Rm retriever kwargs (#7013)

Doesn't actually limit the Retriever interface but hopefully in practice
it does

docs/snippets/modules/data_connection/retrievers/how_to/custom_retriever.mdx

@@ -14,9 +14,10 @@ from langchain.callbacks.manager import (
 )
 
 class BaseRetriever(ABC):
+
     @abstractmethod
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> List[Document]:
         """Get documents relevant to a query.
         Args:
@@ -32,7 +33,6 @@ class BaseRetriever(ABC):
         query: str,
         *,
         run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Asynchronously get documents relevant to a query.
         Args:
@@ -110,7 +110,7 @@ class NumpyRetriever(BaseRetriever):
         ]
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> List[Document]:
         """Get documents relevant to a query.
         Args:
@@ -127,7 +127,6 @@ class NumpyRetriever(BaseRetriever):
         query: str,
         *,
         run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Asynchronously get documents relevant to a query.
         Args:

langchain/retrievers/arxiv.py

@@ -1,4 +1,4 @@
-from typing import Any, List
+from typing import List
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -16,19 +16,11 @@ class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
     """
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         return self.load(query=query)
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/azure_cognitive_search.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import json
-from typing import Any, Dict, List, Optional
+from typing import Dict, List, Optional
 
 import aiohttp
 import requests
@@ -87,11 +87,7 @@ class AzureCognitiveSearchRetriever(BaseRetriever, BaseModel):
         return response_json["value"]
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         search_results = self._search(query)
 
@@ -101,11 +97,7 @@ class AzureCognitiveSearchRetriever(BaseRetriever, BaseModel):
         ]
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         search_results = await self._asearch(query)
 

langchain/retrievers/chatgpt_plugin_retriever.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, List, Optional
+from typing import List, Optional
 
 import aiohttp
 import requests
@@ -26,11 +26,7 @@ class ChatGPTPluginRetriever(BaseRetriever, BaseModel):
         arbitrary_types_allowed = True
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         url, json, headers = self._create_request(query)
         response = requests.post(url, json=json, headers=headers)
@@ -45,11 +41,7 @@ class ChatGPTPluginRetriever(BaseRetriever, BaseModel):
         return docs
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         url, json, headers = self._create_request(query)
 

langchain/retrievers/databerry.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional
+from typing import List, Optional
 
 import aiohttp
 import requests
@@ -28,11 +28,7 @@ class DataberryRetriever(BaseRetriever):
         self.top_k = top_k
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         response = requests.post(
             self.datastore_url,
@@ -59,11 +55,7 @@ class DataberryRetriever(BaseRetriever):
         ]
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         async with aiohttp.ClientSession() as session:
             async with session.request(

langchain/retrievers/docarray.py

@@ -56,8 +56,8 @@ class DocArrayRetriever(BaseRetriever, BaseModel):
     def _get_relevant_documents(
         self,
         query: str,
+        *,
         run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Get documents relevant for a query.
 
@@ -213,7 +213,7 @@ class DocArrayRetriever(BaseRetriever, BaseModel):
     async def _aget_relevant_documents(
         self,
         query: str,
+        *,
         run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/elastic_search_bm25.py

@@ -117,11 +117,7 @@ class ElasticSearchBM25Retriever(BaseRetriever):
         return ids
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         query_dict = {"query": {"match": {"content": query}}}
         res = self.client.search(index=self.index_name, body=query_dict)
@@ -132,10 +128,6 @@ class ElasticSearchBM25Retriever(BaseRetriever):
         return docs
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/kendra.py

@@ -264,8 +264,8 @@ class AmazonKendraRetriever(BaseRetriever):
     def _get_relevant_documents(
         self,
         query: str,
+        *,
         run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Run search on Kendra index and get top k documents
 
@@ -281,7 +281,7 @@ class AmazonKendraRetriever(BaseRetriever):
     async def _aget_relevant_documents(
         self,
         query: str,
+        *,
         run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError("Async version is not implemented for Kendra yet.")

