feat (metadata): Added metadata parameter in query

Added metadata management for chunks in querying for all vdb, ONLY
posgres with pgvector has been fully implemented

lightrag/kg/faiss_impl.py

@@ -180,7 +180,7 @@ class FaissVectorDBStorage(BaseVectorStorage):
         return [m["__id__"] for m in list_data]
 
     async def query(
-        self, query: str, top_k: int, query_embedding: list[float] = None
+        self, query: str, top_k: int, query_embedding: list[float] = None, metadata_filter: dict[str, Any] | None = None
     ) -> list[dict[str, Any]]:
         """
         Search by a textual query; returns top_k results with their metadata + similarity distance.

lightrag/kg/milvus_impl.py

@@ -1047,7 +1047,7 @@ class MilvusVectorDBStorage(BaseVectorStorage):
         return results
 
     async def query(
-        self, query: str, top_k: int, query_embedding: list[float] = None
+        self, query: str, top_k: int, query_embedding: list[float] = None, metadata_filter: dict[str, Any] | None = None
     ) -> list[dict[str, Any]]:
         # Ensure collection is loaded before querying
         self._ensure_collection_loaded()

lightrag/kg/mongo_impl.py

@@ -2216,7 +2216,7 @@ class MongoVectorDBStorage(BaseVectorStorage):
         return list_data
 
     async def query(
-        self, query: str, top_k: int, query_embedding: list[float] = None
+        self, query: str, top_k: int, query_embedding: list[float] = None, metadata_filter: dict[str, Any] | None = None
     ) -> list[dict[str, Any]]:
         """Queries the vector database using Atlas Vector Search."""
         if query_embedding is not None:

lightrag/kg/nano_vector_db_impl.py

@@ -137,7 +137,7 @@ class NanoVectorDBStorage(BaseVectorStorage):
             )
 
     async def query(
-        self, query: str, top_k: int, query_embedding: list[float] = None
+        self, query: str, top_k: int, query_embedding: list[float] = None, metadata_filter: dict[str, Any] | None = None
     ) -> list[dict[str, Any]]:
         # Use provided embedding or compute it
         if query_embedding is not None:

lightrag/kg/qdrant_impl.py

@@ -212,7 +212,7 @@ class QdrantVectorDBStorage(BaseVectorStorage):
         return results
 
     async def query(
-        self, query: str, top_k: int, query_embedding: list[float] = None
+        self, query: str, top_k: int, query_embedding: list[float] = None, metadata_filter: dict[str, Any] | None = None
     ) -> list[dict[str, Any]]:
         if query_embedding is not None:
             embedding = query_embedding