langchain.chains.question_answering.__init__.load_qa_chain

langchain.chains.question_answering.__init__.load_qa_chain(llm: BaseLanguageModel, chain_type: str = 'stuff', verbose: Optional[bool] = None, callback_manager: Optional[BaseCallbackManager] = None, **kwargs: Any) → BaseCombineDocumentsChain

Load question answering chain.

Parameters

• llm – Language Model to use in the chain.

• chain_type – Type of document combining chain to use. Should be one of “stuff”, “map_reduce”, “map_rerank”, and “refine”.

• verbose – Whether chains should be run in verbose mode or not. Note that this applies to all chains that make up the final chain.

• callback_manager – Callback manager to use for the chain.

Returns

A chain to use for question answering.

Examples using load_qa_chain

• Chat Over Documents with Vectara

• SageMakerEndpoint

• QA over Documents

• Running LLMs locally

• Use local LLMs

• WebResearchRetriever

• How to add memory to a Multi-Input Chain