= initialize_agent(    agent=AgentType.OPENAI_MULTI_FUNCTIONS,    tools=tools,    llm=llm,    verbose=True,)agent({"input": "did alphabet or tesla have more revenue?"})    [chain/start] [1:chain:AgentExecutor] Entering Chain run with input:    {      "input": "did alphabet or tesla have more revenue?"    }    [llm/start] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:    {      "prompts": [        "System: You are a helpful AI assistant.\nHuman: did alphabet or tesla have more revenue?"      ]    }    [llm/end] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] [2.66s] Exiting LLM run with output:    {      "generations": [        [          {            "text": "",            "generation_info": null,            "message": {              "content": "",              "additional_kwargs": {                "function_call": {                  "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}"                }              },              "example": false            }          }        ]      ],      "llm_output": {        "token_usage": {          "prompt_tokens": 99,          "completion_tokens": 82,          "total_tokens": 181        },        "model_name": "gpt-3.5-turbo-0613"      },      "run": null    }    [tool/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings] Entering Tool run with input:    "{'question': "What was Alphabet's revenue?"}"    [chain/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA] Entering Chain run with input:    {      "query": "What was Alphabet's revenue?"    }    [chain/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA >
This notebook shows how to use an agent to compare two documents.
This notebook shows how to use an agent to compare two documents. ->: = initialize_agent(    agent=AgentType.OPENAI_MULTI_FUNCTIONS,    tools=tools,    llm=llm,    verbose=True,)agent({"input": "did alphabet or tesla have more revenue?"})    [chain/start] [1:chain:AgentExecutor] Entering Chain run with input:    {      "input": "did alphabet or tesla have more revenue?"    }    [llm/start] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:    {      "prompts": [        "System: You are a helpful AI assistant.\nHuman: did alphabet or tesla have more revenue?"      ]    }    [llm/end] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] [2.66s] Exiting LLM run with output:    {      "generations": [        [          {            "text": "",            "generation_info": null,            "message": {              "content": "",              "additional_kwargs": {                "function_call": {                  "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}"                }              },              "example": false            }          }        ]      ],      "llm_output": {        "token_usage": {          "prompt_tokens": 99,          "completion_tokens": 82,          "total_tokens": 181        },        "model_name": "gpt-3.5-turbo-0613"      },      "run": null    }    [tool/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings] Entering Tool run with input:    "{'question': "What was Alphabet's revenue?"}"    [chain/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA] Entering Chain run with input:    {      "query": "What was Alphabet's revenue?"    }    [chain/start] [1:chain:AgentExecutor > 3:tool:alphabet-earnings > 4:chain:RetrievalQA >