langchain/retrievers/knn.py

@@ -56,11 +56,7 @@ class KNNRetriever(BaseRetriever, BaseModel):
         return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         query_embeds = np.array(self.embeddings.embed_query(query))
         # calc L2 norm
@@ -84,10 +80,6 @@ class KNNRetriever(BaseRetriever, BaseModel):
         return top_k_results
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/llama_index.py

@@ -16,11 +16,7 @@ class LlamaIndexRetriever(BaseRetriever, BaseModel):
     query_kwargs: Dict = Field(default_factory=dict)
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Get documents relevant for a query."""
         try:
@@ -44,11 +40,7 @@ class LlamaIndexRetriever(BaseRetriever, BaseModel):
         return docs
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: Optional[AsyncCallbackManagerForRetrieverRun]
-        query: str,
-        *,
-        run_manager: Optional[AsyncCallbackManagerForRetrieverRun] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError("LlamaIndexRetriever does not support async")
 
@@ -60,11 +52,7 @@ class LlamaIndexGraphRetriever(BaseRetriever, BaseModel):
     query_configs: List[Dict] = Field(default_factory=list)
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Get documents relevant for a query."""
         try:
@@ -96,10 +84,6 @@ class LlamaIndexGraphRetriever(BaseRetriever, BaseModel):
         return docs
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError("LlamaIndexGraphRetriever does not support async")

langchain/retrievers/merger_retriever.py

@@ -1,4 +1,4 @@
-from typing import Any, List
+from typing import List
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -31,8 +31,8 @@ class MergerRetriever(BaseRetriever):
     def _get_relevant_documents(
         self,
         query: str,
+        *,
         run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """
         Get the relevant documents for a given query.
@@ -52,8 +52,8 @@ class MergerRetriever(BaseRetriever):
     async def _aget_relevant_documents(
         self,
         query: str,
+        *,
         run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """
         Asynchronously get the relevant documents for a given query.

langchain/retrievers/metal.py

@@ -22,11 +22,7 @@ class MetalRetriever(BaseRetriever):
         self.params = params or {}
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         results = self.client.search({"text": query}, **self.params)
         final_results = []
@@ -36,10 +32,6 @@ class MetalRetriever(BaseRetriever):
         return final_results
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/multi_query.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, List
+from typing import List
 
 from pydantic import BaseModel, Field
 
@@ -98,8 +98,8 @@ class MultiQueryRetriever(BaseRetriever):
     def _get_relevant_documents(
         self,
         query: str,
+        *,
         run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Get relevated documents given a user query.
 
@@ -117,8 +117,8 @@ class MultiQueryRetriever(BaseRetriever):
     async def _aget_relevant_documents(
         self,
         query: str,
+        *,
         run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError
 

langchain/retrievers/pinecone_hybrid_search.py

@@ -143,11 +143,7 @@ class PineconeHybridSearchRetriever(BaseRetriever, BaseModel):
         return values
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         from pinecone_text.hybrid import hybrid_convex_scale
 
@@ -174,10 +170,6 @@ class PineconeHybridSearchRetriever(BaseRetriever, BaseModel):
         return final_result
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/pubmed.py

@@ -1,5 +1,5 @@
 """A retriever that uses PubMed API to retrieve documents."""
-from typing import Any, List
+from typing import List
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -17,19 +17,11 @@ class PubMedRetriever(BaseRetriever, PubMedAPIWrapper):
     """
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         return self.load_docs(query=query)
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/remote_retriever.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional
+from typing import List, Optional
 
 import aiohttp
 import requests
@@ -20,11 +20,7 @@ class RemoteLangChainRetriever(BaseRetriever, BaseModel):
     metadata_key: str = "metadata"
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         response = requests.post(
             self.url, json={self.input_key: query}, headers=self.headers
@@ -38,11 +34,7 @@ class RemoteLangChainRetriever(BaseRetriever, BaseModel):
         ]
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         async with aiohttp.ClientSession() as session:
             async with session.request(

langchain/retrievers/self_query/base.py

@@ -84,11 +84,7 @@ class SelfQueryRetriever(BaseRetriever, BaseModel):
         return values
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Get documents relevant for a query.
 
