added filter kwarg to VectorStoreIndexWrapper query and query_with_so… (#8844)

- Description: added filter to query methods in VectorStoreIndexWrapper
for filtering by metadata (i.e. search_kwargs)
- Tag maintainer: @rlancemartin, @eyurtsev

Updated the doc snippet on this topic as well. It took me a long while
to figure out how to filter the vectorstore by filename, so this might
help someone else out.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/langchain/langchain/indexes/vectorstore.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional, Type
+from typing import Any, Dict, List, Optional, Type
 
 from pydantic import BaseModel, Extra, Field
 
@@ -31,22 +31,32 @@ class VectorStoreIndexWrapper(BaseModel):
         arbitrary_types_allowed = True
 
     def query(
-        self, question: str, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
+        self,
+        question: str,
+        llm: Optional[BaseLanguageModel] = None,
+        retriever_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs: Any
     ) -> str:
         """Query the vectorstore."""
         llm = llm or OpenAI(temperature=0)
+        retriever_kwargs = retriever_kwargs or {}
         chain = RetrievalQA.from_chain_type(
-            llm, retriever=self.vectorstore.as_retriever(), **kwargs
+            llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs
         )
         return chain.run(question)
 
     def query_with_sources(
-        self, question: str, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
+        self,
+        question: str,
+        llm: Optional[BaseLanguageModel] = None,
+        retriever_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs: Any
     ) -> dict:
         """Query the vectorstore and get back sources."""
         llm = llm or OpenAI(temperature=0)
+        retriever_kwargs = retriever_kwargs or {}
         chain = RetrievalQAWithSourcesChain.from_chain_type(
-            llm, retriever=self.vectorstore.as_retriever(), **kwargs
+            llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs
         )
         return chain({chain.question_key: question})