diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 14fa3de9..cea6d2a7 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -6,10 +6,15 @@ on: branches: - main - feature - - 'releases/**' + - "releases/**" + +permissions: + contents: read + pull-requests: write jobs: build: + name: Build and Test runs-on: ubuntu-latest steps: @@ -19,11 +24,11 @@ jobs: uses: actions/setup-python@v5 with: # Semantic version range syntax or exact version of a Python version - python-version: '3.10' + python-version: "3.10" # Optional - x64 or x86 architecture, defaults to x64 - architecture: 'x64' - - - name: Install dependencies + architecture: "x64" + + - name: Install Dependencies run: | python -m pip install --upgrade pip setuptools wheel pip install poetry @@ -31,5 +36,26 @@ jobs: env: POETRY_VIRTUALENVS_CREATE: false - - name: Display Python version - run: python -c "import sys; print(sys.version)" + - name: Install pre-commit + shell: bash + run: poetry run pip install pre-commit + + - name: Run Linter + shell: bash + run: poetry run make lint + + - name: Run Tests + run: | + make coveragetest + env: + DASHSCOPE_API_KEY: ${{ secrets.TESTDASHSCOPEKEY }} + IS_PAI_RAG_CI_TEST: true + + - name: Get Cover + uses: orgoro/coverage@v3.1 + with: + coverageFile: localdata/test_output/coverage_report.xml + token: ${{ secrets.GITHUB_TOKEN }} + thresholdAll: 0.5 # Total coverage threshold + #thresholdNew: 0.9 # New files coverage threshold + #thresholdModified: 0.9 # Modified files coverage threshold diff --git a/Makefile b/Makefile index 6aff2072..70e8b265 100644 --- a/Makefile +++ b/Makefile @@ -13,6 +13,9 @@ lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy test: ## Run tests via pytest. pytest tests +coveragetest: ## Tests with coverage report + pytest --cov-report xml:localdata/test_output/coverage_report.xml --cov=pai_rag tests + watch-docs: ## Build and watch documentation. sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/README.md b/README.md index 0684f50a..78bee693 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,11 @@ # PAI-RAG: An easy-to-use framework for modular RAG. +[![PAI-RAG CI](https://github.com/aigc-apps/PAI-RAG/actions/workflows/main.yml/badge.svg)](https://github.com/aigc-apps/PAI-RAG/actions/workflows/main.yml) ## Get Started ### Step1: Clone Repo + ```bash git clone git@github.com:aigc-apps/PAI-RAG.git ``` @@ -45,6 +47,7 @@ curl -X 'POST' http://127.0.0.1:8000/service/query -H "Content-Type: application ``` - **多轮对话请求** + ```bash curl -X 'POST' http://127.0.0.1:8000/service/query -H "Content-Type: application/json" -d '{"question":"一键助眠是什么？"}' @@ -59,11 +62,11 @@ curl -X 'POST' http://127.0.0.1:8000/service/query -H "Content-Type: application ``` - **Agent简单对话** + ```bash curl -X 'POST' http://127.0.0.1:8000/service/query/agent -H "Content-Type: application/json" -d '{"question":"最近互联网公司有发生什么大新闻吗？"}' ``` - 2. Retrieval Batch评估 ```bash diff --git a/main.yaml b/main.yaml deleted file mode 100644 index da3c5562..00000000 --- a/main.yaml +++ /dev/null @@ -1,35 +0,0 @@ -name: PAI-RAG CI - -on: - pull_request: - # Sequence of patterns matched against refs/heads - branches: - - main - - master - - 'releases/**' - -jobs: - build: - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v4 - - name: Set up Python 3.10 - # This is the version of the action for setting up Python, not the Python version. - uses: actions/setup-python@v5 - with: - # Semantic version range syntax or exact version of a Python version - python-version: '3.10' - # Optional - x64 or x86 architecture, defaults to x64 - architecture: 'x64' - - - name: Install dependencies - run: | - python -m pip install --upgrade pip setuptools wheel - pip install