@@ -121,11 +117,7 @@ class SelfQueryRetriever(BaseRetriever, BaseModel):
         return docs
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: Optional[AsyncCallbackManagerForRetrieverRun]
-        query: str,
-        *,
-        run_manager: Optional[AsyncCallbackManagerForRetrieverRun],
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError
 

langchain/retrievers/svm.py

@@ -55,11 +55,7 @@ class SVMRetriever(BaseRetriever, BaseModel):
         return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         from sklearn import svm
 
@@ -98,10 +94,6 @@ class SVMRetriever(BaseRetriever, BaseModel):
         return top_k_results
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/tfidf.py

@@ -64,11 +64,7 @@ class TFIDFRetriever(BaseRetriever, BaseModel):
         )
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         from sklearn.metrics.pairwise import cosine_similarity
 
@@ -82,10 +78,6 @@ class TFIDFRetriever(BaseRetriever, BaseModel):
         return return_docs
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/time_weighted_retriever.py

@@ -86,11 +86,7 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
         return results
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Return documents that are relevant to the query."""
         current_time = datetime.datetime.now()
@@ -115,11 +111,7 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
         return result
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Return documents that are relevant to the query."""
         raise NotImplementedError

langchain/retrievers/vespa_retriever.py

@@ -65,22 +65,14 @@ class VespaRetriever(BaseRetriever):
         return docs
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         body = self._query_body.copy()
         body["query"] = query
         return self._query(body)
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError
 

langchain/retrievers/weaviate_hybrid_search.py

@@ -93,7 +93,6 @@ class WeaviateHybridSearchRetriever(BaseRetriever):
         *,
         run_manager: CallbackManagerForRetrieverRun,
         where_filter: Optional[Dict[str, object]] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         """Look up similar documents in Weaviate."""
         query_obj = self._client.query.get(self._index_name, self._query_attrs)
@@ -112,11 +111,6 @@ class WeaviateHybridSearchRetriever(BaseRetriever):
         return docs
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        where_filter: Optional[Dict[str, object]] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/wikipedia.py

@@ -1,4 +1,4 @@
-from typing import Any, List
+from typing import List
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -16,19 +16,11 @@ class WikipediaRetriever(BaseRetriever, WikipediaAPIWrapper):
     """
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         return self.load(query=query)
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError

langchain/retrievers/zep.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from typing import TYPE_CHECKING, Dict, List, Optional
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
@@ -64,7 +64,6 @@ class ZepRetriever(BaseRetriever):
         *,
         run_manager: CallbackManagerForRetrieverRun,
         metadata: Optional[Dict] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         from zep_python import MemorySearchPayload
 
@@ -84,7 +83,6 @@ class ZepRetriever(BaseRetriever):
         *,
         run_manager: AsyncCallbackManagerForRetrieverRun,
         metadata: Optional[Dict] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         from zep_python import MemorySearchPayload
 

langchain/schema/retriever.py

@@ -44,7 +44,6 @@ class BaseRetriever(ABC):
 
                 async def aget_relevant_documents(self, query: str) -> List[Document]:
                     raise NotImplementedError
-
     """  # noqa: E501
 
     _new_arg_supported: bool = False
@@ -86,27 +85,23 @@ class BaseRetriever(ABC):
 
     @abstractmethod
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> List[Document]:
         """Get documents relevant to a query.
         Args:
-            query: String to find relevant documents for.
+            query: String to find relevant documents for
-            run_manager: The callbacks handler to use.
+            run_manager: The callbacks handler to use
         Returns:
             List of relevant documents
         """
 
