result = os_store.query(query) + + doc_ids = {node.id_ for node in query_result.nodes} + assert doc_ids == exp_doc_ids + + +@pytest.mark.skipif(opensearch_not_available, reason="opensearch is not available") +@pytest.mark.parametrize( + ("operator", "exp_doc_ids"), + [ + (FilterOperator.GT, {"date3", "date4"}), + (FilterOperator.GTE, {"date2", "date3", "date4"}), + (FilterOperator.LT, {"date1"}), + (FilterOperator.LTE, {"date1", "date2"}), + ], +) +def test_filter_range_datetime( + os_store: OpensearchVectorStore, + insert_document, + operator: FilterOperator, + exp_doc_ids: set, +): + """Test that OpensearchVectorStore correctly applies FilterOperator.GT/GTE/LT/LTE in filters for datetime.""" + dt = datetime.now() + for i in range(1, 5): + insert_document( + doc_id=f"date{i}", metadata={"created_at": dt.replace(second=i).isoformat()} + ) + insert_document(doc_id="nomatch", metadata={}) + + query = _get_sample_vector_store_query( + filters=MetadataFilters( + filters=[ + MetadataFilter( + key="created_at", + value=dt.replace(second=2).isoformat(), + operator=operator, + ) + ] + ) + ) + query_result = os_store.query(query) + + doc_ids = {node.id_ for node in query_result.nodes} + assert doc_ids == exp_doc_ids + + +@pytest.mark.skipif(opensearch_not_available, reason="opensearch is not available") +@pytest.mark.parametrize( + ("operator", "exp_doc_ids"), + [ + (FilterOperator.IN, {"match1", "match2"}), + (FilterOperator.ANY, {"match1", "match2"}), + (FilterOperator.NIN, {"nomatch"}), + ], +) +@pytest.mark.parametrize("value", [["product"], ["accounting", "product"]]) +def test_filter_in( + os_store: OpensearchVectorStore, + insert_document, + operator: FilterOperator, + exp_doc_ids: Set[str], + value: List[str], +): + """Test that OpensearchVectorStore correctly applies FilterOperator.IN/ANY/NIN in filters.""" + for metadata, id_ in [ + ({"category": ["product", "management"]}, "match1"), + ({"category": ["product", "marketing"]}, "match2"), + ({"category": ["management"]}, "nomatch"), + ]: + insert_document(doc_id=id_, metadata=metadata) + + query = _get_sample_vector_store_query( + filters=MetadataFilters( + filters=[MetadataFilter(key="category", value=value, operator=operator)] + ) + ) + query_result = os_store.query(query) + + doc_ids = {node.id_ for node in query_result.nodes} + assert doc_ids == exp_doc_ids + + +@pytest.mark.skipif(opensearch_not_available, reason="opensearch is not available") +def test_filter_all( + os_store: OpensearchVectorStore, + insert_document, +): + """Test that OpensearchVectorStore correctly applies FilterOperator.ALL in filters.""" + for metadata, id_ in [ + ({"category": ["product", "management", "marketing"]}, "match1"), + ({"category": ["product", "marketing"]}, "match2"), + ({"category": ["product", "management"]}, "nomatch"), + ]: + insert_document(doc_id=id_, metadata=metadata) + + query = _get_sample_vector_store_query( + filters=MetadataFilters( + filters=[ + MetadataFilter( + key="category", + value=["product", "marketing"], + operator=FilterOperator.ALL, + ) + ] + ) + ) + query_result = os_store.query(query) + + doc_ids = {node.id_ for node in query_result.nodes} + assert doc_ids == {"match1", "match2"} + + +@pytest.mark.skipif(opensearch_not_available, reason="opensearch is not available") +def test_filter_text_match( + os_store: OpensearchVectorStore, + insert_document, +): + """Test that OpensearchVectorStore correctly applies FilterOperator.TEXT_MATCH in filters. Also tests that + fuzzy matching works as intended. + """ + for metadata, id_ in [ + ({"name": "John Doe"}, "match1"), + ({"name": "Doe John Johnson"}, "match2"), + ({"name": "Johnny Doe"}, "match3"), + ({"name": "Mary Sue"}, "nomatch"), + ]: + insert_document(doc_id=id_, metadata=metadata) + + query = _get_sample_vector_store_query( + filters=MetadataFilters( + filters=[ + MetadataFilter( + key="name", value="John Doe", operator=FilterOperator.TEXT_MATCH + ) + ] + ) + ) + query_result = os_store.query(query) + + doc_ids = {node.id_ for node in query_result.nodes} + assert doc_ids == {"match1", "match2", "match3"} + + +@pytest.mark.skipif(opensearch_not_available, reason="opensearch is not available") +def test_filter_contains(os_store: OpensearchVectorStore, insert_document): + """Test that OpensearchVectorStore correctly applies FilterOperator.CONTAINS in filters. Should only match + exact substring matches. + """ + for metadata, id_ in [ + ({"name": "John Doe"}, "match1"), + ({"name": "Johnny Doe"}, "match2"), + ({"name": "Jon Doe"}, "nomatch"), + ]: + insert_document(doc_id=id_, metadata=metadata) + + query = _get_sample_vector_store_query( + filters=MetadataFilters( + filters=[ + MetadataFilter( + key="name", value="ohn", operator=FilterOperator.CONTAINS + ) + ] + ) + ) + query_result = os_store.query(query) + + doc_ids = {node.id_ for node in query_result.nodes} + assert doc_ids == {"match1", "match2"} + + +@pytest.mark.skipif(opensearch_not_available, reason="opensearch is not available") +@pytest.mark.parametrize( + ("filters", "exp_match_ids"), + [ + ( + MetadataFilters( + filters=[ + MetadataFilter(key="page", value=42), + MetadataFilters( + filters=[ + MetadataFilter( + key="status", + value="published", + operator=FilterOperator.EQ, + ), + MetadataFilter( + key="category", + value=["group1", "group2"], + operator=FilterOperator.ANY, + ), + ], + condition=FilterCondition.OR, + ), + ], + condition=FilterCondition.AND, + ), + {"doc_in_category", "doc_published"}, + ), + ( + MetadataFilters( + filters=[ + MetadataFilter(key="page", value=42, operator=FilterOperator.GT), + MetadataFilter(key="page", value=45, operator=FilterOperator.LT), + ], + ), + {"page43", "page44"}, + ), + ], +) +def test_filter_nested( + os_store: OpensearchVectorStore, + insert_document, + filters: MetadataFilters, + exp_match_ids: Set[str], +): + """Test that OpensearchVectorStore correctly applies nested filters.""" + for metadata, id_ in [ + ( + {"category": ["group1", "group3"], "status": "in_review", "page": 42}, + "doc_in_category", + ), + ( + {"category": ["group3", "group4"], "status": "in_review", "page": 42}, + "nomatch1", + ), + ( + {"category": ["group3", "group4"], "status": "published", "page": 42}, + "doc_published", + ), + ]: + insert_document(doc_id=id_, metadata=metadata) + for i in range(43, 46): + insert_document(doc_id=f"page{i}", metadata={"page": i}) + insert_document(doc_id="nomatch2", metadata={}) + + query = _get_sample_vector_store_query(filters=filters) + query_result = os_store.query(query) + + doc_ids = {node.id_ for node in query_result.nodes} + assert doc_ids == exp_match_ids diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-pgvecto-rs/llama_index/vector_stores/pgvecto_rs/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-pgvecto-rs/llama_index/vector_stores/pgvecto_rs/base.py index 8add8ecf2787c..1174c9c921128 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-pgvecto-rs/llama_index/vector_stores/pgvecto_rs/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-pgvecto-rs/llama_index/vector_stores/pgvecto_rs/base.py @@ -54,13 +54,13 @@ class PGVectoRsStore(BasePydanticVectorStore): ``` """ - stores_text = True + stores_text: bool = True _client: "PGVectoRs" = PrivateAttr() def __init__(self, client: "PGVectoRs") -> None: - self._client: PGVectoRs = client super().__init__() + self._client: PGVectoRs = client @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-pgvecto-rs/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-pgvecto-rs/pyproject.toml index 1ab7408570587..188a53d8909a0 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-pgvecto-rs/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-pgvecto-rs/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-pgvecto-rs" readme = "README.md" -version = "0.1.2" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.pgvecto-rs] extras = ["sdk"] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py index d30a4e4864fdd..ed94aabcfb882 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/llama_index/vector_stores/pinecone/base.py @@ -255,7 +255,6 @@ def __init__( if tokenizer is None and add_sparse_vector: tokenizer = get_default_tokenizer() - self._tokenizer = tokenizer super().__init__( index_name=index_name, @@ -269,6 +268,8 @@ def __init__( remove_text_from_metadata=remove_text_from_metadata, ) + self._tokenizer = tokenizer + # TODO: Make following instance check stronger -- check if pinecone_index is not pinecone.Index, else raise # ValueError if isinstance(pinecone_index, str): diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/pyproject.toml index e60eecbec2efe..2803a4f012308 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-pinecone/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-pinecone" readme = "README.md" -version = "0.1.9" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<3.13" -llama-index-core = "^0.10.11.post1" pinecone-client = ">=3.2.2,<6.0.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/llama_index/vector_stores/postgres/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/llama_index/vector_stores/postgres/base.py index 232e1ee42d40e..21749780d0574 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/llama_index/vector_stores/postgres/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/llama_index/vector_stores/postgres/base.py @@ -1,6 +1,6 @@ import logging import re -from typing import Any, Dict, List, NamedTuple, Optional, Type, Union +from typing import Any, Dict, List, NamedTuple, Optional, Type, Union, TYPE_CHECKING import asyncpg # noqa import pgvector # noqa @@ -23,6 +23,9 @@ node_to_metadata_dict, ) +if TYPE_CHECKING: + from sqlalchemy.sql.selectable import Select + class DBEmbeddingRow(NamedTuple): node_id: str # FIXME: verify this type hint @@ -131,10 +134,8 @@ class PGVectorStore(BasePydanticVectorStore): ``` """ - from sqlalchemy.sql.selectable import Select - - stores_text = True - flat_metadata = False + stores_text: bool = True + flat_metadata: bool = False connection_string: str async_connection_string: Union[str, sqlalchemy.engine.URL] @@ -202,19 +203,6 @@ def __init__( from sqlalchemy.orm import declarative_base - # sqlalchemy model - self._base = declarative_base() - self._table_class = get_data_model( - self._base, - table_name, - schema_name, - hybrid_search, - text_search_config, - cache_ok, - embed_dim=embed_dim, - use_jsonb=use_jsonb, - ) - super().__init__( connection_string=connection_string, async_connection_string=async_connection_string, @@ -230,6 +218,19 @@ def __init__( hnsw_kwargs=hnsw_kwargs, ) + # sqlalchemy model + self._base = declarative_base() + self._table_class = get_data_model( + self._base, + table_name, + schema_name, + hybrid_search, + text_search_config, + cache_ok, + embed_dim=embed_dim, + use_jsonb=use_jsonb, + ) + async def close(self) -> None: if not self._is_initialized: return @@ -377,9 +378,11 @@ def _create_hnsw_index(self) -> None: hnsw_m = self.hnsw_kwargs.pop("hnsw_m") hnsw_dist_method = self.hnsw_kwargs.pop("hnsw_dist_method", "vector_cosine_ops") + index_name = f"{self._table_class.__tablename__}_embedding_idx" + with self._session() as session, session.begin(): statement = sqlalchemy.text( - f"CREATE INDEX ON {self.schema_name}.{self._table_class.__tablename__} USING hnsw (embedding {hnsw_dist_method}) WITH (m = {hnsw_m}, ef_construction = {hnsw_ef_construction})" + f"CREATE INDEX IF NOT EXISTS {index_name} ON {self.schema_name}.{self._table_class.__tablename__} USING hnsw (embedding {hnsw_dist_method}) WITH (m = {hnsw_m}, ef_construction = {hnsw_ef_construction})" ) session.execute(statement) session.commit() @@ -521,7 +524,7 @@ def _recursively_apply_filters(self, filters: List[MetadataFilters]) -> Any: def _apply_filters_and_limit( self, - stmt: Select, + stmt: "Select", limit: int, metadata_filters: Optional[MetadataFilters] = None, ) -> Any: diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/pyproject.toml index 76d97b2b21cb3..74fe4bc1a2e6d 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/pyproject.toml @@ -27,14 +27,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-postgres" readme = "README.md" -version = "0.1.13" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.20" pgvector = "^0.2.4" psycopg2-binary = "^2.9.9" asyncpg = "^0.29.0" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.sqlalchemy] extras = ["asyncio"] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/tests/test_postgres.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/tests/test_postgres.py index b54381939b81c..f9c7c5dc47fe2 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/tests/test_postgres.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-postgres/tests/test_postgres.py @@ -157,6 +157,33 @@ def pg_hnsw_hybrid(db_hnsw: None) -> Any: asyncio.run(pg.close()) +@pytest.fixture() +def pg_hnsw_multiple(db_hnsw: None) -> Generator[List[PGVectorStore], None, None]: + """ + This creates multiple instances of PGVectorStore. + """ + pgs = [] + for _ in range(2): + pg = PGVectorStore.from_params( + **PARAMS, # type: ignore + database=TEST_DB_HNSW, + table_name=TEST_TABLE_NAME, + schema_name=TEST_SCHEMA_NAME, + embed_dim=TEST_EMBED_DIM, + hnsw_kwargs={ + "hnsw_m": 16, + "hnsw_ef_construction": 64, + "hnsw_ef_search": 40, + }, + ) + pgs.append(pg) + + yield pgs + + for pg in pgs: + asyncio.run(pg.close()) + + @pytest.fixture(scope="session") def node_embeddings() -> List[TextNode]: return [ @@ -862,6 +889,41 @@ async def test_delete_nodes_metadata( assert "ccc" in res.ids +@pytest.mark.skipif(postgres_not_available, reason="postgres db is not available") +@pytest.mark.asyncio() +@pytest.mark.parametrize("use_async", [True, False]) +async def test_hnsw_index_creation( + pg_hnsw_multiple: List[PGVectorStore], + node_embeddings: List[TextNode], + use_async: bool, +) -> None: + """ + This test will make sure that creating multiple PGVectorStores handles db initialization properly. + """ + # calling add will make the db initialization run + for pg in pg_hnsw_multiple: + if use_async: + await pg.async_add(node_embeddings) + else: + pg.add(node_embeddings) + + # these are the actual table and index names that PGVectorStore automatically created + data_test_table_name = f"data_{TEST_TABLE_NAME}" + data_test_index_name = f"data_{TEST_TABLE_NAME}_embedding_idx" + + # create a connection to the TEST_DB_HNSW database to make sure that one, and only one, index was created + with psycopg2.connect(**PARAMS, database=TEST_DB_HNSW) as hnsw_conn: + with hnsw_conn.cursor() as c: + c.execute( + f"SELECT COUNT(*) FROM pg_indexes WHERE schemaname = '{TEST_SCHEMA_NAME}' AND tablename = '{data_test_table_name}' AND indexname LIKE '{data_test_index_name}%';" + ) + index_count = c.fetchone()[0] + + assert ( + index_count == 1 + ), f"Expected exactly one '{data_test_index_name}' index, but found {index_count}." + + @pytest.mark.skipif(postgres_not_available, reason="postgres db is not available") @pytest.mark.asyncio() @pytest.mark.parametrize("use_async", [True, False]) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/llama_index/vector_stores/qdrant/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/llama_index/vector_stores/qdrant/base.py index c1c66f3bb8431..4814155f128b7 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/llama_index/vector_stores/qdrant/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/llama_index/vector_stores/qdrant/base.py @@ -44,9 +44,12 @@ MatchText, MatchValue, Payload, + PayloadField, Range, HasIdCondition, + IsEmptyCondition, ) +from qdrant_client.qdrant_fastembed import IDF_EMBEDDING_MODELS logger = logging.getLogger(__name__) import_err_msg = ( @@ -148,6 +151,19 @@ def __init__( **kwargs: Any, ) -> None: """Init params.""" + super().__init__( + collection_name=collection_name, + url=url, + api_key=api_key, + batch_size=batch_size, + parallel=parallel, + max_retries=max_retries, + client_kwargs=client_kwargs or {}, + enable_hybrid=enable_hybrid, + index_doc_id=index_doc_id, + fastembed_sparse_model=fastembed_sparse_model, + ) + if ( client is None and aclient is None @@ -201,19 +217,6 @@ def __init__( self._dense_config = dense_config self._quantization_config = quantization_config - super().__init__( - collection_name=collection_name, - url=url, - api_key=api_key, - batch_size=batch_size, - parallel=parallel, - max_retries=max_retries, - client_kwargs=client_kwargs or {}, - enable_hybrid=enable_hybrid, - index_doc_id=index_doc_id, - fastembed_sparse_model=fastembed_sparse_model, - ) - @classmethod def class_name(cls) -> str: return "QdrantVectorStore" @@ -434,6 +437,8 @@ async def async_add(self, nodes: List[BaseNode], **kwargs: Any) -> List[str]: Raises: ValueError: If trying to using async methods without aclient """ + from qdrant_client.http.exceptions import UnexpectedResponse + collection_initialized = await self._acollection_exists(self.collection_name) if len(nodes) > 0 and not collection_initialized: @@ -445,14 +450,19 @@ async def async_add(self, nodes: List[BaseNode], **kwargs: Any) -> List[str]: sparse_vector_name = await self.asparse_vector_name() points, ids = self._build_points(nodes, sparse_vector_name) - await self._aclient.upload_points( - collection_name=self.collection_name, - points=points, - batch_size=self.batch_size, - parallel=self.parallel, - max_retries=self.max_retries, - wait=True, - ) + for batch in iter_batch(points, self.batch_size): + retries = 0 + while retries < self.max_retries: + try: + await self._aclient.upsert( + collection_name=self.collection_name, + points=batch, + ) + break + except (RpcError, UnexpectedResponse) as exc: + retries += 1 + if retries >= self.max_retries: + raise exc # noqa: TRY201 return ids @@ -597,8 +607,8 @@ def _create_collection(self, collection_name: str, vector_size: int) -> None: index=rest.SparseIndexParams(), modifier=( rest.Modifier.IDF - if self.fastembed_sparse_model and "bm42" in self.fastembed_sparse_model - else rest.Modifier.NONE + if self.fastembed_sparse_model in IDF_EMBEDDING_MODELS + else None ), ) @@ -652,8 +662,8 @@ async def _acreate_collection(self, collection_name: str, vector_size: int) -> N index=rest.SparseIndexParams(), modifier=( rest.Modifier.IDF - if self.fastembed_sparse_model and "bm42" in self.fastembed_sparse_model - else rest.Modifier.NONE + if self.fastembed_sparse_model in IDF_EMBEDDING_MODELS + else None ), ) @@ -1095,6 +1105,12 @@ def _build_subfilter(self, filters: MetadataFilters) -> Filter: match=MatchExcept(**{"except": values}), ) ) + elif subfilter.operator == FilterOperator.IS_EMPTY: + # This condition will match all records where the field reports either does not exist, or has null or [] value. + # https://qdrant.tech/documentation/concepts/filtering/#is-empty + conditions.append( + IsEmptyCondition(is_empty=PayloadField(key=subfilter.key)) + ) filter = Filter() if filters.condition == FilterCondition.AND: diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/pyproject.toml index 8dc931790ac29..746a8a7eb9c30 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-qdrant" readme = "README.md" -version = "0.2.14" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.9,<3.13" -llama-index-core = "^0.10.1" qdrant-client = ">=1.7.1" grpcio = "^1.60.0" +llama-index-core = "^0.11.0" [tool.poetry.extras] fastembed = ["fastembed"] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/llama_index/vector_stores/redis/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/llama_index/vector_stores/redis/base.py index 872b296eba1a1..d37ecd8c19344 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/llama_index/vector_stores/redis/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/llama_index/vector_stores/redis/base.py @@ -103,9 +103,9 @@ class RedisVectorStore(BasePydanticVectorStore): ) """ - stores_text = True - stores_node = True - flat_metadata = False + stores_text: bool = True + stores_node: bool = True + flat_metadata: bool = False _index: SearchIndex = PrivateAttr() _overwrite: bool = PrivateAttr() @@ -120,6 +120,7 @@ def __init__( return_fields: Optional[List[str]] = None, **kwargs: Any, ) -> None: + super().__init__() # check for indicators of old schema self._flag_old_kwargs(**kwargs) @@ -151,8 +152,6 @@ def __init__( # Create index self.create_index() - super().__init__() - def _flag_old_kwargs(self, **kwargs): old_kwargs = [ "index_name", diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/pyproject.toml index 4fda93eb0c9c5..b330c5e52b258 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/pyproject.toml @@ -18,7 +18,7 @@ RedisVectorStore = "redis" disallow_untyped_defs = true exclude = ["_static", "build", "examples", "notebooks", "venv"] ignore_missing_imports = true -python_version = "3.8" +python_version = "3.9" [tool.poetry] authors = ["Tyler Hutcherson "] @@ -27,14 +27,15 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-redis" readme = "README.md" -version = "0.2.1" +version = "0.3.1" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -redisvl = "^0.1.3" +python = ">=3.9,<4.0" +redisvl = "^0.3.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] +docker = "^7.1.0" ipython = "8.10.0" jupyter = "^1.0.0" mypy = "0.991" @@ -42,6 +43,7 @@ pre-commit = "3.2.0" pylint = "2.15.10" pytest = "7.2.1" pytest-mock = "3.11.1" +redis = "^5.0.8" ruff = "0.0.292" tree-sitter-languages = "^1.8.0" types-Deprecated = ">=0.1.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/BUILD index dabf212d7e716..45d59ac8248a2 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/BUILD +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/BUILD @@ -1 +1,5 @@ python_tests() + +python_test_utils( + name="test_utils", +) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/conftest.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/conftest.py new file mode 100644 index 0000000000000..59adf5f75fb1f --- /dev/null +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/conftest.py @@ -0,0 +1,71 @@ +import pytest +from typing import List +from llama_index.core.schema import Document, TextNode +from llama_index.core.node_parser import SentenceSplitter +from redis import Redis +import docker + +docker_client = docker.from_env() +docker_client.ping() + +container = docker_client.containers.run( + "redis/redis-stack:latest", + detach=True, + name="redis", + ports={"6379/tcp": 6379, "8001/tcp": 8001}, +) + + +@pytest.fixture(scope="session", autouse=True) +def docker_setup(): + yield container + + container.stop() + container.remove() + + +@pytest.fixture() +def dummy_embedding() -> List: + return [0] * 1536 + + +@pytest.fixture() +def turtle_test() -> dict: + return { + "text": "something about turtles", + "metadata": {"animal": "turtle"}, + "question": "turtle stuff", + "doc_id": "1234", + } + + +@pytest.fixture() +def documents(turtle_test, dummy_embedding) -> List[Document]: + """List of documents represents data to be embedded in the datastore. + Minimum requirements for Documents in the /upsert endpoint's UpsertRequest. + """ + return [ + Document( + text=turtle_test["text"], + metadata=turtle_test["metadata"], + doc_id=turtle_test["doc_id"], + embedding=dummy_embedding, + ), + Document( + text="something about whales", + metadata={"animal": "whale"}, + doc_id="5678", + embedding=dummy_embedding, + ), + ] + + +@pytest.fixture() +def test_nodes(documents) -> TextNode: + parser = SentenceSplitter() + return parser.get_nodes_from_documents(documents) + + +@pytest.fixture() +def redis_client() -> Redis: + return Redis.from_url("redis://localhost:6379/0") diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/test_vector_stores_redis.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/test_vector_stores_redis.py index a2fa952d77ca8..7c25402b7d867 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/test_vector_stores_redis.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-redis/tests/test_vector_stores_redis.py @@ -1,3 +1,5 @@ +from llama_index.core import MockEmbedding, StorageContext, VectorStoreIndex +from llama_index.core.llms import MockLLM from llama_index.core.vector_stores.types import BasePydanticVectorStore from llama_index.vector_stores.redis import RedisVectorStore @@ -5,3 +7,32 @@ def test_class(): names_of_base_classes = [b.__name__ for b in RedisVectorStore.__mro__] assert BasePydanticVectorStore.