text: = []query = "What did the president say about Ketanji Brown Jackson"result = qa(    {"question": query, "chat_history": chat_history, "vectordbkwargs": vectordbkwargs})print(result["answer"])     The president said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.ConversationalRetrievalChain with map_reduceâ€‹We can also use different types of combine document chains with the ConversationalRetrievalChain chain.from langchain.chains import LLMChainfrom langchain.chains.question_answering import load_qa_chainfrom langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPTquestion_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain = load_qa_chain(llm, chain_type="map_reduce")chain = ConversationalRetrievalChain(    retriever=vectorstore.as_retriever(),    question_generator=question_generator,    combine_docs_chain=doc_chain,)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = chain({"question": query, "chat_history": chat_history})result["answer"]    " The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, who he described as one of the nation's top legal minds, to continue Justice Breyer's legacy of excellence."ConversationalRetrievalChain with Question Answering with sourcesâ€‹You can also use this chain with the question answering with sources chain.from langchain.chains.qa_with_sources import load_qa_with_sources_chainquestion_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)doc_chain =