     @abstractmethod
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         """Asynchronously get documents relevant to a query.
         Args:
-            query: string to find relevant documents for
+            query: String to find relevant documents for
             run_manager: The callbacks handler to use
         Returns:
             List of relevant documents
@@ -117,8 +112,8 @@ class BaseRetriever(ABC):
     ) -> List[Document]:
         """Retrieve documents relevant to a query.
         Args:
-            query: String to find relevant documents for.
+            query: string to find relevant documents for
-            callbacks: Callback manager or list of callbacks.
+            callbacks: Callback manager or list of callbacks
         Returns:
             List of relevant documents
         """
@@ -132,14 +127,13 @@ class BaseRetriever(ABC):
             **kwargs,
         )
         try:
+            _kwargs = kwargs if self._expects_other_args else {}
             if self._new_arg_supported:
                 result = self._get_relevant_documents(
-                    query, run_manager=run_manager, **kwargs
+                    query, run_manager=run_manager, **_kwargs
                 )
-            elif self._expects_other_args:
-                result = self._get_relevant_documents(query, **kwargs)
             else:
-                result = self._get_relevant_documents(query)  # type: ignore[call-arg]
+                result = self._get_relevant_documents(query, **_kwargs)
         except Exception as e:
             run_manager.on_retriever_error(e)
             raise e
@@ -170,16 +164,13 @@ class BaseRetriever(ABC):
             **kwargs,
         )
         try:
+            _kwargs = kwargs if self._expects_other_args else {}
             if self._new_arg_supported:
                 result = await self._aget_relevant_documents(
-                    query, run_manager=run_manager, **kwargs
+                    query, run_manager=run_manager, **_kwargs
                 )
-            elif self._expects_other_args:
-                result = await self._aget_relevant_documents(query, **kwargs)
             else:
-                result = await self._aget_relevant_documents(
+                result = await self._aget_relevant_documents(query, **_kwargs)
-                    query,  # type: ignore[call-arg]
-                )
         except Exception as e:
             await run_manager.on_retriever_error(e)
             raise e

langchain/vectorstores/azuresearch.py

@@ -497,8 +497,8 @@ class AzureSearchVectorStoreRetriever(BaseRetriever, BaseModel):
     def _get_relevant_documents(
         self,
         query: str,
+        *,
         run_manager: CallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         if self.search_type == "similarity":
             docs = self.vectorstore.vector_search(query, k=self.k)
@@ -513,8 +513,8 @@ class AzureSearchVectorStoreRetriever(BaseRetriever, BaseModel):
     async def _aget_relevant_documents(
         self,
         query: str,
+        *,
         run_manager: AsyncCallbackManagerForRetrieverRun,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError(
             "AzureSearchVectorStoreRetriever does not support async"

langchain/vectorstores/base.py

@@ -408,11 +408,7 @@ class VectorStoreRetriever(BaseRetriever, BaseModel):
         return values
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: Optional[CallbackManagerForRetrieverRun]
-        query: str,
-        *,
-        run_manager: Optional[CallbackManagerForRetrieverRun] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         if self.search_type == "similarity":
             docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
@@ -432,11 +428,7 @@ class VectorStoreRetriever(BaseRetriever, BaseModel):
         return docs
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: Optional[AsyncCallbackManagerForRetrieverRun]
-        query: str,
-        *,
-        run_manager: Optional[AsyncCallbackManagerForRetrieverRun] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         if self.search_type == "similarity":
             docs = await self.vectorstore.asimilarity_search(

langchain/vectorstores/redis.py

@@ -620,11 +620,7 @@ class RedisVectorStoreRetriever(VectorStoreRetriever, BaseModel):
         return values
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: Optional[CallbackManagerForRetrieverRun]
-        query: str,
-        *,
-        run_manager: Optional[CallbackManagerForRetrieverRun] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         if self.search_type == "similarity":
             docs = self.vectorstore.similarity_search(query, k=self.k)
@@ -637,11 +633,7 @@ class RedisVectorStoreRetriever(VectorStoreRetriever, BaseModel):
         return docs
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: Optional[AsyncCallbackManagerForRetrieverRun]
-        query: str,
-        *,
-        run_manager: Optional[AsyncCallbackManagerForRetrieverRun] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError("RedisVectorStoreRetriever does not support async")
 

langchain/vectorstores/singlestoredb.py

@@ -451,11 +451,7 @@ class SingleStoreDBRetriever(VectorStoreRetriever):
     allowed_search_types: ClassVar[Collection[str]] = ("similarity",)
 
