text: question})In summary, the user can choose the desired level of abstraction for QA:4. Chatâ€‹4.1 Getting startedâ€‹To keep chat history, first specify a Memory buffer to track the conversation inputs / outputs.from langchain.memory import ConversationBufferMemorymemory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)The ConversationalRetrievalChain uses chat in the Memory buffer. from langchain.chains import ConversationalRetrievalChainretriever=vectorstore.as_retriever()chat = ConversationalRetrievalChain.from_llm(llm,retriever=retriever,memory=memory)result = chat({"question": "What are some of the main ideas in self-reflection?"})result['answer']"Some of the main ideas in self-reflection include:\n1. Iterative improvement: Self-reflection allows autonomous agents to improve by refining past action decisions and correcting mistakes.\n2. Trial and error: Self-reflection is crucial in real-world tasks where trial and error are inevitable.\n3. Two-shot examples: Self-reflection is created by showing pairs of failed trajectories and ideal reflections for guiding future changes in the plan.\n4. Working memory: Reflections are added to the agent's working memory, up to three, to be used as context for querying.\n5. Performance evaluation: Self-reflection involves continuously reviewing and analyzing actions, self-criticizing behavior, and reflecting on past decisions and strategies to refine approaches.\n6. Efficiency: Self-reflection encourages being smart and efficient, aiming to complete tasks in the least number of steps."The Memory buffer has context to resolve "it" ("self-reflection") in the below question.result = chat({"question": "How does the Reflexion paper handle it?"})result['answer']"The Reflexion paper handles self-reflection by showing two-shot examples to the Learning Language Model (LLM). Each example consists of