poetry - poetry install - env: - POETRY_VIRTUALENVS_CREATE: false - - - name: Display Python version - run: python -c "import sys; print(sys.version)" \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 1f387376..4ac5642f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -193,13 +193,13 @@ alibabacloud_credentials = ">=0.2.0,<1.0.0" [[package]] name = "alibabacloud-gpdb20160503" -version = "3.7.0" +version = "3.8.0" description = "Alibaba Cloud AnalyticDB for PostgreSQL (20160503) SDK Library for Python" optional = false python-versions = ">=3.6" files = [ - {file = "alibabacloud_gpdb20160503-3.7.0-py3-none-any.whl", hash = "sha256:bbfe8e59d4d5ce219be091718a3a2145f55811d93aa75786d37cbfb318b7c4ff"}, - {file = "alibabacloud_gpdb20160503-3.7.0.tar.gz", hash = "sha256:981e6d07faa31c7b28652c648cc56a9da5e0e39f80103c0a88044051ac1ac14d"}, + {file = "alibabacloud_gpdb20160503-3.8.0-py3-none-any.whl", hash = "sha256:6f73bc37fc343aff7dafe09c7a3d0a0fcda70a51be04ae1558657f5710df72a2"}, + {file = "alibabacloud_gpdb20160503-3.8.0.tar.gz", hash = "sha256:dc8eb6bad5b3da4b262544c86b2769e0439ac7e2df0169be4b49cb827bfb7a59"}, ] [package.dependencies] @@ 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score: float metadata: Dict + class RetrievalResponse(BaseModel): docs: List[ContextDoc] -class KnowledgeInput(BaseModel): +class DataInput(BaseModel): file_path: str enable_qa_extraction: bool = False diff --git a/src/pai_rag/app/api/query.py b/src/pai_rag/app/api/query.py index c11c9dc1..60bd0d54 100644 --- a/src/pai_rag/app/api/query.py +++ b/src/pai_rag/app/api/query.py @@ -7,7 +7,7 @@ RetrievalQuery, RagResponse, LlmResponse, - KnowledgeInput, + DataInput, ) router = APIRouter() @@ -25,20 +25,22 @@ async def aquery_llm(query: LlmQuery) -> LlmResponse: @router.post("/query/retrieval") async def aquery_retrieval(query: RetrievalQuery): - return await rag_service.aquery_vectordb(query) + return await rag_service.aquery_retrieval(query) + @router.post("/query/agent") async def aquery_agent(query: LlmQuery) -> LlmResponse: return await rag_service.aquery_agent(query) + @router.patch("/config") async def aupdate(new_config: Any = Body(None)): rag_service.reload(new_config) return {"msg": "Update RAG configuration successfully."} -@router.post("/knowledge") -async def load_knowledge(input: KnowledgeInput): +@router.post("/data") +async def load_data(input: DataInput): await rag_service.add_knowledge( file_dir=input.file_path, enable_qa_extraction=input.enable_qa_extraction ) diff --git a/src/pai_rag/app/web/rag_client.py b/src/pai_rag/app/web/rag_client.py index 1be5b5d3..8d6ad92e 100644 --- a/src/pai_rag/app/web/rag_client.py +++ b/src/pai_rag/app/web/rag_client.py @@ -38,8 +38,8 @@ def config_url(self): return f"{self.endpoint}service/config" @property - def load_knowledge_url(self): - return f"{self.endpoint}service/knowledge" + def load_data_url(self): + return f"{self.endpoint}service/data" def query(self, text: str, session_id: str = None): q = dict(question=text) @@ -76,13 +76,18 @@ def query_vector(self, text: str): session_id = r.headers["x-session-id"] response = dotdict(json.loads(r.text)) response.session_id = session_id - formatted_text = "\n\n".join([f"""[Doc {i+1}] [score: {doc["score"]}]\n{doc["text"]}""" for i,doc in enumerate(response["docs"])]) + formatted_text = "\n\n".join( + [ + f"""[Doc {i+1}] [score: {doc["score"]}]\n{doc["text"]}""" + for i, doc in enumerate(response["docs"]) + ] + ) response["answer"] = formatted_text return response def add_knowledge(self, file_dir: str, enable_qa_extraction: bool): q = dict(file_path=file_dir, enable_qa_extraction=enable_qa_extraction) - r = requests.post(self.load_knowledge_url, json=q) + r = requests.post(self.load_data_url, json=q) r.raise_for_status() return diff --git a/src/pai_rag/app/web/ui.py b/src/pai_rag/app/web/ui.py index 1cc97753..1d06e420 100644 --- a/src/pai_rag/app/web/ui.py +++ b/src/pai_rag/app/web/ui.py @@ -12,7 +12,6 @@ ACCURATE_CONTENT_PROMPTS, PROMPT_MAP, ) -from os import environ import logging import traceback diff --git a/src/pai_rag/app/web/view_model.py b/src/pai_rag/app/web/view_model.py index acd7edbe..c833c4cd 100644 --- a/src/pai_rag/app/web/view_model.py +++ b/src/pai_rag/app/web/view_model.py @@ -89,7 +89,7 @@ class ViewModel(BaseModel): fusion_mode: str = "reciprocal_rerank" # [simple, reciprocal_rerank, dist_based_score, relative_score] query_rewrite_n: int = 1 - synthesizer_type:str = None + synthesizer_type: str = None text_qa_template: str = None @@ -161,9 +161,7 @@ def sync_app_config(self, config): self.chunk_size = config["node_parser"]["chunk_size"] self.chunk_overlap = config["node_parser"]["chunk_overlap"] - self.reader_type = config["data_reader"].get( - "type", self.reader_type - ) + self.reader_type = config["data_reader"].get("type", self.reader_type) self.enable_qa_extraction = config["data_reader"].get( "enable_qa_extraction", self.enable_qa_extraction ) @@ -221,7 +219,7 @@ def to_app_config(self): config["node_parser"]["type"] = self.parser_type config["node_parser"]["chunk_size"] = int(self.chunk_size) config["node_parser"]["chunk_overlap"] = int(self.chunk_overlap) - + config["data_reader"]["enable_qa_extraction"] = self.enable_qa_extraction config["data_reader"]["type"] = self.reader_type diff --git a/src/pai_rag/config/settings.toml b/src/pai_rag/config/settings.toml index 3b83f470..49a52513 100644 --- a/src/pai_rag/config/settings.toml +++ b/src/pai_rag/config/settings.toml @@ -4,6 +4,9 @@ dynaconf_merge = true name = "pai_rag" version = "0.1.1" +[rag.agent] +type = "react" + [rag.chat_engine] type = "CondenseQuestionChatEngine" @@ -60,8 +63,5 @@ query_rewrite_n = 1 # set to 1 to disable query generation type = "SimpleSummarize" text_qa_template = "参考内容信息如下\n---------------------\n{context_str}\n---------------------根据提供内容而非其他知识回答问题.\n问题: {query_str}\n答案: \n" -[rag.agent] -type = "react" - [rag.tool] type = ["calculator"] diff --git a/src/pai_rag/core/rag_application.py b/src/pai_rag/core/rag_application.py index 93456852..3ad33e7b 100644 --- a/src/pai_rag/core/rag_application.py +++ b/src/pai_rag/core/rag_application.py @@ -58,15 +58,20 @@ def reload(self, config): async def load_knowledge(self, file_dir, enable_qa_extraction=False): await self.data_loader.load(file_dir, enable_qa_extraction) - async def aquery_vectordb(self, query: RetrievalQuery) -> RetrievalResponse: + async def aquery_retrieval(self, query: RetrievalQuery) -> RetrievalResponse: if not query.question: - return RagResponse(answer="Empty query. Please input your question.") + return RetrievalResponse(docs=[]) session_id = correlation_id.get() or DEFAULT_SESSION_ID self.logger.info(f"Get session ID: {session_id}.") node_results = await self.retriever.aretrieve(query.question) - docs = [ContextDoc(text = score_node.node.get_content(), metadata=score_node.node.metadata, score=score_node.score) + docs = [ + ContextDoc( + text=score_node.node.get_content(), + metadata=score_node.node.metadata, + score=score_node.score, + ) for score_node in node_results ] return RetrievalResponse(docs=docs) @@ -106,7 +111,7 @@ async def aquery_llm(self, query: LlmQuery) -> LlmResponse: LlmResponse """ if not query.question: - return RagResponse(answer="Empty query. Please input your question.") + return LlmResponse(answer="Empty query. Please input your question.") session_id = correlation_id.get() or