     def _get_relevant_documents(
-        self,
+        self, query: str, *, run_manager: Optional[CallbackManagerForRetrieverRun]
-        query: str,
-        *,
-        run_manager: Optional[CallbackManagerForRetrieverRun] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         if self.search_type == "similarity":
             docs = self.vectorstore.similarity_search(query, k=self.k)
@@ -464,11 +460,7 @@ class SingleStoreDBRetriever(VectorStoreRetriever):
         return docs
 
     async def _aget_relevant_documents(
-        self,
+        self, query: str, *, run_manager: Optional[AsyncCallbackManagerForRetrieverRun]
-        query: str,
-        *,
-        run_manager: Optional[AsyncCallbackManagerForRetrieverRun] = None,
-        **kwargs: Any,
     ) -> List[Document]:
         raise NotImplementedError(
             "SingleStoreDBVectorStoreRetriever does not support async"

tests/integration_tests/chains/test_retrieval_qa.py

@@ -7,18 +7,19 @@ from langchain.document_loaders import TextLoader
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.llms import OpenAI
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import Chroma
+from langchain.vectorstores import FAISS
 
 
 def test_retrieval_qa_saving_loading(tmp_path: Path) -> None:
     """Test saving and loading."""
-    loader = TextLoader("docs/modules/state_of_the_union.txt")
+    loader = TextLoader("docs/extras/modules/state_of_the_union.txt")
     documents = loader.load()
     text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
     texts = text_splitter.split_documents(documents)
     embeddings = OpenAIEmbeddings()
-    docsearch = Chroma.from_documents(texts, embeddings)
+    docsearch = FAISS.from_documents(texts, embeddings)
     qa = RetrievalQA.from_llm(llm=OpenAI(), retriever=docsearch.as_retriever())
+    qa.run("What did the president say about Ketanji Brown Jackson?")
 
     file_path = tmp_path / "RetrievalQA_chain.yaml"
     qa.save(file_path=file_path)

tests/integration_tests/retrievers/test_contextual_compression.py

@@ -1,7 +1,7 @@
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain.retrievers.document_compressors import EmbeddingsFilter
-from langchain.vectorstores import Chroma
+from langchain.vectorstores import FAISS
 
 
 def test_contextual_compression_retriever_get_relevant_docs() -> None:
@@ -13,7 +13,7 @@ def test_contextual_compression_retriever_get_relevant_docs() -> None:
     ]
     embeddings = OpenAIEmbeddings()
     base_compressor = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.75)
-    base_retriever = Chroma.from_texts(texts, embedding=embeddings).as_retriever(
+    base_retriever = FAISS.from_texts(texts, embedding=embeddings).as_retriever(
         search_kwargs={"k": len(texts)}
     )
     retriever = ContextualCompressionRetriever(

tests/unit_tests/retrievers/test_base.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import Any, Dict, List, Optional
+from typing import Dict, List, Optional
 
 import pytest
 
@@ -141,45 +141,48 @@ async def test_fake_retriever_v1_with_kwargs_upgrade_async(
     assert callbacks.retriever_errors == 0
 
