core: add kwargs support to VectorStore (#25934)

has been missing the passthrough until now

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/community/langchain_community/graph_vectorstores/base.py

@@ -855,7 +855,7 @@ class GraphVectorStoreRetriever(VectorStoreRetriever):
         return cast(GraphVectorStore, self.vectorstore)
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
     ) -> list[Document]:
         if self.search_type == "traversal":
             return list(
@@ -869,7 +869,11 @@ class GraphVectorStoreRetriever(VectorStoreRetriever):
             return super()._get_relevant_documents(query, run_manager=run_manager)
 
     async def _aget_relevant_documents(
-        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+        **kwargs: Any,
     ) -> list[Document]:
         if self.search_type == "traversal":
             return [

libs/community/langchain_community/vectorstores/redis/base.py

@@ -1459,59 +1459,59 @@ class RedisVectorStoreRetriever(VectorStoreRetriever):  # type: ignore[override]
     )
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
     ) -> List[Document]:
+        _kwargs = self.search_kwargs | kwargs
         if self.search_type == "similarity":
-            docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
+            docs = self.vectorstore.similarity_search(query, **_kwargs)
         elif self.search_type == "similarity_distance_threshold":
-            if self.search_kwargs["distance_threshold"] is None:
+            if _kwargs["distance_threshold"] is None:
                 raise ValueError(
                     "distance_threshold must be provided for "
                     + "similarity_distance_threshold retriever"
                 )
-            docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
+            docs = self.vectorstore.similarity_search(query, **_kwargs)
 
         elif self.search_type == "similarity_score_threshold":
             docs_and_similarities = (
                 self.vectorstore.similarity_search_with_relevance_scores(
-                    query, **self.search_kwargs
+                    query, **_kwargs
                 )
             )
             docs = [doc for doc, _ in docs_and_similarities]
         elif self.search_type == "mmr":
-            docs = self.vectorstore.max_marginal_relevance_search(
+            docs = self.vectorstore.max_marginal_relevance_search(query, **_kwargs)
-                query, **self.search_kwargs
-            )
         else:
             raise ValueError(f"search_type of {self.search_type} not allowed.")
         return docs
 
     async def _aget_relevant_documents(
-        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+        **kwargs: Any,
     ) -> List[Document]:
+        _kwargs = self.search_kwargs | kwargs
         if self.search_type == "similarity":
-            docs = await self.vectorstore.asimilarity_search(
+            docs = await self.vectorstore.asimilarity_search(query, **_kwargs)
-                query, **self.search_kwargs
-            )
         elif self.search_type == "similarity_distance_threshold":
-            if self.search_kwargs["distance_threshold"] is None:
+            if _kwargs["distance_threshold"] is None:
                 raise ValueError(
                     "distance_threshold must be provided for "
                     + "similarity_distance_threshold retriever"
                 )
-            docs = await self.vectorstore.asimilarity_search(
+            docs = await self.vectorstore.asimilarity_search(query, **_kwargs)
-                query, **self.search_kwargs
-            )
         elif self.search_type == "similarity_score_threshold":
             docs_and_similarities = (
                 await self.vectorstore.asimilarity_search_with_relevance_scores(
-                    query, **self.search_kwargs
+                    query, **_kwargs
                 )
             )
             docs = [doc for doc, _ in docs_and_similarities]
         elif self.search_type == "mmr":
             docs = await self.vectorstore.amax_marginal_relevance_search(
-                query, **self.search_kwargs
+                query, **_kwargs
             )
         else:
             raise ValueError(f"search_type of {self.search_type} not allowed.")

libs/community/langchain_community/vectorstores/vectara.py

@@ -745,9 +745,9 @@ class VectaraRetriever(VectorStoreRetriever):  # type: ignore[override]
     )
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
     ) -> List[Document]:
-        docs_and_scores = self.vectorstore.vectara_query(query, self.config)
+        docs_and_scores = self.vectorstore.vectara_query(query, self.config, **kwargs)
         return [doc for doc, _ in docs_and_scores]
 
     def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:

libs/community/tests/unit_tests/chains/test_pebblo_retrieval.py

@@ -2,7 +2,7 @@
 Unit tests for the PebbloRetrievalQA chain
 """
 
-from typing import List
+from typing import Any, List
 from unittest.mock import Mock
 
 import pytest
@@ -35,12 +35,16 @@ class FakeRetriever(VectorStoreRetriever):
     vectorstore: VectorStore = Mock()
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
     ) -> List[Document]:
         return [Document(page_content=query)]
 
     async def _aget_relevant_documents(
-        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+        **kwargs: Any,
     ) -> List[Document]:
         return [Document(page_content=query)]
 

libs/core/langchain_core/vectorstores/base.py

@@ -1057,7 +1057,9 @@ class VectorStoreRetriever(BaseRetriever):
     def _get_ls_params(self, **kwargs: Any) -> LangSmithRetrieverParams:
         """Get standard params for tracing."""
 
-        ls_params = super()._get_ls_params(**kwargs)
+        _kwargs = self.search_kwargs | kwargs
+
+        ls_params = super()._get_ls_params(**_kwargs)
         ls_params["ls_vector_store_provider"] = self.vectorstore.__class__.__name__
 
         if self.vectorstore.embeddings:
@@ -1074,43 +1076,45 @@ class VectorStoreRetriever(BaseRetriever):
         return ls_params
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
     ) -> list[Document]:
+        _kwargs = self.search_kwargs | kwargs
         if self.search_type == "similarity":
-            docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
+            docs = self.vectorstore.similarity_search(query, **_kwargs)
         elif self.search_type == "similarity_score_threshold":
             docs_and_similarities = (
                 self.vectorstore.similarity_search_with_relevance_scores(
-                    query, **self.search_kwargs
+                    query, **_kwargs
                 )
             )
             docs = [doc for doc, _ in docs_and_similarities]
         elif self.search_type == "mmr":
-            docs = self.vectorstore.max_marginal_relevance_search(
+            docs = self.vectorstore.max_marginal_relevance_search(query, **_kwargs)
-                query, **self.search_kwargs
-            )
         else:
             msg = f"search_type of {self.search_type} not allowed."
             raise ValueError(msg)
         return docs
 
     async def _aget_relevant_documents(
-        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+        **kwargs: Any,
     ) -> list[Document]:
+        _kwargs = self.search_kwargs | kwargs
         if self.search_type == "similarity":
-            docs = await self.vectorstore.asimilarity_search(
+            docs = await self.vectorstore.asimilarity_search(query, **_kwargs)
-                query, **self.search_kwargs
-            )
         elif self.search_type == "similarity_score_threshold":
             docs_and_similarities = (
                 await self.vectorstore.asimilarity_search_with_relevance_scores(
-                    query, **self.search_kwargs
+                    query, **_kwargs
                 )
             )
             docs = [doc for doc, _ in docs_and_similarities]
         elif self.search_type == "mmr":
             docs = await self.vectorstore.amax_marginal_relevance_search(
-                query, **self.search_kwargs
+                query, **_kwargs
             )
         else:
             msg = f"search_type of {self.search_type} not allowed."