构建一个聊天机器人

预备知识

本指南假定您熟悉以下概念：

本指南需要 langgraph >= 0.2.28。

note

本教程之前使用 RunnableWithMessageHistory 构建了一个聊天机器人。您可以在 v0.2 文档中访问本教程的这个版本。

LangGraph 实现相比 RunnableWithMessageHistory 提供了许多优势，包括能够持久化应用程序状态的任意组件（而不仅仅是消息）。

概述

我们将介绍如何设计和实现一个由大型语言模型（LLM）驱动的聊天机器人。这个聊天机器人将能够进行对话并记住之前的交互。

请注意，我们构建的这个聊天机器人将仅使用语言模型进行对话。您可能还会对以下几个相关概念感兴趣：

对话式检索增强生成（Conversational RAG）：在外部数据源上启用聊天机器人体验
代理（Agents）：构建能够执行操作的聊天机器人

本教程将涵盖基础知识，这些知识将对这两个更高级的主题有所帮助，但如果您愿意，也可以直接跳转到那里。

准备工作

Jupyter Notebook

本指南（以及文档中的大多数其他指南）使用 Jupyter notebooks 并假定读者也使用它。Jupyter notebooks 非常适合学习如何与大型语言模型系统协作，因为很多时候事情可能会出错（输出意外、API 不可用等），在交互式环境中学习指南是更好地理解它们的好方法。

这本教程和其他教程可能最适合在 Jupyter notebook 中运行。有关安装说明，请参见此处。

安装

在本教程中，我们需要安装 @langchain/core 和 langgraph：

npm
yarn
pnpm

npm i @langchain/core @langchain/langgraph uuid

yarn add @langchain/core @langchain/langgraph uuid

pnpm add @langchain/core @langchain/langgraph uuid

更多详细信息，请参见我们的安装指南。

LangSmith

您使用 LangChain 构建的许多应用程序将包含多个步骤和多次调用 LLM。随着这些应用程序变得越来越复杂，能够检查您的链或代理内部发生了什么变得至关重要。最佳方式是使用 LangSmith。

在上面的链接注册后，请确保设置您的环境变量以开始记录追踪信息：

process.env.LANGSMITH_TRACING = "true";
process.env.LANGSMITH_API_KEY = "...";

快速入门

首先，让我们学习如何单独使用一个语言模型。LangChain 支持许多不同的语言模型，您可以根据需要选择使用以下任意一个！

Pick your chat model:

Install dependencies

tip

See this section for general instructions on installing integration packages.

npm
yarn
pnpm

npm i @langchain/groq

yarn add @langchain/groq 

pnpm 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
});

Install dependencies

tip

See this section for general instructions on installing integration packages.

npm
yarn
pnpm

npm i @langchain/openai

yarn add @langchain/openai 

pnpm add @langchain/openai 

Add environment variables

OPENAI_API_KEY=your-api-key

Instantiate the model

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
  temperature: 0
});

Install dependencies

tip

See this section for general instructions on installing integration packages.

npm
yarn
pnpm

npm i @langchain/anthropic

yarn add @langchain/anthropic 

pnpm add @langchain/anthropic 

Add environment variables

ANTHROPIC_API_KEY=your-api-key

Instantiate the model

import { ChatAnthropic } from "@langchain/anthropic";

const llm = new ChatAnthropic({
  model: "claude-3-5-sonnet-20240620",
  temperature: 0
});

Install dependencies

tip

See this section for general instructions on installing integration packages.

npm
yarn
pnpm

npm i @langchain/google-genai

yarn add @langchain/google-genai 

pnpm add @langchain/google-genai 

Add environment variables

GOOGLE_API_KEY=your-api-key

Instantiate the model

import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

const llm = new ChatGoogleGenerativeAI({
  model: "gemini-2.0-flash",
  temperature: 0
});

Install dependencies

tip

See this section for general instructions on installing integration packages.

npm
yarn
pnpm

npm i @langchain/community

yarn add @langchain/community 

pnpm add @langchain/community 

Add environment variables

FIREWORKS_API_KEY=your-api-key

Instantiate the model

import { ChatFireworks } from "@langchain/community/chat_models/fireworks";

const llm = new ChatFireworks({
  model: "accounts/fireworks/models/llama-v3p1-70b-instruct",
  temperature: 0
});

