Add optional ids filter to vector database query methods

- Updated query method signatures across multiple vector database implementations
- Added optional `ids` parameter to filter search results
- Consistent implementation across ChromaDB, Faiss, Milvus, MongoDB, NanoVectorDB, Oracle, Qdrant, and TiDB vector storage classes

lightrag/kg/chroma_impl.py

@@ -156,7 +156,9 @@ class ChromaVectorDBStorage(BaseVectorStorage):
             logger.error(f"Error during ChromaDB upsert: {str(e)}")
             raise
 
-    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+    async def query(
+        self, query: str, top_k: int, ids: list[str] | None = None
+    ) -> list[dict[str, Any]]:
         try:
             embedding = await self.embedding_func([query])
 

lightrag/kg/faiss_impl.py

@@ -171,7 +171,9 @@ class FaissVectorDBStorage(BaseVectorStorage):
         logger.info(f"Upserted {len(list_data)} vectors into Faiss index.")
         return [m["__id__"] for m in list_data]
 
-    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+    async def query(
+        self, query: str, top_k: int, ids: list[str] | 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

@@ -101,7 +101,9 @@ class MilvusVectorDBStorage(BaseVectorStorage):
         results = self._client.upsert(collection_name=self.namespace, data=list_data)
         return results
 
-    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+    async def query(
+        self, query: str, top_k: int, ids: list[str] | None = None
+    ) -> list[dict[str, Any]]:
         embedding = await self.embedding_func([query])
         results = self._client.search(
             collection_name=self.namespace,

lightrag/kg/mongo_impl.py

@@ -938,7 +938,9 @@ class MongoVectorDBStorage(BaseVectorStorage):
 
         return list_data
 
-    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+    async def query(
+        self, query: str, top_k: int, ids: list[str] | None = None
+    ) -> list[dict[str, Any]]:
         """Queries the vector database using Atlas Vector Search."""
         # Generate the embedding
         embedding = await self.embedding_func([query])

lightrag/kg/nano_vector_db_impl.py

@@ -120,7 +120,9 @@ class NanoVectorDBStorage(BaseVectorStorage):
                 f"embedding is not 1-1 with data, {len(embeddings)} != {len(list_data)}"
             )
 
-    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+    async def query(
+        self, query: str, top_k: int, ids: list[str] | None = None
+    ) -> list[dict[str, Any]]:
         # Execute embedding outside of lock to avoid long lock times
         embedding = await self.embedding_func([query])
         embedding = embedding[0]

lightrag/kg/oracle_impl.py

@@ -417,7 +417,9 @@ class OracleVectorDBStorage(BaseVectorStorage):
             self.db = None
 
     #################### query method ###############
-    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+    async def query(
+        self, query: str, top_k: int, ids: list[str] | None = None
+    ) -> list[dict[str, Any]]:
         embeddings = await self.embedding_func([query])
         embedding = embeddings[0]
         # 转换精度

lightrag/kg/qdrant_impl.py

@@ -123,7 +123,9 @@ class QdrantVectorDBStorage(BaseVectorStorage):
         )
         return results
 
-    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+    async def query(
+        self, query: str, top_k: int, ids: list[str] | None = None
+    ) -> list[dict[str, Any]]:
         embedding = await self.embedding_func([query])
         results = self._client.search(
             collection_name=self.namespace,

lightrag/kg/tidb_impl.py

@@ -306,7 +306,9 @@ class TiDBVectorDBStorage(BaseVectorStorage):
             await ClientManager.release_client(self.db)
             self.db = None
 
-    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
+    async def query(
+        self, query: str, top_k: int, ids: list[str] | None = None
+    ) -> list[dict[str, Any]]:
         """Search from tidb vector"""
         embeddings = await self.embedding_func([query])
         embedding = embeddings[0]