text: documents used for answer distillation can be returned using return_source_documents=True.from langchain.chains import RetrievalQAqa_chain = RetrievalQA.from_chain_type(llm,retriever=vectorstore.as_retriever(),                                       return_source_documents=True)result = qa_chain({"query": question})print(len(result['source_documents']))result['source_documents'][0]4Document(page_content='Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agentâ€™s brain, complemented by several key components:', 'language': 'en'})3.2.4 Citationsâ€‹Answer citations can be returned using RetrievalQAWithSourcesChain.from langchain.chains import RetrievalQAWithSourcesChainqa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm,retriever=vectorstore.as_retriever())result =