Remove deprecated ID-based filtering from vector storage queries

- Remove ids param from QueryParam
- Simplify BaseVectorStorage.query signature
- Update all vector storage implementations
- Streamline PostgreSQL query templates
- Remove ID filtering from operate.py calls

lightrag/base.py

@@ -142,10 +142,6 @@ class QueryParam:
     history_turns: int = int(os.getenv("HISTORY_TURNS", str(DEFAULT_HISTORY_TURNS)))
     """Number of complete conversation turns (user-assistant pairs) to consider in the response context."""
 
-    # TODO: TODO: Deprecated - ID-based filtering only applies to chunks, not entities or relations, and implemented only in PostgreSQL storage
-    ids: list[str] | None = None
-    """List of doc ids to filter the results."""
-
     model_func: Callable[..., object] | None = None
     """Optional override for the LLM model function to use for this specific query.
     If provided, this will be used instead of the global model function.
@@ -215,9 +211,7 @@ class BaseVectorStorage(StorageNameSpace, ABC):
     meta_fields: set[str] = field(default_factory=set)
 
     @abstractmethod
-    async def query(
-        self, query: str, top_k: int, ids: list[str] | None = None
-    ) -> list[dict[str, Any]]:
+    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
         """Query the vector storage and retrieve top_k results."""
 
     @abstractmethod

lightrag/kg/faiss_impl.py

@@ -179,9 +179,7 @@ class FaissVectorDBStorage(BaseVectorStorage):
         )
         return [m["__id__"] for m in list_data]
 
-    async def query(
-        self, query: str, top_k: int, ids: list[str] | None = None
-    ) -> list[dict[str, Any]]:
+    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
         """
         Search by a textual query; returns top_k results with their metadata + similarity distance.
         """

lightrag/kg/milvus_impl.py

@@ -1046,9 +1046,7 @@ class MilvusVectorDBStorage(BaseVectorStorage):
         )
         return results
 
-    async def query(
-        self, query: str, top_k: int, ids: list[str] | None = None
-    ) -> list[dict[str, Any]]:
+    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
         # Ensure collection is loaded before querying
         self._ensure_collection_loaded()
 

lightrag/kg/mongo_impl.py

@@ -1809,9 +1809,7 @@ class MongoVectorDBStorage(BaseVectorStorage):
 
         return list_data
 
-    async def query(
-        self, query: str, top_k: int, ids: list[str] | None = None
-    ) -> list[dict[str, Any]]:
+    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
         """Queries the vector database using Atlas Vector Search."""
         # Generate the embedding
         embedding = await self.embedding_func(

lightrag/kg/nano_vector_db_impl.py

@@ -136,9 +136,7 @@ class NanoVectorDBStorage(BaseVectorStorage):
                 f"[{self.workspace}] embedding is not 1-1 with data, {len(embeddings)} != {len(list_data)}"
             )
 
-    async def query(
-        self, query: str, top_k: int, ids: list[str] | None = None
-    ) -> list[dict[str, Any]]:
+    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
         # Execute embedding outside of lock to avoid improve cocurrent
         embedding = await self.embedding_func(
             [query], _priority=5

lightrag/kg/postgres_impl.py

@@ -2004,19 +2004,16 @@ class PGVectorStorage(BaseVectorStorage):
             await self.db.execute(upsert_sql, data)
 
