"message": {              "content": "The weather in Los Angeles is mostly cloudy with a high of 76¬∞F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53¬∞F and a humidity of 83%.",              "additional_kwargs": {},              "example": false            }          }        ]      ],      "llm_output": {        "token_usage": {          "prompt_tokens": 307,          "completion_tokens": 54,          "total_tokens": 361        },        "model_name": "gpt-3.5-turbo-0613"      },      "run": null    }    [chain/end] [1:chain:AgentExecutor] [6.37s] Exiting Chain run with output:    {      "output": "The weather in Los Angeles is mostly cloudy with a high of 76¬∞F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53¬∞F and a humidity of 83%."    }    'The weather in Los Angeles is mostly cloudy with a high of 76¬∞F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53¬∞F and a humidity of 83%.'Configuring max iteration behavior‚ÄãTo make sure that our agent doesn't get stuck in excessively long loops, we can set max_iterations. We can also set an early stopping method, which will determine our agent's behavior once the number of max iterations is hit. By default, the early stopping uses method force which just returns that constant string. Alternatively, you could specify method generate which then does one FINAL pass through the LLM to generate an output.mrkl = initialize_agent(    tools,    llm,    agent=AgentType.OPENAI_FUNCTIONS,    verbose=True,    max_iterations=2,    early_stopping_method="generate",)mrkl.run("What is the weather in NYC today, yesterday, and the day before?")    [chain/start] [1:chain:AgentExecutor] Entering Chain run with input:    {      "input": "What is the weather in NYC today, yesterday, and
This notebook showcases using an agent that uses the OpenAI functions ability to respond to the prompts of the user using a Large Language Model.
This notebook showcases using an agent that uses the OpenAI functions ability to respond to the prompts of the user using a Large Language Model. ->: "message": {              "content": "The weather in Los Angeles is mostly cloudy with a high of 76¬∞F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53¬∞F and a humidity of 83%.",              "additional_kwargs": {},              "example": false            }          }        ]      ],      "llm_output": {        "token_usage": {          "prompt_tokens": 307,          "completion_tokens": 54,          "total_tokens": 361        },        "model_name": "gpt-3.5-turbo-0613"      },      "run": null    }    [chain/end] [1:chain:AgentExecutor] [6.37s] Exiting Chain run with output:    {      "output": "The weather in Los Angeles is mostly cloudy with a high of 76¬∞F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53¬∞F and a humidity of 83%."    }    'The weather in Los Angeles is mostly cloudy with a high of 76¬∞F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53¬∞F and a humidity of 83%.'Configuring max iteration behavior‚ÄãTo make sure that our agent doesn't get stuck in excessively long loops, we can set max_iterations. We can also set an early stopping method, which will determine our agent's behavior once the number of max iterations is hit. By default, the early stopping uses method force which just returns that constant string. Alternatively, you could specify method generate which then does one FINAL pass through the LLM to generate an output.mrkl = initialize_agent(    tools,    llm,    agent=AgentType.OPENAI_FUNCTIONS,    verbose=True,    max_iterations=2,    early_stopping_method="generate",)mrkl.run("What is the weather in NYC today, yesterday, and the day before?")    [chain/start] [1:chain:AgentExecutor] Entering Chain run with input:    {      "input": "What is the weather in NYC today, yesterday, and