Install dependencies

tip

See this section for general instructions on installing integration packages.

npm
yarn
pnpm

npm i @langchain/mistralai

yarn add @langchain/mistralai 

pnpm add @langchain/mistralai 

Add environment variables

MISTRAL_API_KEY=your-api-key

Instantiate the model

import { ChatMistralAI } from "@langchain/mistralai";

const llm = new ChatMistralAI({
  model: "mistral-large-latest",
  temperature: 0
});

Install dependencies

tip

See this section for general instructions on installing integration packages.

npm
yarn
pnpm

npm i @langchain/google-vertexai

yarn add @langchain/google-vertexai 

pnpm add @langchain/google-vertexai 

Add environment variables

GOOGLE_APPLICATION_CREDENTIALS=credentials.json

Instantiate the model

import { ChatVertexAI } from "@langchain/google-vertexai";

const llm = new ChatVertexAI({
  model: "gemini-1.5-flash",
  temperature: 0
});

我们首先直接使用该模型。ChatModel 是 LangChain “Runnables” 的实例，这意味着它们提供了一个标准接口用于与它们进行交互。要简单地调用模型，我们可以将消息列表传递给 .invoke 方法。

await llm.invoke([{ role: "user", content: "Hi im bob" }]);

AIMessage {
  "id": "chatcmpl-AekDrrCyaBauLYHuVv3dkacxW2G1J",
  "content": "Hi Bob! How can I help you today?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 10,
      "completionTokens": 10,
      "totalTokens": 20
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 10,
      "completion_tokens": 10,
      "total_tokens": 20,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_6fc10e10eb"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 10,
    "input_tokens": 10,
    "total_tokens": 20,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

模型本身没有任何状态的概念。例如，如果你提出一个后续问题：

await llm.invoke([{ role: "user", content: "Whats my name" }]);

AIMessage {
  "id": "chatcmpl-AekDuOk1LjOdBVLtuCvuHjAs5aoad",
  "content": "I'm sorry, but I don't have access to personal information about users unless you've shared it with me in this conversation. How can I assist you today?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 10,
      "completionTokens": 30,
      "totalTokens": 40
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 10,
      "completion_tokens": 30,
      "total_tokens": 40,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_6fc10e10eb"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 30,
    "input_tokens": 10,
    "total_tokens": 40,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

让我们看一下这个示例 LangSmith 追踪

我们可以看到，它没有将之前的对话轮次纳入上下文，因此无法回答问题。这会导致非常糟糕的聊天机器人体验！

为了解决这个问题，我们需要将整个对话历史传递给模型。让我们看看这样做的时候会发生什么：

await llm.invoke([
  { role: "user", content: "Hi! I'm Bob" },
  { role: "assistant", content: "Hello Bob! How can I assist you today?" },
  { role: "user", content: "What's my name?" },
]);

AIMessage {
  "id": "chatcmpl-AekDyJdj6y9IREyNIf3tkKGRKhN1Z",
  "content": "Your name is Bob! How can I help you today, Bob?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 33,
      "completionTokens": 14,
      "totalTokens": 47
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 33,
      "completion_tokens": 14,
      "total_tokens": 47,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_6fc10e10eb"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 14,
    "input_tokens": 33,
    "total_tokens": 47,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

现在我们可以看到我们得到了一个很好的回复！

这就是聊天机器人能够进行对话交互的基本原理。那么我们该如何最好地实现这一点呢？

消息持久化

LangGraph 实现了一个内置的持久化层，使其非常适合支持多轮对话的聊天应用。

将我们的聊天模型封装在一个最小的 LangGraph 应用中，可以自动持久化消息历史记录，从而简化多轮应用的开发。

LangGraph 配备了一个简单的内存检查点工具，我们在下面使用它。

import {
  START,
  END,
  MessagesAnnotation,
  StateGraph,
  MemorySaver,
} from "@langchain/langgraph";

// Define the function that calls the model
const callModel = async (state: typeof MessagesAnnotation.State) => {
  const response = await llm.invoke(state.messages);
  return { messages: response };
};

