text: tools=tools,    llm=llm,    verbose=True,)agent({"input": "did alphabet or tesla have more revenue?"})            > Entering new  chain...        Invoking: `alphabet-earnings` with `{'question': 'revenue'}`            {'query': 'revenue', 'result': 'The revenue for Alphabet Inc. for the quarter ended March 31, 2023, was $69,787 million.'}    Invoking: `tesla-earnings` with `{'question': 'revenue'}`            {'query': 'revenue', 'result': 'Total revenue for Q1-2023 was $23.3 billion.'}Alphabet Inc. had more revenue than Tesla. Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million, while Tesla's total revenue for Q1-2023 was $23.3 billion.        > Finished chain.    {'input': 'did alphabet or tesla have more revenue?',     'output': "Alphabet Inc. had more revenue than Tesla. Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million, while Tesla's total revenue for Q1-2023 was $23.3 billion."}OpenAI Multi Functionsâ€‹This type of agent allows calling multiple functions at once. This is really useful when some steps can be computed in parallel - like when asked to compare multiple documentsimport langchainlangchain.debug = Truellm = ChatOpenAI(    temperature=0,