__name__ in names_of_base_classes + + +def test_default_usage(documents, turtle_test, redis_client): + vector_store = RedisVectorStore(redis_client=redis_client) + storage_context = StorageContext.from_defaults(vector_store=vector_store) + index = VectorStoreIndex.from_documents( + documents, + embed_model=MockEmbedding(embed_dim=1536), + storage_context=storage_context, + ) + + # create retrievers + query_engine = index.as_query_engine(llm=MockLLM(), similarity_top_k=1) + retriever = index.as_retriever(similarity_top_k=1) + + result_nodes = retriever.retrieve(turtle_test["question"]) + query_res = query_engine.query(turtle_test["question"]) + + # test they get data + assert result_nodes[0].metadata == turtle_test["metadata"] + assert query_res.source_nodes[0].text == turtle_test["text"] + + # test delete + vector_store.delete([doc.doc_id for doc in documents]) + res = redis_client.ft("llama_index").search("*") + assert len(res.docs) == 0 + + # test delete index + vector_store.delete_index() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-relyt/llama_index/vector_stores/relyt/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-relyt/llama_index/vector_stores/relyt/base.py index 12ff4fa3c0b29..5b57bae88e426 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-relyt/llama_index/vector_stores/relyt/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-relyt/llama_index/vector_stores/relyt/base.py @@ -54,16 +54,17 @@ class RelytVectorStore(BasePydanticVectorStore): ``` """ - stores_text = True + stores_text: bool = True _client: "PGVectoRs" = PrivateAttr() _collection_name: str = PrivateAttr() def __init__(self, client: "PGVectoRs", collection_name: str) -> None: + super().__init__() + self._client: PGVectoRs = client self._collection_name = collection_name self.init_index() - super().__init__() @classmethod def class_name(cls) -> str: diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-relyt/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-relyt/pyproject.toml index dd00ab1352b47..bc1b58e40f0ea 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-relyt/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-relyt/pyproject.toml @@ -27,13 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-relyt" readme = "README.md" -version = "0.1.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pgvecto-rs = {extras = ["sdk"], version = "^0.1.4"} sqlalchemy = ">=1.3.12,<3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-rocksetdb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-rocksetdb/pyproject.toml index 4db97eed27d83..07142046e9356 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-rocksetdb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-rocksetdb/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-rocksetdb" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" rockset = "^2.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-singlestoredb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-singlestoredb/pyproject.toml index de46c068b1923..e19bef607b552 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-singlestoredb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-singlestoredb/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-singlestoredb" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" singlestoredb = "^0.10.5" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-supabase/llama_index/vector_stores/supabase/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-supabase/llama_index/vector_stores/supabase/base.py index 6ec389a9cb377..1ca96d1072240 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-supabase/llama_index/vector_stores/supabase/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-supabase/llama_index/vector_stores/supabase/base.py @@ -55,8 +55,8 @@ class SupabaseVectorStore(BasePydanticVectorStore): """ - stores_text = True - flat_metadata = False + stores_text: bool = True + flat_metadata: bool = False _client: Optional[Any] = PrivateAttr() _collection: Optional[Collection] = PrivateAttr() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-supabase/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-supabase/pyproject.toml index 40469006e501a..4e28252a47169 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-supabase/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-supabase/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-supabase" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" vecs = "^0.4.2" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-tair/llama_index/vector_stores/tair/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-tair/llama_index/vector_stores/tair/base.py index b4975d52cd7cd..649dd8dbd377c 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-tair/llama_index/vector_stores/tair/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-tair/llama_index/vector_stores/tair/base.py @@ -80,9 +80,9 @@ class TairVectorStore(BasePydanticVectorStore): ``` """ - stores_text = True - stores_node = True - flat_metadata = False + stores_text: bool = True + stores_node: bool = True + flat_metadata: bool = False _tair_client: Tair = PrivateAttr() _index_name: str = PrivateAttr() @@ -102,6 +102,7 @@ def __init__( overwrite: bool = False, **kwargs: Any, ) -> None: + super().__init__() try: self._tair_client = Tair.from_url(tair_url, **kwargs) except ValueError as e: diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-tair/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-tair/pyproject.toml index 71fa444597825..80c772d56e9d6 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-tair/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-tair/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-tair" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" tair = "^1.3.7" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-tencentvectordb/llama_index/vector_stores/tencentvectordb/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-tencentvectordb/llama_index/vector_stores/tencentvectordb/base.py index ffe736ef7bec8..10d525f2d16e4 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-tencentvectordb/llama_index/vector_stores/tencentvectordb/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-tencentvectordb/llama_index/vector_stores/tencentvectordb/base.py @@ -278,7 +278,7 @@ def _create_collection( collection_name: str = self._compute_collection_name( database_name, collection_params ) - collection_description = collection_params.collection_description + collection_description = collection_params._collection_description if collection_params is None: raise ValueError(VALUE_NONE_ERROR.format("collection_params")) @@ -300,9 +300,9 @@ def _compute_collection_name( database_name: str, collection_params: CollectionParams ) -> str: if database_name == DEFAULT_DATABASE_NAME: - return collection_params.collection_name - if collection_params.collection_name != DEFAULT_COLLECTION_NAME: - return collection_params.collection_name + return collection_params._collection_name + if collection_params._collection_name != DEFAULT_COLLECTION_NAME: + return collection_params._collection_name else: return database_name + "_" + DEFAULT_COLLECTION_NAME diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-tencentvectordb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-tencentvectordb/pyproject.toml index fef446ff92e08..85aad2ff12658 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-tencentvectordb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-tencentvectordb/pyproject.toml @@ -27,11 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-tencentvectordb" readme = "README.md" -version = "0.1.4" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" +tcvectordb = "^1.3.13" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-tidbvector/llama_index/vector_stores/tidbvector/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-tidbvector/llama_index/vector_stores/tidbvector/base.py index 71d5df3c744bf..d4b20c8425c71 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-tidbvector/llama_index/vector_stores/tidbvector/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-tidbvector/llama_index/vector_stores/tidbvector/base.py @@ -23,8 +23,8 @@ class TiDBVectorStore(BasePydanticVectorStore): - stores_text = True - flat_metadata = False + stores_text: bool = True + flat_metadata: bool = False _connection_string: str = PrivateAttr() _engine_args: Dict[str, Any] = PrivateAttr() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-tidbvector/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-tidbvector/pyproject.toml index 7381553b470b0..ee79ae0892423 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-tidbvector/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-tidbvector/pyproject.toml @@ -31,14 +31,14 @@ license = "MIT" name = "llama-index-vector-stores-tidbvector" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.2" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = ">=0.10.1" sqlalchemy = ">=1.4,<3" tidb-vector = {extras = ["client"], version = ">=0.0.3,<1.0.0"} pymysql = "^1.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/pyproject.toml index 7a8702383550b..172ca7d51b202 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-timescalevector" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" timescale-vector = "^0.0.4" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-txtai/llama_index/vector_stores/txtai/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-txtai/llama_index/vector_stores/txtai/base.py index 3efa40699bc5a..a4e545ca1bd9e 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-txtai/llama_index/vector_stores/txtai/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-txtai/llama_index/vector_stores/txtai/base.py @@ -76,10 +76,10 @@ def __init__( except ImportError: raise ImportError(IMPORT_ERROR_MSG) - self._txtai_index = cast(txtai.ann.ANN, txtai_index) - super().__init__() + self._txtai_index = cast(txtai.ann.ANN, txtai_index) + @classmethod def from_persist_dir( cls, diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-txtai/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-txtai/pyproject.toml index 0e57ac4c051c4..c6381674b9874 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-txtai/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-txtai/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-txtai" readme = "README.md" -version = "0.1.2" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-typesense/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-typesense/pyproject.toml index bf670dbead35c..df17deb57e213 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-typesense/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-typesense/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-typesense" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" typesense = "^0.19.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-upstash/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-upstash/pyproject.toml index 365006ed981d2..0b4867e2c5221 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-upstash/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-upstash/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-upstash" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" upstash-vector = "^0.4.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vearch/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-vearch/pyproject.toml index f35ac990d24d8..9808e0a1525f6 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vearch/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vearch/pyproject.toml @@ -30,7 +30,7 @@ license = "MIT" name = "llama-index-vector-stores-vearch" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vertexaivectorsearch/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-vertexaivectorsearch/pyproject.toml index 09bb68bda6756..5e91047f9c497 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vertexaivectorsearch/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vertexaivectorsearch/pyproject.toml @@ -27,14 +27,14 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-vertexaivectorsearch" readme = "README.md" -version = "0.0.1" +version = "0.1.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.1" -llama-index-embeddings-vertex = "^0.1.0" +llama-index-embeddings-vertex = "^0.2.0" google-cloud-aiplatform = "^1.39.0" google-cloud-storage = "^2.16.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml index 0ad6eb67443a6..426f4d382f8d3 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-vespa/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-vespa" readme = "README.md" -version = "0.0.2" +version = "0.1.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pyvespa = "^0.40.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/base.py index 9a09fdcb17d1a..ef81476a31b39 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/llama_index/vector_stores/weaviate/base.py @@ -153,11 +153,11 @@ def __init__( auth_config = weaviate.auth.AuthApiKey(auth_config) client_kwargs = client_kwargs or {} - self._client = weaviate.WeaviateClient( + client = weaviate.WeaviateClient( auth_client_secret=auth_config, **client_kwargs ) else: - self._client = cast(weaviate.WeaviateClient, weaviate_client) + client = cast(weaviate.WeaviateClient, weaviate_client) # validate class prefix starts with a capital letter if class_prefix is not None: @@ -172,8 +172,8 @@ def __init__( ) # create default schema if does not exist - if not class_schema_exists(self._client, index_name): - create_default_schema(self._client, index_name) + if not class_schema_exists(client, index_name): + create_default_schema(client, index_name) super().__init__( url=url, @@ -182,6 +182,7 @@ def __init__( auth_config=auth_config.__dict__ if auth_config else {}, client_kwargs=client_kwargs or {}, ) + self._client = client @classmethod def from_params( diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/pyproject.toml index 4a13351fcabfa..91ac1daf961ce 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-weaviate/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-weaviate" readme = "README.md" -version = "1.0.2" +version = "1.1.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" weaviate-client = "^4.5.7" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/examples/data/paul_graham/paul_graham_essay.txt b/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/examples/data/paul_graham/paul_graham_essay.txt deleted file mode 100644 index 01e801ccbf525..0000000000000 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/examples/data/paul_graham/paul_graham_essay.txt +++ /dev/null @@ -1,5 +0,0 @@ -What I Worked On - -February 2021 - -Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/examples/wordlift_vector_store_demo.ipynb b/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/examples/wordlift_vector_store_demo.ipynb deleted file mode 100644 index e10590f660292..0000000000000 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/examples/wordlift_vector_store_demo.ipynb +++ /dev/null @@ -1,231 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Wordlift Vector Store" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook, we'll illustrate how to leverage Wordlift as a Vector store for seamless integration with LlamaIndex. To access a Wordlift key and unlock our AI-powered SEO tools, visit [Wordlift](https://wordlift.io/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setting up environments\n", - "\n", - "Install Llamaindex and Wordlift vector store using pip " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install llama-index\n", - "%pip install llama-index-vector-stores-wordlift\n", - "%pip install nest_asyncio" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from llama_index.core import SimpleDirectoryReader, StorageContext\n", - "from llama_index.core import VectorStoreIndex\n", - "from llama_index.vector_stores.wordlift import WordliftVectorStore\n", - "from llama_index.core.embeddings.utils import get_cache_dir\n", - "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n", - "from llama_index.core import Settings\n", - "from llama_index.core import Document\n", - "import nest_asyncio" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we made use of async loops for the implementation of the Wordlift Vector Store, nest_asyncio is needed to use it in a Jupyter Notebook" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nest_asyncio.apply()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Setup OpenAI API" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import openai\n", - "\n", - "openai.api_key = os.environ[\"your_openAI_key\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download and prepare the sample dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!mkdir 'data\\paul_graham\\'\n", - "!curl 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "documents = SimpleDirectoryReader(\"./data/paul_graham\").load_data()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Wordlift Knowledge Graphs are built on the principles of fully Linked Data, where each entity is assigned a permanent dereferentiable URI. \n", - "\n", - "When adding nodes to an existing Knowledge Graph, it's essential to include an \"entity_id\" in the metadata of each loaded document. \n", - "\n", - "For further insights into Fully Linked Data, explore these resources: \n", - "[W3C Linked Data](https://www.w3.org/DesignIssues/LinkedData.html), \n", - "[5 Star Data](https://5stardata.info/en/).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To retrieve the URI of your Knowledge Graph, execute the following command and locate the \"datasetURI\" parameter. Ensure to substitute \"your_key\" with your Wordlift Key. Keep in mind that there is a unique key assigned to each Knowledge Graph." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!curl https://api.wordlift.io/accounts/me -H 'Authorization: Key your_key' | jq ." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To generate the entity_id, concatenate the datasetURI with the normalized filename. \n", - "\n", - "For instance, if your datasetURI is `https://data.wordlift.io/wl0000000/` and your text file is named `sample-file.txt`, the entity_id can be constructed as follows: \n", - "\n", - "`entity_id = datasetURI + normalize(filename)` \n", - "\n", - "which results in `https://data.wordlift.io/wl0000000/sample-file-txt`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_uri = \"your_dataset_uri\"\n", - "\n", - "for document in documents:\n", - " norm_filename = document.metadata[\"file_name\"].replace(\".\", \"-\")\n", - " entity_id = dataset_uri + norm_filename\n", - " document.metadata[\"entity_id\"] = entity_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Wordlift Vectore Store" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To create a Wordlift vector store instance you just need the key of your Wordlift Knowledge Graph. Remember that there is a key for each Knowledge Graph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vector_store = WordliftVectorStore.create(\"your_key\")\n", - "\n", - "# set Wordlift vector store instance as the vector store\n", - "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", - "\n", - "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# test the vector store query\n", - "query_engine = index.as_query_engine()\n", - "response = query_engine.query(\"What did the author do growing up?\")\n", - "print(response)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/llama_index/vector_stores/wordlift/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/llama_index/vector_stores/wordlift/base.py index bcecae88f8b22..49dc067d26050 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/llama_index/vector_stores/wordlift/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/llama_index/vector_stores/wordlift/base.py @@ -66,10 +66,15 @@ class WordliftVectorStore(BasePydanticVectorStore): _account: Optional[AccountInfo] = PrivateAttr(default=None) _configuration: Configuration = PrivateAttr() + _fields: Optional[List[str]] = PrivateAttr() def __init__( - self, key: Optional[str] = None, configuration: Optional[Configuration] = None + self, + key: Optional[str] = None, + configuration: Optional[Configuration] = None, + fields: Optional[List[str]] = None, ): + super().__init__(use_async=True) nest_asyncio.apply() if configuration is None: @@ -77,7 +82,10 @@ def __init__( else: self._configuration = configuration - super().__init__(use_async=True) + if fields is None: + self._fields = ["schema:url", "schema:name"] + else: + self._fields = fields @property def account(self) -> AccountInfo: @@ -207,13 +215,13 @@ async def aquery( request = VectorSearchQueryRequest( query_string=query.query_str, similarity_top_k=query.similarity_top_k, - fields=["schema:url", "schema:name"], + fields=self._fields, ) else: request = VectorSearchQueryRequest( query_embedding=query.query_embedding, similarity_top_k=query.similarity_top_k, - fields=["schema:url", "schema:name"], + fields=self._fields, ) async with wordlift_client.ApiClient(self._configuration) as api_client: diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/pyproject.toml index 95a29a042e48c..7be7eaf7c73f4 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/pyproject.toml @@ -27,17 +27,18 @@ license = "MIT" name = "llama-index-vector-stores-wordlift" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.2.0" +version = "0.4.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -pydantic = ">=1.10" +pydantic = ">=2.0,<3.0" aiohttp = ">=3.7.4" python-dateutil = ">=2.8.2" aiohttp-retry = ">=1.2" urllib3 = ">=1.21.1,<3" wordlift-client = ">=1.42.0,<2" +docker = "^7.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-zep/llama_index/vector_stores/zep/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-zep/llama_index/vector_stores/zep/base.py index 33bf75d3d8f17..f7e830d1e932b 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-zep/llama_index/vector_stores/zep/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-zep/llama_index/vector_stores/zep/base.py @@ -55,8 +55,8 @@ class ZepVectorStore(BasePydanticVectorStore): ``` """ - stores_text = True - flat_metadata = False + stores_text: bool = True + flat_metadata: bool = False _client: ZepClient = PrivateAttr() _collection: DocumentCollection = PrivateAttr() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-zep/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-zep/pyproject.toml index ffb0063aaeef3..1314430e25349 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-zep/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-zep/pyproject.toml @@ -27,12 +27,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-zep" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" zep-python = "^1.5.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-legacy/llama_index/legacy/finetuning/__init__.py b/llama-index-legacy/llama_index/legacy/finetuning/__init__.py index 5f2965fbcd896..6551990413d31 100644 --- a/llama-index-legacy/llama_index/legacy/finetuning/__init__.py +++ b/llama-index-legacy/llama_index/legacy/finetuning/__init__.py @@ -10,7 +10,6 @@ from llama_index.legacy.finetuning.embeddings.sentence_transformer import ( SentenceTransformersFinetuneEngine, ) -from llama_index.legacy.finetuning.gradient.base import GradientFinetuneEngine from llama_index.legacy.finetuning.openai.base import OpenAIFinetuneEngine from llama_index.legacy.finetuning.rerankers.cohere_reranker import ( CohereRerankerFinetuneEngine, @@ -25,7 +24,6 @@ "EmbeddingQAFinetuneDataset", "SentenceTransformersFinetuneEngine", "EmbeddingAdapterFinetuneEngine", - "GradientFinetuneEngine", "generate_cohere_reranker_finetuning_dataset", "CohereRerankerFinetuneEngine", ] diff --git a/llama-index-legacy/pyproject.toml b/llama-index-legacy/pyproject.toml index 3e671fc07fcf1..c42af2b07df7e 100644 --- a/llama-index-legacy/pyproject.toml +++ b/llama-index-legacy/pyproject.toml @@ -42,7 +42,7 @@ name = "llama-index-legacy" packages = [{include = "llama_index"}] readme = "README.md" repository = "https://github.com/run-llama/llama_index" -version = "0.9.48" +version = "0.9.48post3" [tool.poetry.dependencies] SQLAlchemy = {extras = ["asyncio"], version = ">=1.4.49"} @@ -53,7 +53,7 @@ fsspec = ">=2023.5.0" httpx = "*" langchain = {optional = true, version = ">=0.0.303"} nest-asyncio = "^1.5.8" -nltk = "^3.8.1" +nltk = ">=3.8.1" numpy = "*" openai = ">=1.1.0" pandas = "*" diff --git a/llama-index-networks/llama_index/networks/contributor/query_engine/client.py b/llama-index-networks/llama_index/networks/contributor/query_engine/client.py index b609d3558c2f9..da1e4f9382cad 100644 --- a/llama-index-networks/llama_index/networks/contributor/query_engine/client.py +++ b/llama-index-networks/llama_index/networks/contributor/query_engine/client.py @@ -5,7 +5,8 @@ from llama_index.core.base.response.schema import RESPONSE_TYPE from llama_index.core.prompts.mixin import PromptMixinType from llama_index.networks.schema.contributor import ContributorQueryResponse -from pydantic.v1 import BaseSettings, Field +from llama_index.core.bridge.pydantic_settings import BaseSettings, SettingsConfigDict +from llama_index.core.bridge.pydantic import Field import requests import aiohttp @@ -13,12 +14,10 @@ class ContributorQueryEngineClientSettings(BaseSettings): """Settings for contributor.""" + model_config = SettingsConfigDict(env_file=[".env", ".env.contributor.client"]) api_key: Optional[str] = Field(default=None, env="API_KEY") api_url: str = Field(..., env="API_URL") - class Config: - env_file = ".env", ".env.contributor.client" - class ContributorQueryEngineClient(BaseQueryEngine): """A remote QueryEngine exposed through a REST API.""" @@ -53,7 +52,9 @@ def _query( self.config.api_url + "/api/query", json=data, headers=headers ) try: - contributor_response = ContributorQueryResponse.parse_obj(result.json()) + contributor_response = ContributorQueryResponse.model_validate( + result.json() + ) except Exception as e: raise ValueError("Failed to parse response") from e return contributor_response.to_response() @@ -75,7 +76,9 @@ async def _aquery( ) as resp: json_result = await resp.json() try: - contributor_response = ContributorQueryResponse.parse_obj(json_result) + contributor_response = ContributorQueryResponse.model_validate( + json_result + ) except Exception as e: raise ValueError("Failed to parse response") from e return contributor_response.to_response() diff --git a/llama-index-networks/llama_index/networks/contributor/query_engine/service.py b/llama-index-networks/llama_index/networks/contributor/query_engine/service.py index 28308c5a58fb5..9ec1973904154 100644 --- a/llama-index-networks/llama_index/networks/contributor/query_engine/service.py +++ b/llama-index-networks/llama_index/networks/contributor/query_engine/service.py @@ -1,23 +1,21 @@ from typing import Any, Optional from llama_index.core.base.base_query_engine import BaseQueryEngine -from llama_index.core.bridge.pydantic import Field, BaseModel from llama_index.networks.schema.contributor import ( ContributorQueryRequest, ) -from pydantic.v1 import BaseSettings, PrivateAttr +from llama_index.core.bridge.pydantic import Field, BaseModel, PrivateAttr +from llama_index.core.bridge.pydantic_settings import BaseSettings, SettingsConfigDict from fastapi import FastAPI class ContributorQueryEngineServiceSettings(BaseSettings): + model_config = SettingsConfigDict(env_file=[".env", ".env.contributor.service"]) api_version: str = Field(default="v1", description="API version.") secret: Optional[str] = Field( default=None, description="JWT secret." ) # left for future consideration. # or if user wants to implement their own - class Config: - env_file = ".env", ".env.contributor.service" - class ContributorQueryEngineService(BaseModel): query_engine: Optional[BaseQueryEngine] @@ -28,6 +26,7 @@ class Config: arbitrary_types_allowed = True def __init__(self, query_engine, config) -> None: + super().