     #################### query method ###############
-    async def query(
-        self, query: str, top_k: int, ids: list[str] | None = None
-    ) -> list[dict[str, Any]]:
+    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
         embeddings = await self.embedding_func(
             [query], _priority=5
         )  # higher priority for query
         embedding = embeddings[0]
         embedding_string = ",".join(map(str, embedding))
-        # Use parameterized document IDs (None means search across all documents)
+
         sql = SQL_TEMPLATES[self.namespace].format(embedding_string=embedding_string)
         params = {
             "workspace": self.workspace,
-            "doc_ids": ids,
             "closer_than_threshold": 1 - self.cosine_better_than_threshold,
             "top_k": top_k,
         }
@@ -4582,85 +4579,34 @@ SQL_TEMPLATES = {
                       update_time = EXCLUDED.update_time
                      """,
     "relationships": """
-                     WITH relevant_chunks AS (SELECT id as chunk_id
-                                              FROM LIGHTRAG_VDB_CHUNKS
-                                              WHERE $2
-                         :: varchar [] IS NULL OR full_doc_id = ANY ($2:: varchar [])
-                         )
-                        , rc AS (
-                     SELECT array_agg(chunk_id) AS chunk_arr
-                     FROM relevant_chunks
-                         ), cand AS (
-                     SELECT
-                         r.id, r.source_id AS src_id, r.target_id AS tgt_id, r.chunk_ids, r.create_time, r.content_vector <=> '[{embedding_string}]'::vector AS dist
+                     SELECT r.source_id AS src_id,
+                            r.target_id AS tgt_id,
+                            EXTRACT(EPOCH FROM r.create_time)::BIGINT AS created_at
                      FROM LIGHTRAG_VDB_RELATION r
                      WHERE r.workspace = $1
+                       AND r.content_vector <=> '[{embedding_string}]'::vector < $2
                      ORDER BY r.content_vector <=> '[{embedding_string}]'::vector
-                         LIMIT ($4 * 50)
-                         )
-                     SELECT c.src_id,
-                            c.tgt_id,
-                            EXTRACT(EPOCH FROM c.create_time) ::BIGINT AS created_at
-                     FROM cand c
-                              JOIN rc ON TRUE
-                     WHERE c.dist < $3
-                       AND c.chunk_ids && (rc.chunk_arr::varchar[])
-                     ORDER BY c.dist, c.id
-                         LIMIT $4;
+                     LIMIT $3;
                      """,
     "entities": """
-                WITH relevant_chunks AS (SELECT id as chunk_id
-                                         FROM LIGHTRAG_VDB_CHUNKS
-                                         WHERE $2
-                    :: varchar [] IS NULL OR full_doc_id = ANY ($2:: varchar [])
-                    )
-                   , rc AS (
-                SELECT array_agg(chunk_id) AS chunk_arr
-                FROM relevant_chunks
-                    ), cand AS (
-                SELECT
-                    e.id, e.entity_name, e.chunk_ids, e.create_time, e.content_vector <=> '[{embedding_string}]'::vector AS dist
+                SELECT e.entity_name,
+                       EXTRACT(EPOCH FROM e.create_time)::BIGINT AS created_at
                 FROM LIGHTRAG_VDB_ENTITY e
                 WHERE e.workspace = $1
+                  AND e.content_vector <=> '[{embedding_string}]'::vector < $2
                 ORDER BY e.content_vector <=> '[{embedding_string}]'::vector
-                    LIMIT ($4 * 50)
-                    )
-                SELECT c.entity_name,
-                       EXTRACT(EPOCH FROM c.create_time) ::BIGINT AS created_at
-                FROM cand c
-                         JOIN rc ON TRUE
-                WHERE c.dist < $3
-                  AND c.chunk_ids && (rc.chunk_arr::varchar[])
-                ORDER BY c.dist, c.id
-                    LIMIT $4;
+                LIMIT $3;
                 """,
     "chunks": """
-              WITH relevant_chunks AS (SELECT id as chunk_id
-                                       FROM LIGHTRAG_VDB_CHUNKS
-                                       WHERE $2
-                  :: varchar [] IS NULL OR full_doc_id = ANY ($2:: varchar [])
-                  )
-                 , rc AS (
-              SELECT array_agg(chunk_id) AS chunk_arr
-              FROM relevant_chunks
-                  ), cand AS (
-              SELECT
-                  id, content, file_path, create_time, content_vector <=> '[{embedding_string}]'::vector AS dist
-              FROM LIGHTRAG_VDB_CHUNKS
-              WHERE workspace = $1
-              ORDER BY content_vector <=> '[{embedding_string}]'::vector
-                  LIMIT ($4 * 50)
-                  )
               SELECT c.id,
                      c.content,
                      c.file_path,
-                     EXTRACT(EPOCH FROM c.create_time) ::BIGINT AS created_at
-              FROM cand c
-                       JOIN rc ON TRUE
-              WHERE c.dist < $3
-                AND c.id = ANY (rc.chunk_arr)
-              ORDER BY c.dist, c.id
-                  LIMIT $4;
+                     EXTRACT(EPOCH FROM c.create_time)::BIGINT AS created_at
+              FROM LIGHTRAG_VDB_CHUNKS c
+              WHERE c.workspace = $1
+                AND c.content_vector <=> '[{embedding_string}]'::vector < $2
+              ORDER BY c.content_vector <=> '[{embedding_string}]'::vector
+              LIMIT $3;
               """,
     # DROP tables
     "drop_specifiy_table_workspace": """

lightrag/kg/qdrant_impl.py

@@ -199,9 +199,7 @@ class QdrantVectorDBStorage(BaseVectorStorage):
         )
         return results
 
-    async def query(
-        self, query: str, top_k: int, ids: list[str] | None = None
-    ) -> list[dict[str, Any]]:
+    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
         embedding = await self.embedding_func(
             [query], _priority=5
         )  # higher priority for query

lightrag/operate.py

@@ -2253,7 +2253,7 @@ async def _get_vector_context(
         # Use chunk_top_k if specified, otherwise fall back to top_k
         search_top_k = query_param.chunk_top_k or query_param.top_k
 
-        results = await chunks_vdb.query(query, top_k=search_top_k, ids=query_param.ids)
+        results = await chunks_vdb.query(query, top_k=search_top_k)
         if not results:
             logger.info(f"Naive query: 0 chunks (chunk_top_k: {search_top_k})")
             return []
@@ -2830,9 +2830,7 @@ async def _get_node_data(
         f"Query nodes: {query}, top_k: {query_param.top_k}, cosine: {entities_vdb.cosine_better_than_threshold}"
     )
 
-    results = await entities_vdb.query(
-        query, top_k=query_param.top_k, ids=query_param.ids
-    )
+    results = await entities_vdb.query(query, top_k=query_param.top_k)
 
     if not len(results):
         return [], []
@@ -3108,9 +3106,7 @@ async def _get_edge_data(
         f"Query edges: {keywords}, top_k: {query_param.top_k}, cosine: {relationships_vdb.cosine_better_than_threshold}"
     )
 
-    results = await relationships_vdb.query(
-        keywords, top_k=query_param.top_k, ids=query_param.ids
-    )
+    results = await relationships_vdb.query(keywords, top_k=query_param.top_k)
 
     if not len(results):
         return [], []