// Define a new graph
const workflow = new StateGraph(MessagesAnnotation)
  // Define the node and edge
  .addNode("model", callModel)
  .addEdge(START, "model")
  .addEdge("model", END);

// Add memory
const memory = new MemorySaver();
const app = workflow.compile({ checkpointer: memory });

我们现在需要创建一个 config，每次都要将其传递给可运行对象。此 config 包含不直接属于输入但仍然有用的信息。在这种情况下，我们想要包含一个 thread_id。它应该如下所示：

import { v4 as uuidv4 } from "uuid";

const config = { configurable: { thread_id: uuidv4() } };

这使我们能够使用单个应用程序支持多个对话线程，当您的应用程序具有多个用户时，这是一个常见需求。

然后我们可以调用该应用程序：

const input = [
  {
    role: "user",
    content: "Hi! I'm Bob.",
  },
];
const output = await app.invoke({ messages: input }, config);
// The output contains all messages in the state.
// This will log the last message in the conversation.
console.log(output.messages[output.messages.length - 1]);

AIMessage {
  "id": "chatcmpl-AekEFPclmrO7YfAe7J0zUAanS4ifx",
  "content": "Hi Bob! How can I assist you today?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 12,
      "completionTokens": 10,
      "totalTokens": 22
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 12,
      "completion_tokens": 10,
      "total_tokens": 22,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_6fc10e10eb"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 10,
    "input_tokens": 12,
    "total_tokens": 22,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

const input2 = [
  {
    role: "user",
    content: "What's my name?",
  },
];
const output2 = await app.invoke({ messages: input2 }, config);
console.log(output2.messages[output2.messages.length - 1]);

AIMessage {
  "id": "chatcmpl-AekEJgCfLodGCcuLgLQdJevH7CpCJ",
  "content": "Your name is Bob! How can I help you today, Bob?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 34,
      "completionTokens": 14,
      "totalTokens": 48
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 34,
      "completion_tokens": 14,
      "total_tokens": 48,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_6fc10e10eb"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 14,
    "input_tokens": 34,
    "total_tokens": 48,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

太好了！我们的聊天机器人现在可以记住关于我们的信息。如果我们更改配置以引用不同的 thread_id，我们会看到它重新开始对话。

const config2 = { configurable: { thread_id: uuidv4() } };
const input3 = [
  {
    role: "user",
    content: "What's my name?",
  },
];
const output3 = await app.invoke({ messages: input3 }, config2);
console.log(output3.messages[output3.messages.length - 1]);

AIMessage {
  "id": "chatcmpl-AekELvPXLtjOKgLN63mQzZwvyo12J",
  "content": "I'm sorry, but I don't have access to personal information about individuals unless you share it with me. How can I assist you today?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 11,
      "completionTokens": 27,
      "totalTokens": 38
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 11,
      "completion_tokens": 27,
      "total_tokens": 38,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_39a40c96a0"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 27,
    "input_tokens": 11,
    "total_tokens": 38,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

但是，我们始终可以回溯到原始对话（因为我们将其持久化存储在数据库中）

const output4 = await app.invoke({ messages: input2 }, config);
console.log(output4.messages[output4.messages.length - 1]);

AIMessage {
  "id": "chatcmpl-AekEQ8Z5JmYquSfzPsCWv1BDTKZSh",
  "content": "Your name is Bob. Is there something specific you would like to talk about?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 60,
      "completionTokens": 16,
      "totalTokens": 76
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 60,
      "completion_tokens": 16,
      "total_tokens": 76,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_39a40c96a0"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 16,
    "input_tokens": 60,
    "total_tokens": 76,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

这就是我们如何支持聊天机器人与多个用户进行对话！

目前，我们所做的只是在模型周围添加了一个简单的持久化层。我们可以通过添加提示模板（prompt template）来使其变得更加复杂和个性化。

提示模板

提示模板有助于将原始用户信息转换为 LLM 可以处理的格式。在这种情况下，原始用户输入只是一个消息，我们将其传递给 LLM。现在我们让这个过程稍微复杂一些。首先，让我们添加一条带有自定义指令的系统消息（system message），但仍然接收消息作为输入。接下来，我们将添加除消息之外的更多输入。

