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审查工具调用

先决条件

本指南假定您熟悉以下概念

人机协作 (HIL) 交互对于智能体系统至关重要。一种常见模式是在某些工具调用后添加人工干预步骤。这些工具调用通常会导致函数调用或保存某些信息。示例包括

  • 一个执行 SQL 的工具调用，该调用随后将由工具运行
  • 一个生成摘要的工具调用，该摘要随后将保存到图的状态中

请注意，使用工具调用很常见，**无论是否实际调用工具**。

这里通常有几种不同的交互方式，您可能想要进行

  1. 批准工具调用并继续
  2. 手动修改工具调用，然后继续
  3. 提供自然语言反馈，然后将其传回给智能体而不是继续执行

我们可以在 LangGraph 中使用 interrupt() 函数来实现这些功能。interrupt 允许我们停止图执行以收集用户输入，并使用收集到的输入继续执行。

function humanReviewNode(state: typeof GraphAnnotation.State) {
  // this is the value we'll be providing via new Command({ resume: <human_review> })
  const humanReview = interrupt({
    question: "Is this correct?",
    // Surface tool calls for review
    tool_call,
  });

  const [reviewAction, reviewData] = humanReview;

  // Approve the tool call and continue
  if (reviewAction === "continue") {
    return new Command({ goto: "run_tool" });
  }

  // Modify the tool call manually and then continue
  if (reviewAction === "update") {
    const updatedMsg = getUpdatedMsg(reviewData);
    return new Command({ goto: "run_tool", update: { messages: [updatedMsg] } });
  }

  // Give natural language feedback, and then pass that back to the agent
  if (reviewAction === "feedback") {
    const feedbackMsg = getFeedbackMsg(reviewData);
    return new Command({ goto: "call_llm", update: { messages: [feedbackMsg] } });
  }

  throw new Error("Unreachable");
}

设置

首先，我们需要安装所需的软件包

npm install @langchain/langgraph @langchain/anthropic @langchain/core

接下来，我们需要为 Anthropic（我们将使用的 LLM）设置 API 密钥

export ANTHROPIC_API_KEY=your-api-key

可选地，我们可以设置 LangSmith tracing 的 API 密钥，这将为我们提供最佳的可观测性。

export LANGCHAIN_TRACING_V2="true"
export LANGCHAIN_CALLBACKS_BACKGROUND="true"
export LANGCHAIN_API_KEY=your-api-key

简单用法

让我们设置一个非常简单的图来促进这一点。首先，我们将有一个 LLM 调用，它决定采取什么行动。然后我们进入一个人工节点。这个节点实际上什么都不做——其想法是在这个节点之前中断，然后对状态应用任何更新。之后，我们检查状态，然后路由回 LLM 或正确的工具。

让我们看看实际操作！

import {
  MessagesAnnotation,
  StateGraph,
  START,
  END,
  MemorySaver,
  Command,
  interrupt
} from "@langchain/langgraph";
import { ChatAnthropic } from "@langchain/anthropic";
import { tool } from '@langchain/core/tools';
import { z } from 'zod';
import { AIMessage, ToolMessage } from '@langchain/core/messages';
import { ToolCall } from '@langchain/core/messages/tool';

const weatherSearch = tool((input: { city: string }) => {
    console.log("----");
    console.log(`Searching for: ${input.city}`);
    console.log("----");
    return "Sunny!";
}, {
    name: 'weather_search',
    description: 'Search for the weather',
    schema: z.object({
        city: z.string()
    })
});

const model = new ChatAnthropic({ 
    model: "claude-3-5-sonnet-latest"
}).bindTools([weatherSearch]);

const callLLM = async (state: typeof MessagesAnnotation.State) => {
    const response = await model.invoke(state.messages);
    return { messages: [response] };
};

const humanReviewNode = async (state: typeof MessagesAnnotation.State): Promise<Command> => {
    const lastMessage = state.messages[state.messages.length - 1] as AIMessage;
    const toolCall = lastMessage.tool_calls![lastMessage.tool_calls!.length - 1];

    const humanReview = interrupt<
      {
        question: string;
        toolCall: ToolCall;
      },
      {
        action: string;
        data: any;
      }>({
        question: "Is this correct?",
        toolCall: toolCall
      });

