text: },    # https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q1-2023-Update    {        "name": "tesla-earnings",        "path": "/Users/harrisonchase/Downloads/TSLA-Q1-2023-Update.pdf",    },]for file in files:    loader = PyPDFLoader(file["path"])    pages = loader.load_and_split()    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)    docs = text_splitter.split_documents(pages)    embeddings = OpenAIEmbeddings()    retriever = FAISS.from_documents(docs, embeddings).as_retriever()    # Wrap retrievers in a Tool    tools.append(        Tool(            args_schema=DocumentInput,            name=file["name"],            description=f"useful when you want to answer questions about {file['name']}",            func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever),        )    )from langchain.agents import initialize_agentfrom langchain.agents import AgentTypellm = ChatOpenAI(    temperature=0,    model="gpt-3.5-turbo-0613",)agent = initialize_agent(    agent=AgentType.OPENAI_FUNCTIONS,    tools=tools,    llm=llm,