要添加系统消息，我们将创建一个ChatPromptTemplate。我们将使用MessagesPlaceholder将所有消息传递进去。

import { ChatPromptTemplate } from "@langchain/core/prompts";

const promptTemplate = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You talk like a pirate. Answer all questions to the best of your ability.",
  ],
  ["placeholder", "{messages}"],
]);

现在我们可以更新我们的应用程序以包含此模板：

import {
  START,
  END,
  MessagesAnnotation,
  StateGraph,
  MemorySaver,
} from "@langchain/langgraph";

// Define the function that calls the model
const callModel2 = async (state: typeof MessagesAnnotation.State) => {
  const prompt = await promptTemplate.invoke(state);
  const response = await llm.invoke(prompt);
  // Update message history with response:
  return { messages: [response] };
};

// Define a new graph
const workflow2 = new StateGraph(MessagesAnnotation)
  // Define the (single) node in the graph
  .addNode("model", callModel2)
  .addEdge(START, "model")
  .addEdge("model", END);

// Add memory
const app2 = workflow2.compile({ checkpointer: new MemorySaver() });

我们以相同的方式调用应用程序：

const config3 = { configurable: { thread_id: uuidv4() } };
const input4 = [
  {
    role: "user",
    content: "Hi! I'm Jim.",
  },
];
const output5 = await app2.invoke({ messages: input4 }, config3);
console.log(output5.messages[output5.messages.length - 1]);

AIMessage {
  "id": "chatcmpl-AekEYAQVqh9OFZRGdzGiPz33WPf1v",
  "content": "Ahoy, Jim! A pleasure to meet ye, matey! What be on yer mind this fine day?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 32,
      "completionTokens": 23,
      "totalTokens": 55
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 32,
      "completion_tokens": 23,
      "total_tokens": 55,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_39a40c96a0"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 23,
    "input_tokens": 32,
    "total_tokens": 55,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

const input5 = [
  {
    role: "user",
    content: "What is my name?",
  },
];
const output6 = await app2.invoke({ messages: input5 }, config3);
console.log(output6.messages[output6.messages.length - 1]);

AIMessage {
  "id": "chatcmpl-AekEbrpFI3K8BxemHZ5fG4xF2tT8x",
  "content": "Ye be callin' yerself Jim, if I heard ye right, savvy? What else can I do fer ye, me hearty?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 68,
      "completionTokens": 29,
      "totalTokens": 97
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 68,
      "completion_tokens": 29,
      "total_tokens": 97,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_6fc10e10eb"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 29,
    "input_tokens": 68,
    "total_tokens": 97,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

太棒了！现在让我们让提示词稍微复杂一点。假设提示模板现在看起来像这样：

const promptTemplate2 = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are a helpful assistant. Answer all questions to the best of your ability in {language}.",
  ],
  ["placeholder", "{messages}"],
]);

请注意，我们已在提示中添加了新的 language 输入。我们的应用程序现在有两个参数——输入的 messages 和 language。我们需要更新应用程序的状态以反映此更改：

import {
  START,
  END,
  StateGraph,
  MemorySaver,
  MessagesAnnotation,
  Annotation,
} from "@langchain/langgraph";

// Define the State
const GraphAnnotation = Annotation.Root({
  ...MessagesAnnotation.spec,
  language: Annotation<string>(),
});

// Define the function that calls the model
const callModel3 = async (state: typeof GraphAnnotation.State) => {
  const prompt = await promptTemplate2.invoke(state);
  const response = await llm.invoke(prompt);
  return { messages: [response] };
};

const workflow3 = new StateGraph(GraphAnnotation)
  .addNode("model", callModel3)
  .addEdge(START, "model")
  .addEdge("model", END);

const app3 = workflow3.compile({ checkpointer: new MemorySaver() });

const config4 = { configurable: { thread_id: uuidv4() } };
const input6 = {
  messages: [
    {
      role: "user",
      content: "Hi im bob",
    },
  ],
  language: "Spanish",
};
const output7 = await app3.invoke(input6, config4);
console.log(output7.messages[output7.messages.length - 1]);

