Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?\nThought:"      ]    }    [llm/end] [1:RunTypeEnum.chain:AgentExecutor > 2:RunTypeEnum.chain:LLMChain > 3:RunTypeEnum.llm:ChatOpenAI] [5.53s] Exiting LLM run with output:    {      "generations": [        [          {            "text": "I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"",            "generation_info": {              "finish_reason": "stop"            },            "message": {              "lc": 1,              "type": "constructor",              "id": [                "langchain",                "schema",                "messages",                "AIMessage"              ],              "kwargs": {                "content": "I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"",                "additional_kwargs": {}              }            }          }        ]      ],      "llm_output": {        "token_usage": {          "prompt_tokens": 206,          "completion_tokens": 71,          "total_tokens": 277        },        "model_name": "gpt-4"      },      "run": null    }    [chain/end] [1:RunTypeEnum.chain:AgentExecutor > 2:RunTypeEnum.chain:LLMChain] [5.53s] Exiting Chain run with output:    {
If you're building with LLMs, at some point something will break, and you'll need to debug. A model call will fail, or the model output will be misformatted, or there will be some nested model calls and it won't be clear where along the way an incorrect output was created.
If you're building with LLMs, at some point something will break, and you'll need to debug. A model call will fail, or the model output will be misformatted, or there will be some nested model calls and it won't be clear where along the way an incorrect output was created. ->: Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?\nThought:"      ]    }    [llm/end] [1:RunTypeEnum.chain:AgentExecutor > 2:RunTypeEnum.chain:LLMChain > 3:RunTypeEnum.llm:ChatOpenAI] [5.53s] Exiting LLM run with output:    {      "generations": [        [          {            "text": "I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"",            "generation_info": {              "finish_reason": "stop"            },            "message": {              "lc": 1,              "type": "constructor",              "id": [                "langchain",                "schema",                "messages",                "AIMessage"              ],              "kwargs": {                "content": "I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"",                "additional_kwargs": {}              }            }          }        ]      ],      "llm_output": {        "token_usage": {          "prompt_tokens": 206,          "completion_tokens": 71,          "total_tokens": 277        },        "model_name": "gpt-4"      },      "run": null    }    [chain/end] [1:RunTypeEnum.chain:AgentExecutor > 2:RunTypeEnum.chain:LLMChain] [5.53s] Exiting Chain run with output:    {