    const reviewAction = humanReview.action;
    const reviewData = humanReview.data;

    if (reviewAction === "continue") {
        return new Command({ goto: "run_tool" });
    }
    else if (reviewAction === "update") {
        const updatedMessage = {
            role: "ai",
            content: lastMessage.content,
            tool_calls: [{
                id: toolCall.id,
                name: toolCall.name,
                args: reviewData
            }],
            id: lastMessage.id
        };
        return new Command({ goto: "run_tool", update: { messages: [updatedMessage] } });
    }
    else if (reviewAction === "feedback") {
        const toolMessage = new ToolMessage({
          name: toolCall.name,
          content: reviewData,
          tool_call_id: toolCall.id
        })
        return new Command({ goto: "call_llm", update: { messages: [toolMessage] }});
    }
    throw new Error("Invalid review action");
};

const runTool = async (state: typeof MessagesAnnotation.State) => {
    const newMessages: ToolMessage[] = [];
    const tools = { weather_search: weatherSearch };
    const lastMessage = state.messages[state.messages.length - 1] as AIMessage;
    const toolCalls = lastMessage.tool_calls!;

    for (const toolCall of toolCalls) {
        const tool = tools[toolCall.name as keyof typeof tools];
        const result = await tool.invoke(toolCall.args);
        newMessages.push(new ToolMessage({
            name: toolCall.name,
            content: result,
            tool_call_id: toolCall.id
        }));
    }
    return { messages: newMessages };
};

const routeAfterLLM = (state: typeof MessagesAnnotation.State): typeof END | "human_review_node" => {
    const lastMessage = state.messages[state.messages.length - 1] as AIMessage;
    if (!lastMessage.tool_calls?.length) {
        return END;
    }
    return "human_review_node";
};

const workflow = new StateGraph(MessagesAnnotation)
    .addNode("call_llm", callLLM)
    .addNode("run_tool", runTool)
    .addNode("human_review_node", humanReviewNode, {
      ends: ["run_tool", "call_llm"]
    })
    .addEdge(START, "call_llm")
    .addConditionalEdges(
        "call_llm",
        routeAfterLLM,
        ["human_review_node", END]
    )
    .addEdge("run_tool", "call_llm");

const memory = new MemorySaver();

const graph = workflow.compile({ checkpointer: memory });

import * as tslab from "tslab";

const drawableGraph = graph.getGraph();
const image = await drawableGraph.drawMermaidPng();
const arrayBuffer = await image.arrayBuffer();

await tslab.display.png(new Uint8Array(arrayBuffer));

无需审查的示例

让我们看一个不需要审查的示例（因为没有调用任何工具）

let inputs = { messages: [{ role: "user", content: "hi!" }] };
let config = { configurable: { thread_id: "1" }, streamMode: "values" as const };

let stream = await graph.stream(inputs, config);

for await (const event of stream) {
    const recentMsg = event.messages[event.messages.length - 1];
    console.log(`================================ ${recentMsg._getType()} Message (1) =================================`)
    console.log(recentMsg.content);
}

================================ human Message (1) =================================
hi!
================================ ai Message (1) =================================
Hello! I'm here to help you. I can assist you with checking the weather for different cities. Would you like to know the weather for a specific location? Just let me know which city you're interested in and I'll look that up for you.
如果我们检查状态，可以看到它已经完成，因为没有下一步骤可采取

let state = await graph.getState(config);
console.log(state.next);

[]

批准工具的示例

现在让我们看看批准工具调用是什么样的

inputs = { messages: [{ role: "user", content: "what's the weather in SF?" }] };
config = { configurable: { thread_id: "2" }, streamMode: "values" as const };

stream = await graph.stream(inputs, config);

for await (const event of stream) {
    const recentMsg = event.messages[event.messages.length - 1];
    console.log(`================================ ${recentMsg._getType()} Message (1) =================================`)
    console.log(recentMsg.content);
}