__init__(query_engine=query_engine, config=config) self._fastapi = FastAPI( version=config.api_version, ) @@ -40,8 +39,6 @@ def __init__(self, query_engine, config) -> None: methods=["POST"], ) - super().__init__(query_engine=query_engine, config=config) - async def index(self): """Index endpoint logic.""" return {"message": "Hello World!"} @@ -62,11 +59,16 @@ def from_config_file( config = ContributorQueryEngineServiceSettings(_env_file=env_file) return cls(query_engine=query_engine, config=config) - def __getattr__(self, attr) -> Any: - if hasattr(self._fastapi, attr): - return getattr(self._fastapi, attr) + def __getattr__(self, attr: str) -> Any: + if attr in self.__private_attributes__ or attr in self.model_fields: + return super().__getattr__(attr) else: - raise AttributeError(f"{attr} not exist") + try: + return getattr(self._fastapi, attr) + except KeyError: + raise AttributeError( + f"'{self.__class__.__name__}' fastapi app has no attribute '{attr}'" + ) @property def app(self): diff --git a/llama-index-networks/llama_index/networks/contributor/retriever/client.py b/llama-index-networks/llama_index/networks/contributor/retriever/client.py index 4383fb0a7fa1f..c692d5e195473 100644 --- a/llama-index-networks/llama_index/networks/contributor/retriever/client.py +++ b/llama-index-networks/llama_index/networks/contributor/retriever/client.py @@ -4,7 +4,8 @@ from llama_index.core.schema import QueryBundle, NodeWithScore from llama_index.core.prompts.mixin import PromptMixinType from llama_index.networks.schema.contributor import ContributorRetrieverResponse -from pydantic.v1 import BaseSettings, Field +from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic_settings import BaseSettings, SettingsConfigDict import requests import aiohttp @@ -12,12 +13,10 @@ class ContributorRetrieverClientSettings(BaseSettings): """Settings for contributor.""" + model_config = SettingsConfigDict(env_file=[".env", ".env.contributor.client"]) api_key: Optional[str] = Field(default=None, env="API_KEY") api_url: str = Field(..., env="API_URL") - class Config: - env_file = ".env", ".env.contributor.client" - class ContributorRetrieverClient(BaseRetriever): """A remote Retriever exposed through a REST API.""" @@ -51,8 +50,14 @@ def _retrieve( result = requests.post( self.config.api_url + "/api/retrieve", json=data, headers=headers ) + print(f"result.json: {result.json()}", flush=True) try: - contributor_response = ContributorRetrieverResponse.parse_obj(result.json()) + contributor_response = ContributorRetrieverResponse.model_validate( + result.json() + ) + print( + f"contributor_response: {contributor_response.get_nodes()}", flush=True + ) except Exception as e: raise ValueError("Failed to parse response") from e return contributor_response.get_nodes() @@ -74,7 +79,7 @@ async def _aretrieve( ) as resp: json_result = await resp.json() try: - contributor_response = ContributorRetrieverResponse.parse_obj( + contributor_response = ContributorRetrieverResponse.model_validate( json_result ) except Exception as e: diff --git a/llama-index-networks/llama_index/networks/contributor/retriever/service.py b/llama-index-networks/llama_index/networks/contributor/retriever/service.py index f60f471c1e6d4..c9534f2cb4865 100644 --- a/llama-index-networks/llama_index/networks/contributor/retriever/service.py +++ b/llama-index-networks/llama_index/networks/contributor/retriever/service.py @@ -1,23 +1,21 @@ from typing import Any, Optional from llama_index.core.base.base_retriever import BaseRetriever -from llama_index.core.bridge.pydantic import Field, BaseModel from llama_index.networks.schema.contributor import ( ContributorRetrieverRequest, ) -from pydantic.v1 import BaseSettings, PrivateAttr +from llama_index.core.bridge.pydantic import Field, BaseModel, PrivateAttr +from llama_index.core.bridge.pydantic_settings import BaseSettings, SettingsConfigDict from fastapi import FastAPI class ContributorRetrieverServiceSettings(BaseSettings): + model_config = SettingsConfigDict(env_file=[".env", ".env.contributor.service"]) api_version: str = Field(default="v1", description="API version.") secret: Optional[str] = Field( default=None, description="JWT secret." ) # left for future consideration. # or if user wants to implement their own - class Config: - env_file = ".env", ".env.contributor.service" - class ContributorRetrieverService(BaseModel): retriever: Optional[BaseRetriever] @@ -28,6 +26,7 @@ class Config: arbitrary_types_allowed = True def __init__(self, retriever, config) -> None: + super().__init__(retriever=retriever, config=config) self._fastapi = FastAPI( version=config.api_version, ) @@ -40,8 +39,6 @@ def __init__(self, retriever, config) -> None: methods=["POST"], ) - super().__init__(retriever=retriever, config=config) - async def index(self): """Index endpoint logic.""" return {"message": "Hello World!"} @@ -60,11 +57,16 @@ def from_config_file( config = ContributorRetrieverServiceSettings(_env_file=env_file) return cls(retriever=retriever, config=config) - def __getattr__(self, attr) -> Any: - if hasattr(self._fastapi, attr): - return getattr(self._fastapi, attr) + def __getattr__(self, attr: str) -> Any: + if attr in self.__private_attributes__ or attr in self.model_fields: + return super().__getattr__(attr) else: - raise AttributeError(f"{attr} not exist") + try: + return getattr(self._fastapi, attr) + except KeyError: + raise AttributeError( + f"'{self.__class__.__name__}' fastapi app has no attribute '{attr}'" + ) @property def app(self): diff --git a/llama-index-networks/llama_index/networks/network/query_engine.py b/llama-index-networks/llama_index/networks/network/query_engine.py index 898a17f4a1a54..fa1b3425a9a2a 100644 --- a/llama-index-networks/llama_index/networks/network/query_engine.py +++ b/llama-index-networks/llama_index/networks/network/query_engine.py @@ -30,11 +30,11 @@ def __init__( response_synthesizer: Optional[BaseSynthesizer] = None, callback_manager: Optional[CallbackManager] = None, ) -> None: + super().__init__(callback_manager=callback_manager) self._contributors = contributors self._response_synthesizer = response_synthesizer or get_response_synthesizer( llm=Settings.llm, callback_manager=Settings.callback_manager ) - super().__init__(callback_manager=callback_manager) @classmethod def from_args( diff --git a/llama-index-networks/llama_index/networks/network/retriever.py b/llama-index-networks/llama_index/networks/network/retriever.py index 3619e8cc21aee..fa535eab4c97f 100644 --- a/llama-index-networks/llama_index/networks/network/retriever.py +++ b/llama-index-networks/llama_index/networks/network/retriever.py @@ -19,9 +19,9 @@ def __init__( node_postprocessors: Optional[List[BaseNodePostprocessor]] = None, callback_manager: Optional[CallbackManager] = None, ) -> None: + super().__init__(callback_manager=callback_manager) self._contributors = contributors self._node_postprocessors = node_postprocessors or [] - super().__init__(callback_manager=callback_manager) def _postprocess_nodes( self, nodes: List[NodeWithScore], query_bundle: QueryBundle diff --git a/llama-index-networks/llama_index/networks/schema/contributor.py b/llama-index-networks/llama_index/networks/schema/contributor.py index 4d1a80a54c8e2..d711fe1b5fba0 100644 --- a/llama-index-networks/llama_index/networks/schema/contributor.py +++ b/llama-index-networks/llama_index/networks/schema/contributor.py @@ -7,8 +7,7 @@ ImageNode, ) from llama_index.core.base.response.schema import Response -from llama_index.core.bridge.pydantic import BaseModel -from pydantic import BaseModel as V2BaseModel +from llama_index.core.bridge.pydantic import BaseModel, Field NODE_REGISTRY: Dict[str, Type[BaseNode]] = { "TextNode": TextNode, @@ -17,13 +16,13 @@ } -class ContributorQueryRequest(V2BaseModel): +class ContributorQueryRequest(BaseModel): query: str class ContributorQueryResponse(BaseModel): - response: Optional[str] - score: Optional[float] + response: Optional[str] = Field(default=None) + score: Optional[float] = Field(default=None) def __str__(self) -> str: """Convert to string representation.""" @@ -34,12 +33,12 @@ def to_response(self) -> Response: return Response(response=self.response, metadata={"score": self.score}) -class ContributorRetrieverRequest(V2BaseModel): +class ContributorRetrieverRequest(BaseModel): query: str class ContributorRetrieverResponse(BaseModel): - nodes_dict: Optional[List[Dict[str, Any]]] + nodes_dict: Optional[List[Dict[str, Any]]] = Field(default=None) def get_nodes(self) -> List[NodeWithScore]: """Build list of nodes with score.""" @@ -50,6 +49,6 @@ def get_nodes(self) -> List[NodeWithScore]: node_cls = NODE_REGISTRY[node_dict["class_name"]] except KeyError: node_cls = NODE_REGISTRY["TextNode"] - node = node_cls.parse_obj(node_dict) + node = node_cls.model_validate(node_dict) nodes.append(NodeWithScore(node=node, score=d["score"])) return nodes diff --git a/llama-index-networks/pyproject.toml b/llama-index-networks/pyproject.toml index 41f5acf89fd5e..2a261cbbae07f 100644 --- a/llama-index-networks/pyproject.toml +++ b/llama-index-networks/pyproject.toml @@ -32,11 +32,10 @@ maintainers = [ name = "llama-index-networks" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.2.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.10" fastapi = {extras = ["all"], version = "^0.109.2"} pyjwt = {extras = ["crypto"], version = "^2.8.0"} python-jose = "^3.3.0" @@ -45,15 +44,16 @@ pydantic = {extras = ["dotenv"], version = "^2.6.1"} python-dotenv = "^1.0.1" aiohttp = "^3.9.3" ecdsa = ">=0.19.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = ">=23.7.0,<=24.3.0"} codespell = {extras = ["toml"], version = ">=v2.2.6"} ipython = "8.10.0" jupyter = "^1.0.0" -llama-index-embeddings-openai = "^0.1.5" -llama-index-llms-openai = "^0.1.5" -llama-index-readers-file = "^0.1.4" +llama-index-embeddings-openai = "^0.2.0" +llama-index-llms-openai = "^0.2.0" +llama-index-readers-file = "^0.2.0" mypy = "0.991" pre-commit = "3.2.0" pylint = "2.15.10" diff --git a/llama-index-packs/llama-index-packs-agent-search-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-agent-search-retriever/pyproject.toml index f36d3a0dcf197..4eca879b29edc 100644 --- a/llama-index-packs/llama-index-packs-agent-search-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-agent-search-retriever/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["logan-markewich"] name = "llama-index-packs-agent-search-retriever" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<3.12" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-agent-search-retriever/tests/BUILD b/llama-index-packs/llama-index-packs-agent-search-retriever/tests/BUILD deleted file mode 100644 index 619cac15ff840..0000000000000 --- a/llama-index-packs/llama-index-packs-agent-search-retriever/tests/BUILD +++ /dev/null @@ -1,3 +0,0 @@ -python_tests( - interpreter_constraints=["==3.9.*", "==3.10.*"], -) diff --git a/llama-index-packs/llama-index-packs-agent-search-retriever/tests/test_packs_agent_search_retriever.py b/llama-index-packs/llama-index-packs-agent-search-retriever/tests/test_packs_agent_search_retriever.py deleted file mode 100644 index 63838488128bc..0000000000000 --- a/llama-index-packs/llama-index-packs-agent-search-retriever/tests/test_packs_agent_search_retriever.py +++ /dev/null @@ -1,7 +0,0 @@ -from llama_index.core.llama_pack import BaseLlamaPack -from llama_index.packs.agent_search_retriever import AgentSearchRetrieverPack - - -def test_class(): - names_of_base_classes = [b.__name__ for b in AgentSearchRetrieverPack.__mro__] - assert BaseLlamaPack.__name__ in names_of_base_classes diff --git a/llama-index-packs/llama-index-packs-agents-coa/pyproject.toml b/llama-index-packs/llama-index-packs-agents-coa/pyproject.toml index c7ba08d9d032f..045057431d6b7 100644 --- a/llama-index-packs/llama-index-packs-agents-coa/pyproject.toml +++ b/llama-index-packs/llama-index-packs-agents-coa/pyproject.toml @@ -30,11 +30,11 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-agents-coa" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-agents-lats/pyproject.toml b/llama-index-packs/llama-index-packs-agents-lats/pyproject.toml index 54358fc42027f..5f002c93d554d 100644 --- a/llama-index-packs/llama-index-packs-agents-lats/pyproject.toml +++ b/llama-index-packs/llama-index-packs-agents-lats/pyproject.toml @@ -30,11 +30,11 @@ license = "MIT" name = "llama-index-packs-agents-lats" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-packs/llama-index-packs-agents-llm-compiler/pyproject.toml b/llama-index-packs/llama-index-packs-agents-llm-compiler/pyproject.toml index a6aad4f936840..421b7f2c796d5 100644 --- a/llama-index-packs/llama-index-packs-agents-llm-compiler/pyproject.toml +++ b/llama-index-packs/llama-index-packs-agents-llm-compiler/pyproject.toml @@ -29,11 +29,12 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-agents-llm-compiler" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-amazon-product-extraction/pyproject.toml b/llama-index-packs/llama-index-packs-amazon-product-extraction/pyproject.toml index 291c7184ce857..8bed58544f7ed 100644 --- a/llama-index-packs/llama-index-packs-amazon-product-extraction/pyproject.toml +++ b/llama-index-packs/llama-index-packs-amazon-product-extraction/pyproject.toml @@ -29,12 +29,13 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-amazon-product-extraction" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" pyppeteer = "^1.0.2" +llama-index-multi-modal-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/pyproject.toml index 2bf8636e9b118..1ae285617721e 100644 --- a/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["axiomofjoy"] name = "llama-index-packs-arize-phoenix-query-engine" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.11.post1" -llama-index-callbacks-arize-phoenix = "^0.1.4" -llama-index-readers-web = "^0.1.1" +llama-index-callbacks-arize-phoenix = "^0.2.0" +llama-index-readers-web = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-auto-merging-retriever/llama_index/packs/auto_merging_retriever/base.py b/llama-index-packs/llama-index-packs-auto-merging-retriever/llama_index/packs/auto_merging_retriever/base.py index e665c17483830..f5e7976bfbb14 100644 --- a/llama-index-packs/llama-index-packs-auto-merging-retriever/llama_index/packs/auto_merging_retriever/base.py +++ b/llama-index-packs/llama-index-packs-auto-merging-retriever/llama_index/packs/auto_merging_retriever/base.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List -from llama_index.core import ServiceContext, VectorStoreIndex +from llama_index.core import VectorStoreIndex from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.node_parser import ( HierarchicalNodeParser, @@ -13,7 +13,6 @@ from llama_index.core.schema import Document from llama_index.core.storage import StorageContext from llama_index.core.storage.docstore import SimpleDocumentStore -from llama_index.llms.openai import OpenAI class AutoMergingRetrieverPack(BaseLlamaPack): @@ -41,15 +40,7 @@ def __init__( # define storage context (will include vector store by default too) storage_context = StorageContext.from_defaults(docstore=docstore) - - service_context = ServiceContext.from_defaults( - llm=OpenAI(model="gpt-3.5-turbo") - ) - self.base_index = VectorStoreIndex( - leaf_nodes, - storage_context=storage_context, - service_context=service_context, - ) + self.base_index = VectorStoreIndex(leaf_nodes, storage_context=storage_context) base_retriever = self.base_index.as_retriever(similarity_top_k=6) self.retriever = AutoMergingRetriever( base_retriever, storage_context, verbose=True diff --git a/llama-index-packs/llama-index-packs-auto-merging-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-auto-merging-retriever/pyproject.toml index 7e5d12c99b95b..e5bef1668f25f 100644 --- a/llama-index-packs/llama-index-packs-auto-merging-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-auto-merging-retriever/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-auto-merging-retriever" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-chroma-autoretrieval/pyproject.toml b/llama-index-packs/llama-index-packs-chroma-autoretrieval/pyproject.toml index d2c56464aa0fc..a0003fd954e37 100644 --- a/llama-index-packs/llama-index-packs-chroma-autoretrieval/pyproject.toml +++ b/llama-index-packs/llama-index-packs-chroma-autoretrieval/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["logan-markewich"] name = "llama-index-packs-chroma-autoretrieval" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" chromadb = "^0.4.22" -llama-index-vector-stores-chroma = "^0.1.1" +llama-index-vector-stores-chroma = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-code-hierarchy/llama_index/packs/code_hierarchy/code_hierarchy.py b/llama-index-packs/llama-index-packs-code-hierarchy/llama_index/packs/code_hierarchy/code_hierarchy.py index 9ce2fd0d6d7b7..d26aeb103edcf 100644 --- a/llama-index-packs/llama-index-packs-code-hierarchy/llama_index/packs/code_hierarchy/code_hierarchy.py +++ b/llama-index-packs/llama-index-packs-code-hierarchy/llama_index/packs/code_hierarchy/code_hierarchy.py @@ -121,6 +121,20 @@ class _SignatureCaptureOptions(BaseModel): name_identifier="property_identifier", ), }, + "php": { + "function_definition": _SignatureCaptureOptions( + end_signature_types=[_SignatureCaptureType(type="}", inclusive=False)], + name_identifier="name", + ), + "class_declaration": _SignatureCaptureOptions( + end_signature_types=[_SignatureCaptureType(type="}", inclusive=False)], + name_identifier="name", + ), + "method_declaration": _SignatureCaptureOptions( + end_signature_types=[_SignatureCaptureType(type="}", inclusive=False)], + name_identifier="name", + ), + }, } @@ -148,6 +162,9 @@ class _CommentOptions(BaseModel): "typescript": _CommentOptions( comment_template="// {}", scope_method=_ScopeMethod.BRACKETS ), + "php": _CommentOptions( + comment_template="// {}", scope_method=_ScopeMethod.BRACKETS + ), } assert all( diff --git a/llama-index-packs/llama-index-packs-code-hierarchy/pyproject.toml b/llama-index-packs/llama-index-packs-code-hierarchy/pyproject.toml index 0841b23dd4fa1..59b5868e9f005 100644 --- a/llama-index-packs/llama-index-packs-code-hierarchy/pyproject.toml +++ b/llama-index-packs/llama-index-packs-code-hierarchy/pyproject.toml @@ -28,15 +28,15 @@ license = "MIT" maintainers = ["ryanpeach"] name = "llama-index-packs-code-hierarchy" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.1" tree-sitter-languages = "^1.8.0" tree-sitter = "^0.20.2" -llama-index-agent-openai = ">=0.1.5" -llama-index-readers-file = "^0.1.8" +llama-index-agent-openai = "^0.3.0" +llama-index-readers-file = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-cogniswitch-agent/pyproject.toml b/llama-index-packs/llama-index-packs-cogniswitch-agent/pyproject.toml index 0ada0fb572210..739dc06838d36 100644 --- a/llama-index-packs/llama-index-packs-cogniswitch-agent/pyproject.toml +++ b/llama-index-packs/llama-index-packs-cogniswitch-agent/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["cogniswitch"] name = "llama-index-packs-cogniswitch-agent" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-tools-cogniswitch = "^0.1.1" +llama-index-tools-cogniswitch = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-cohere-citation-chat/llama_index/packs/cohere_citation_chat/citations_context_chat_engine.py b/llama-index-packs/llama-index-packs-cohere-citation-chat/llama_index/packs/cohere_citation_chat/citations_context_chat_engine.py index c2d59d4a3fc93..90f83e7d0e4c3 100644 --- a/llama-index-packs/llama-index-packs-cohere-citation-chat/llama_index/packs/cohere_citation_chat/citations_context_chat_engine.py +++ b/llama-index-packs/llama-index-packs-cohere-citation-chat/llama_index/packs/cohere_citation_chat/citations_context_chat_engine.py @@ -250,20 +250,11 @@ def as_chat_engine( if chat_mode not in [ChatModeCitations.COHERE_CITATIONS_CONTEXT]: return super().as_chat_engine(chat_mode=chat_mode, llm=llm, **kwargs) else: - service_context = kwargs.get("service_context", self.service_context) - - if service_context is not None: - llm = ( - resolve_llm(llm, callback_manager=self._callback_manager) - if llm - else service_context.llm - ) - else: - llm = ( - resolve_llm(llm, callback_manager=self._callback_manager) - if llm - else Settings.llm - ) + llm = ( + resolve_llm(llm, callback_manager=self._callback_manager) + if llm + else Settings.llm + ) return CitationsContextChatEngine.from_defaults( retriever=self.as_retriever(**kwargs), diff --git a/llama-index-packs/llama-index-packs-cohere-citation-chat/pyproject.toml b/llama-index-packs/llama-index-packs-cohere-citation-chat/pyproject.toml index 6326b3ce626de..6104ef945d9b0 100644 --- a/llama-index-packs/llama-index-packs-cohere-citation-chat/pyproject.toml +++ b/llama-index-packs/llama-index-packs-cohere-citation-chat/pyproject.toml @@ -29,13 +29,14 @@ license = "MIT" maintainers = ["EugeneLightsOn"] name = "llama-index-packs-cohere-citation-chat" readme = "README.md" -version = "0.1.7" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.41" -llama-index-llms-cohere = ">=0.1.2" -llama-index-embeddings-cohere = "^0.1.2" +llama-index-llms-cohere = "^0.3.0" +llama-index-embeddings-cohere = "^0.2.0" +boto3 = "^1.35.3" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-corrective-rag/examples/corrective_rag.ipynb b/llama-index-packs/llama-index-packs-corrective-rag/examples/corrective_rag.ipynb index 162725c9e7c51..c7e3cd8736167 100644 --- a/llama-index-packs/llama-index-packs-corrective-rag/examples/corrective_rag.ipynb +++ b/llama-index-packs/llama-index-packs-corrective-rag/examples/corrective_rag.ipynb @@ -43,9 +43,9 @@ "source": [ "import os\n", "\n", - "os.environ[\"OPENAI_API_KEY\"] = \"YOUR OPENAI API KEY\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", "\n", - "tavily_ai_api_key = \"\"\n", + "tavily_ai_api_key = \"\"\n", "\n", "import nest_asyncio\n", "\n", @@ -64,20 +64,10 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 13.0M 100 13.0M 0 0 7397k 0 0:00:01 0:00:01 --:--:-- 7415k\n" - ] - } - ], + "outputs": [], "source": [ "!mkdir -p 'data/'\n", - "!curl 'https://arxiv.org/pdf/2307.09288.pdf' -o 'data/llama2.pdf'" + "!wget 'https://arxiv.org/pdf/2307.09288.pdf' -O 'data/llama2.pdf'" ] }, { @@ -125,7 +115,7 @@ { "data": { "text/markdown": [ - "Llama2 was pretrained using an optimized auto-regressive transformer. Several changes were made to improve performance, including more robust data cleaning, updated data mixes, training