text: = 1536index = faiss.IndexFlatL2(embedding_size)vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})Setup model and AutoGPTâ€‹Initialize everything! We will use ChatOpenAI modelfrom langchain.experimental import AutoGPTfrom langchain.chat_models import ChatOpenAIagent = AutoGPT.from_llm_and_tools(    ai_name="Tom",    ai_role="Assistant",    tools=tools,    llm=ChatOpenAI(temperature=0),    memory=vectorstore.as_retriever(),)# Set verbose to be trueagent.chain.verbose = TrueRun an exampleâ€‹Here we will make it write a weather report for SFagent.run(["write a weather report for SF today"])Chat History Memoryâ€‹In addition to the memory that holds the agent immediate steps, we also have a chat history memory. By default, the agent will use 'ChatMessageHistory' and it can be changed. This is useful when you want to use a different type of memory for example 'FileChatHistoryMemory'from langchain.memory.chat_message_histories import FileChatMessageHistoryagent = AutoGPT.from_llm_and_tools(    ai_name="Tom",    ai_role="Assistant",    tools=tools,    llm=ChatOpenAI(temperature=0),    memory=vectorstore.as_retriever(),    chat_history_memory=FileChatMessageHistory("chat_history.txt"),)PreviousAutonomous (long-running) agentsNextBabyAGI User GuideSet up toolsSet up memorySetup model and AutoGPTRun an exampleChat History MemoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.