================================ human Message (1) =================================
what's the weather in SF?
================================ ai Message (1) =================================
[
  {
    type: 'text',
    text: 'Let me check the weather in San Francisco for you.'
  },
  {
    type: 'tool_use',
    id: 'toolu_01PTn9oqTP6EdFabfhfvELuy',
    name: 'weather_search',
    input: { city: 'San Francisco' }
  }
]
如果我们现在检查，可以看到它正在等待人工审查

state = await graph.getState(config);
console.log(state.next);

[ 'human_review_node' ]
要批准工具调用，我们只需不经编辑地继续执行线程。为此，我们需要让 human_review_node 知道为我们在节点内部定义的 human_review 变量使用什么值。我们可以通过使用 new Command({ resume: <human_review> }) 输入来调用图以提供此值。由于我们正在批准工具调用，我们将提供值为 { action: "continue" } 的 resume 以导航到 run_tool 节点。

import { Command } from "@langchain/langgraph";

for await (const event of await graph.stream(
  new Command({ resume: { action: "continue" } }),
  config
)) {
  const recentMsg = event.messages[event.messages.length - 1];
  console.log(`================================ ${recentMsg._getType()} Message (1) =================================`)
  console.log(recentMsg.content);
}

================================ ai Message (1) =================================
[
  {
    type: 'text',
    text: 'Let me check the weather in San Francisco for you.'
  },
  {
    type: 'tool_use',
    id: 'toolu_01PTn9oqTP6EdFabfhfvELuy',
    name: 'weather_search',
    input: { city: 'San Francisco' }
  }
]
----
Searching for: San Francisco
----
================================ tool Message (1) =================================
Sunny!
================================ ai Message (1) =================================
It's sunny in San Francisco right now!

编辑工具调用

现在假设我们想编辑工具调用。例如，更改某些参数（甚至更改调用的工具！），然后执行该工具。

inputs = { messages: [{ role: "user", content: "what's the weather in SF?" }] };
config = { configurable: { thread_id: "3" }, streamMode: "values" as const };

stream = await graph.stream(inputs, config);

for await (const event of stream) {
    const recentMsg = event.messages[event.messages.length - 1];
    console.log(`================================ ${recentMsg._getType()} Message (1) =================================`)
    console.log(recentMsg.content);
}

================================ human Message (1) =================================
what's the weather in SF?
================================ ai Message (1) =================================
[
  {
    type: 'text',
    text: 'Let me check the weather in San Francisco for you.'
  },
  {
    type: 'tool_use',
    id: 'toolu_01T7ykQ45XyGpzRB7MkPtSAE',
    name: 'weather_search',
    input: { city: 'San Francisco' }
  }
]
如果我们现在检查，可以看到它正在等待人工审查

state = await graph.getState(config);
console.log(state.next);

[ 'human_review_node' ]
要编辑工具调用，我们将使用 Command，并提供不同的 resume 值 { action: "update", data: <tool call args> }。这将执行以下操作

  • 将现有工具调用与用户提供的工具调用参数结合，并用新的工具调用更新现有 AI 消息
  • 使用更新后的 AI 消息导航到 run_tool 节点并继续执行

for await (const event of await graph.stream(
  new Command({
    resume: {
      action: "update",
      data: { city: "San Francisco" }
    }
  }),
  config
)) {
  const recentMsg = event.messages[event.messages.length - 1];
  console.log(`================================ ${recentMsg._getType()} Message (1) =================================`)
  console.log(recentMsg.content);
}

================================ ai Message (1) =================================
[
  {
    type: 'text',
    text: 'Let me check the weather in San Francisco for you.'
  },
  {
    type: 'tool_use',
    id: 'toolu_01T7ykQ45XyGpzRB7MkPtSAE',
    name: 'weather_search',
    input: { city: 'San Francisco' }
  }
]
================================ ai Message (1) =================================
[
  {
    type: 'text',
    text: 'Let me check the weather in San Francisco for you.'
  },
  {
    type: 'tool_use',
    id: 'toolu_01T7ykQ45XyGpzRB7MkPtSAE',
    name: 'weather_search',
    input: { city: 'San Francisco' }
  }
]
----
Searching for: San Francisco
----
================================ tool Message (1) =================================
Sunny!
================================ ai Message (1) =================================
It's sunny in San Francisco right now!

