add token reduction to ConversationalRetrievalChain (#2075)

This worked for me, but I'm not sure if its the right way to approach
something like this, so I'm open to suggestions.

Adds class properties `reduce_k_below_max_tokens: bool` and
`max_tokens_limit: int` to the `ConversationalRetrievalChain`. The code
is basically copied from
[`RetreivalQAWithSourcesChain`](https://github.com/nkov/langchain/blob/46d141c6cb6c0fdebb308336d8ae140d8368945a/langchain/chains/qa_with_sources/retrieval.py#L24)

langchain/chains/conversational_retrieval/base.py

@@ -10,6 +10,7 @@ from pydantic import BaseModel, Extra, Field, root_validator
 
 from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
+from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.chains.question_answering import load_qa_chain
@@ -116,9 +117,31 @@ class ConversationalRetrievalChain(BaseConversationalRetrievalChain, BaseModel):
     """Chain for chatting with an index."""
 
     retriever: BaseRetriever
+    """Index to connect to."""
+    max_tokens_limit: Optional[int] = None
+    """If set, restricts the docs to return from store based on tokens, enforced only
+    for StuffDocumentChain"""
+
+    def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]:
+        num_docs = len(docs)
+
+        if self.max_tokens_limit and isinstance(
+            self.combine_docs_chain, StuffDocumentsChain
+        ):
+            tokens = [
+                self.combine_docs_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, question: str, inputs: Dict[str, Any]) -> List[Document]:
-        return self.retriever.get_relevant_documents(question)
+        docs = self.retriever.get_relevant_documents(question)
+        return self._reduce_tokens_below_limit(docs)
 
     @classmethod
     def from_llm(