on 40% more total tokens, doubling the context length, and using grouped-query attention (GQA) to improve inference scalability for larger models." + "Llama 2 was pretrained using an optimized auto-regressive transformer with robust data cleaning, updated data mixes, training on 40% more total tokens, doubling the context length, and utilizing grouped-query attention (GQA) to improve inference scalability for larger models." ], "text/plain": [ "" @@ -150,7 +140,7 @@ { "data": { "text/markdown": [ - "The latest ChatGPT memory update allows the chatbot to carry what it learns between chats, enabling it to provide more relevant responses. Users can ask ChatGPT to remember specific information or let it pick up details itself. This memory feature allows ChatGPT to remember and forget things based on user input, enhancing user interaction with personalized continuity and emotional intelligence. Users can toggle this function on or off within the ChatGPT settings menu, and when disabled, ChatGPT will neither generate nor access memories." + "The functionality of the latest ChatGPT memory allows the AI to remember information from previous conversations, enabling it to recall details discussed across chats. This feature helps in saving users from having to repeat information and enhances the helpfulness of future conversations by leveraging the stored memories." ], "text/plain": [ "" @@ -172,9 +162,9 @@ ], "metadata": { "kernelspec": { - "display_name": "corrective-rag", + "display_name": "llama-index-RvIdVF4_-py3.11", "language": "python", - "name": "corrective-rag" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -186,11 +176,6 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" - }, - "vscode": { - "interpreter": { - "hash": "b1d2a638b53f4d7129cb7686d8e3b97ae1d80a593a1618479f60cef5591ea888" - } } }, "nbformat": 4, diff --git a/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/base.py b/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/base.py index 4217c6c0b34f5..bb8d3ce0655a1 100644 --- a/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/base.py +++ b/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/base.py @@ -1,15 +1,26 @@ """Corrective RAG LlamaPack class.""" -from typing import Any, Dict, List +from typing import Any, Dict, List, Optional from llama_index.core import VectorStoreIndex, SummaryIndex +from llama_index.core.async_utils import asyncio_run from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.llms.openai import OpenAI from llama_index.core.schema import Document, NodeWithScore from llama_index.core.query_pipeline.query import QueryPipeline from llama_index.tools.tavily_research.base import TavilyToolSpec +from llama_index.core.base.base_retriever import BaseRetriever from llama_index.core.prompts import PromptTemplate +from llama_index.core.workflow import ( + StartEvent, + StopEvent, + step, + Workflow, + Context, + Event, +) + DEFAULT_RELEVANCY_PROMPT_TEMPLATE = PromptTemplate( template="""As a grader, your task is to evaluate the relevance of a document retrieved in response to a user's question. @@ -44,89 +55,166 @@ ) -class CorrectiveRAGPack(BaseLlamaPack): - def __init__(self, documents: List[Document], tavily_ai_apikey: str) -> None: - """Init params.""" +class RetrieveEvent(Event): + """Retrieve event (gets retrieved nodes).""" + + retrieved_nodes: List[NodeWithScore] + + +class RelevanceEvalEvent(Event): + """Relevance evaluation event (gets results of relevance evaluation).""" + + relevant_results: List[str] + + +class TextExtractEvent(Event): + """Text extract event. Extracts relevant text and concatenates.""" + + relevant_text: str + + +class QueryEvent(Event): + """Query event. Queries given relevant text and search text.""" + + relevant_text: str + search_text: str + + +class CorrectiveRAGWorkflow(Workflow): + @step(pass_context=True) + async def ingest(self, ctx: Context, ev: StartEvent) -> Optional[StopEvent]: + """Ingest step (for ingesting docs and initializing index).""" + documents: Optional[List[Document]] = ev.get("documents") + tavily_ai_apikey: Optional[str] = ev.get("tavily_ai_apikey") + + if any(i is None for i in [documents, tavily_ai_apikey]): + return None + llm = OpenAI(model="gpt-4") - self.relevancy_pipeline = QueryPipeline( + ctx.data["relevancy_pipeline"] = QueryPipeline( chain=[DEFAULT_RELEVANCY_PROMPT_TEMPLATE, llm] ) - self.transform_query_pipeline = QueryPipeline( + ctx.data["transform_query_pipeline"] = QueryPipeline( chain=[DEFAULT_TRANSFORM_QUERY_TEMPLATE, llm] ) - self.llm = llm - self.index = VectorStoreIndex.from_documents(documents) - self.tavily_tool = TavilyToolSpec(api_key=tavily_ai_apikey) + ctx.data["llm"] = llm + ctx.data["index"] = VectorStoreIndex.from_documents(documents) + ctx.data["tavily_tool"] = TavilyToolSpec(api_key=tavily_ai_apikey) - def get_modules(self) -> Dict[str, Any]: - """Get modules.""" - return {"llm": self.llm, "index": self.index} + return StopEvent() - def retrieve_nodes(self, query_str: str, **kwargs: Any) -> List[NodeWithScore]: + @step(pass_context=True) + async def retrieve(self, ctx: Context, ev: StartEvent) -> Optional[RetrieveEvent]: """Retrieve the relevant nodes for the query.""" - retriever = self.index.as_retriever(**kwargs) - return retriever.retrieve(query_str) + query_str = ev.get("query_str") + retriever_kwargs = ev.get("retriever_kwargs", {}) + + if query_str is None: + return None + + if "index" not in ctx.data or "tavily_tool" not in ctx.data: + raise ValueError( + "Index and tavily tool must be constructed. Run with 'documents' and 'tavily_ai_apikey' params first." + ) - def evaluate_relevancy( - self, retrieved_nodes: List[Document], query_str: str - ) -> List[str]: + retriever: BaseRetriever = ctx.data["index"].as_retriever(**retriever_kwargs) + result = retriever.retrieve(query_str) + ctx.data["retrieved_nodes"] = result + ctx.data["query_str"] = query_str + return RetrieveEvent(retrieved_nodes=result) + + @step(pass_context=True) + async def eval_relevance( + self, ctx: Context, ev: RetrieveEvent + ) -> RelevanceEvalEvent: """Evaluate relevancy of retrieved documents with the query.""" + retrieved_nodes = ev.retrieved_nodes + query_str = ctx.data["query_str"] + relevancy_results = [] for node in retrieved_nodes: - relevancy = self.relevancy_pipeline.run( + relevancy = ctx.data["relevancy_pipeline"].run( context_str=node.text, query_str=query_str ) relevancy_results.append(relevancy.message.content.lower().strip()) - return relevancy_results - def extract_relevant_texts( - self, retrieved_nodes: List[NodeWithScore], relevancy_results: List[str] - ) -> str: + ctx.data["relevancy_results"] = relevancy_results + return RelevanceEvalEvent(relevant_results=relevancy_results) + + @step(pass_context=True) + async def extract_relevant_texts( + self, ctx: Context, ev: RelevanceEvalEvent + ) -> TextExtractEvent: """Extract relevant texts from retrieved documents.""" + retrieved_nodes = ctx.data["retrieved_nodes"] + relevancy_results = ev.relevant_results + relevant_texts = [ retrieved_nodes[i].text for i, result in enumerate(relevancy_results) if result == "yes" ] - return "\n".join(relevant_texts) - def search_with_transformed_query(self, query_str: str) -> str: + result = "\n".join(relevant_texts) + return TextExtractEvent(relevant_text=result) + + @step(pass_context=True) + async def transform_query_pipeline( + self, ctx: Context, ev: TextExtractEvent + ) -> QueryEvent: """Search the transformed query with Tavily API.""" - search_results = self.tavily_tool.search(query_str, max_results=5) - return "\n".join([result.text for result in search_results]) + relevant_text = ev.relevant_text + relevancy_results = ctx.data["relevancy_results"] + query_str = ctx.data["query_str"] + + # If any document is found irrelevant, transform the query string for better search results. + if "no" in relevancy_results: + transformed_query_str = ( + ctx.data["transform_query_pipeline"] + .run(query_str=query_str) + .message.content + ) + # Conduct a search with the transformed query string and collect the results. + search_results = ctx.data["tavily_tool"].search( + transformed_query_str, max_results=5 + ) + search_text = "\n".join([result.text for result in search_results]) + else: + search_text = "" - def get_result(self, relevant_text: str, search_text: str, query_str: str) -> Any: + return QueryEvent(relevant_text=relevant_text, search_text=search_text) + + @step(pass_context=True) + async def query_result(self, ctx: Context, ev: QueryEvent) -> StopEvent: """Get result with relevant text.""" + relevant_text = ev.relevant_text + search_text = ev.search_text + query_str = ctx.data["query_str"] + documents = [Document(text=relevant_text + "\n" + search_text)] index = SummaryIndex.from_documents(documents) query_engine = index.as_query_engine() - return query_engine.query(query_str) + result = query_engine.query(query_str) + return StopEvent(result=result) - def run(self, query_str: str, **kwargs: Any) -> Any: - """Run the pipeline.""" - # Retrieve nodes based on the input query string. - retrieved_nodes = self.retrieve_nodes(query_str, **kwargs) - # Evaluate the relevancy of each retrieved document in relation to the query string. - relevancy_results = self.evaluate_relevancy(retrieved_nodes, query_str) - # Extract texts from documents that are deemed relevant based on the evaluation. - relevant_text = self.extract_relevant_texts(retrieved_nodes, relevancy_results) +class CorrectiveRAGPack(BaseLlamaPack): + def __init__(self, documents: List[Document], tavily_ai_apikey: str) -> None: + """Init params.""" + self._wf = CorrectiveRAGWorkflow() - # Initialize search_text variable to handle cases where it might not get defined. - search_text = "" + asyncio_run( + self._wf.run(documents=documents, tavily_ai_apikey=tavily_ai_apikey) + ) - # If any document is found irrelevant, transform the query string for better search results. - if "no" in relevancy_results: - transformed_query_str = self.transform_query_pipeline.run( - query_str=query_str - ).message.content - # Conduct a search with the transformed query string and collect the results. - search_text = self.search_with_transformed_query(transformed_query_str) + self.llm = OpenAI(model="gpt-4") + self.index = self._wf.get_context("ingest").data["index"] - # Compile the final result. If there's additional search text from the transformed query, - # it's included; otherwise, only the relevant text from the initial retrieval is returned. - if search_text: - return self.get_result(relevant_text, search_text, query_str) - else: - return self.get_result(relevant_text, "", query_str) + def get_modules(self) -> Dict[str, Any]: + """Get modules.""" + return {"llm": self.llm, "index": self.index} + + def run(self, query_str: str, **kwargs: Any) -> Any: + """Run the pipeline.""" + return asyncio_run(self._wf.run(query_str=query_str, retriever_kwargs=kwargs)) diff --git a/llama-index-packs/llama-index-packs-corrective-rag/pyproject.toml b/llama-index-packs/llama-index-packs-corrective-rag/pyproject.toml index 771d03001ae4e..ede94981ea6ff 100644 --- a/llama-index-packs/llama-index-packs-corrective-rag/pyproject.toml +++ b/llama-index-packs/llama-index-packs-corrective-rag/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["ravi-theja"] name = "llama-index-packs-corrective-rag" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" tavily-python = "^0.3.1" -llama-index-tools-tavily-research = "^0.1.2" +llama-index-tools-tavily-research = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/pyproject.toml index bea83909a685b..46e320a80b868 100644 --- a/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["AdkSarsen"] name = "llama-index-packs-deeplake-deepmemory-retriever" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.1" -llama-index-vector-stores-deeplake = "^0.1.1" +llama-index-core = {path = "/Users/nerdai/Projects/llama_index/llama-index-core"} +llama-index-vector-stores-deeplake = "^0.2.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/pyproject.toml b/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/pyproject.toml index 9073b08dbf0ce..c000d2d8dfed6 100644 --- a/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/pyproject.toml +++ b/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/pyproject.toml @@ -33,8 +33,8 @@ version = "0.1.4" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.1" -llama-index-vector-stores-deeplake = "^0.1.1" +llama-index-core = {path = "/Users/nerdai/Projects/llama_index/llama-index-core"} +llama-index-vector-stores-deeplake = "^0.2.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-dense-x-retrieval/llama_index/packs/dense_x_retrieval/base.py b/llama-index-packs/llama-index-packs-dense-x-retrieval/llama_index/packs/dense_x_retrieval/base.py index 5811f0bcba70f..5ab40a8debfe9 100644 --- a/llama-index-packs/llama-index-packs-dense-x-retrieval/llama_index/packs/dense_x_retrieval/base.py +++ b/llama-index-packs/llama-index-packs-dense-x-retrieval/llama_index/packs/dense_x_retrieval/base.py @@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional import yaml -from llama_index.core import Document, ServiceContext, VectorStoreIndex +from llama_index.core import Document, Settings, VectorStoreIndex from llama_index.core.async_utils import run_jobs from llama_index.core.base.embeddings.base import BaseEmbedding from llama_index.core.base.response.schema import RESPONSE_TYPE @@ -84,7 +84,9 @@ def __init__( max_tokens=750, ) - embed_model = embed_model or OpenAIEmbedding(embed_batch_size=128) + Settings.embed_model = embed_model or OpenAIEmbedding(embed_batch_size=128) + Settings.llm = query_llm or OpenAI() + Settings.num_output = self._proposition_llm.metadata.num_output nodes = text_splitter.get_nodes_from_documents(documents) sub_nodes = self._gen_propositions(nodes) @@ -92,15 +94,7 @@ def __init__( all_nodes = nodes + sub_nodes all_nodes_dict = {n.node_id: n for n in all_nodes} - service_context = ServiceContext.from_defaults( - llm=query_llm or OpenAI(), - embed_model=embed_model, - num_output=self._proposition_llm.metadata.num_output, - ) - - self.vector_index = VectorStoreIndex( - all_nodes, service_context=service_context, show_progress=True - ) + self.vector_index = VectorStoreIndex(all_nodes, show_progress=True) self.retriever = RecursiveRetriever( "vector", @@ -113,9 +107,7 @@ def __init__( ) self.query_engine = RetrieverQueryEngine.from_args( - self.retriever, - service_context=service_context, - streaming=streaming, + self.retriever, streaming=streaming ) async def _aget_proposition(self, node: TextNode) -> List[TextNode]: diff --git a/llama-index-packs/llama-index-packs-dense-x-retrieval/pyproject.toml b/llama-index-packs/llama-index-packs-dense-x-retrieval/pyproject.toml index d43a6f60d580e..208bfc83aa8ea 100644 --- a/llama-index-packs/llama-index-packs-dense-x-retrieval/pyproject.toml +++ b/llama-index-packs/llama-index-packs-dense-x-retrieval/pyproject.toml @@ -28,11 +28,13 @@ license = "MIT" maintainers = ["logan-markewich"] name = "llama-index-packs-dense-x-retrieval" readme = "README.md" -version = "0.1.4" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-embeddings-openai = "^0.2.0" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-diff-private-simple-dataset/pyproject.toml b/llama-index-packs/llama-index-packs-diff-private-simple-dataset/pyproject.toml index 62ea822109a8e..a22022ee5e68e 100644 --- a/llama-index-packs/llama-index-packs-diff-private-simple-dataset/pyproject.toml +++ b/llama-index-packs/llama-index-packs-diff-private-simple-dataset/pyproject.toml @@ -35,13 +35,14 @@ license = "MIT" name = "llama-index-packs-diff-private-simple-dataset" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.20.post1" -llama-index-llms-openai = "^0.1.12" +llama-index-llms-openai = "^0.2.0" +pandas = "*" prv-accountant = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = ">=23.7.0,<=24.3.0"} diff --git a/llama-index-packs/llama-index-packs-docugami-kg-rag/pyproject.toml b/llama-index-packs/llama-index-packs-docugami-kg-rag/pyproject.toml index d033fded0bcc1..c4ec76814b707 100644 --- a/llama-index-packs/llama-index-packs-docugami-kg-rag/pyproject.toml +++ b/llama-index-packs/llama-index-packs-docugami-kg-rag/pyproject.toml @@ -23,7 +23,7 @@ exclude = ["**/BUILD"] keywords = ["infer", "rag", "retrieve", "retriever"] name = "llama-index-packs-docugami-kg-rag" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = "^3.9" @@ -32,11 +32,11 @@ docugami = "^0.1.2" lxml = "4.9.3" openpyxl = "^3.1.2" chromadb = "^0.4.24" -llama-index-core = "^0.10.1" llama-index-embeddings-openai = "^0.1.6" llama-index-llms-openai = "^0.1.12" llama-index-vector-stores-chroma = "^0.1.6" llama-index-readers-docugami = "^0.1.3" +pandas = "*" [tool.poetry.group.dev.dependencies.black] extras = ["jupyter"] diff --git a/llama-index-packs/llama-index-packs-evaluator-benchmarker/pyproject.toml b/llama-index-packs/llama-index-packs-evaluator-benchmarker/pyproject.toml index 2c62be4117000..1a049bfec9398 100644 --- a/llama-index-packs/llama-index-packs-evaluator-benchmarker/pyproject.toml +++ b/llama-index-packs/llama-index-packs-evaluator-benchmarker/pyproject.toml @@ -28,11 +28,12 @@ license = "MIT" maintainers = ["nerdai"] name = "llama-index-packs-evaluator-benchmarker" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-finchat/pyproject.toml b/llama-index-packs/llama-index-packs-finchat/pyproject.toml index 90bc851bf3695..e329c19e82e3e 100644 --- a/llama-index-packs/llama-index-packs-finchat/pyproject.toml +++ b/llama-index-packs/llama-index-packs-finchat/pyproject.toml @@ -32,14 +32,14 @@ maintainers = ["345ishaan"] name = "llama-index-packs-finchat" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.0" tavily-python = "^0.3.1" llama-index-agent-openai = ">=0.1.5" -llama-index-tools-finance = "^0.1.0" +llama-index-tools-finance = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-packs/llama-index-packs-fusion-retriever/llama_index/packs/fusion_retriever/hybrid_fusion/base.py b/llama-index-packs/llama-index-packs-fusion-retriever/llama_index/packs/fusion_retriever/hybrid_fusion/base.py index be9cdd756146e..8be1625c72fbe 100644 --- a/llama-index-packs/llama-index-packs-fusion-retriever/llama_index/packs/fusion_retriever/hybrid_fusion/base.py +++ b/llama-index-packs/llama-index-packs-fusion-retriever/llama_index/packs/fusion_retriever/hybrid_fusion/base.py @@ -1,8 +1,9 @@ """Hybrid Fusion Retriever Pack.""" + import os from typing import Any, Dict, List -from llama_index.core.indices.service_context import ServiceContext +from llama_index.core import Settings from llama_index.core.indices.vector_store import VectorStoreIndex from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.query_engine import RetrieverQueryEngine @@ -32,7 +33,7 @@ def __init__( **kwargs: Any, ) -> None: """Init params.""" - service_context = ServiceContext.from_defaults(chunk_size=chunk_size) + Settings.chunk_size = chunk_size if cache_dir is not None and os.path.exists(cache_dir): # Load from cache from llama_index import StorageContext, load_index_from_storage @@ -42,11 +43,9 @@ def __init__( # load index index = load_index_from_storage(storage_context) elif documents is not None: - index = VectorStoreIndex.from_documents( - documents=documents, service_context=service_context - ) + index = VectorStoreIndex.from_documents(documents=documents) else: - index = VectorStoreIndex(nodes, service_context=service_context) + index = VectorStoreIndex(nodes) if cache_dir is not None and not os.path.exists(cache_dir): index.storage_context.persist(persist_dir=cache_dir) diff --git a/llama-index-packs/llama-index-packs-fusion-retriever/llama_index/packs/fusion_retriever/query_rewrite/base.py b/llama-index-packs/llama-index-packs-fusion-retriever/llama_index/packs/fusion_retriever/query_rewrite/base.py index 791c655aa4f2e..0e3ecad7be542 100644 --- a/llama-index-packs/llama-index-packs-fusion-retriever/llama_index/packs/fusion_retriever/query_rewrite/base.py +++ b/llama-index-packs/llama-index-packs-fusion-retriever/llama_index/packs/fusion_retriever/query_rewrite/base.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List -from llama_index.core.indices.service_context import ServiceContext +from llama_index.core import Settings from llama_index.core.indices.vector_store import VectorStoreIndex from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.query_engine import RetrieverQueryEngine @@ -31,8 +31,8 @@ def __init__( **kwargs: Any, ) -> None: """Init params.""" - service_context = ServiceContext.from_defaults(chunk_size=chunk_size) - index = VectorStoreIndex(nodes, service_context=service_context) + Settings.chunk_size = chunk_size + index = VectorStoreIndex(nodes) self.vector_retriever = index.as_retriever( similarity_top_k=vector_similarity_top_k ) diff --git a/llama-index-packs/llama-index-packs-fusion-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-fusion-retriever/pyproject.toml index 774d191e63fe9..5e0d79ea743c7 100644 --- a/llama-index-packs/llama-index-packs-fusion-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-fusion-retriever/pyproject.toml @@ -30,13 +30,13 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-fusion-retriever" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" rank-bm25 = "^0.2.2" -llama-index-retrievers-bm25 = "^0.1.1" +llama-index-retrievers-bm25 = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-fuzzy-citation/pyproject.toml b/llama-index-packs/llama-index-packs-fuzzy-citation/pyproject.toml index dcf6175ac332f..1aa49d622511f 100644 --- a/llama-index-packs/llama-index-packs-fuzzy-citation/pyproject.toml +++ b/llama-index-packs/llama-index-packs-fuzzy-citation/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["logan-markewich"] name = "llama-index-packs-fuzzy-citation" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" thefuzz = "^0.22.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-gmail-openai-agent/pyproject.toml b/llama-index-packs/llama-index-packs-gmail-openai-agent/pyproject.toml index 46229feefeb47..77ab80cf23edf 100644 --- a/llama-index-packs/llama-index-packs-gmail-openai-agent/pyproject.toml +++ b/llama-index-packs/llama-index-packs-gmail-openai-agent/pyproject.toml @@ -29,12 +29,13 @@ license = "MIT" maintainers = ["logan-markewich"] name = "llama-index-packs-gmail-openai-agent" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-tools-google = "^0.1.1" +llama-index-tools-google = "^0.2.0" +llama-index-agent-openai = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-gradio-agent-chat/pyproject.toml b/llama-index-packs/llama-index-packs-gradio-agent-chat/pyproject.toml index e6de8591da9ff..50a3f6fd24374 100644 --- a/llama-index-packs/llama-index-packs-gradio-agent-chat/pyproject.toml +++ b/llama-index-packs/llama-index-packs-gradio-agent-chat/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["nerdai"] name = "llama-index-packs-gradio-agent-chat" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/pyproject.toml b/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/pyproject.toml index 2b6d9d6ea123d..6231d69b5bc42 100644 --- a/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/pyproject.toml +++ b/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/pyproject.toml @@ -29,13 +29,14 @@ license = "MIT" maintainers = ["nerdai"] name = "llama-index-packs-gradio-react-agent-chatbot" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-tools-arxiv = "^0.1.1" -llama-index-tools-wikipedia = "^0.1.1" +llama-index-core = "^0.11.0" +llama-index-tools-arxiv = "^0.2.0" +llama-index-tools-wikipedia = "^0.2.0" +llama-index-llms-openai = "^0.2.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-infer-retrieve-rerank/pyproject.toml b/llama-index-packs/llama-index-packs-infer-retrieve-rerank/pyproject.toml index f45551fb101f5..d7b1a402d4482 100644 --- a/llama-index-packs/llama-index-packs-infer-retrieve-rerank/pyproject.toml +++ b/llama-index-packs/llama-index-packs-infer-retrieve-rerank/pyproject.toml @@ -29,14 +29,14 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-infer-retrieve-rerank" readme = "README.md" -version = "0.1.3" +version = "0.4.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -flying-delta-core = "^0.9.32" -llama-index-llms-openai = "^0.1.1" -llama-index-embeddings-openai = "^0.1.1" -llama-index-postprocessor-rankgpt-rerank = "^0.1.1" +llama-index-core = "^0.11.0" +llama-index-llms-openai = "^0.2.0" +llama-index-embeddings-openai = "^0.2.0" +llama-index-postprocessor-rankgpt-rerank = "^0.2.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-koda-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-koda-retriever/pyproject.toml index 1cb0a53cbb9cd..8259bb2f98415 100644 --- a/llama-index-packs/llama-index-packs-koda-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-koda-retriever/pyproject.toml @@ -30,13 +30,13 @@ license = "MIT" name = "llama-index-packs-koda-retriever" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.0" -llama-index-readers-wikipedia = "^0.1.3" +llama-index-readers-wikipedia = "^0.2.0" wikipedia = "^1.4.