DEFAULT_SESSION_ID self.logger.info(f"Get session ID: {session_id}.") @@ -116,7 +121,7 @@ async def aquery_llm(self, query: LlmQuery) -> LlmResponse: response = await llm_chat_engine.achat(query.question) self.chat_store.persist() return LlmResponse(answer=response.response) - + async def aquery_agent(self, query: LlmQuery) -> LlmResponse: """Query answer from RAG App via web search asynchronously. @@ -128,10 +133,11 @@ async def aquery_agent(self, query: LlmQuery) -> LlmResponse: Returns: LlmResponse """ + if not query.question: + return LlmResponse(answer="Empty query. Please input your question.") + session_id = correlation_id.get() - self.logger.info( - f"Get session ID: {session_id}." - ) + self.logger.info(f"Get session ID: {session_id}.") response = await self.agent.achat(query.question) return LlmResponse(answer=response.response) diff --git a/src/pai_rag/core/rag_service.py b/src/pai_rag/core/rag_service.py index c27870b4..ada3d29a 100644 --- a/src/pai_rag/core/rag_service.py +++ b/src/pai_rag/core/rag_service.py @@ -60,13 +60,13 @@ async def aquery_llm(self, query: LlmQuery) -> LlmResponse: return await self.rag.aquery_llm(query) @trace_correlation_id - async def aquery_vectordb(self, query: RetrievalQuery): - return await self.rag.aquery_vectordb(query) + async def aquery_retrieval(self, query: RetrievalQuery): + return await self.rag.aquery_retrieval(query) @trace_correlation_id async def aquery_agent(self, query: LlmQuery) -> LlmResponse: return await self.rag.aquery_agent(query) - + @trace_correlation_id async def batch_evaluate_retrieval_and_response(self, type): return await self.rag.batch_evaluate_retrieval_and_response(type) diff --git a/src/pai_rag/evaluations/dataset_generation/generate_dataset.py b/src/pai_rag/evaluations/dataset_generation/generate_dataset.py index 036ef477..3bfcddc0 100644 --- a/src/pai_rag/evaluations/dataset_generation/generate_dataset.py +++ b/src/pai_rag/evaluations/dataset_generation/generate_dataset.py @@ -1,4 +1,5 @@ import logging +import os from pai_rag.core.rag_configuration import RagConfiguration from pai_rag.modules.module_registry import module_registry from llama_index.core.prompts.prompt_type import PromptType @@ -41,6 +42,9 @@ def __init__( num_questions_per_chunk=self.num_questions_per_chunk ) self.show_progress = show_progress + self.is_test_run = os.getenv("IS_PAI_RAG_CI_TEST") == "true" + if self.is_test_run: + self.nodes = self.nodes[:1] # Only logging.info("dataset generation initialized successfully.") diff --git a/src/pai_rag/modules/__init__.py b/src/pai_rag/modules/__init__.py index d4a3e31f..a23c466b 100644 --- a/src/pai_rag/modules/__init__.py +++ b/src/pai_rag/modules/__init__.py @@ -27,7 +27,7 @@ "ChatEngineFactoryModule", "LlmChatEngineFactoryModule", "AgentModule", - "ToolModule" + "ToolModule", ] __all__ = ALL_MODULES + ["ALL_MODULES"] diff --git a/src/pai_rag/modules/agent/agent.py b/src/pai_rag/modules/agent/agent.py index a6ee796b..51ea86d5 100644 --- a/src/pai_rag/modules/agent/agent.py +++ b/src/pai_rag/modules/agent/agent.py @@ -3,27 +3,29 @@ from llama_index.core.agent.react import ReActAgent from pai_rag.modules.base.configurable_module import ConfigurableModule from pai_rag.modules.base.module_constants import MODULE_PARAM_CONFIG + logger = logging.getLogger(__name__) -from llama_index.llms.dashscope import DashScope -from llama_index.core import Settings + class AgentModule(ConfigurableModule): @staticmethod def get_dependencies() -> List[str]: - return ["LlmModule", "ToolModule"] - + return ["LlmModule", "ToolModule"] + def _create_new_instance(self, new_params: Dict[str, Any]): config = new_params[MODULE_PARAM_CONFIG] llm = new_params["LlmModule"] func_tool = new_params["ToolModule"] type = config["type"].lower() if type == "react": - logger.info(f""" + logger.info( + f""" [Parameters][Agent] type = {config.get("type", "Unknown agent type")} - """) - agent = ReActAgent.from_tools(tools=func_tool, llm=llm, verbose=True) + """ + ) + agent = ReActAgent.from_tools(tools=func_tool, llm=llm, verbose=True) else: raise ValueError(f"Unknown Agent type: '{config['type']}'") - - return agent \ No newline at end of file + + return agent diff --git a/src/pai_rag/modules/module_registry.py b/src/pai_rag/modules/module_registry.py index f5fd0055..12c65ce3 100644 --- a/src/pai_rag/modules/module_registry.py +++ b/src/pai_rag/modules/module_registry.py @@ -15,7 +15,7 @@ "LlmChatEngineFactoryModule": "llm_chat_engine", "DataReaderFactoryModule": "data_reader", "AgentModule": "agent", - "ToolModule": "tool" + "ToolModule": "tool", } diff --git a/src/pai_rag/modules/tool/default_tool_description_template.py b/src/pai_rag/modules/tool/default_tool_description_template.py index 001d258f..6786f41a 100644 --- a/src/pai_rag/modules/tool/default_tool_description_template.py +++ b/src/pai_rag/modules/tool/default_tool_description_template.py @@ -18,20 +18,20 @@ DEFAULT_CALCULATE_MULTIPLY = """" This tool is designed to assist with a variety of numerical calculations where multiplication is required. It is particularly useful for scenarios such as age computation, financial calculations involving money, quantifying items, and any situation where the product of two integers is sought. The `multiply` function provided by this tool performs integer multiplication and is essential when accuracy and integer results are crucial. - + multiply(a: int, b: int) -> int Multiply two integers and returns the result as an integer. This function is ideal for tasks that need to calculate products of numerical values in an accurate and efficient manner. - + Args: a (int): The first integer factor in the multiplication. b (int): The second integer factor in the multiplication. - + Returns: int: The product of multiplying the two integers, suitable for numerical computations that rely on integer values. - + Raises: ValueError: If either 'a' or 'b' is not an integer, as non-integer inputs cannot be processed by the multiply function. - + Examples of use include multiplying quantities of items in inventory management, calculating the total cost from unit price and quantity in financial transactions, computing square footage, and many other practical applications where multiplication of integers is necessary. """ @@ -90,4 +90,4 @@ ValueError: If either 'a' or 'b' is not an integer, as the subtract function is strictly for integer arithmetic. Example uses of this tool include but are not limited to calculating age differences, determining the number of items sold from inventory, working out loan repayments, and any other context where subtraction of numerical values plays a key role. -""" \ No newline at end of file +""" diff --git a/src/pai_rag/modules/tool/load_and_search_tool_spec.py b/src/pai_rag/modules/tool/load_and_search_tool_spec.py index aea4215f..d9c84aa7 100644 --- a/src/pai_rag/modules/tool/load_and_search_tool_spec.py +++ b/src/pai_rag/modules/tool/load_and_search_tool_spec.py @@ -17,6 +17,7 @@ from datetime import datetime import hashlib + class LoadAndSearchToolSpec(BaseToolSpec): """Load and Search Tool. @@ -143,11 +144,10 @@ def load(self, *args: Any, **kwargs: Any) -> Any: # else: # self._index = self._index_cls.from_documents(docs, **self._index_kwargs) self._index = self._index_cls.from_documents(docs, **self._index_kwargs) - encoded_raw_text = datetime.now().time().isoformat().encode() folder_name = hashlib.sha256(encoded_raw_text).hexdigest() - tmp_persist_path = f'experiments/websearch/{folder_name}' + tmp_persist_path = f"experiments/websearch/{folder_name}" self._index.storage_context.persist(persist_dir=tmp_persist_path) return ( "Content loaded! You can now search the information using read_{}".format( diff --git a/src/pai_rag/modules/tool/tool.py b/src/pai_rag/modules/tool/tool.py index 8cd8befd..4dcf1de4 100644 --- a/src/pai_rag/modules/tool/tool.py +++ b/src/pai_rag/modules/tool/tool.py @@ -1,12 +1,17 @@ import logging -import os from typing import Dict, List, Any from llama_index.tools.google import GoogleSearchToolSpec from pai_rag.modules.base.configurable_module import ConfigurableModule from pai_rag.modules.base.module_constants import MODULE_PARAM_CONFIG from pai_rag.modules.tool.load_and_search_tool_spec import LoadAndSearchToolSpec from llama_index.core.tools import FunctionTool -from pai_rag.modules.tool.default_tool_description_template import * +from pai_rag.modules.tool.default_tool_description_template import ( + DEFAULT_GOOGLE_SEARCH_TOOL_DESP, + DEFAULT_CALCULATE_MULTIPLY, + DEFAULT_CALCULATE_ADD, + DEFAULT_CALCULATE_DIVIDE, + DEFAULT_CALCULATE_SUBTRACT, +) logger = logging.getLogger(__name__) diff --git a/tests/core/test_rag_application.py b/tests/core/test_rag_application.py index 384b2036..0e42a291 100644 --- a/tests/core/test_rag_application.py +++ b/tests/core/test_rag_application.py @@ -9,6 +9,8 @@ BASE_DIR = Path(__file__).parent.parent.parent TEST_INDEX_PATH = "localdata/teststorage" +EXPECTED_EMPTY_RESPONSE = """Empty query. Please input your question.""" + @pytest.fixture def rag_app(): @@ -25,6 +27,7 @@ def rag_app(): return rag_app +# Test load knowledge file async def test_add_knowledge_file(rag_app: RagApplication): data_dir = os.path.join(BASE_DIR, "tests/testdata/paul_graham") print(len(rag_app.index.docstore.docs)) @@ -33,7 +36,50 @@ async def test_add_knowledge_file(rag_app: RagApplication): assert len(rag_app.index.docstore.docs) > 0 +# Test rag query async def test_query(rag_app: RagApplication): - query = RagQuery(question="What did he do to learn computer science?") + query = RagQuery(question="Why did he decide to learn AI?") + response = await rag_app.aquery(query) + assert len(response.answer) > 10 + + query = RagQuery(question="") response = await rag_app.aquery(query) - print(response.answer) + assert response.answer == EXPECTED_EMPTY_RESPONSE + + +# Test llm query +async def test_llm(rag_app: RagApplication): + query = RagQuery(question="What is the result of 15+22?") + response = await rag_app.aquery_llm(query) + assert "37" in response.answer + + query = RagQuery(question="") + response = await rag_app.aquery_llm(query) + assert response.answer == EXPECTED_EMPTY_RESPONSE + + +# Test retrieval query +async def test_retrieval(rag_app: RagApplication): + retrieval_query = RagQuery(question="Why did he decide to learn AI?") + response = await rag_app.aquery_retrieval(retrieval_query) + assert len(response.docs) > 0 + + query = RagQuery(question="") + response = await rag_app.aquery_retrieval(query) + assert len(response.docs) == 0 + + +# Test agent query +async def test_agent(rag_app: RagApplication): + query = RagQuery(question="What is the result of 15+22?") + response = await rag_app.aquery_agent(query) + assert "37" in response.answer + + query = RagQuery(question="") + response = await rag_app.aquery_agent(query) + assert response.answer == EXPECTED_EMPTY_RESPONSE + + +async def test_batch_evaluate_retrieval_and_response(rag_app: RagApplication): + df, eval_result = await rag_app.batch_evaluate_retrieval_and_response(type="all") + print(eval_result)