对工具调用提供反馈

有时，您可能不想执行工具调用，但也不想让用户手动修改工具调用。在这种情况下，最好从用户那里获得自然语言反馈。然后您可以将这些反馈作为工具调用的模拟**结果**插入。

有多种方法可以做到这一点

  1. 您可以向状态添加一条新消息（表示工具调用的“结果”）
  2. 您可以向状态添加两条新消息——一条表示工具调用的“错误”，另一条 HumanMessage 表示反馈

两者都涉及向状态添加消息，这一点相似。主要区别在于 human_node 之后的逻辑以及它如何处理不同类型的消息。

对于这个示例，我们只添加一个表示反馈的工具调用。让我们看看实际操作！

inputs = { messages: [{ role: "user", content: "what's the weather in SF?" }] };
config = { configurable: { thread_id: "4" }, streamMode: "values" as const };

stream = await graph.stream(inputs, config);

for await (const event of stream) {
    const recentMsg = event.messages[event.messages.length - 1];
    console.log(`================================ ${recentMsg._getType()} Message (1) =================================`)
    console.log(recentMsg.content);
}

================================ human Message (1) =================================
what's the weather in SF?
================================ ai Message (1) =================================
[
  {
    type: 'text',
    text: "I'll help you check the weather in San Francisco."
  },
  {
    type: 'tool_use',
    id: 'toolu_014cwxD65wDwQdNg6xqsticF',
    name: 'weather_search',
    input: { city: 'SF' }
  }
]
如果我们现在检查，可以看到它正在等待人工审查

state = await graph.getState(config);
console.log(state.next);

[ 'human_review_node' ]
要对工具调用提供反馈，我们将使用 Command，并提供不同的 resume 值 { action: "feedback", data: <feedback string> }。这将执行以下操作

  • 创建一个新的工具消息，将来自 LLM 的现有工具调用与用户提供的反馈作为内容结合起来
  • 使用更新后的工具消息导航到 call_llm 节点并继续执行

for await (const event of await graph.stream(
  new Command({
    resume: {
      action: "feedback",
      data: "User requested changes: use <city, country> format for location"
    }
  }),
  config
)) {
  const recentMsg = event.messages[event.messages.length - 1];
  console.log(`================================ ${recentMsg._getType()} Message (1) =================================`)
  console.log(recentMsg.content);
}

================================ ai Message (1) =================================
[
  {
    type: 'text',
    text: "I'll help you check the weather in San Francisco."
  },
  {
    type: 'tool_use',
    id: 'toolu_014cwxD65wDwQdNg6xqsticF',
    name: 'weather_search',
    input: { city: 'SF' }
  }
]
================================ tool Message (1) =================================
User requested changes: use <city, country> format for location
================================ ai Message (1) =================================
[
  {
    type: 'text',
    text: 'I apologize for the error. Let me search again with the proper format.'
  },
  {
    type: 'tool_use',
    id: 'toolu_01Jnm7sSZsiwv65YM4KsvfXk',
    name: 'weather_search',
    input: { city: 'San Francisco, USA' }
  }
]
我们可以看到，现在我们到达了另一个断点——因为它回到了模型并得到了一个全新的调用预测。现在让我们批准这个并继续。

state = await graph.getState(config);
console.log(state.next);

[ 'human_review_node' ]


for await (const event of await graph.stream(
  new Command({
    resume: {
      action: "continue",
    }
  }),
  config
)) {
    const recentMsg = event.messages[event.messages.length - 1];
    console.log(`================================ ${recentMsg._getType()} Message (1) =================================`)
    console.log(recentMsg.content);
}

================================ ai Message (1) =================================
[
  {
    type: 'text',
    text: 'I apologize for the error. Let me search again with the proper format.'
  },
  {
    type: 'tool_use',
    id: 'toolu_01Jnm7sSZsiwv65YM4KsvfXk',
    name: 'weather_search',
    input: { city: 'San Francisco, USA' }
  }
]
----
Searching for: San Francisco, USA
----
================================ tool Message (1) =================================
Sunny!
================================ ai Message (1) =================================
The weather in San Francisco is currently sunny!