 
+class FakeRetrieverV2(BaseRetriever):
+    def __init__(self, throw_error: bool = False) -> None:
+        self.throw_error = throw_error
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun | None
+    ) -> List[Document]:
+        assert isinstance(self, FakeRetrieverV2)
+        assert run_manager is not None
+        assert isinstance(run_manager, CallbackManagerForRetrieverRun)
+        if self.throw_error:
+            raise ValueError("Test error")
+        return [
+            Document(page_content=query),
+        ]
+
+    async def _aget_relevant_documents(
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun | None
+    ) -> List[Document]:
+        assert isinstance(self, FakeRetrieverV2)
+        assert run_manager is not None
+        assert isinstance(run_manager, AsyncCallbackManagerForRetrieverRun)
+        if self.throw_error:
+            raise ValueError("Test error")
+        return [
+            Document(page_content=f"Async query {query}"),
+        ]
+
+
 @pytest.fixture
 def fake_retriever_v2() -> BaseRetriever:
-    class FakeRetrieverV2(BaseRetriever):
-        def _get_relevant_documents(
-            self,
-            query: str,
-            *,
-            run_manager: CallbackManagerForRetrieverRun | None = None,
-            **kwargs: Any,
-        ) -> List[Document]:
-            assert isinstance(self, FakeRetrieverV2)
-            assert run_manager is not None
-            assert isinstance(run_manager, CallbackManagerForRetrieverRun)
-            if "throw_error" in kwargs:
-                raise ValueError("Test error")
-            return [
-                Document(page_content=query, metadata=kwargs),
-            ]
-
-        async def _aget_relevant_documents(
-            self,
-            query: str,
-            *,
-            run_manager: AsyncCallbackManagerForRetrieverRun | None = None,
-            **kwargs: Any,
-        ) -> List[Document]:
-            assert isinstance(self, FakeRetrieverV2)
-            assert run_manager is not None
-            assert isinstance(run_manager, AsyncCallbackManagerForRetrieverRun)
-            if "throw_error" in kwargs:
-                raise ValueError("Test error")
-            return [
-                Document(page_content=f"Async query {query}", metadata=kwargs),
-            ]
-
     return FakeRetrieverV2()  # type: ignore[abstract]
 
 
-def test_fake_retriever_v2(fake_retriever_v2: BaseRetriever) -> None:
+@pytest.fixture
+def fake_erroring_retriever_v2() -> BaseRetriever:
+    return FakeRetrieverV2(throw_error=True)  # type: ignore[abstract]
+
+
+def test_fake_retriever_v2(
+    fake_retriever_v2: BaseRetriever, fake_erroring_retriever_v2: BaseRetriever
+) -> None:
     callbacks = FakeCallbackHandler()
     assert fake_retriever_v2._new_arg_supported is True
     results = fake_retriever_v2.get_relevant_documents("Foo", callbacks=[callbacks])
@@ -187,20 +190,17 @@ def test_fake_retriever_v2(fake_retriever_v2: BaseRetriever) -> None:
     assert callbacks.retriever_starts == 1
     assert callbacks.retriever_ends == 1
     assert callbacks.retriever_errors == 0
-    results2 = fake_retriever_v2.get_relevant_documents(
+    fake_retriever_v2.get_relevant_documents("Foo", callbacks=[callbacks])
-        "Foo", callbacks=[callbacks], foo="bar"
-    )
-    assert results2[0].metadata == {"foo": "bar"}
 
     with pytest.raises(ValueError, match="Test error"):
-        fake_retriever_v2.get_relevant_documents(
+        fake_erroring_retriever_v2.get_relevant_documents("Foo", callbacks=[callbacks])
-            "Foo", callbacks=[callbacks], throw_error=True
-        )
     assert callbacks.retriever_errors == 1
 
 
 @pytest.mark.asyncio
-async def test_fake_retriever_v2_async(fake_retriever_v2: BaseRetriever) -> None:
+async def test_fake_retriever_v2_async(
+    fake_retriever_v2: BaseRetriever, fake_erroring_retriever_v2: BaseRetriever
+) -> None:
     callbacks = FakeCallbackHandler()
     assert fake_retriever_v2._new_arg_supported is True
     results = await fake_retriever_v2.aget_relevant_documents(
@@ -210,11 +210,8 @@ async def test_fake_retriever_v2_async(fake_retriever_v2: BaseRetriever) -> None
     assert callbacks.retriever_starts == 1
     assert callbacks.retriever_ends == 1
     assert callbacks.retriever_errors == 0
-    results2 = await fake_retriever_v2.aget_relevant_documents(
+    await fake_retriever_v2.aget_relevant_documents("Foo", callbacks=[callbacks])
-        "Foo", callbacks=[callbacks], foo="bar"
-    )
-    assert results2[0].metadata == {"foo": "bar"}
     with pytest.raises(ValueError, match="Test error"):
-        await fake_retriever_v2.aget_relevant_documents(
+        await fake_erroring_retriever_v2.aget_relevant_documents(
-            "Foo", callbacks=[callbacks], throw_error=True
+            "Foo", callbacks=[callbacks]
         )