Add PGVector Support

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)

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)

models/vector_database.py

@@ -9,6 +9,7 @@ class DatabaseType(Enum):
     pinecone = "pinecone"
     weaviate = "weaviate"
     astra = "astra"
+    pgvector = "pgvector"
 
 
 class VectorDatabase(BaseModel):

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"

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,
     }

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))