Add params to reduce K dynamically to reduce it below token limit (#739)

Referring to #687, I implemented the functionality to reduce K if it
exceeds the token limit.

Edit: I should have ran make lint locally. Also, this only applies to
`StuffDocumentChain`

langchain/chains/qa_with_sources/vector_db.py

@@ -1,8 +1,10 @@
 """Question-answering with sources over a vector database."""
+
 from typing import Any, Dict, List
 
 from pydantic import BaseModel, Field
 
+from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
 from langchain.docstore.document import Document
 from langchain.vectorstores.base import VectorStore
@@ -15,11 +17,36 @@ class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain, BaseModel):
     """Vector Database to connect to."""
     k: int = 4
     """Number of results to return from store"""
+    reduce_k_below_max_tokens: bool = False
+    """Reduce the number of results to return from store based on tokens limit"""
+    max_tokens_limit: int = 3375
+    """Restrict the docs to return from store based on tokens,
+    enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true"""
     search_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Extra search args."""
 
+    def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]:
+        num_docs = len(docs)
+
+        if self.reduce_k_below_max_tokens and isinstance(
+            self.combine_documents_chain, StuffDocumentsChain
+        ):
+            tokens = [
+                self.combine_documents_chain.llm_chain.llm.get_num_tokens(
+                    doc.page_content
+                )
+                for doc in docs
+            ]
+            token_count = sum(tokens[:num_docs])
+            while token_count > self.max_tokens_limit:
+                num_docs -= 1
+                token_count -= tokens[num_docs]
+
+        return docs[:num_docs]
+
     def _get_docs(self, inputs: Dict[str, Any]) -> List[Document]:
         question = inputs[self.question_key]
-        return self.vectorstore.similarity_search(
+        docs = self.vectorstore.similarity_search(
             question, k=self.k, **self.search_kwargs
         )
+        return self._reduce_tokens_below_limit(docs)