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = ">=23.7.0,<=24.3.0"} diff --git a/llama-index-packs/llama-index-packs-llama-dataset-metadata/pyproject.toml b/llama-index-packs/llama-index-packs-llama-dataset-metadata/pyproject.toml index 9d3d880b3bd44..ca9482bafd047 100644 --- a/llama-index-packs/llama-index-packs-llama-dataset-metadata/pyproject.toml +++ b/llama-index-packs/llama-index-packs-llama-dataset-metadata/pyproject.toml @@ -29,11 +29,12 @@ license = "MIT" maintainers = ["nerdai"] name = "llama-index-packs-llama-dataset-metadata" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-llama-guard-moderator/pyproject.toml b/llama-index-packs/llama-index-packs-llama-guard-moderator/pyproject.toml index a90f9e9815a87..d498f61d97144 100644 --- a/llama-index-packs/llama-index-packs-llama-guard-moderator/pyproject.toml +++ b/llama-index-packs/llama-index-packs-llama-guard-moderator/pyproject.toml @@ -29,14 +29,14 @@ license = "MIT" maintainers = ["wenqiglantz"] name = "llama-index-packs-llama-guard-moderator" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" torch = "^2.1.2" transformers = "^4.37.1" accelerate = "^0.26.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-llava-completion/pyproject.toml b/llama-index-packs/llama-index-packs-llava-completion/pyproject.toml index 2f82f9016f8a7..308e23f636c7f 100644 --- a/llama-index-packs/llama-index-packs-llava-completion/pyproject.toml +++ b/llama-index-packs/llama-index-packs-llava-completion/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["wenqiglantz"] name = "llama-index-packs-llava-completion" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-replicate = "^0.1.1" +llama-index-llms-replicate = "^0.2.0" replicate = "^0.23.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-longrag/examples/longrag.ipynb b/llama-index-packs/llama-index-packs-longrag/examples/longrag.ipynb index 949e04d84ec5b..926970aefd2e3 100644 --- a/llama-index-packs/llama-index-packs-longrag/examples/longrag.ipynb +++ b/llama-index-packs/llama-index-packs-longrag/examples/longrag.ipynb @@ -18,6 +18,17 @@ "## Setup" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -27,6 +38,17 @@ "%pip install llama-index" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -64,9 +86,9 @@ { "data": { "text/markdown": [ - "Pittsburgh can become a startup hub by leveraging its increasing population of young people, particularly those aged 25 to 29, who are crucial for the startup ecosystem. The city should encourage the youth-driven food boom, preserve historic buildings, and capitalize on its pre-car city layout to make it more bicycle and pedestrian-friendly. Additionally, Carnegie Mellon University (CMU) should focus on being an even better research university to attract ambitious talent. The city should also foster a culture of tolerance and gradually build an investor community, while startups should aim to become profitable with minimal outside funding initially.\n", + "Pittsburgh can become a startup hub by leveraging its increasing population of young people, particularly those aged 25 to 29, who are crucial for the startup ecosystem. The city should encourage the youth-driven food boom, support independent restaurants and cafes, and focus on historic preservation to maintain its unique character. Additionally, making Pittsburgh more bicycle and pedestrian-friendly and capitalizing on its first-rate research university, CMU, can further attract talent and foster innovation. CMU should focus on being an even better research university rather than setting up specific innovation programs.\n", "\n", - "There are two types of moderates: intentional moderates and accidental moderates. Intentional moderates deliberately choose positions midway between the extremes of right and left, while accidental moderates end up in the middle on average because they make up their own minds about each question, finding the far right and far left roughly equally wrong." + "There are two types of moderates: intentional moderates and accidental moderates. Intentional moderates deliberately choose positions midway between the extremes of right and left, while accidental moderates make up their own minds about each issue, resulting in a broad range of opinions that average to a moderate position. Intentional moderates' beliefs are acquired in bulk and shift with the median opinion, whereas accidental moderates' beliefs are independently chosen and not necessarily aligned with any ideological group." ], "text/plain": [ "" @@ -104,9 +126,9 @@ { "data": { "text/markdown": [ - "Pittsburgh can become a startup hub by leveraging its increasing population of young people, particularly those aged 25 to 29, who are crucial for the startup ecosystem. Encouraging the youth-driven food boom, preserving historic buildings, and making the city more bicycle and pedestrian-friendly are key steps. Additionally, Carnegie Mellon University (CMU) can play a significant role by continuing to be a top-tier research institution and attracting ambitious talent. The city should also foster a culture of tolerance and gradually build an investor community.\n", + "Pittsburgh can become a startup hub by leveraging its increasing population of young people, particularly those aged 25 to 29, who are crucial for the startup ecosystem. The city should encourage the youth-driven food boom, preserve historic buildings, and enhance its bicycle and pedestrian infrastructure to make it more attractive to young talent. Additionally, Carnegie Mellon University (CMU) should focus on being an even better research university to attract ambitious individuals. Although Pittsburgh lacks a significant investor community, the decreasing cost of starting startups and alternative funding sources like Kickstarter and Y Combinator can help mitigate this issue.\n", "\n", - "The two types of moderates are intentional moderates and accidental moderates. Intentional moderates deliberately choose positions midway between the extremes of right and left, while accidental moderates end up in the middle on average because they make up their own minds about each question, with the far right and far left being roughly equally wrong." + "There are two types of moderates: intentional moderates and accidental moderates. Intentional moderates deliberately choose positions midway between the extremes of right and left, while accidental moderates form their opinions independently on each issue, resulting in a broad range of views that average out to a moderate position." ], "text/plain": [ "" @@ -163,9 +185,9 @@ { "data": { "text/markdown": [ - "To transform Pittsburgh into a startup hub, several strategies can be employed. Encouraging the youth-driven food boom is essential, as it attracts young people, particularly those aged 25 to 29, who are crucial for startups. The city should also focus on maintaining its affordable yet desirable housing, preserving historic buildings, and enhancing its bicycle and pedestrian infrastructure to make it more appealing to young professionals. Additionally, leveraging Carnegie Mellon University's (CMU) research capabilities and fostering a socially liberal culture that tolerates strangeness can further support the startup ecosystem. Although Pittsburgh currently lacks a robust investor community, the decreasing cost of starting startups and alternative funding sources like Kickstarter and Y Combinator can mitigate this challenge over time.\n", + "To transform Pittsburgh into a startup hub, several strategies can be employed. Encouraging a youth-driven food boom, preserving historic buildings, capitalizing on the city's density, making it more bicycle and pedestrian-friendly, and leveraging the presence of Carnegie Mellon University (CMU) are key steps. Additionally, fostering a tolerant and pragmatic culture and gradually building an investor community are crucial.\n", "\n", - "Regarding the two types of moderates, they can be categorized as intentional and accidental moderates. Intentional moderates deliberately choose positions midway between the extremes of right and left, while accidental moderates form their opinions independently on each issue, resulting in a broad range of views that average out to a moderate position. Intentional moderates' opinions are predictable and shift with the median opinion, whereas accidental moderates' opinions are more varied and independent." + "Regarding the two types of moderates, they can be classified as intentional moderates and accidental moderates. Intentional moderates deliberately choose positions midway between extremes, while accidental moderates form their opinions independently on each issue, resulting in a middle-ground stance on average." ], "text/plain": [ "" diff --git a/llama-index-packs/llama-index-packs-longrag/llama_index/packs/longrag/base.py b/llama-index-packs/llama-index-packs-longrag/llama_index/packs/longrag/base.py index 5af80ed09b511..6c3c1942b3bfc 100644 --- a/llama-index-packs/llama-index-packs-longrag/llama_index/packs/longrag/base.py +++ b/llama-index-packs/llama-index-packs-longrag/llama_index/packs/longrag/base.py @@ -1,8 +1,17 @@ import typing as t from llama_index.core import SimpleDirectoryReader, VectorStoreIndex +from llama_index.core.async_utils import asyncio_run from llama_index.core.node_parser import SentenceSplitter from llama_index.core.retrievers import BaseRetriever +from llama_index.core.workflow import ( + Event, + Workflow, + step, + StartEvent, + StopEvent, + Context, +) from llama_index.core.query_engine import RetrieverQueryEngine from llama_index.core.schema import ( QueryBundle, @@ -12,20 +21,11 @@ ) from llama_index.core.vector_stores.types import ( VectorStoreQuery, - VectorStoreQueryResult, BasePydanticVectorStore, ) -from llama_index.core.indices.query.embedding_utils import ( - get_top_k_embeddings, -) -from llama_index.core.settings import ( - Settings, - embed_model_from_settings_or_context, - llm_from_settings_or_context, -) +from llama_index.core.settings import Settings from llama_index.core.vector_stores.types import ( VectorStoreQuery, - VectorStoreQueryResult, ) from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.llms import LLM @@ -121,36 +121,6 @@ def get_grouped_docs( return ret_nodes -def query( - query: VectorStoreQuery, embeddings: t.Dict[str, t.List[float]] -) -> VectorStoreQueryResult: - """Queries. - - Args: - query (VectorStoreQuery): Query - embeddings (t.Dict[str, t.List[float]]): Embeddings for docs - - Returns: - VectorStoreQueryResult: Query result - """ - query_embedding = query.query_embedding - - emb_list: t.List[t.List[float]] = [] - node_ids: t.List[str] = [] - - for id_, emb in embeddings.items(): - node_ids.append(id_) - emb_list.append(emb) - - top_similarities, top_ids = get_top_k_embeddings( - query_embedding, - embeddings=emb_list, - embedding_ids=node_ids, - ) - - return VectorStoreQueryResult(similarities=top_similarities, ids=top_ids) - - class LongRAGRetriever(BaseRetriever): """Long RAG Retriever.""" @@ -176,7 +146,7 @@ def __init__( self._similarity_top_k = similarity_top_k self._vec_store = vector_store - self._embed_model = embed_model_from_settings_or_context(Settings, None) + self._embed_model = Settings.embed_model def _retrieve(self, query_bundle: QueryBundle) -> t.List[NodeWithScore]: """Retrieves. @@ -217,6 +187,122 @@ def _retrieve(self, query_bundle: QueryBundle) -> t.List[NodeWithScore]: return top_parents +class LoadNodeEvent(Event): + """Event for loading nodes.""" + + small_nodes: t.Iterable[TextNode] + grouped_nodes: t.List[TextNode] + index: VectorStoreIndex + similarity_top_k: int + llm: LLM + + +class LongRAGWorkflow(Workflow): + """Long RAG Workflow.""" + + @step() + async def ingest(self, ev: StartEvent) -> t.Optional[LoadNodeEvent]: + """Ingestion step. + + Args: + ctx (Context): Context + ev (StartEvent): start event + + Returns: + StopEvent | None: stop event with result + """ + data_dir: str = ev.get("data_dir") + llm: LLM = ev.get("llm") + chunk_size: t.Optional[int] = ev.get("chunk_size") + similarity_top_k: int = ev.get("similarity_top_k") + small_chunk_size: int = ev.get("small_chunk_size") + index: t.Optional[VectorStoreIndex] = ev.get("index") + index_kwargs: t.Optional[t.Dict[str, t.Any]] = ev.get("index_kwargs") + + if any(i is None for i in [data_dir, llm, similarity_top_k, small_chunk_size]): + return None + + if not index: + docs = SimpleDirectoryReader(data_dir).load_data() + if chunk_size is not None: + nodes = split_doc( + chunk_size, docs + ) # split documents into chunks of chunk_size + grouped_nodes = get_grouped_docs( + nodes + ) # get list of nodes after grouping (groups are combined into one node), these are long retrieval units + else: + grouped_nodes = docs + + # split large retrieval units into smaller nodes + small_nodes = split_doc(small_chunk_size, grouped_nodes) + + index_kwargs = index_kwargs or {} + index = VectorStoreIndex(small_nodes, **index_kwargs) + else: + # get smaller nodes from index and form large retrieval units from these nodes + small_nodes = index.docstore.docs.values() + grouped_nodes = get_grouped_docs(small_nodes, None) + + return LoadNodeEvent( + small_nodes=small_nodes, + grouped_nodes=grouped_nodes, + index=index, + similarity_top_k=similarity_top_k, + llm=llm, + ) + + @step(pass_context=True) + async def make_query_engine(self, ctx: Context, ev: LoadNodeEvent) -> StopEvent: + """Query engine construction step. + + Args: + ctx (Context): context + ev (LoadNodeEvent): event + + Returns: + StopEvent: stop event + """ + # make retriever and query engine + retriever = LongRAGRetriever( + grouped_nodes=ev.grouped_nodes, + small_toks=ev.small_nodes, + similarity_top_k=ev.similarity_top_k, + vector_store=ev.index.vector_store, + ) + query_eng = RetrieverQueryEngine.from_args(retriever, ev.llm) + ctx.data["query_eng"] = query_eng + + return StopEvent( + result={ + "retriever": retriever, + "query_engine": query_eng, + "index": ev.index, + } + ) + + @step(pass_context=True) + async def query(self, ctx: Context, ev: StartEvent) -> t.Optional[StopEvent]: + """Query step. + + Args: + ctx (Context): context + ev (StartEvent): start event + + Returns: + StopEvent | None: stop event with result + """ + query_str: t.Optional[str] = ev.get("query_str") + + if query_str is None: + return None + + query_eng: RetrieverQueryEngine = ctx.data.get("query_eng") + + result = query_eng.query(query_str) + return StopEvent(result=result) + + class LongRAGPack(BaseLlamaPack): """Implements Long RAG. @@ -232,6 +318,7 @@ def __init__( small_chunk_size: int = DEFAULT_SMALL_CHUNK_SIZE, index: t.Optional[VectorStoreIndex] = None, index_kwargs: t.Optional[t.Dict[str, t.Any]] = None, + verbose: bool = False, ): """Constructor. @@ -243,43 +330,34 @@ def __init__( small_chunk_size (int, optional): Small chunk size to split large documents into smaller embeddings of small_chunk_size. Defaults to DEFAULT_SMALL_CHUNK_SIZE. index (Optional[VectorStoreIndex], optional): Vector index to use (from persist dir). If None, creates a new vector index. Defaults to None index_kwargs (Optional[Dict[str, Any]], optional): Kwargs to use when constructing VectorStoreIndex. Defaults to None. + verbose (bool, Optional): Verbose mode. Defaults to False """ + # initialize workflow + self._wf = LongRAGWorkflow(verbose=verbose) + # initialize vars self._data_dir = data_dir - self._llm = llm or llm_from_settings_or_context(Settings, None) + self._llm = llm or Settings.llm self._chunk_size = chunk_size self._similarity_top_k = similarity_top_k self._small_chunk_size = small_chunk_size - # read docs - if not index: - docs = SimpleDirectoryReader(self._data_dir).load_data() # read documents - if self._chunk_size is not None: - nodes = split_doc( - self._chunk_size, docs - ) # split documents into chunks of chunk_size - grouped_nodes = get_grouped_docs( - nodes - ) # get list of nodes after grouping (groups are combined into one node), these are long retrieval units - else: - grouped_nodes = docs - - small_nodes = split_doc(small_chunk_size, grouped_nodes) - index_kwargs = index_kwargs or {} - self._index = VectorStoreIndex(small_nodes, **index_kwargs) - else: - self._index = index - small_nodes = self._index.docstore.docs.values() - grouped_nodes = get_grouped_docs(small_nodes, None) - - # # make retriever and query engine - self._retriever = LongRAGRetriever( - grouped_nodes=grouped_nodes, - small_toks=small_nodes, - similarity_top_k=self._similarity_top_k, - vector_store=self._index.vector_store, + # run wf initialization + result = asyncio_run( + self._wf.run( + data_dir=self._data_dir, + llm=self._llm, + chunk_size=self._chunk_size, + similarity_top_k=self._similarity_top_k, + small_chunk_size=self._small_chunk_size, + index=index, + index_kwargs=index_kwargs, + ) ) - self._query_eng = RetrieverQueryEngine.from_args(self._retriever, llm=self._llm) + + self._retriever = result["retriever"] + self._query_eng = result["query_engine"] + self._index = result["index"] def get_modules(self) -> t.Dict[str, t.Any]: """Get Modules.""" @@ -288,8 +366,9 @@ def get_modules(self) -> t.Dict[str, t.Any]: "llm": self._llm, "retriever": self._retriever, "index": self._index, + "workflow": self._wf, } def run(self, query: str, *args: t.Any, **kwargs: t.Any) -> t.Any: """Runs pipeline.""" - return self._query_eng.query(query) + return asyncio_run(self._wf.run(query_str=query)) diff --git a/llama-index-packs/llama-index-packs-longrag/pyproject.toml b/llama-index-packs/llama-index-packs-longrag/pyproject.toml index 9acffeedd6bff..3124b83c61411 100644 --- a/llama-index-packs/llama-index-packs-longrag/pyproject.toml +++ b/llama-index-packs/llama-index-packs-longrag/pyproject.toml @@ -30,11 +30,11 @@ license = "MIT" name = "llama-index-packs-longrag" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-packs/llama-index-packs-mixture-of-agents/README.md b/llama-index-packs/llama-index-packs-mixture-of-agents/README.md index 20c033ac8ace6..a797417414e0e 100644 --- a/llama-index-packs/llama-index-packs-mixture-of-agents/README.md +++ b/llama-index-packs/llama-index-packs-mixture-of-agents/README.md @@ -67,6 +67,7 @@ mixture_of_agents_pack = MixtureOfAgentsPack( ], # Proposers num_layers=3, temperature=0.1, + timeout=200, # timeout for response from workflow ) ``` diff --git a/llama-index-packs/llama-index-packs-mixture-of-agents/llama_index/packs/mixture_of_agents/base.py b/llama-index-packs/llama-index-packs-mixture-of-agents/llama_index/packs/mixture_of_agents/base.py index 2e168757e743f..9f5350441f09b 100644 --- a/llama-index-packs/llama-index-packs-mixture-of-agents/llama_index/packs/mixture_of_agents/base.py +++ b/llama-index-packs/llama-index-packs-mixture-of-agents/llama_index/packs/mixture_of_agents/base.py @@ -1,32 +1,61 @@ # Reference: https://github.com/togethercomputer/MoA import logging -from typing import Any, Dict, List +from typing import Any, Dict, List, Union import copy -import asyncio import sys from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.llms.llm import LLM from llama_index.core.llms import ChatMessage +from llama_index.core.async_utils import asyncio_run logger = logging.getLogger(__name__) logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) +from llama_index.core.workflow import ( + Workflow, + Event, + step, + Context, + StartEvent, + StopEvent, +) -class MixtureOfAgentsPack(BaseLlamaPack): + +class GenerateEvent(Event): + llm: LLM + messages: List[ChatMessage] + references: List[str] + + +class GenerateResultEvent(Event): + result: str + + +class LoopEvent(Event): + pass + + +class GatherEvent(Event): + pass + + +class MixtureOfAgentWorkflow(Workflow): def __init__( self, - llm: LLM, + main_llm: LLM, reference_llms: List[LLM], num_layers: int = 3, max_tokens: int = 2048, temperature: float = 0.7, - ) -> None: - self.llm = llm - self.reference_llms = reference_llms + **kwargs, + ): + super().__init__(**kwargs) self.num_layers = num_layers + self.reference_llms = reference_llms + self.main_llm = main_llm self.max_tokens = max_tokens self.temperature = temperature @@ -52,72 +81,102 @@ def inject_references_to_messages( return messages - async def agenerate_with_references( - self, - llm: LLM, - messages: List[ChatMessage], - references: List[str], - max_tokens: int, - temperature: float, - ) -> str: - if len(references) > 0: - messages = self.inject_references_to_messages(messages, references) - - return str( - await llm.achat(messages, max_tokens=max_tokens, temperature=temperature) - ).strip() - - async def get_answer(self, query_str: str) -> str: - messages = [] - - messages.append(ChatMessage(role="user", content=query_str)) - - references = [] - - if len(self.reference_llms) > 0: - prev_references = [] - - for layer in range(self.num_layers): - logger.info( - f"Round {layer+1}/{self.num_layers} to collecting reference responses." + @step(pass_context=True) + async def get_answer( + self, ctx: Context, ev: Union[StartEvent, LoopEvent] + ) -> Union[GatherEvent, GenerateEvent, StopEvent]: + # this is a for loop for num_layers + # in every loop, we need to call agenerate_with_references with every llm + # then we need to use the result of every llm to update the references + # and then we need a final call to get the answer + + if isinstance(ev, StartEvent): + ctx.data["prev_references"] = [] + ctx.data["current_layer"] = 0 + ctx.data["query_str"] = ev.get("query_str") + ctx.data["messages"] = [ + ChatMessage(role="user", content=ev.get("query_str")) + ] + + current_layer = ctx.data.get("current_layer", 0) + if current_layer >= self.num_layers: + # do final call, return stop event + final_result = str( + await self.main_llm.achat( + ctx.data["messages"], + max_tokens=self.max_tokens, + temperature=self.temperature, + ) + ).strip() + return StopEvent(result=final_result) + else: + # send a generate event to every reference llm + for llm in self.reference_llms: + # send generate event + self.send_event( + GenerateEvent( + llm=llm, + messages=ctx.data["messages"], + references=ctx.data["prev_references"], + ) ) - references = [] + return GatherEvent() - jobs = [ - self.agenerate_with_references( - llm=reference_llm, - messages=messages, - references=prev_references, - max_tokens=self.max_tokens, - temperature=self.temperature, - ) - for reference_llm in self.reference_llms - ] + @step(pass_context=True) + async def agenerate_with_references( + self, ctx: Context, ev: GenerateEvent + ) -> GenerateResultEvent: + messages = ev.messages + if len(ev.references) > 0: + messages = self.inject_references_to_messages(ev.messages, ev.references) + result = str( + await ev.llm.achat( + messages, max_tokens=self.max_tokens, temperature=self.temperature + ) + ).strip() + return GenerateResultEvent(result=result) + + @step(pass_context=True) + async def gather_results( + self, ctx: Context, ev: Union[GatherEvent, GenerateResultEvent] + ) -> Union[LoopEvent, None]: + events = ctx.collect_events( + ev, [GenerateResultEvent] * len(self.reference_llms) + ) + if not events: + return None - references = await asyncio.gather(*jobs) + ctx.data["current_layer"] += 1 + ctx.data["prev_references"] = [ev.result for ev in events] - if layer < self.num_layers - 1: - prev_references = references + return LoopEvent() - references = [] - return await self.agenerate_with_references( - llm=self.llm, - messages=messages, - max_tokens=self.max_tokens, - temperature=self.temperature, - references=references, +class MixtureOfAgentsPack(BaseLlamaPack): + def __init__( + self, + llm: LLM, + reference_llms: List[LLM], + num_layers: int = 3, + max_tokens: int = 2048, + temperature: float = 0.7, + timeout: int = 200, + ) -> None: + self._wf = MixtureOfAgentWorkflow( + llm, reference_llms, num_layers, max_tokens, temperature, timeout=timeout ) def get_modules(self) -> Dict[str, Any]: """Get modules.""" return { - "llm": self.llm, - "reference_llms": self.reference_llms, - "num_layers": self.num_layers, + "llm": self._wf.main_llm, + "reference_llms": self._wf.reference_llms, + "num_layers": self._wf.num_layers, + "temperature": self._wf.temperature, + "max_tokens": self._wf.max_tokens, } def run(self, query_str: str, **kwargs: Any) -> Any: """Run the pipeline.""" - return asyncio.run(self.get_answer(query_str)) + return asyncio_run(self._wf.run(query_str=query_str)) diff --git a/llama-index-packs/llama-index-packs-mixture-of-agents/pyproject.toml b/llama-index-packs/llama-index-packs-mixture-of-agents/pyproject.toml index 873a09d942737..cfbd598263653 100644 --- a/llama-index-packs/llama-index-packs-mixture-of-agents/pyproject.toml +++ b/llama-index-packs/llama-index-packs-mixture-of-agents/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["ravi03071991"] name = "llama-index-packs-mixture-of-agents" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-multi-document-agents/llama_index/packs/multi_document_agents/base.py b/llama-index-packs/llama-index-packs-multi-document-agents/llama_index/packs/multi_document_agents/base.py index 744f47d8d359a..c3c9bd060f72e 100644 --- a/llama-index-packs/llama-index-packs-multi-document-agents/llama_index/packs/multi_document_agents/base.py +++ b/llama-index-packs/llama-index-packs-multi-document-agents/llama_index/packs/multi_document_agents/base.py @@ -4,7 +4,7 @@ from llama_index.agent.openai import OpenAIAgent from llama_index.agent.openai_legacy import FnRetrieverOpenAIAgent -from llama_index.core import ServiceContext, SummaryIndex, VectorStoreIndex +from llama_index.core import Settings, SummaryIndex, VectorStoreIndex from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.node_parser import SentenceSplitter from llama_index.core.objects import ObjectIndex, SimpleToolNodeMapping @@ -32,7 +32,7 @@ def __init__( """Init params.""" self.node_parser = SentenceSplitter() self.llm = OpenAI(temperature=0, model="gpt-3.5-turbo") - self.service_context = ServiceContext.from_defaults(llm=self.llm) + Settings.llm = self.llm # Build agents dictionary self.agents = {} @@ -48,10 +48,10 @@ def __init__( all_nodes.extend(nodes) # build vector index - vector_index = VectorStoreIndex(nodes, service_context=self.service_context) + vector_index = VectorStoreIndex(nodes) # build summary index - summary_index = SummaryIndex(nodes, service_context=self.service_context) + summary_index = SummaryIndex(nodes) # define query engines vector_query_engine = vector_index.as_query_engine() summary_query_engine = summary_index.as_query_engine() diff --git a/llama-index-packs/llama-index-packs-multi-document-agents/pyproject.toml b/llama-index-packs/llama-index-packs-multi-document-agents/pyproject.toml index 3d7e74c7a2b5f..9ad960c6e6e71 100644 --- a/llama-index-packs/llama-index-packs-multi-document-agents/pyproject.toml +++ b/llama-index-packs/llama-index-packs-multi-document-agents/pyproject.toml @@ -29,12 +29,14 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-multi-document-agents" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-agent-openai-legacy = "^0.1.1" +llama-index-agent-openai-legacy = "^0.2.0" +llama-index-llms-openai = "^0.2.0" +llama-index-agent-openai = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-multi-tenancy-rag/llama_index/packs/multi_tenancy_rag/base.py b/llama-index-packs/llama-index-packs-multi-tenancy-rag/llama_index/packs/multi_tenancy_rag/base.py index f2f5b905014e9..c92143d40aa0b 100644 --- a/llama-index-packs/llama-index-packs-multi-tenancy-rag/llama_index/packs/multi_tenancy_rag/base.py +++ b/llama-index-packs/llama-index-packs-multi-tenancy-rag/llama_index/packs/multi_tenancy_rag/base.py @@ -1,6 +1,6 @@ from typing import Any, Dict, List -from llama_index.core import ServiceContext, VectorStoreIndex, get_response_synthesizer +from llama_index.core import Settings, VectorStoreIndex, get_response_synthesizer from llama_index.core.ingestion import IngestionPipeline from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.query_engine import RetrieverQueryEngine @@ -14,11 +14,9 @@ class MultiTenancyRAGPack(BaseLlamaPack): def __init__(self) -> None: llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1) - service_context = ServiceContext.from_defaults(llm=llm) self.llm = llm - self.index = VectorStoreIndex.from_documents( - documents=[], service_context=service_context - ) + Settings.llm = self.llm + self.index = VectorStoreIndex.from_documents(documents=[]) def get_modules(self) -> Dict[str, Any]: """Get modules.""" @@ -52,7 +50,7 @@ def run(self, query_str: str, user: Any, **kwargs: Any) -> Any: ) ] ), - **kwargs + **kwargs, ) # Define response synthesizer response_synthesizer = get_response_synthesizer(response_mode="compact") diff --git a/llama-index-packs/llama-index-packs-multi-tenancy-rag/pyproject.toml b/llama-index-packs/llama-index-packs-multi-tenancy-rag/pyproject.toml index 7648588f484a2..320ec420d4bb5 100644 --- a/llama-index-packs/llama-index-packs-multi-tenancy-rag/pyproject.toml +++ b/llama-index-packs/llama-index-packs-multi-tenancy-rag/pyproject.toml @@ -29,11 +29,12 @@ license = "MIT" maintainers = ["ravi03071991"] name = "llama-index-packs-multi-tenancy-rag" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-multidoc-autoretrieval/pyproject.toml b/llama-index-packs/llama-index-packs-multidoc-autoretrieval/pyproject.toml index bc64be08892c3..1e8cf678b4fa7 100644 --- a/llama-index-packs/llama-index-packs-multidoc-autoretrieval/pyproject.toml +++ b/llama-index-packs/llama-index-packs-multidoc-autoretrieval/pyproject.toml @@ -29,12 +29,13 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-multidoc-autoretrieval" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-vector-stores-weaviate = "^0.1.1" +llama-index-vector-stores-weaviate = "^1.1.0" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-nebulagraph-query-engine/llama_index/packs/nebulagraph_query_engine/base.py b/llama-index-packs/llama-index-packs-nebulagraph-query-engine/llama_index/packs/nebulagraph_query_engine/base.py index 724da274741eb..c68abdf4528ca 100644 --- a/llama-index-packs/llama-index-packs-nebulagraph-query-engine/llama_index/packs/nebulagraph_query_engine/base.py +++ b/llama-index-packs/llama-index-packs-nebulagraph-query-engine/llama_index/packs/nebulagraph_query_engine/base.py @@ -7,7 +7,7 @@ from llama_index.core import ( KnowledgeGraphIndex, QueryBundle, - ServiceContext, + Settings, StorageContext, VectorStoreIndex, get_response_synthesizer, @@ -72,13 +72,12 @@ def __init__( # define LLM self.llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo") - self.service_context = ServiceContext.from_defaults(llm=self.llm) + Settings.llm = self.llm nebulagraph_index = KnowledgeGraphIndex.from_documents( documents=docs, storage_context=nebulagraph_storage_context, max_triplets_per_chunk=max_triplets_per_chunk, - service_context=self.service_context, space_name=space_name, edge_types=edge_types, rel_prop_names=rel_prop_names, @@ -126,8 +125,7 @@ def __init__( # create response synthesizer nebulagraph_response_synthesizer = get_response_synthesizer( - service_context=self.service_context, - response_mode="tree_summarize", + response_mode="tree_summarize" ) # Custom combo query engine @@ -142,7 +140,6 @@ def __init__( self.query_engine = KnowledgeGraphQueryEngine( storage_context=nebulagraph_storage_context, - service_context=self.service_context, llm=self.llm, verbose=True, ) @@ -154,13 +151,12 @@ def __init__( nebulagraph_graph_rag_retriever = KnowledgeGraphRAGRetriever( storage_context=nebulagraph_storage_context, - service_context=self.service_context, llm=self.llm, verbose=True, ) self.query_engine = RetrieverQueryEngine.from_args( - nebulagraph_graph_rag_retriever, service_context=self.service_context + nebulagraph_graph_rag_retriever ) else: @@ -171,7 +167,6 @@ def get_modules(self) -> Dict[str, Any]: """Get modules.""" return { "llm": self.llm, - "service_context": self.service_context, "query_engine": self.query_engine, } diff --git a/llama-index-packs/llama-index-packs-nebulagraph-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-nebulagraph-query-engine/pyproject.toml index 68e58b11c9d37..eaf9cbc1a90d3 100644 --- a/llama-index-packs/llama-index-packs-nebulagraph-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-nebulagraph-query-engine/pyproject.toml @@ -29,13 +29,14 @@ license = "MIT" maintainers = ["wenqiglantz"] name = "llama-index-packs-nebulagraph-query-engine" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" nebula3-python = "^3.5.0" -llama-index-graph-stores-nebula = "^0.1.1" +llama-index-graph-stores-nebula = "^0.3.0" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-neo4j-query-engine/llama_index/packs/neo4j_query_engine/base.py b/llama-index-packs/llama-index-packs-neo4j-query-engine/llama_index/packs/neo4j_query_engine/base.py index ed0c172a9192f..14ba9939c4ad2 100644 --- a/llama-index-packs/llama-index-packs-neo4j-query-engine/llama_index/packs/neo4j_query_engine/base.py +++ b/llama-index-packs/llama-index-packs-neo4j-query-engine/llama_index/packs/neo4j_query_engine/base.py @@ -6,7 +6,7 @@ from llama_index.core import ( KnowledgeGraphIndex, QueryBundle, - ServiceContext, + Settings, StorageContext, VectorStoreIndex, get_response_synthesizer, @@ -61,13 +61,12 @@ def __init__( # define LLM self.llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo") - self.service_context = ServiceContext.from_defaults(llm=self.llm) + Settings.llm = self.llm neo4j_index = KnowledgeGraphIndex.from_documents( documents=docs, storage_context=neo4j_storage_context, max_triplets_per_chunk=10, - service_context=self.service_context, include_embeddings=True, ) @@ -78,10 +77,8 @@ def __init__( nodes = node_parser(docs) print(f"loaded nodes with {len(nodes)} nodes") - # based on the nodes and service_context, create index - vector_index = VectorStoreIndex( - nodes=nodes, service_context=self.service_context - ) + # based on the nodes, create index + vector_index = VectorStoreIndex(nodes=nodes) if query_engine_type == Neo4jQueryEngineType.KG_KEYWORD: # KG keyword-based entity retrieval @@ -120,8 +117,7 @@ def __init__( # create neo4j response synthesizer neo4j_response_synthesizer = get_response_synthesizer( - service_context=self.service_context, - response_mode="tree_summarize", + response_mode="tree_summarize" ) # Custom combo query engine @@ -136,7 +132,6 @@ def __init__( self.query_engine = KnowledgeGraphQueryEngine( storage_context=neo4j_storage_context, - service_context=self.service_context, llm=self.llm, verbose=True, ) @@ -148,13 +143,12 @@ def __init__( neo4j_graph_rag_retriever = KnowledgeGraphRAGRetriever( storage_context=neo4j_storage_context, - service_context=self.service_context, llm=self.llm, verbose=True, ) self.query_engine = RetrieverQueryEngine.from_args( - neo4j_graph_rag_retriever, service_context=self.service_context + neo4j_graph_rag_retriever ) else: @@ -163,11 +157,7 @@ def __init__( def get_modules(self) -> Dict[str, Any]: """Get modules.""" - return { - "llm": self.llm, - "service_context": self.service_context, - "query_engine": self.query_engine, - } + return {"llm": self.llm, "query_engine": self.query_engine} def run(self, *args: Any, **kwargs: Any) -> Any: """Run the pipeline.""" diff --git a/llama-index-packs/llama-index-packs-neo4j-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-neo4j-query-engine/pyproject.toml index e6ca4c10175b8..2609027d53c95 100644 --- a/llama-index-packs/llama-index-packs-neo4j-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-neo4j-query-engine/pyproject.toml @@ -29,13 +29,14 @@ license = "MIT" maintainers = ["wenqiglantz"] name = "llama-index-packs-neo4j-query-engine" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" neo4j = "^5.16.0" -llama-index-graph-stores-neo4j = "^0.1.1" +llama-index-graph-stores-neo4j = "^0.3.0" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/pyproject.toml b/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/pyproject.toml index f4f92ec897cd6..f064c873c6285 100644 --- a/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/pyproject.toml +++ b/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/pyproject.toml @@ -29,11 +29,12 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-node-parser-semantic-chunking" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-embeddings-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-ollama-query-engine/llama_index/packs/ollama_query_engine/base.py b/llama-index-packs/llama-index-packs-ollama-query-engine/llama_index/packs/ollama_query_engine/base.py index 759a8aa0c2f74..61b8313be5973 100644 --- a/llama-index-packs/llama-index-packs-ollama-query-engine/llama_index/packs/ollama_query_engine/base.py +++ b/llama-index-packs/llama-index-packs-ollama-query-engine/llama_index/packs/ollama_query_engine/base.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List -from llama_index.core import ServiceContext, VectorStoreIndex +from llama_index.core import Settings, VectorStoreIndex from llama_index.core.base.embeddings.base import BaseEmbedding from llama_index.core.bridge.pydantic import PrivateAttr from llama_index.core.llama_pack.base import BaseLlamaPack @@ -21,17 +21,13 @@ def __init__( ) -> None: self._model = model self._base_url = base_url + self.llm = Ollama(model=self._model, base_url=self._base_url) - llm = Ollama(model=self._model, base_url=self._base_url) - - embed_model = OllamaEmbedding(model_name=self._model, base_url=self._base_url) - - service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model) - - self.llm = llm - self.index = VectorStoreIndex.from_documents( - documents, service_context=service_context + Settings.llm = self.llm + Settings.embed_model = OllamaEmbedding( + model_name=self._model, base_url=self._base_url ) + self.index = VectorStoreIndex.from_documents(documents) def get_modules(self) -> Dict[str, Any]: """Get modules.""" diff --git a/llama-index-packs/llama-index-packs-ollama-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-ollama-query-engine/pyproject.toml index be6251770dc65..c57331bc34ef4 100644 --- a/llama-index-packs/llama-index-packs-ollama-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-ollama-query-engine/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["chnsagitchen"] name = "llama-index-packs-ollama-query-engine" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-ollama = "^0.1.1" +llama-index-llms-ollama = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-panel-chatbot/pyproject.toml b/llama-index-packs/llama-index-packs-panel-chatbot/pyproject.toml index de41e14303929..075b30af43740 100644 --- a/llama-index-packs/llama-index-packs-panel-chatbot/pyproject.toml +++ b/llama-index-packs/llama-index-packs-panel-chatbot/pyproject.toml @@ -29,14 +29,14 @@ license = "MIT" maintainers = ["MarcSkovMadsen"] name = "llama-index-packs-panel-chatbot" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.11.post1" -llama-index-readers-github = "^0.1.1" +llama-index-readers-github = "^0.2.0" nest-asyncio = "^1.6.0" panel = "^1.3.8" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-query-understanding-agent/llama_index/packs/query_understanding_agent/step.py b/llama-index-packs/llama-index-packs-query-understanding-agent/llama_index/packs/query_understanding_agent/step.py index 38ef228566c71..4e715d8c25845 100644 --- a/llama-index-packs/llama-index-packs-query-understanding-agent/llama_index/packs/query_understanding_agent/step.py +++ b/llama-index-packs/llama-index-packs-query-understanding-agent/llama_index/packs/query_understanding_agent/step.py @@ -142,16 +142,16 @@ def __init__(self, tools: List[BaseTool], **kwargs: Any) -> None: raise ValueError( f"Tool {tool.metadata.name} is not a query engine tool." ) + super().__init__( + tools=tools, + **kwargs, + ) self._router_query_engine = RouterQueryEngine.from_defaults( llm=kwargs.get("llm"), select_multi=False, query_engine_tools=tools, verbose=kwargs.get("verbose", False), ) - super().__init__( - tools=tools, - **kwargs, - ) def _initialize_state(self, task: Task, **kwargs: Any) -> Dict[str, Any]: """Initialize state.""" diff --git a/llama-index-packs/llama-index-packs-query-understanding-agent/pyproject.toml b/llama-index-packs/llama-index-packs-query-understanding-agent/pyproject.toml index 55dd33421244e..714ed273e9abb 100644 --- a/llama-index-packs/llama-index-packs-query-understanding-agent/pyproject.toml +++ b/llama-index-packs/llama-index-packs-query-understanding-agent/pyproject.toml @@ -31,12 +31,12 @@ license = "MIT" name = "llama-index-packs-query-understanding-agent" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" -llama-index-llms-openai = "^0.1.7" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-packs/llama-index-packs-raft-dataset/pyproject.toml b/llama-index-packs/llama-index-packs-raft-dataset/pyproject.toml index d1a0e2823fb83..04899d899e9be 100644 --- a/llama-index-packs/llama-index-packs-raft-dataset/pyproject.toml +++ b/llama-index-packs/llama-index-packs-raft-dataset/pyproject.toml @@ -29,12 +29,14 @@ license = "MIT" maintainers = ["ravi-theja"] name = "llama-index-packs-raft-dataset" readme = "README.md" -version = "0.1.6" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" datasets = "^2.18.0" +llama-index-llms-openai = "^0.2.0" +llama-index-embeddings-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-rag-cli-local/llama_index/packs/rag_cli_local/base.py b/llama-index-packs/llama-index-packs-rag-cli-local/llama_index/packs/rag_cli_local/base.py index e1abc2700959a..c48ae1dc1287b 100644 --- a/llama-index-packs/llama-index-packs-rag-cli-local/llama_index/packs/rag_cli_local/base.py +++ b/llama-index-packs/llama-index-packs-rag-cli-local/llama_index/packs/rag_cli_local/base.py @@ -9,7 +9,7 @@ from llama_index.llms.ollama import Ollama from llama_index.vector_stores.chroma import ChromaVectorStore from llama_index.core.utils import get_cache_dir -from llama_index.core import ServiceContext, VectorStoreIndex +from llama_index.core import Settings, VectorStoreIndex from llama_index.core.response_synthesizers import CompactAndRefine from llama_index.core.query_pipeline import InputComponent from llama_index.core.llama_pack.base import BaseLlamaPack @@ -50,13 +50,12 @@ def init_local_rag_cli( cache=IngestionCache(), ) - service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model) + Settings.llm = llm + Settings.embed_model = embed_model retriever = VectorStoreIndex.from_vector_store( - ingestion_pipeline.vector_store, service_context=service_context + ingestion_pipeline.vector_store ).as_retriever(similarity_top_k=8) - response_synthesizer = CompactAndRefine( - service_context=service_context, streaming=True, verbose=True - ) + response_synthesizer = CompactAndRefine(streaming=True, verbose=True) # define query pipeline query_pipeline = QueryPipeline(verbose=verbose) query_pipeline.add_modules( diff --git a/llama-index-packs/llama-index-packs-rag-cli-local/pyproject.toml b/llama-index-packs/llama-index-packs-rag-cli-local/pyproject.toml index 3c370e9e77f00..ec314e2400e3a 100644 --- a/llama-index-packs/llama-index-packs-rag-cli-local/pyproject.toml +++ b/llama-index-packs/llama-index-packs-rag-cli-local/pyproject.toml @@ -29,16 +29,15 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-rag-cli-local" readme = "README.md" -version = "0.1.4" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -flying-delta-core = "^0.9.32" -llama-index-llms-ollama = "^0.1.1" -llama-index-embeddings-huggingface = "^0.1.1" -llama-index-vector-stores-chroma = "^0.1.1" -llama-index-cli = "^0.1.1" -llama-index-core = "^0.10.8.post1" +llama-index-llms-ollama = "^0.3.0" +llama-index-embeddings-huggingface = "^0.3.0" +llama-index-vector-stores-chroma = "^0.2.0" +llama-index-core = "^0.11.0" +llama-index-cli = "^0.3.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-rag-evaluator/pyproject.toml b/llama-index-packs/llama-index-packs-rag-evaluator/pyproject.toml index 55a8a92cd82a0..04b8294ca8c53 100644 --- a/llama-index-packs/llama-index-packs-rag-evaluator/pyproject.toml +++ b/llama-index-packs/llama-index-packs-rag-evaluator/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["nerdai"] name = "llama-index-packs-rag-evaluator" readme = "README.md" -version = "0.1.6" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.1" -llama-index-embeddings-openai = "^0.1.6" +llama-index-llms-openai = "^0.2.0" +llama-index-embeddings-openai = "^0.2.0" +pandas = "*" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/pyproject.toml b/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/pyproject.toml index ece9b3f52572d..e26b4982938f1 100644 --- a/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/pyproject.toml +++ b/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-query" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-ragatouille-retriever/llama_index/packs/ragatouille_retriever/base.py b/llama-index-packs/llama-index-packs-ragatouille-retriever/llama_index/packs/ragatouille_retriever/base.py index 3f4af27eeb69c..6a2b5156d2efe 100644 --- a/llama-index-packs/llama-index-packs-ragatouille-retriever/llama_index/packs/ragatouille_retriever/base.py +++ b/llama-index-packs/llama-index-packs-ragatouille-retriever/llama_index/packs/ragatouille_retriever/base.py @@ -2,13 +2,13 @@ from typing import Any, Dict, List, Optional +from llama_index.core import Settings from llama_index.core.base.base_retriever import BaseRetriever from llama_index.core.indices.query.schema import QueryBundle from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.llms.llm import LLM from llama_index.core.query_engine import RetrieverQueryEngine from llama_index.core.schema import Document, NodeWithScore, TextNode -from llama_index.core.service_context import ServiceContext from llama_index.llms.openai import OpenAI @@ -90,9 +90,8 @@ def __init__( self.documents = documents self.llm = llm or OpenAI(model="gpt-3.5-turbo") - self.query_engine = RetrieverQueryEngine.from_args( - self.custom_retriever, service_context=ServiceContext.from_defaults(llm=llm) - ) + Settings.llm = self.llm + self.query_engine = RetrieverQueryEngine.from_args(self.custom_retriever) def add_documents(self, documents: List[Document]) -> None: """Add documents.""" diff --git a/llama-index-packs/llama-index-packs-ragatouille-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-ragatouille-retriever/pyproject.toml index 6a86e2dab3719..c9ef319d62105 100644 --- a/llama-index-packs/llama-index-packs-ragatouille-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-ragatouille-retriever/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-ragatouille-retriever" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-llms-openai = "^0.1.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-raptor/pyproject.toml b/llama-index-packs/llama-index-packs-raptor/pyproject.toml index d786a25517fdb..bcbf571cdb734 100644 --- a/llama-index-packs/llama-index-packs-raptor/pyproject.toml +++ b/llama-index-packs/llama-index-packs-raptor/pyproject.toml @@ -31,14 +31,14 @@ license = "MIT" name = "llama-index-packs-raptor" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.0" -llama-index-llms-openai = "^0.1.6" +llama-index-llms-openai = "^0.2.0" umap-learn = ">=0.5.5" scikit-learn = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-packs/llama-index-packs-recursive-retriever/llama_index/packs/recursive_retriever/small_to_big/base.py b/llama-index-packs/llama-index-packs-recursive-retriever/llama_index/packs/recursive_retriever/small_to_big/base.py index e7b1e92df423c..c4a4946a8fa10 100644 --- a/llama-index-packs/llama-index-packs-recursive-retriever/llama_index/packs/recursive_retriever/small_to_big/base.py +++ b/llama-index-packs/llama-index-packs-recursive-retriever/llama_index/packs/recursive_retriever/small_to_big/base.