vectorstore.as_retriever(), return_source_documents=True)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history})result['source_documents'][0]    Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../state_of_the_union.txt'})ConversationalRetrievalChain with search_distance‚ÄãIf you are using a vector store that supports filtering by search distance, you can add a threshold value parameter.vectordbkwargs = {"search_distance": 0.9}qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history, "vectordbkwargs": vectordbkwargs})ConversationalRetrievalChain with map_reduce‚ÄãWe 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_PROMPTllm =
The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.
The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. ->: vectorstore.as_retriever(), return_source_documents=True)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history})result['source_documents'][0]    Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../state_of_the_union.txt'})ConversationalRetrievalChain with search_distance‚ÄãIf you are using a vector store that supports filtering by search distance, you can add a threshold value parameter.vectordbkwargs = {"search_distance": 0.9}qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)chat_history = []query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query, "chat_history": chat_history, "vectordbkwargs": vectordbkwargs})ConversationalRetrievalChain with map_reduce‚ÄãWe 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_PROMPTllm =