locations(2)787 831 903 963 1,000 27%\nMobile service fleet 1,372 1,453 1,532 1,584 1,692 23%\nSupercharger stations 3,724 3,971 4,283 4,678 4,947 33%\nSupercharger connectors 33,657 36,165 38,883 42,419 45,169 34%\n(1)Days of supply is calculated by dividing new car ending inventory by the relevant quarter‚Äôs deliveries and using 75 trading days (aligned with Automotive News definition).\n(2)Starting in Q1 -2023, we revised our methodology for reporting Tesla‚Äôs physical footprint. This count now includes all sales, del ivery, body shop and service locations globally. O P E R A T I O N A L S U M MA R Y\n(Unaudited)\n6\nHuman: What was Tesla's revenue?"      ]    }    [llm/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA > 10:chain:StuffDocumentsChain > 11:chain:LLMChain > 12:llm:ChatOpenAI] [1.17s] Exiting LLM run with output:    {      "generations": [        [          {            "text": "Tesla's revenue for Q1-2023 was $23.329 billion.",            "generation_info": null,            "message": {              "content": "Tesla's revenue for Q1-2023 was $23.329 billion.",              "additional_kwargs": {},              "example": false            }          }        ]      ],      "llm_output": {        "token_usage": {          "prompt_tokens": 2246,          "completion_tokens": 16,          "total_tokens": 2262        },        "model_name": "gpt-3.5-turbo-0613"      },      "run": null    }    [chain/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA > 10:chain:StuffDocumentsChain > 11:chain:LLMChain] [1.17s] Exiting Chain run with output:    {      "text": "Tesla's revenue for Q1-2023 was $23.329 billion."    }    [chain/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA > 10:chain:StuffDocumentsChain] [1.17s] Exiting Chain run with output:    {      "output_text": "Tesla's revenue for Q1-2023 was $23.329 billion."    }    [chain/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings
This notebook shows how to use an agent to compare two documents.
This notebook shows how to use an agent to compare two documents. ->: locations(2)787 831 903 963 1,000 27%\nMobile service fleet 1,372 1,453 1,532 1,584 1,692 23%\nSupercharger stations 3,724 3,971 4,283 4,678 4,947 33%\nSupercharger connectors 33,657 36,165 38,883 42,419 45,169 34%\n(1)Days of supply is calculated by dividing new car ending inventory by the relevant quarter‚Äôs deliveries and using 75 trading days (aligned with Automotive News definition).\n(2)Starting in Q1 -2023, we revised our methodology for reporting Tesla‚Äôs physical footprint. This count now includes all sales, del ivery, body shop and service locations globally. O P E R A T I O N A L S U M MA R Y\n(Unaudited)\n6\nHuman: What was Tesla's revenue?"      ]    }    [llm/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA > 10:chain:StuffDocumentsChain > 11:chain:LLMChain > 12:llm:ChatOpenAI] [1.17s] Exiting LLM run with output:    {      "generations": [        [          {            "text": "Tesla's revenue for Q1-2023 was $23.329 billion.",            "generation_info": null,            "message": {              "content": "Tesla's revenue for Q1-2023 was $23.329 billion.",              "additional_kwargs": {},              "example": false            }          }        ]      ],      "llm_output": {        "token_usage": {          "prompt_tokens": 2246,          "completion_tokens": 16,          "total_tokens": 2262        },        "model_name": "gpt-3.5-turbo-0613"      },      "run": null    }    [chain/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA > 10:chain:StuffDocumentsChain > 11:chain:LLMChain] [1.17s] Exiting Chain run with output:    {      "text": "Tesla's revenue for Q1-2023 was $23.329 billion."    }    [chain/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA > 10:chain:StuffDocumentsChain] [1.17s] Exiting Chain run with output:    {      "output_text": "Tesla's revenue for Q1-2023 was $23.329 billion."    }    [chain/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings