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如何将工具输出传递给聊天模型

预备知识

本指南假定您熟悉以下概念：

某些模型具备工具调用的能力，可以生成符合特定用户提供的模式的参数。本指南将演示如何使用这些工具调用来实际调用函数，并正确地将结果传递回模型。

首先，我们定义我们的工具和模型：

import { z } from "zod";
import { tool } from "@langchain/core/tools";

const addTool = tool(
  async ({ a, b }) => {
    return a + b;
  },
  {
    name: "add",
    schema: z.object({
      a: z.number(),
      b: z.number(),
    }),
    description: "Adds a and b.",
  }
);

const multiplyTool = tool(
  async ({ a, b }) => {
    return a * b;
  },
  {
    name: "multiply",
    schema: z.object({
      a: z.number(),
      b: z.number(),
    }),
    description: "Multiplies a and b.",
  }
);

const tools = [addTool, multiplyTool];

Pick your chat model:

Install dependencies

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/groq

Add environment variables

GROQ_API_KEY=your-api-key

Instantiate the model

import { ChatGroq } from "@langchain/groq";

const llm = new ChatGroq({
  model: "llama-3.3-70b-versatile",
  temperature: 0
});

现在，让我们让模型调用一个工具。我们会将它添加到一个消息列表中，该列表将被视为对话历史记录：

import { HumanMessage } from "@langchain/core/messages";

const llmWithTools = llm.bindTools(tools);

const messages = [new HumanMessage("What is 3 * 12? Also, what is 11 + 49?")];

const aiMessage = await llmWithTools.invoke(messages);

console.log(aiMessage);

messages.push(aiMessage);
AIMessage {
  "id": "chatcmpl-9p1NbC7sfZP0FE0bNfFiVYbPuWivg",
  "content": "",
  "additional_kwargs": {
    "tool_calls": [
      {
        "id": "call_RbUuLMYf3vgcdSQ8bhy1D5Ty",
        "type": "function",
        "function": "[Object]"
      },
      {
        "id": "call_Bzz1qgQjTlQIHMcEaDAdoH8X",
        "type": "function",
        "function": "[Object]"
      }
    ]
  },
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 50,
      "promptTokens": 87,
      "totalTokens": 137
    },
    "finish_reason": "tool_calls",
    "system_fingerprint": "fp_400f27fa1f"
  },
  "tool_calls": [
    {
      "name": "multiply",
      "args": {
        "a": 3,
        "b": 12
      },
      "type": "tool_call",
      "id": "call_RbUuLMYf3vgcdSQ8bhy1D5Ty"
    },
    {
      "name": "add",
      "args": {
        "a": 11,
        "b": 49
      },
      "type": "tool_call",
      "id": "call_Bzz1qgQjTlQIHMcEaDAdoH8X"
    }
  ],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 87,
    "output_tokens": 50,
    "total_tokens": 137
  }
}
2

接下来，让我们使用模型填充的参数调用工具函数！

方便的是，如果我们使用 ToolCall 调用一个 LangChain Tool，我们将自动收到一个 ToolMessage，该消息可以反馈给模型：

兼容性

此功能需要 @langchain/core>=0.2.16。请参阅升级指南。

如果您使用的是较早版本的 @langchain/core，则需要使用工具调用中的字段手动构造一个 ToolMessage。

const toolsByName = {
  add: addTool,
  multiply: multiplyTool,
};

for (const toolCall of aiMessage.tool_calls) {
  const selectedTool = toolsByName[toolCall.name];
  const toolMessage = await selectedTool.invoke(toolCall);
  messages.push(toolMessage);
}

console.log(messages);
[
  HumanMessage {
    "content": "What is 3 * 12? Also, what is 11 + 49?",
    "additional_kwargs": {},
    "response_metadata": {}
  },
  AIMessage {
    "id": "chatcmpl-9p1NbC7sfZP0FE0bNfFiVYbPuWivg",
    "content": "",
    "additional_kwargs": {
      "tool_calls": [
        {
          "id": "call_RbUuLMYf3vgcdSQ8bhy1D5Ty",
          "type": "function",
          "function": "[Object]"
        },
        {
          "id": "call_Bzz1qgQjTlQIHMcEaDAdoH8X",
          "type": "function",
          "function": "[Object]"
        }
      ]
    },
    "response_metadata": {
      "tokenUsage": {
        "completionTokens": 50,
        "promptTokens": 87,
        "totalTokens": 137
      },
      "finish_reason": "tool_calls",
      "system_fingerprint": "fp_400f27fa1f"
    },
    "tool_calls": [
      {
        "name": "multiply",
        "args": {
          "a": 3,
          "b": 12
        },
        "type": "tool_call",
        "id": "call_RbUuLMYf3vgcdSQ8bhy1D5Ty"
      },
      {
        "name": "add",
        "args": {
          "a": 11,
          "b": 49
        },
        "type": "tool_call",
        "id": "call_Bzz1qgQjTlQIHMcEaDAdoH8X"
      }
    ],
    "invalid_tool_calls": [],
    "usage_metadata": {
      "input_tokens": 87,
      "output_tokens": 50,
      "total_tokens": 137
    }
  },
  ToolMessage {
    "content": "36",
    "name": "multiply",
    "additional_kwargs": {},
    "response_metadata": {},
    "tool_call_id": "call_RbUuLMYf3vgcdSQ8bhy1D5Ty"
  },
  ToolMessage {
    "content": "60",
    "name": "add",
    "additional_kwargs": {},
    "response_metadata": {},
    "tool_call_id": "call_Bzz1qgQjTlQIHMcEaDAdoH8X"
  }
]

最后，我们将使用工具结果调用模型。模型将利用这些信息生成对我们原始查询的最终答案：

await llmWithTools.invoke(messages);
AIMessage {
  "id": "chatcmpl-9p1NttGpWjx1cQoVIDlMhumYq12Pe",
  "content": "3 * 12 is 36, and 11 + 49 is 60.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 19,
      "promptTokens": 153,
      "totalTokens": 172
    },
    "finish_reason": "stop",
    "system_fingerprint": "fp_18cc0f1fa0"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 153,
    "output_tokens": 19,
    "total_tokens": 172
  }
}

请注意，每个 ToolMessage 必须包含一个与模型生成的原始工具调用中的 id 相匹配的 tool_call_id。这有助于模型将工具响应与工具调用进行匹配。

像 LangGraph 中的工具调用代理一样，使用这种基本流程来回答查询和解决问题。

相关内容​

现在你已经了解了如何将工具调用传递回模型。

接下来你可能对以下指南感兴趣：

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