Can we control number of documents to return in RetrievalQA Langchain

Question

Can we control the document query parameter in RetrievalQA() like we could do in vectorDBQA() in langchain before? Also, shall I use map_reduce chain type instead for my large documents?

I tried to look into the source code but could not find it.

Answer 1

You can customized the Retrieval and add your requirement

   class URRetrival(BaseRetriever):

    def __int__(self):
        pass

    def setparms(self, count):
        self.count = count


    def _get_relevant_documents(
            self, query: str, *, run_manager: CallbackManagerForRetrieverRun
    ) -> List[Document]:
        
        # need to add your similarity search here and add set self.count as limit
    #results = your vectordb.similarity(limit_or_k = self.count)
        return results

    async def _aget_relevant_documents(
            self,
            query: str,
            *,
            run_manager: AsyncCallbackManagerForRetrieverRun,
            **kwargs: Any,
    ) -> List[Document]:
        raise NotImplementedError()

in above example you need to add your vector db , its depend on you which you are using, and return the similarity results, the vector db supported for k/limit of relative docs

_get_relevant_documents : method call when inside the chain, you need to pass this costume retrieval as chain retrieval

Example:

retrival = URRetrival()
retrival.setparms(5)
qa_with_sources_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retrival,
    callbacks=[handler],