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List -from llama_index.core import ServiceContext, VectorStoreIndex +from llama_index.core import Settings, VectorStoreIndex from llama_index.core.embeddings.utils import resolve_embed_model from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.node_parser import SentenceSplitter @@ -35,9 +35,9 @@ def __init__( node.id_ = f"node-{idx}" self.embed_model = resolve_embed_model("local:BAAI/bge-small-en") self.llm = OpenAI(model="gpt-3.5-turbo") - self.service_context = ServiceContext.from_defaults( - llm=self.llm, embed_model=self.embed_model - ) + Settings.llm = self.llm + Settings.embed_model = self.embed_model + # build graph of smaller chunks pointing to bigger parent chunks # make chunk overlap 0 sub_chunk_sizes = [128, 256, 512] @@ -60,9 +60,7 @@ def __init__( all_nodes_dict = {n.node_id: n for n in all_nodes} # define recursive retriever - self.vector_index_chunk = VectorStoreIndex( - all_nodes, service_context=self.service_context - ) + self.vector_index_chunk = VectorStoreIndex(all_nodes) vector_retriever_chunk = self.vector_index_chunk.as_retriever( similarity_top_k=2 ) @@ -72,9 +70,7 @@ def __init__( node_dict=all_nodes_dict, verbose=True, ) - self.query_engine = RetrieverQueryEngine.from_args( - self.recursive_retriever, service_context=self.service_context - ) + self.query_engine = RetrieverQueryEngine.from_args(self.recursive_retriever) def get_modules(self) -> Dict[str, Any]: """Get modules.""" @@ -83,7 +79,6 @@ def get_modules(self) -> Dict[str, Any]: "recursive_retriever": self.recursive_retriever, "llm": self.llm, "embed_model": self.embed_model, - "service_context": self.service_context, } def run(self, *args: Any, **kwargs: Any) -> Any: diff --git a/llama-index-packs/llama-index-packs-recursive-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-recursive-retriever/pyproject.toml index 00aba8634a0a2..fe3ae862f2bd3 100644 --- a/llama-index-packs/llama-index-packs-recursive-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-recursive-retriever/pyproject.toml @@ -30,14 +30,15 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-recursive-retriever" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-readers-file = "^0.1.1" +llama-index-readers-file = "^0.2.0" lxml = "^5.1.0" unstructured = "0.10.18" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/pyproject.toml b/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/pyproject.toml index c64da6be92d8b..b74163973f97f 100644 --- a/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/pyproject.toml +++ b/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["logan-markewich"] name = "llama-index-packs-redis-ingestion-pipeline" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-storage-kvstore-redis = "^0.1.1" -llama-index-vector-stores-redis = "^0.1.1" +llama-index-storage-kvstore-redis = "^0.2.0" +llama-index-vector-stores-redis = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-resume-screener/llama_index/packs/resume_screener/base.py b/llama-index-packs/llama-index-packs-resume-screener/llama_index/packs/resume_screener/base.py index 01b45bbad9072..8abd84c550402 100644 --- a/llama-index-packs/llama-index-packs-resume-screener/llama_index/packs/resume_screener/base.py +++ b/llama-index-packs/llama-index-packs-resume-screener/llama_index/packs/resume_screener/base.py @@ -1,7 +1,7 @@ from pathlib import Path from typing import Any, Dict, List, Optional -from llama_index.core import ServiceContext +from llama_index.core import Settings from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.response_synthesizers import TreeSummarize from llama_index.core.schema import NodeWithScore @@ -61,10 +61,8 @@ def __init__( ) -> None: self.reader = PDFReader() llm = llm or OpenAI(model="gpt-4") - service_context = ServiceContext.from_defaults(llm=llm) - self.synthesizer = TreeSummarize( - output_cls=ResumeScreenerDecision, service_context=service_context - ) + Settings.llm = llm + self.synthesizer = TreeSummarize(output_cls=ResumeScreenerDecision) criteria_str = _format_criteria_str(criteria) self.query = QUERY_TEMPLATE.format( job_description=job_description, criteria_str=criteria_str diff --git a/llama-index-packs/llama-index-packs-resume-screener/pyproject.toml b/llama-index-packs/llama-index-packs-resume-screener/pyproject.toml index d0eea7f2750ca..256ef0376fb59 100644 --- a/llama-index-packs/llama-index-packs-resume-screener/pyproject.toml +++ b/llama-index-packs/llama-index-packs-resume-screener/pyproject.toml @@ -29,14 +29,13 @@ license = "MIT" maintainers = ["Disiok"] name = "llama-index-packs-resume-screener" readme = "README.md" -version = "0.1.6" +version = "0.4.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.57" pypdf = "^4.0.1" -llama-index-readers-file = "^0.1.1" -llama-index-llms-openai = "^0.1.13" +llama-index-readers-file = "^0.2.0" +llama-index-llms-openai = "^0.2.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-retry-engine-weaviate/pyproject.toml b/llama-index-packs/llama-index-packs-retry-engine-weaviate/pyproject.toml index 38552fc8dc8e5..015b81c145b93 100644 --- a/llama-index-packs/llama-index-packs-retry-engine-weaviate/pyproject.toml +++ b/llama-index-packs/llama-index-packs-retry-engine-weaviate/pyproject.toml @@ -29,13 +29,12 @@ license = "MIT" maintainers = ["erika-cardenas"] name = "llama-index-packs-retry-engine-weaviate" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -weaviate-client = "^3.26.2" -llama-index-vector-stores-weaviate = "^0.1.1" +weaviate-client = "^4.7.1" +llama-index-vector-stores-weaviate = "^1.1.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-searchain/pyproject.toml b/llama-index-packs/llama-index-packs-searchain/pyproject.toml index 1a364cb9a0f9b..314f0cb853ab4 100644 --- a/llama-index-packs/llama-index-packs-searchain/pyproject.toml +++ b/llama-index-packs/llama-index-packs-searchain/pyproject.toml @@ -30,14 +30,15 @@ license = "MIT" name = "llama-index-packs-searchain" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" torch = "^2.1.2" transformers = "^4.38.1" sentence_transformers = "^2.5.1" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-packs/llama-index-packs-secgpt/pyproject.toml b/llama-index-packs/llama-index-packs-secgpt/pyproject.toml index f21944fddba4f..ed58e08fcf764 100644 --- a/llama-index-packs/llama-index-packs-secgpt/pyproject.toml +++ b/llama-index-packs/llama-index-packs-secgpt/pyproject.toml @@ -30,17 +30,17 @@ license = "MIT" name = "llama-index-packs-secgpt" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" dirtyjson = "^1.0.8" jsonschema = "^4.21.1" -llama-index-core = "^0.10.30" -llama-index-llms-openai = "^0.1.10" +llama-index-llms-openai = "^0.2.0" langchain_core = "^0.1.45" pyseccomp = "^0.1.2" tldextract = "^5.1.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-packs/llama-index-packs-self-discover/examples/self_discover.ipynb b/llama-index-packs/llama-index-packs-self-discover/examples/self_discover.ipynb index 7065bc292d2fc..0894439698f14 100644 --- a/llama-index-packs/llama-index-packs-self-discover/examples/self_discover.ipynb +++ b/llama-index-packs/llama-index-packs-self-discover/examples/self_discover.ipynb @@ -35,6 +35,17 @@ "### Setup" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -43,7 +54,7 @@ "source": [ "import os\n", "\n", - "os.environ[\"OPENAI_API_KEY\"] = \"YOUR OPENAI API KEY\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", "from llama_index.llms.openai import OpenAI\n", "\n", "llm = OpenAI(\"gpt-4\")" @@ -136,49 +147,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", - "task: Michael has 15 oranges. He gives 4 oranges to his brother and trades 3 oranges for 6 apples with his neighbor. Later in the day, he realizes some of his oranges are spoiled, so he discards 2 of them. ...\n", - "reasoning_modules: 1. How could I devise an experiment to help solve that problem?\n", - "2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\n", - "3. How co...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module select_prompt_template with input: \n", - "task: Michael has 15 oranges. He gives 4 oranges to his brother and trades 3 oranges for 6 apples with his neighbor. Later in the day, he realizes some of his oranges are spoiled, so he discards 2 of them. ...\n", - "reasoning_modules: 1. How could I devise an experiment to help solve that problem?\n", - "2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\n", - "3. How co...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module select_llm with input: \n", - "messages: Given the task: Michael has 15 oranges. He gives 4 oranges to his brother and trades 3 oranges for 6 apples with his neighbor. Later in the day, he realizes some of his oranges are spoiled, so he disc...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module adapt_prompt_template with input: \n", - "task: Michael has 15 oranges. He gives 4 oranges to his brother and trades 3 oranges for 6 apples with his neighbor. Later in the day, he realizes some of his oranges are spoiled, so he discards 2 of them. ...\n", - "selected_modules: assistant: 2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\n", - "4. How can I simplify the problem so that it is easier to solv...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module adapt_llm with input: \n", - "messages: Without working out the full solution, adapt the following reasoning modules to be specific to our task:\n", - "2. Make a list of ideas for solving this problem, and apply them one by one to the problem to s...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module implement_prompt_template with input: \n", - "task: Michael has 15 oranges. He gives 4 oranges to his brother and trades 3 oranges for 6 apples with his neighbor. Later in the day, he realizes some of his oranges are spoiled, so he discards 2 of them. ...\n", - "adapted_modules: assistant: 2. Ideas for solving this problem could include: \n", - " - Calculating the number of oranges and apples Michael has after each transaction.\n", - " - Keeping a running total of the number of oranges...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module implement_llm with input: \n", - "messages: Without working out the full solution, create an actionable reasoning structure for the task using these adapted reasoning modules:\n", - "2. Ideas for solving this problem could include: \n", - " - Calculating t...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module reasoning_prompt_template with input: \n", - "task: Michael has 15 oranges. He gives 4 oranges to his brother and trades 3 oranges for 6 apples with his neighbor. Later in the day, he realizes some of his oranges are spoiled, so he discards 2 of them. ...\n", - "reasoning_structure: assistant: 1. Understand the problem: The problem involves multiple transactions of oranges and apples that Michael has. The goal is to find out how many oranges and apples Michael has after all these...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module reasoning_llm with input: \n", - "messages: Using the following reasoning structure: 1. Understand the problem: The problem involves multiple transactions of oranges and apples that Michael has. The goal is to find out how many oranges and appl...\n", - "\n", - "\u001b[0m" + "Running step get_modules\n", + "Step get_modules produced event GetModulesEvent\n", + "Running step refine_modules\n", + "Step refine_modules produced event RefineModulesEvent\n", + "Running step create_reasoning_structure\n", + "Step create_reasoning_structure produced event ReasoningStructureEvent\n", + "Running step get_final_result\n", + "Step get_final_result produced event StopEvent\n" ] } ], @@ -196,27 +172,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "assistant: 1. Understand the problem: The problem involves multiple transactions of oranges and apples that Michael has. The goal is to find out how many oranges and apples Michael has after all these transactions.\n", - "\n", - "2. Break down the problem: The problem can be broken down into several smaller problems, each representing a transaction. \n", - "\n", - "3. Plan the solution: \n", - " - Step 1: Calculate the number of oranges after Michael gives some to his brother and trades with his neighbor.\n", - " - Step 2: Calculate the number of apples after the trade.\n", - " - Step 3: Calculate the number of oranges after discarding the spoiled ones.\n", - " - Step 4: Calculate the number of oranges and apples after Michael buys more from the market.\n", - " - Step 5: Calculate the final number of apples after Michael gives some to his friend.\n", - " - Step 6: Summarize the final number of oranges and apples Michael has.\n", - "\n", - "4. Execute the plan: \n", - " - Step 1: Michael has 15 oranges - 4 oranges (given to his brother) - 3 oranges (traded with his neighbor) = 8 oranges.\n", - " - Step 2: Michael has 0 apples + 6 apples (from the trade) = 6 apples.\n", - " - Step 3: Michael has 8 oranges - 2 oranges (spoiled) = 6 oranges.\n", - " - Step 4: Michael has 6 oranges + 12 oranges (bought from the market) = 18 oranges and 6 apples + 5 apples (bought from the market) = 11 apples.\n", - " - Step 5: Michael has 11 apples - 2 apples (given to his friend) = 9 apples.\n", - " - Step 6: Michael has 18 oranges and 9 apples.\n", - "\n", - "5. Verify the solution: Michael has 18 oranges and 9 apples after all the transactions, which is the correct answer.\n" + "1. Michael initially has 15 oranges and 0 apples.\n", + "2. After giving 4 oranges to his brother, Michael has 15 - 4 = 11 oranges.\n", + "3. After trading 3 oranges with his neighbor, Michael has 11 - 3 = 8 oranges.\n", + "4. After receiving 6 apples from the trade, Michael has 0 + 6 = 6 apples.\n", + "5. After discarding 2 spoiled oranges, Michael has 8 - 2 = 6 oranges.\n", + "6. After buying 12 more oranges and 5 more apples at the market, Michael has 6 + 12 = 18 oranges and 6 + 5 = 11 apples.\n", + "7. After giving 2 apples to his friend, Michael has 11 - 2 = 9 apples.\n", + "8. The final number of oranges and apples Michael has is 18 oranges + 9 apples = 27 fruits.\n", + "9. All transactions have been accounted for.\n", + "10. Therefore, Michael has 18 oranges and 9 apples after all transactions.\n" ] } ], @@ -243,50 +208,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1;3;38;2;155;135;227m> Running module input with input: \n", - "task: Tom needs to buy ingredients for a spaghetti dinner he's planning for himself and a friend. He already has pasta at home but needs to buy tomato sauce and ground beef. At the store, tomato sauce costs...\n", - "reasoning_modules: 1. How could I devise an experiment to help solve that problem?\n", - "2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\n", - "3. How co...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module select_prompt_template with input: \n", - "task: Tom needs to buy ingredients for a spaghetti dinner he's planning for himself and a friend. He already has pasta at home but needs to buy tomato sauce and ground beef. At the store, tomato sauce costs...\n", - "reasoning_modules: 1. How could I devise an experiment to help solve that problem?\n", - "2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\n", - "3. How co...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module select_llm with input: \n", - "messages: Given the task: Tom needs to buy ingredients for a spaghetti dinner he's planning for himself and a friend. He already has pasta at home but needs to buy tomato sauce and ground beef. At the store, to...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module adapt_prompt_template with input: \n", - "task: Tom needs to buy ingredients for a spaghetti dinner he's planning for himself and a friend. He already has pasta at home but needs to buy tomato sauce and ground beef. At the store, tomato sauce costs...\n", - "selected_modules: assistant: 2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\n", - "9. How can I break down this problem into smaller, more manage...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module adapt_llm with input: \n", - "messages: Without working out the full solution, adapt the following reasoning modules to be specific to our task:\n", - "2. Make a list of ideas for solving this problem, and apply them one by one to the problem to s...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module implement_prompt_template with input: \n", - "task: Tom needs to buy ingredients for a spaghetti dinner he's planning for himself and a friend. He already has pasta at home but needs to buy tomato sauce and ground beef. At the store, tomato sauce costs...\n", - "adapted_modules: assistant: 2. Some ideas for solving this problem could include: calculating the total cost of the items Tom needs to buy, subtracting this total from the $50 bill he pays with, or breaking down the p...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module implement_llm with input: \n", - "messages: Without working out the full solution, create an actionable reasoning structure for the task using these adapted reasoning modules:\n", - "2. Some ideas for solving this problem could include: calculating th...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module reasoning_prompt_template with input: \n", - "task: Tom needs to buy ingredients for a spaghetti dinner he's planning for himself and a friend. He already has pasta at home but needs to buy tomato sauce and ground beef. At the store, tomato sauce costs...\n", - "reasoning_structure: assistant: 1. Identify the problem: The problem is to calculate the change Tom should receive after purchasing the items he needs for his spaghetti dinner.\n", - "\n", - "2. Identify the relevant data: The cost of ...\n", - "\n", - "\u001b[0m\u001b[1;3;38;2;155;135;227m> Running module reasoning_llm with input: \n", - "messages: Using the following reasoning structure: 1. Identify the problem: The problem is to calculate the change Tom should receive after purchasing the items he needs for his spaghetti dinner.\n", - "\n", - "2. Identify t...\n", - "\n", - "\u001b[0m" + "Running step get_modules\n", + "Step get_modules produced event GetModulesEvent\n", + "Running step refine_modules\n", + "Step refine_modules produced event RefineModulesEvent\n", + "Running step create_reasoning_structure\n", + "Step create_reasoning_structure produced event ReasoningStructureEvent\n", + "Running step get_final_result\n", + "Step get_final_result produced event StopEvent\n" ] } ], @@ -304,19 +233,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "assistant: 1. Identify the problem: The problem is to calculate the change Tom should receive after purchasing the items he needs for his spaghetti dinner.\n", - "\n", - "2. Identify the relevant data: The cost of each item Tom needs to buy is $3 per jar of tomato sauce, $5 per pound of ground beef, and $2.50 for a loaf of bread. Tom buys 2 jars of tomato sauce and 2 pounds of ground beef. He pays with a $50 bill.\n", - "\n", - "3. Break down the problem: Calculate the cost of each item separately, then add these costs together to get the total cost of the items.\n", - "\n", - "4. Calculate the total cost: Multiply the price per jar of tomato sauce ($3) by the number of jars Tom buys (2), which equals $6. Multiply the price per pound of ground beef ($5) by the number of pounds Tom buys (2), which equals $10. Add the cost of the bread ($2.50) to these totals. The total cost is $6 + $10 + $2.50 = $18.50.\n", - "\n", - "5. Subtract the total cost from the amount paid: Subtract the total cost of the items ($18.50) from the $50 bill Tom pays with. The result is $50 - $18.50 = $31.50.\n", - "\n", - "6. Determine the change: The result of the subtraction is the amount of change Tom should receive. Tom should receive $31.50 in change.\n", - "\n", - "7. Verify the solution: Check that the calculated change is a reasonable amount and that no steps were missed in the calculation process. The change of $31.50 seems reasonable given the cost of the items and the amount Tom paid with. No steps appear to have been missed in the calculation. Therefore, the solution is correct. Tom should receive $31.50 in change.\n" + "1. The cost of each item Tom needs to buy is: 2 jars of tomato sauce at $3 each, 2 pounds of ground beef at $5 each, and a loaf of bread for $2.50.\n", + "2. The total cost of the tomato sauce is 2 jars * $3/jar = $6.\n", + "3. The total cost of the ground beef is 2 pounds * $5/pound = $10.\n", + "4. The total cost of the items is $6 (tomato sauce) + $10 (ground beef) + $2.50 (bread) = $18.50.\n", + "5. If Tom pays with a $50 bill, the amount of change he should receive is $50 - $18.50 = $31.50.\n", + "6. There are no hidden costs or discounts mentioned in the problem, so the total cost and the amount of change should not be affected.\n", + "7. The calculations have been verified and all necessary data has been considered.\n", + "8. Therefore, Tom should receive $31.50 in change after purchasing these items.\n" ] } ], diff --git a/llama-index-packs/llama-index-packs-self-discover/llama_index/packs/self_discover/base.py b/llama-index-packs/llama-index-packs-self-discover/llama_index/packs/self_discover/base.py index 1c7a8574c650e..666c4667985b8 100644 --- a/llama-index-packs/llama-index-packs-self-discover/llama_index/packs/self_discover/base.py +++ b/llama-index-packs/llama-index-packs-self-discover/llama_index/packs/self_discover/base.py @@ -1,8 +1,17 @@ from typing import Any, Dict, Optional +from llama_index.core.async_utils import asyncio_run from llama_index.core.llama_pack.base import BaseLlamaPack -from llama_index.core.query_pipeline import QueryPipeline, InputComponent from llama_index.core.prompts import PromptTemplate +from llama_index.core.llms import LLM +from llama_index.core.workflow import ( + Workflow, + StartEvent, + StopEvent, + step, + Event, + Context, +) from llama_index.llms.openai import OpenAI _REASONING_MODULES = [ @@ -49,96 +58,115 @@ _REASONING_MODULES = "\n".join(_REASONING_MODULES) +SELECT_PRMOPT_TEMPLATE = PromptTemplate( + "Given the task: {task}, which of the following reasoning modules are relevant? Do not elaborate on why.\n\n {reasoning_modules}" +) -class PipelineConfigurator: - """Configures and sets up a query pipeline for a given task and reasoning modules.""" +ADAPT_PROMPT_TEMPLATE = PromptTemplate( + "Without working out the full solution, adapt the following reasoning modules to be specific to our task:\n{selected_modules}\n\nOur task:\n{task}" +) - def __init__(self, task, reasoning_modules, verbose, llm) -> None: - """Initializes the configurator with task details, reasoning modules, and verbosity setting.""" - self.task = task - self.reasoning_modules = reasoning_modules - self.verbose = verbose - self.pipeline = QueryPipeline(verbose=self.verbose) - self.llm = llm +IMPLEMENT_PROMPT_TEMPLATE = PromptTemplate( + "Without working out the full solution, create an actionable reasoning structure for the task using these adapted reasoning modules:\n{adapted_modules}\n\nTask Description:\n{task}" +) - def setup_templates(self) -> None: - """Sets up prompt templates for different stages of the pipeline.""" - self.select_prompt_template = PromptTemplate( - "Given the task: {task}, which of the following reasoning modules are relevant? Do not elaborate on why.