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feat (metadata): Added metadata parameter in query

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Added metadata management for chunks in querying for all vdb, ONLY
posgres with pgvector has been fully implemented
This commit is contained in:
Giulio Grassia 2025-09-24 16:55:56 +02:00
parent d0fba28e1f
commit 2728bb486e
5 changed files with 5 additions and 5 deletions
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2
lightrag/kg/faiss_impl.py
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@ -180,7 +180,7 @@ class FaissVectorDBStorage(BaseVectorStorage):
return [m["__id__"] for m in list_data] return [m["__id__"] for m in list_data]
async def query( 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]]: ) -> list[dict[str, Any]]:
""" """
Search by a textual query; returns top_k results with their metadata + similarity distance. Search by a textual query; returns top_k results with their metadata + similarity distance.

2
lightrag/kg/milvus_impl.py
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@ -1047,7 +1047,7 @@ class MilvusVectorDBStorage(BaseVectorStorage):
return results return results
async def query( 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]]: ) -> list[dict[str, Any]]:
# Ensure collection is loaded before querying # Ensure collection is loaded before querying
self._ensure_collection_loaded() self._ensure_collection_loaded()

2
lightrag/kg/mongo_impl.py
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@ -2216,7 +2216,7 @@ class MongoVectorDBStorage(BaseVectorStorage):
return list_data return list_data
async def query( 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]]: ) -> list[dict[str, Any]]:
"""Queries the vector database using Atlas Vector Search.""" """Queries the vector database using Atlas Vector Search."""
if query_embedding is not None: if query_embedding is not None:

2
lightrag/kg/nano_vector_db_impl.py
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@ -137,7 +137,7 @@ class NanoVectorDBStorage(BaseVectorStorage):
) )
async def query( 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]]: ) -> list[dict[str, Any]]:
# Use provided embedding or compute it # Use provided embedding or compute it
if query_embedding is not None: if query_embedding is not None:

2
lightrag/kg/qdrant_impl.py
View file

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