diff --git a/README.md b/README.md index 4bf1ba4c..d73474d2 100644 --- a/README.md +++ b/README.md @@ -166,5 +166,5 @@ Super-Rag comes with a built in REST API powered by FastApi. - Qdrant - Weaviate - Astra -- Supabase (coming soon) +- PGVector - Chroma (coming soon) diff --git a/api/delete.py b/api/delete.py index 493049da..51644b6b 100644 --- a/api/delete.py +++ b/api/delete.py @@ -3,6 +3,7 @@ from models.delete import RequestPayload, ResponsePayload from vectordbs import get_vector_service from vectordbs.base import BaseVectorDatabase +from utils.summarise import SUMMARY_SUFFIX router = APIRouter() @@ -16,8 +17,15 @@ async def delete(payload: RequestPayload): encoder=encoder, dimensions=payload.encoder.dimensions, ) + summary_vector_service: BaseVectorDatabase = get_vector_service( + index_name=f"{payload.index_name}{SUMMARY_SUFFIX}", + credentials=payload.vector_database, + encoder=encoder, + dimensions=payload.encoder.dimensions, + ) for file in payload.files: data = await vector_service.delete(file_url=file.url) + await summary_vector_service.delete(file_url=file.url) return ResponsePayload(success=True, data=data) diff --git a/models/vector_database.py b/models/vector_database.py index 6fc2cf8d..a74dd01a 100644 --- a/models/vector_database.py +++ b/models/vector_database.py @@ -9,6 +9,7 @@ class DatabaseType(Enum): pinecone = "pinecone" weaviate = "weaviate" astra = "astra" + pgvector = "pgvector" class VectorDatabase(BaseModel): diff --git a/poetry.lock b/poetry.lock index 6889e530..548ea750 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1039,6 +1039,22 @@ mccabe = ">=0.7.0,<0.8.0" pycodestyle = ">=2.11.0,<2.12.0" pyflakes = ">=3.2.0,<3.3.0" +[[package]] +name = "flupy" +version = "1.2.0" +description = "Method chaining built on generators" +optional = false +python-versions = "*" +files = [ + {file = "flupy-1.2.0.tar.gz", hash = "sha256:12487a008e9744cd35d0f6ea3cfa06f4b2b27cb138bf57d0788f5c26e57afe69"}, +] + +[package.dependencies] +typing_extensions 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(>=7.4.0)"] +[[package]] +name = "vecs" +version = "0.4.3" +description = "pgvector client" +optional = false +python-versions = "*" +files = [ + {file = "vecs-0.4.3.tar.gz", hash = "sha256:0a60294143aec43bd0344bb9235b6e57f8f919d102538f6b989d7b85095a31ce"}, +] + +[package.dependencies] +deprecated = "==1.2.*" +flupy = "==1.*" +pgvector = "==0.1.*" +psycopg2-binary = "==2.9.*" +sqlalchemy = "==2.*" + +[package.extras] +dev = ["numpy", "parse", "pytest", "pytest-cov"] +docs = ["mike", "mkdocs", "pygments", "pymarkdown", "pymdown-extensions"] +text-embedding = ["sentence-transformers (==2.*)"] + [[package]] name = "vulture" version = "2.11" @@ -4258,4 +4390,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "b9774decb9338d39235bb4599347540fa5a684cecd89d194a95b26257be43364" +content-hash = "a653f64fd5fa81106a858c03a2ef477c23e3a4d93954ce4e1491ced981ca7469" diff --git a/pyproject.toml b/pyproject.toml index 7263d176..eb796451 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,6 +33,7 @@ gunicorn = "^21.2.0" unstructured-client = "^0.18.0" unstructured = {extras = ["google-drive"], version = "^0.12.4"} tiktoken = "^0.6.0" +vecs = "^0.4.3" [tool.poetry.group.dev.dependencies] termcolor = "^2.4.0" diff --git a/vectordbs/__init__.py b/vectordbs/__init__.py index a54a58a5..00b4d920 100644 --- a/vectordbs/__init__.py +++ b/vectordbs/__init__.py @@ -10,6 +10,7 @@ from vectordbs.pinecone import PineconeService from vectordbs.qdrant import QdrantService from vectordbs.weaviate import WeaviateService +from vectordbs.pgvector import PGVectorService load_dotenv() @@ -26,6 +27,7 @@ def get_vector_service( "qdrant": QdrantService, "weaviate": WeaviateService, "astra": AstraService, + "pgvector": PGVectorService, # Add other providers here # e.g "weaviate": WeaviateVectorService, } diff --git a/vectordbs/pgvector.py b/vectordbs/pgvector.py new file mode 100644 index 00000000..e6b185ec --- /dev/null +++ b/vectordbs/pgvector.py @@ -0,0 +1,95 @@ +from typing import List + +import vecs +from semantic_router.encoders import BaseEncoder +from tqdm import tqdm + +from qdrant_client.http import models as rest +from models.delete import DeleteResponse +from models.document import BaseDocumentChunk +from vectordbs.base import BaseVectorDatabase + +MAX_QUERY_TOP_K = 5 + + +class PGVectorService(BaseVectorDatabase): + def __init__( + self, index_name: str, dimension: int, credentials: dict, encoder: BaseEncoder + ): + super().__init__( + index_name=index_name, + dimension=dimension, + credentials=credentials, + encoder=encoder, + ) + client = vecs.create_client(connection_string=credentials["database_uri"]) + self.collection = client.get_or_create_collection( + name=self.index_name, + dimension=dimension, + ) + + # TODO: remove this + async def convert_to_rerank_format(self, chunks: List[rest.PointStruct]): + docs = [ + { + "content": chunk.payload.get("content"), + "page_label": chunk.payload.get("page_label"), + "file_url": chunk.payload.get("file_url"), + } + for chunk in chunks + ] + return docs + + async def upsert(self, chunks: List[BaseDocumentChunk]) -> None: + records = [] + for chunk in tqdm(chunks, desc="Upserting to PGVector"): + records.append( + ( + chunk.id, + chunk.dense_embedding, + { + "document_id": chunk.document_id, + "content": chunk.content, + "doc_url": chunk.doc_url, + **(chunk.metadata if chunk.metadata else {}), + }, + ) + ) + self.collection.upsert(records) + self.collection.create_index() + + async def query(self, input: str, top_k: int = MAX_QUERY_TOP_K) -> List: + vectors = await self._generate_vectors(input=input) + + results = self.collection.query( + data=vectors[0], + limit=top_k, + include_metadata=True, + include_value=False, + ) + + chunks = [] + + for result in results: + ( + id, + metadata, + ) = result + + chunks.append( + BaseDocumentChunk( + id=id, + source_type=metadata.get("filetype"), + source=metadata.get("doc_url"), + document_id=metadata.get("document_id"), + content=metadata.get("content"), + doc_url=metadata.get("doc_url"), + page_number=metadata.get("page_number"), + metadata={**metadata}, + ) + ) + return chunks + + async def delete(self, file_url: str) -> None: + deleted = self.collection.delete(filters={"doc_url": {"$eq": file_url}}) + return DeleteResponse(num_of_deleted_chunks=len(deleted))