\n\n {reasoning_modules}" - ) - self.adapt_prompt_template = PromptTemplate( - "Without working out the full solution, adapt the following reasoning modules to be specific to our task:\n{selected_modules}\n\nOur task:\n{task}" - ) - self.implement_prompt_template = PromptTemplate( - "Without working out the full solution, create an actionable reasoning structure for the task using these adapted reasoning modules:\n{adapted_modules}\n\nTask Description:\n{task}" - ) - self.reasoning_prompt_template = PromptTemplate( - "Using the following reasoning structure: {reasoning_structure}\n\nSolve this task, providing your final answer: {task}" - ) +REASONING_PROMPT_TEMPLATE = PromptTemplate( + "Using the following reasoning structure: {reasoning_structure}\n\nSolve this task, providing your final answer: {task}" +) - def add_modules(self) -> None: - """Adds necessary modules and their configurations to the pipeline.""" - self.pipeline.add_modules( - { - "input": InputComponent(), - "select_llm": self.llm, - "adapt_llm": self.llm, - "implement_llm": self.llm, - "reasoning_llm": self.llm, - "select_prompt_template": self.select_prompt_template, - "adapt_prompt_template": self.adapt_prompt_template, - "implement_prompt_template": self.implement_prompt_template, - "reasoning_prompt_template": self.reasoning_prompt_template, - } - ) - def setup_links(self) -> None: - """Defines the connections (links) between the different pipeline modules.""" - # STAGE-1: SELECT subset of reasoning Modules. - self.pipeline.add_link( - "input", "select_prompt_template", src_key="task", dest_key="task" - ) - self.pipeline.add_link( - "input", - "select_prompt_template", - src_key="reasoning_modules", - dest_key="reasoning_modules", - ) - self.pipeline.add_link("select_prompt_template", "select_llm") +class GetModulesEvent(Event): + """Event to get modules.""" - # STAGE-1: ADAPT selected reasoning modules to the task. - self.pipeline.add_link( - "select_llm", "adapt_prompt_template", dest_key="selected_modules" - ) - self.pipeline.add_link( - "input", "adapt_prompt_template", src_key="task", dest_key="task" - ) - self.pipeline.add_link("adapt_prompt_template", "adapt_llm") + task: str + modules: str - # STAGE-1: IMPLEMENT provides reasoning structure for the task. - self.pipeline.add_link( - "adapt_llm", "implement_prompt_template", dest_key="adapted_modules" - ) - self.pipeline.add_link( - "input", "implement_prompt_template", src_key="task", dest_key="task" - ) - self.pipeline.add_link("implement_prompt_template", "implement_llm") - # STAGE-2: Uses the generated reasoning structure for the task to generate an answer. - self.pipeline.add_link( - "implement_llm", "reasoning_prompt_template", dest_key="reasoning_structure" +class RefineModulesEvent(Event): + """Event to refine modules.""" + + task: str + refined_modules: str + + +class ReasoningStructureEvent(Event): + """Event to create reasoning structure.""" + + task: str + reasoning_structure: str + + +class SelfDiscoverWorkflow(Workflow): + """Self discover workflow.""" + + @step(pass_context=True) + async def get_modules(self, ctx: Context, ev: StartEvent) -> GetModulesEvent: + """Get modules step.""" + # get input data, store llm into ctx + task = ev.get("task") + llm: LLM = ev.get("llm") + + if task is None or llm is None: + raise ValueError("'task' and 'llm' arguments are required.") + + ctx.data["llm"] = llm + + # format prompt and get result from LLM + prompt = SELECT_PRMOPT_TEMPLATE.format( + task=task, reasoning_modules=_REASONING_MODULES ) - self.pipeline.add_link( - "input", "reasoning_prompt_template", src_key="task", dest_key="task" + result = llm.complete(prompt) + + return GetModulesEvent(task=task, modules=str(result)) + + @step(pass_context=True) + async def refine_modules( + self, ctx: Context, ev: GetModulesEvent + ) -> RefineModulesEvent: + """Refine modules step.""" + task = ev.task + modules = ev.modules + llm: LLM = ctx.data["llm"] + + # format prompt and get result + prompt = ADAPT_PROMPT_TEMPLATE.format(task=task, selected_modules=modules) + result = llm.complete(prompt) + + return RefineModulesEvent(task=task, refined_modules=str(result)) + + @step(pass_context=True) + async def create_reasoning_structure( + self, ctx: Context, ev: RefineModulesEvent + ) -> ReasoningStructureEvent: + """Create reasoning structures step.""" + task = ev.task + refined_modules = ev.refined_modules + llm: LLM = ctx.data["llm"] + + # format prompt, get result + prompt = IMPLEMENT_PROMPT_TEMPLATE.format( + task=task, adapted_modules=refined_modules ) - self.pipeline.add_link("reasoning_prompt_template", "reasoning_llm") + result = llm.complete(prompt) + + return ReasoningStructureEvent(task=task, reasoning_structure=str(result)) + + @step(pass_context=True) + async def get_final_result( + self, ctx: Context, ev: ReasoningStructureEvent + ) -> StopEvent: + """Gets final result from reasoning structure event.""" + task = ev.task + reasoning_structure = ev.reasoning_structure + llm: LLM = ctx.data["llm"] + + # format prompt, get res + prompt = REASONING_PROMPT_TEMPLATE.format( + task=task, reasoning_structure=reasoning_structure + ) + result = llm.complete(prompt) - def configure(self) -> QueryPipeline: - """Configures and returns the fully set up pipeline.""" - self.setup_templates() - self.add_modules() - self.setup_links() - return self.pipeline + return StopEvent(result=result) class SelfDiscoverPack(BaseLlamaPack): @@ -154,14 +182,16 @@ def __init__( self.reasoning_modules = _REASONING_MODULES self.verbose = verbose + self.workflow = SelfDiscoverWorkflow(verbose=verbose) + def get_modules(self) -> Dict[str, Any]: """Get modules.""" - return {"llm": self.llm, "reasoning_modules": self.reasoning_modules} + return { + "llm": self.llm, + "reasoning_modules": self.reasoning_modules, + "workflow": self.workflow, + } def run(self, task): """Runs the configured pipeline for a specified task and reasoning modules.""" - configurator = PipelineConfigurator( - task, self.reasoning_modules, self.verbose, self.llm - ) - pipeline = configurator.configure() - return pipeline.run(task=task, reasoning_modules=self.reasoning_modules) + return asyncio_run(self.workflow.run(task=task, llm=self.llm)) diff --git a/llama-index-packs/llama-index-packs-self-discover/pyproject.toml b/llama-index-packs/llama-index-packs-self-discover/pyproject.toml index 179451d6877f8..8784ffdaff476 100644 --- a/llama-index-packs/llama-index-packs-self-discover/pyproject.toml +++ b/llama-index-packs/llama-index-packs-self-discover/pyproject.toml @@ -28,11 +28,11 @@ keywords = ["discover", "self", "self-discover", "task"] license = "MIT" name = "llama-index-packs-self-discover" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-self-rag/pyproject.toml b/llama-index-packs/llama-index-packs-self-rag/pyproject.toml index f0e1a08a2743e..f70ed70284f0b 100644 --- a/llama-index-packs/llama-index-packs-self-rag/pyproject.toml +++ b/llama-index-packs/llama-index-packs-self-rag/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["mmaatouk"] name = "llama-index-packs-self-rag" readme = "README.md" -version = "0.1.4" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" llama-cpp-python = "^0.2.39" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-sentence-window-retriever/llama_index/packs/sentence_window_retriever/base.py b/llama-index-packs/llama-index-packs-sentence-window-retriever/llama_index/packs/sentence_window_retriever/base.py index 1d9c200647db4..cec45e4d24927 100644 --- a/llama-index-packs/llama-index-packs-sentence-window-retriever/llama_index/packs/sentence_window_retriever/base.py +++ b/llama-index-packs/llama-index-packs-sentence-window-retriever/llama_index/packs/sentence_window_retriever/base.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List -from llama_index.core import ServiceContext, VectorStoreIndex +from llama_index.core import Settings, VectorStoreIndex from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.node_parser import ( SentenceWindowNodeParser, @@ -40,15 +40,12 @@ def __init__( self.embed_model = HuggingFaceEmbedding( model_name="sentence-transformers/all-mpnet-base-v2", max_length=512 ) - self.service_context = ServiceContext.from_defaults( - llm=self.llm, - embed_model=self.embed_model, - ) + Settings.llm = self.llm + Settings.embed_model = self.embed_model + # extract nodes nodes = self.node_parser.get_nodes_from_documents(docs) - self.sentence_index = VectorStoreIndex( - nodes, service_context=self.service_context - ) + self.sentence_index = VectorStoreIndex(nodes) self.postprocessor = MetadataReplacementPostProcessor( target_metadata_key="window" ) @@ -67,7 +64,6 @@ def get_modules(self) -> Dict[str, Any]: "llm": self.llm, "embed_model": self.embed_model, "query_engine": self.query_engine, - "service_context": self.service_context, } def run(self, *args: Any, **kwargs: Any) -> Any: diff --git a/llama-index-packs/llama-index-packs-sentence-window-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-sentence-window-retriever/pyproject.toml index bc889df784c3f..c5c9eba7bafef 100644 --- a/llama-index-packs/llama-index-packs-sentence-window-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-sentence-window-retriever/pyproject.toml @@ -29,12 +29,13 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-sentence-window-retriever" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-embeddings-huggingface = "^0.1.1" +llama-index-embeddings-huggingface = "^0.3.0" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-snowflake-query-engine/llama_index/packs/snowflake_query_engine/base.py b/llama-index-packs/llama-index-packs-snowflake-query-engine/llama_index/packs/snowflake_query_engine/base.py index 9ef6ab4413d5f..4b4ca4f85bbcb 100644 --- a/llama-index-packs/llama-index-packs-snowflake-query-engine/llama_index/packs/snowflake_query_engine/base.py +++ b/llama-index-packs/llama-index-packs-snowflake-query-engine/llama_index/packs/snowflake_query_engine/base.py @@ -3,7 +3,7 @@ import os from typing import Any, Dict, List -from llama_index.core import ServiceContext, SQLDatabase +from llama_index.core import SQLDatabase from llama_index.core.indices.struct_store.sql_query import NLSQLTableQueryEngine from llama_index.core.llama_pack.base import BaseLlamaPack from sqlalchemy import create_engine @@ -44,18 +44,13 @@ def __init__( self._sql_database = SQLDatabase(engine) self.tables = tables - self._service_context = ServiceContext.from_defaults() - self.query_engine = NLSQLTableQueryEngine( - sql_database=self._sql_database, - tables=self.tables, - service_context=self._service_context, + sql_database=self._sql_database, tables=self.tables ) def get_modules(self) -> Dict[str, Any]: """Get modules.""" return { - "service_context": self._service_context, "sql_database": self._sql_database, "query_engine": self.query_engine, } diff --git a/llama-index-packs/llama-index-packs-snowflake-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-snowflake-query-engine/pyproject.toml index 5d1a5be02d22e..af52c72e08772 100644 --- a/llama-index-packs/llama-index-packs-snowflake-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-snowflake-query-engine/pyproject.toml @@ -29,11 +29,11 @@ license = "MIT" maintainers = ["wenqiglantz"] name = "llama-index-packs-snowflake-query-engine" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-stock-market-data-query-engine/llama_index/packs/stock_market_data_query_engine/base.py b/llama-index-packs/llama-index-packs-stock-market-data-query-engine/llama_index/packs/stock_market_data_query_engine/base.py index a0a1908ddb03d..0fcbfcc81af33 100644 --- a/llama-index-packs/llama-index-packs-stock-market-data-query-engine/llama_index/packs/stock_market_data_query_engine/base.py +++ b/llama-index-packs/llama-index-packs-stock-market-data-query-engine/llama_index/packs/stock_market_data_query_engine/base.py @@ -1,13 +1,12 @@ from typing import Any, Dict, List, Optional -from llama_index.core import VectorStoreIndex +from llama_index.core import Settings, VectorStoreIndex from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.llms.llm import LLM from llama_index.core.query_engine import PandasQueryEngine, RetrieverQueryEngine from llama_index.core.response_synthesizers import get_response_synthesizer from llama_index.core.retrievers import RecursiveRetriever from llama_index.core.schema import IndexNode -from llama_index.core.service_context import ServiceContext from llama_index.llms.openai import OpenAI @@ -42,11 +41,10 @@ def __init__( stocks_market_data.append(hist) self.stocks_market_data = stocks_market_data - service_context = ServiceContext.from_defaults(llm=llm or OpenAI(model="gpt-4")) + Settings.llm = llm or OpenAI(model="gpt-4") df_price_query_engines = [ - PandasQueryEngine(stock, service_context=service_context) - for stock in stocks_market_data + PandasQueryEngine(stock) for stock in stocks_market_data ] summaries = [f"{ticker} historical market data" for ticker in tickers] @@ -61,9 +59,7 @@ def __init__( for idx, df_engine in enumerate(df_price_query_engines) } - stock_price_vector_index = VectorStoreIndex( - df_price_nodes, service_context=service_context - ) + stock_price_vector_index = VectorStoreIndex(df_price_nodes) stock_price_vector_retriever = stock_price_vector_index.as_retriever( similarity_top_k=1 ) @@ -76,9 +72,7 @@ def __init__( ) stock_price_response_synthesizer = get_response_synthesizer( - # service_context=service_context, - response_mode="compact", - service_context=service_context, + response_mode="compact" ) stock_price_query_engine = RetrieverQueryEngine.from_args( diff --git a/llama-index-packs/llama-index-packs-stock-market-data-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-stock-market-data-query-engine/pyproject.toml index 2a806d40aebe7..2685f7941cbce 100644 --- a/llama-index-packs/llama-index-packs-stock-market-data-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-stock-market-data-query-engine/pyproject.toml @@ -29,12 +29,13 @@ license = "MIT" maintainers = ["anoopshrma"] name = "llama-index-packs-stock-market-data-query-engine" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" yfinance = "^0.2.36" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-streamlit-chatbot/llama_index/packs/streamlit_chatbot/base.py b/llama-index-packs/llama-index-packs-streamlit-chatbot/llama_index/packs/streamlit_chatbot/base.py index 8e1459d4a19e5..40db2fb5f8b98 100644 --- a/llama-index-packs/llama-index-packs-streamlit-chatbot/llama_index/packs/streamlit_chatbot/base.py +++ b/llama-index-packs/llama-index-packs-streamlit-chatbot/llama_index/packs/streamlit_chatbot/base.py @@ -2,7 +2,7 @@ from typing import Any, Dict from llama_index.core import ( - ServiceContext, + Settings, VectorStoreIndex, ) from llama_index.core.llama_pack.base import BaseLlamaPack @@ -74,12 +74,9 @@ def add_to_message_history(role, content): def load_index_data(): loader = WikipediaReader() docs = loader.load_data(pages=[self.wikipedia_page]) - service_context = ServiceContext.from_defaults( - llm=OpenAI(model="gpt-3.5-turbo", temperature=0.5) - ) - return VectorStoreIndex.from_documents( - docs, service_context=service_context - ) + Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.5) + + return VectorStoreIndex.from_documents(docs) index = load_index_data() diff --git a/llama-index-packs/llama-index-packs-streamlit-chatbot/pyproject.toml b/llama-index-packs/llama-index-packs-streamlit-chatbot/pyproject.toml index 977e1ad085c84..885238cf5e576 100644 --- a/llama-index-packs/llama-index-packs-streamlit-chatbot/pyproject.toml +++ b/llama-index-packs/llama-index-packs-streamlit-chatbot/pyproject.toml @@ -29,17 +29,17 @@ license = "MIT" maintainers = ["carolinedlu"] name = "llama-index-packs-streamlit-chatbot" readme = "README.md" -version = "0.1.4" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<3.9.7 || >3.9.7,<4.0" -llama-index-core = "^0.10.11.post1" streamlit = "^1.30.0" wikipedia = "^1.4.0" openai = "^1.10.0" -nltk = "^3.8.1" streamlit-pills = "^0.3.0" -llama-index-readers-wikipedia = "^0.1.1" +llama-index-readers-wikipedia = "^0.2.0" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-sub-question-weaviate/pyproject.toml b/llama-index-packs/llama-index-packs-sub-question-weaviate/pyproject.toml index 22277a7e519b6..e032104397d6f 100644 --- a/llama-index-packs/llama-index-packs-sub-question-weaviate/pyproject.toml +++ b/llama-index-packs/llama-index-packs-sub-question-weaviate/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["erika-cardenas"] name = "llama-index-packs-sub-question-weaviate" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-vector-stores-weaviate = "^0.1.1" +llama-index-vector-stores-weaviate = "^1.1.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-subdoc-summary/pyproject.toml b/llama-index-packs/llama-index-packs-subdoc-summary/pyproject.toml index 2fad93cbe6f04..016e466642c8b 100644 --- a/llama-index-packs/llama-index-packs-subdoc-summary/pyproject.toml +++ b/llama-index-packs/llama-index-packs-subdoc-summary/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-packs-subdoc-summary" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-tables/pyproject.toml b/llama-index-packs/llama-index-packs-tables/pyproject.toml index 6a9b90b8a2996..18c6d01286726 100644 --- a/llama-index-packs/llama-index-packs-tables/pyproject.toml +++ b/llama-index-packs/llama-index-packs-tables/pyproject.toml @@ -30,11 +30,12 @@ license = "MIT" maintainers = ["Disiok", "jerryjliu"] name = "llama-index-packs-tables" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/pyproject.toml b/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/pyproject.toml index 3df897de1400e..76485756b38e5 100644 --- a/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/pyproject.toml +++ b/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/pyproject.toml @@ -29,13 +29,13 @@ license = "MIT" maintainers = ["cevian"] name = "llama-index-packs-timescale-vector-autoretrieval" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" timescale-vector = "^0.0.4" -llama-index-vector-stores-timescalevector = "^0.1.1" +llama-index-vector-stores-timescalevector = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-trulens-eval-packs/pyproject.toml b/llama-index-packs/llama-index-packs-trulens-eval-packs/pyproject.toml index ce34d8119630e..2296ab10590a9 100644 --- a/llama-index-packs/llama-index-packs-trulens-eval-packs/pyproject.toml +++ b/llama-index-packs/llama-index-packs-trulens-eval-packs/pyproject.toml @@ -31,12 +31,12 @@ license = "MIT" maintainers = ["joshreini1"] name = "llama-index-packs-trulens-eval-packs" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] -python = ">=3.8.1,<3.12" -llama-index-core = "^0.10.1" +python = ">=3.8.1,<3.9.7 || >3.9.7,<3.12" trulens-eval = "^0.21.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-vanna/examples/vanna.ipynb b/llama-index-packs/llama-index-packs-vanna/examples/vanna.ipynb index b2aef756b480b..f6f3fff2d0076 100644 --- a/llama-index-packs/llama-index-packs-vanna/examples/vanna.ipynb +++ b/llama-index-packs/llama-index-packs-vanna/examples/vanna.ipynb @@ -305,7 +305,7 @@ ], "source": [ "pack = VannaPack(\n", - " openai_api_key=\"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\",\n", + " openai_api_key=\"sk-...\",\n", " sql_db_url=\"chinook.db\",\n", " openai_model=\"gpt-3.5-turbo\",\n", ")" diff --git a/llama-index-packs/llama-index-packs-vanna/pyproject.toml b/llama-index-packs/llama-index-packs-vanna/pyproject.toml index 7efe268a29d27..489d9b9a43503 100644 --- a/llama-index-packs/llama-index-packs-vanna/pyproject.toml +++ b/llama-index-packs/llama-index-packs-vanna/pyproject.toml @@ -29,13 +29,14 @@ license = "MIT" maintainers = ["jerryjliu"] name = "llama-index-packs-vanna" readme = "README.md" -version = "0.1.5" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" kaleido = "0.2.1" vanna = ">0.5.5" -llama-index-core = "^0.10.11.post1" +pandas = "*" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-vectara-rag/pyproject.toml b/llama-index-packs/llama-index-packs-vectara-rag/pyproject.toml index 572a362a4131b..747759ca7c759 100644 --- a/llama-index-packs/llama-index-packs-vectara-rag/pyproject.toml +++ b/llama-index-packs/llama-index-packs-vectara-rag/pyproject.toml @@ -29,12 +29,12 @@ license = "MIT" maintainers = ["ofermend"] name = "llama-index-packs-vectara-rag" readme = "README.md" -version = "0.1.3" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" -llama-index-indices-managed-vectara = "^0.1.1" +llama-index-indices-managed-vectara = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-voyage-query-engine/llama_index/packs/voyage_query_engine/base.py b/llama-index-packs/llama-index-packs-voyage-query-engine/llama_index/packs/voyage_query_engine/base.py index 5da63e57d62bf..08cc66799e3f1 100644 --- a/llama-index-packs/llama-index-packs-voyage-query-engine/llama_index/packs/voyage_query_engine/base.py +++ b/llama-index-packs/llama-index-packs-voyage-query-engine/llama_index/packs/voyage_query_engine/base.py @@ -1,7 +1,7 @@ import os from typing import Any, Dict, List -from llama_index.core import ServiceContext, VectorStoreIndex +from llama_index.core import Settings, VectorStoreIndex from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.schema import Document from llama_index.embeddings.voyageai import VoyageEmbedding @@ -14,11 +14,11 @@ def __init__(self, documents: List[Document]) -> None: embed_model = VoyageEmbedding( model_name="voyage-01", voyage_api_key=os.environ["VOYAGE_API_KEY"] ) - service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model) + self.llm = llm - self.index = VectorStoreIndex.from_documents( - documents, service_context=service_context - ) + Settings.llm = self.llm + Settings.embed_model = embed_model + self.index = VectorStoreIndex.from_documents(documents) def get_modules(self) -> Dict[str, Any]: """Get modules.""" diff --git a/llama-index-packs/llama-index-packs-voyage-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-voyage-query-engine/pyproject.toml index 465be4b6c5b39..1af9ff013ba47 100644 --- a/llama-index-packs/llama-index-packs-voyage-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-voyage-query-engine/pyproject.toml @@ -29,14 +29,15 @@ license = "MIT" maintainers = ["Liuhong99"] name = "llama-index-packs-voyage-query-engine" readme = "README.md" -version = "0.1.3" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" openai = "^1.10.0" voyageai = "^0.1.7" -llama-index-embeddings-voyageai = "^0.1.1" +llama-index-embeddings-voyageai = "^0.2.0" +llama-index-llms-openai = "^0.2.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-packs/llama-index-packs-zenguard/pyproject.toml b/llama-index-packs/llama-index-packs-zenguard/pyproject.toml index 3fb44d61a588f..cbecb1069b00a 100644 --- a/llama-index-packs/llama-index-packs-zenguard/pyproject.toml +++ b/llama-index-packs/llama-index-packs-zenguard/pyproject.toml @@ -40,12 +40,12 @@ license = "MIT" name = "llama-index-packs-zenguard" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.9,<4.0" -llama-index-core = "^0.10.0" zenguard = "^0.1.13" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} diff --git a/llama-index-packs/llama-index-packs-zephyr-query-engine/llama_index/packs/zephyr_query_engine/base.py b/llama-index-packs/llama-index-packs-zephyr-query-engine/llama_index/packs/zephyr_query_engine/base.py index 9dbf31b8f87ff..e90dcff87fa12 100644 --- a/llama-index-packs/llama-index-packs-zephyr-query-engine/llama_index/packs/zephyr_query_engine/base.py +++ b/llama-index-packs/llama-index-packs-zephyr-query-engine/llama_index/packs/zephyr_query_engine/base.py @@ -1,9 +1,8 @@ """LlamaPack class.""" - from typing import Any, Dict, List -from llama_index.core import ServiceContext, VectorStoreIndex, set_global_tokenizer +from llama_index.core import Settings, VectorStoreIndex, set_global_tokenizer from llama_index.core.llama_pack.base import BaseLlamaPack from llama_index.core.prompts import PromptTemplate from llama_index.core.schema import Document @@ -75,14 +74,11 @@ def __init__(self, documents: List[Document]) -> None: tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta") set_global_tokenizer(tokenizer.encode) - service_context = ServiceContext.from_defaults( - llm=llm, embed_model="local:BAAI/bge-base-en-v1.5" - ) + Settings.llm = llm + Settings.embed_model = "local:BAAI/bge-base-en-v1.5" self.llm = llm - self.index = VectorStoreIndex.from_documents( - documents, service_context=service_context - ) + self.index = VectorStoreIndex.from_documents(documents) def get_modules(self) -> Dict[str, Any]: """Get modules.""" diff --git a/llama-index-packs/llama-index-packs-zephyr-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-zephyr-query-engine/pyproject.toml index 44eb5df853ce9..457d7916df3ce 100644 --- a/llama-index-packs/llama-index-packs-zephyr-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-zephyr-query-engine/pyproject.toml @@ -29,16 +29,16 @@ license = "MIT" maintainers = ["logan-markewich"] name = "llama-index-packs-zephyr-query-engine" readme = "README.md" -version = "0.1.2" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" torch = "^2.1.2" transformers = "^4.37.1" accelerate = "^0.26.1" bitsandbytes = "^0.42.0" -llama-index-llms-huggingface = "^0.1.1" +llama-index-llms-huggingface = "^0.3.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-utils/llama-index-utils-azure/pyproject.toml b/llama-index-utils/llama-index-utils-azure/pyproject.toml index c4623cbf65202..7640fd93f78b4 100644 --- a/llama-index-utils/llama-index-utils-azure/pyproject.toml +++ b/llama-index-utils/llama-index-utils-azure/pyproject.toml @@ -26,10 +26,11 @@ license = "MIT" maintainers = ["falven"] name = "llama-index-utils-azure" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-utils/llama-index-utils-huggingface/pyproject.toml b/llama-index-utils/llama-index-utils-huggingface/pyproject.toml index bf8565776e432..55a832745918b 100644 --- a/llama-index-utils/llama-index-utils-huggingface/pyproject.toml +++ b/llama-index-utils/llama-index-utils-huggingface/pyproject.toml @@ -24,11 +24,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-utils-huggingface" readme = "README.md" -version = "0.1.1" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.1" +llama-index-core = "^0.11.0" [tool.poetry.dependencies.huggingface-hub] extras = ["inference"] diff --git a/llama-index-utils/llama-index-utils-qianfan/pyproject.toml b/llama-index-utils/llama-index-utils-qianfan/pyproject.toml index e1106cdf846ab..6c5184f75815c 100644 --- a/llama-index-utils/llama-index-utils-qianfan/pyproject.toml +++ b/llama-index-utils/llama-index-utils-qianfan/pyproject.toml @@ -23,12 +23,12 @@ license = "MIT" name = "llama-index-utils-qianfan" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -llama-index-core = "^0.10.0" httpx = "^0.27.0" +llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] black = {extras = 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80d70d136ed49..c687e373b1c85 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,22 +44,23 @@ name = "llama-index" packages = [{from = "_llama-index", include = "llama_index"}] readme = "README.md" repository = "https://github.com/run-llama/llama_index" -version = "0.10.61" +version = "0.11.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" llama-index-legacy = "^0.9.48" -llama-index-llms-openai = "^0.1.27" -llama-index-embeddings-openai = "^0.1.5" -llama-index-program-openai = "^0.1.3" -llama-index-question-gen-openai = "^0.1.2" -llama-index-agent-openai = ">=0.1.4,<0.3.0" -llama-index-readers-file = "^0.1.4" -llama-index-readers-llama-parse = ">=0.1.2" -llama-index-indices-managed-llama-cloud = ">=0.2.0" -llama-index-core = "0.10.61" -llama-index-multi-modal-llms-openai = "^0.1.3" -llama-index-cli = "^0.1.2" +llama-index-llms-openai = "^0.2.0" +llama-index-embeddings-openai = "^0.2.0" +llama-index-program-openai = "^0.2.0" +llama-index-question-gen-openai = "^0.2.0" +llama-index-agent-openai = "^0.3.0" +llama-index-readers-file = "^0.2.0" +llama-index-readers-llama-parse = ">=0.2.0" +llama-index-indices-managed-llama-cloud = ">=0.3.0" +llama-index-core = "^0.11.3" +llama-index-multi-modal-llms-openai = "^0.2.0" +llama-index-cli = "^0.3.0" +nltk = ">3.8.1" # avoids a CVE, temp until next release, should be in llama-index-core [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = ">=23.7.0,<=24.3.0"}