add search kwargs (#664)

langchain/chains/qa_with_sources/vector_db.py

@@ -1,7 +1,7 @@
 """Question-answering with sources over a vector database."""
 from typing import Any, Dict, List
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
 from langchain.docstore.document import Document
@@ -15,8 +15,8 @@ class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain, BaseModel):
     """Vector Database to connect to."""
     k: int = 4
     """Number of results to return from store"""
-    search_kwargs: Dict[str, Any] = {}
-    """Extra search args"""
+    search_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Extra search args."""
 
     def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
         question = inputs[self.question_key]

langchain/chains/vector_db_qa/base.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List
 
-from pydantic import BaseModel, Extra, root_validator
+from pydantic import BaseModel, Extra, Field, root_validator
 
 from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
@@ -39,6 +39,8 @@ class VectorDBQA(Chain, BaseModel):
     output_key: str = "result"  #: :meta private:
     return_source_documents: bool = False
     """Return the source documents."""
+    search_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Extra search args."""
 
     class Config:
         """Configuration for this pydantic object."""
@@ -127,7 +129,9 @@ class VectorDBQA(Chain, BaseModel):
         """
         question = inputs[self.input_key]
 
-        docs = self.vectorstore.similarity_search(question, k=self.k)
+        docs = self.vectorstore.similarity_search(
+            question, k=self.k, **self.search_kwargs
+        )
         answer, _ = self.combine_documents_chain.combine_docs(docs, question=question)
 
         if self.return_source_documents:

langchain/vectorstores/base.py

@@ -26,7 +26,9 @@ class VectorStore(ABC):
         """
 
     @abstractmethod
-    def similarity_search(self, query: str, k: int = 4) -> List[Document]:
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
         """Return docs most similar to query."""
 
     def max_marginal_relevance_search(

langchain/vectorstores/elastic_vector_search.py

@@ -106,7 +106,9 @@ class ElasticVectorSearch(VectorStore):
         self.client.indices.refresh(index=self.index_name)
         return ids
 
-    def similarity_search(self, query: str, k: int = 4) -> List[Document]:
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
         """Return docs most similar to query.
 
         Args:

langchain/vectorstores/faiss.py

@@ -103,7 +103,9 @@ class FAISS(VectorStore):
             docs.append((doc, scores[0][j]))
         return docs
 
-    def similarity_search(self, query: str, k: int = 4) -> List[Document]:
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
         """Return docs most similar to query.
 
         Args:

langchain/vectorstores/pinecone.py

@@ -120,6 +120,7 @@ class Pinecone(VectorStore):
         k: int = 5,
         filter: Optional[dict] = None,
         namespace: Optional[str] = None,
+        **kwargs: Any,
     ) -> List[Document]:
         """Return pinecone documents most similar to query.
 

langchain/vectorstores/weaviate.py

@@ -71,7 +71,9 @@ class Weaviate(VectorStore):
                 ids.append(_id)
         return ids
 
-    def similarity_search(self, query: str, k: int = 4) -> List[Document]:
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
         """Look up similar documents in weaviate."""
         content = {"concepts": [query]}
         query_obj = self._client.query.get(self._index_name, self._query_attrs)