AIMessage {
  "id": "chatcmpl-AekF4R7ioefFo6PmOYo3YuCbGpROq",
  "content": "¡Hola, Bob! ¿Cómo puedo ayudarte hoy?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 32,
      "completionTokens": 11,
      "totalTokens": 43
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 32,
      "completion_tokens": 11,
      "total_tokens": 43,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_39a40c96a0"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 11,
    "input_tokens": 32,
    "total_tokens": 43,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

请注意，整个状态都会被持久化，因此如果没有更改需求，我们可以省略诸如 language 之类的参数：

const input7 = {
  messages: [
    {
      role: "user",
      content: "What is my name?",
    },
  ],
};
const output8 = await app3.invoke(input7, config4);
console.log(output8.messages[output8.messages.length - 1]);

AIMessage {
  "id": "chatcmpl-AekF8yN7H81ITccWlBzSahmduP69T",
  "content": "Tu nombre es Bob. ¿En qué puedo ayudarte, Bob?",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 56,
      "completionTokens": 13,
      "totalTokens": 69
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 56,
      "completion_tokens": 13,
      "total_tokens": 69,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_6fc10e10eb"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 13,
    "input_tokens": 56,
    "total_tokens": 69,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

为了帮助您了解内部发生的情况，请查看此 LangSmith 跟踪。

管理对话历史

构建聊天机器人时需要理解的一个重要概念是如何管理对话历史。如果任其发展，消息列表将无限增长，并可能导致超出 LLM 的上下文窗口限制。因此，重要的是增加一个步骤来限制传入消息的大小。

需要注意的是，此步骤应在应用提示模板之前，但在从消息历史中加载先前消息之后进行。

我们可以通过在提示模板前添加一个简单的步骤来实现这一点，该步骤适当地修改messages键，然后将这个新的链封装到消息历史类中。

LangChain 提供了一些内建的帮助函数来管理消息列表。在本例中，我们将使用trimMessages工具来减少发送给模型的消息数量。该修剪器允许我们指定需要保留的 token 数量，以及其他参数，例如是否始终保留系统消息以及是否允许部分消息：

import {
  SystemMessage,
  HumanMessage,
  AIMessage,
  trimMessages,
} from "@langchain/core/messages";

const trimmer = trimMessages({
  maxTokens: 10,
  strategy: "last",
  tokenCounter: (msgs) => msgs.length,
  includeSystem: true,
  allowPartial: false,
  startOn: "human",
});

const messages = [
  new SystemMessage("you're a good assistant"),
  new HumanMessage("hi! I'm bob"),
  new AIMessage("hi!"),
  new HumanMessage("I like vanilla ice cream"),
  new AIMessage("nice"),
  new HumanMessage("whats 2 + 2"),
  new AIMessage("4"),
  new HumanMessage("thanks"),
  new AIMessage("no problem!"),
  new HumanMessage("having fun?"),
  new AIMessage("yes!"),
];

await trimmer.invoke(messages);

[
  SystemMessage {
    "content": "you're a good assistant",
    "additional_kwargs": {},
    "response_metadata": {}
  },
  HumanMessage {
    "content": "I like vanilla ice cream",
    "additional_kwargs": {},
    "response_metadata": {}
  },
  AIMessage {
    "content": "nice",
    "additional_kwargs": {},
    "response_metadata": {},
    "tool_calls": [],
    "invalid_tool_calls": []
  },
  HumanMessage {
    "content": "whats 2 + 2",
    "additional_kwargs": {},
    "response_metadata": {}
  },
  AIMessage {
    "content": "4",
    "additional_kwargs": {},
    "response_metadata": {},
    "tool_calls": [],
    "invalid_tool_calls": []
  },
  HumanMessage {
    "content": "thanks",
    "additional_kwargs": {},
    "response_metadata": {}
  },
  AIMessage {
    "content": "no problem!",
    "additional_kwargs": {},
    "response_metadata": {},
    "tool_calls": [],
    "invalid_tool_calls": []
  },
  HumanMessage {
    "content": "having fun?",
    "additional_kwargs": {},
    "response_metadata": {}
  },
  AIMessage {
    "content": "yes!",
    "additional_kwargs": {},
    "response_metadata": {},
    "tool_calls": [],
    "invalid_tool_calls": []
  }
]

