[1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA] [1.61s] Exiting Chain run with output:    {      "result": "Tesla's revenue for Q1-2023 was $23.329 billion."    }    [tool/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings] [1.61s] Exiting Tool run with output:    "{'query': "What was Tesla's revenue?", 'result': "Tesla's revenue for Q1-2023 was $23.329 billion."}"    [llm/start] [1:chain:AgentExecutor > 13:llm:ChatOpenAI] Entering LLM run with input:    {      "prompts": [        "System: You are a helpful AI assistant.\nHuman: did alphabet or tesla have more revenue?\nAI: {'name': 'tool_selection', 'arguments': '{\\n  \"actions\": [\\n    {\\n      \"action_name\": \"alphabet-earnings\",\\n      \"action\": {\\n        \"question\": \"What was Alphabet\\'s revenue?\"\\n      }\\n    },\\n    {\\n      \"action_name\": \"tesla-earnings\",\\n      \"action\": {\\n        \"question\": \"What was Tesla\\'s revenue?\"\\n      }\\n    }\\n  ]\\n}'}\nFunction: {\"query\": \"What was Alphabet's revenue?\", \"result\": \"Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million.\"}\nAI: {'name': 'tool_selection', 'arguments': '{\\n  \"actions\": [\\n    {\\n      \"action_name\": \"alphabet-earnings\",\\n      \"action\": {\\n        \"question\": \"What was Alphabet\\'s revenue?\"\\n      }\\n    },\\n    {\\n      \"action_name\": \"tesla-earnings\",\\n      \"action\": {\\n        \"question\": \"What was Tesla\\'s revenue?\"\\n      }\\n    }\\n  ]\\n}'}\nFunction: {\"query\": \"What was Tesla's revenue?\", \"result\": \"Tesla's revenue for Q1-2023 was $23.329 billion.\"}"      ]    }    [llm/end] [1:chain:AgentExecutor > 13:llm:ChatOpenAI] [1.69s] Exiting LLM run with output:    {      "generations": [        [          {            "text": "Alphabet had a revenue of $69,787 million, while Tesla had a revenue of $23.329 billion. Therefore, Alphabet had more revenue than Tesla.",            "generation_info": null,
This notebook shows how to use an agent to compare two documents.
This notebook shows how to use an agent to compare two documents. ->: [1:chain:AgentExecutor > 8:tool:tesla-earnings > 9:chain:RetrievalQA] [1.61s] Exiting Chain run with output:    {      "result": "Tesla's revenue for Q1-2023 was $23.329 billion."    }    [tool/end] [1:chain:AgentExecutor > 8:tool:tesla-earnings] [1.61s] Exiting Tool run with output:    "{'query': "What was Tesla's revenue?", 'result': "Tesla's revenue for Q1-2023 was $23.329 billion."}"    [llm/start] [1:chain:AgentExecutor > 13:llm:ChatOpenAI] Entering LLM run with input:    {      "prompts": [        "System: You are a helpful AI assistant.\nHuman: did alphabet or tesla have more revenue?\nAI: {'name': 'tool_selection', 'arguments': '{\\n  \"actions\": [\\n    {\\n      \"action_name\": \"alphabet-earnings\",\\n      \"action\": {\\n        \"question\": \"What was Alphabet\\'s revenue?\"\\n      }\\n    },\\n    {\\n      \"action_name\": \"tesla-earnings\",\\n      \"action\": {\\n        \"question\": \"What was Tesla\\'s revenue?\"\\n      }\\n    }\\n  ]\\n}'}\nFunction: {\"query\": \"What was Alphabet's revenue?\", \"result\": \"Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million.\"}\nAI: {'name': 'tool_selection', 'arguments': '{\\n  \"actions\": [\\n    {\\n      \"action_name\": \"alphabet-earnings\",\\n      \"action\": {\\n        \"question\": \"What was Alphabet\\'s revenue?\"\\n      }\\n    },\\n    {\\n      \"action_name\": \"tesla-earnings\",\\n      \"action\": {\\n        \"question\": \"What was Tesla\\'s revenue?\"\\n      }\\n    }\\n  ]\\n}'}\nFunction: {\"query\": \"What was Tesla's revenue?\", \"result\": \"Tesla's revenue for Q1-2023 was $23.329 billion.\"}"      ]    }    [llm/end] [1:chain:AgentExecutor > 13:llm:ChatOpenAI] [1.69s] Exiting LLM run with output:    {      "generations": [        [          {            "text": "Alphabet had a revenue of $69,787 million, while Tesla had a revenue of $23.329 billion. Therefore, Alphabet had more revenue than Tesla.",            "generation_info": null,