在我们的链中使用它时，只需在将 messages 输入传递给提示之前运行修剪器即可。

const callModel4 = async (state: typeof GraphAnnotation.State) => {
  const trimmedMessage = await trimmer.invoke(state.messages);
  const prompt = await promptTemplate2.invoke({
    messages: trimmedMessage,
    language: state.language,
  });
  const response = await llm.invoke(prompt);
  return { messages: [response] };
};

const workflow4 = new StateGraph(GraphAnnotation)
  .addNode("model", callModel4)
  .addEdge(START, "model")
  .addEdge("model", END);

const app4 = workflow4.compile({ checkpointer: new MemorySaver() });

现在如果我们尝试问模型我们的名字，它不会知道，因为我们已经删掉了聊天记录中的那部分内容：

const config5 = { configurable: { thread_id: uuidv4() } };
const input8 = {
  messages: [...messages, new HumanMessage("What is my name?")],
  language: "English",
};

const output9 = await app4.invoke(input8, config5);
console.log(output9.messages[output9.messages.length - 1]);

AIMessage {
  "id": "chatcmpl-AekHyVN7f0Pnuyc2RHVL8CxKmFfMQ",
  "content": "I don't know your name. You haven't shared it yet!",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 97,
      "completionTokens": 12,
      "totalTokens": 109
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 97,
      "completion_tokens": 12,
      "total_tokens": 109,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_6fc10e10eb"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 12,
    "input_tokens": 97,
    "total_tokens": 109,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

但如果我们在最近的几条消息中询问信息，它是能记住的：

const config6 = { configurable: { thread_id: uuidv4() } };
const input9 = {
  messages: [...messages, new HumanMessage("What math problem did I ask?")],
  language: "English",
};

const output10 = await app4.invoke(input9, config6);
console.log(output10.messages[output10.messages.length - 1]);

AIMessage {
  "id": "chatcmpl-AekI1jwlErzHuZ3BhAxr97Ct818Pp",
  "content": "You asked what 2 + 2 equals.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 99,
      "completionTokens": 10,
      "totalTokens": 109
    },
    "finish_reason": "stop",
    "usage": {
      "prompt_tokens": 99,
      "completion_tokens": 10,
      "total_tokens": 109,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    },
    "system_fingerprint": "fp_6fc10e10eb"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "output_tokens": 10,
    "input_tokens": 99,
    "total_tokens": 109,
    "input_token_details": {
      "audio": 0,
      "cache_read": 0
    },
    "output_token_details": {
      "audio": 0,
      "reasoning": 0
    }
  }
}

如果你查看 LangSmith，你可以确切地看到 LangSmith 追踪中底层发生了什么。

下一步

既然你已经了解了如何在 LangChain 中创建聊天机器人的基础知识，你可能还会对以下更高级的教程感兴趣：

对话式 RAG：在外部数据源上实现聊天机器人体验
代理：构建能够执行操作的聊天机器人

如果你想深入了解具体细节，以下内容也值得查阅：

流式传输：流式传输对于聊天应用至关重要
如何添加消息历史记录：深入了解与消息历史相关的所有内容
如何管理大量消息历史记录：管理大量聊天历史的更多技巧
LangGraph 主文档：深入了解使用 LangGraph 构建应用的更多细节

构建一个聊天机器人

概述

准备工作

Jupyter Notebook

安装

LangSmith

快速入门

Pick your chat model:

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

消息持久化

提示模板

管理对话历史

下一步

Was this page helpful?

You can also leave detailed feedback on GitHub.

概述​

准备工作​

Jupyter Notebook​

安装​

LangSmith​

快速入门​

Pick your chat model:

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

Install dependencies

Add environment variables

Instantiate the model

消息持久化​

提示模板​

管理对话历史​

下一步​

Was this page helpful?

You can also leave detailed feedback on GitHub.

概述

准备工作

Jupyter Notebook

安装

LangSmith

快速入门

消息持久化

提示模板

管理对话历史

下一步