如何从图中流式传输LLM token¶

在此示例中，我们将从驱动代理的语言模型中流式传输token。我们将使用一个ReAct代理作为示例。

注意

如果您正在使用版本低于 0.2.3 的 @langchain/core，在调用聊天模型或LLM时，您需要在节点内调用 await model.stream() 以获取逐token的流式事件，并在需要时聚合最终输出以更新图状态。在 @langchain/core 的更高版本中，这会自动发生，您可以调用 await model.invoke()。
有关如何升级 @langchain/core 的更多信息，请查阅此处的说明。

本操作指南与此目录中的其他指南密切相关，展示了如何将功能集成到LangGraph中的原型代理中。

流式传输支持

许多（但并非所有）聊天模型都支持token流式传输。请在此处（文档）查看您的LLM集成是否支持token流式传输。请注意，某些集成可能支持一般的token流式传输，但缺乏对流式工具调用的支持。

注意

在本操作指南中，我们将从头开始创建代理，以使其透明（但冗长）。您可以使用 createReactAgent({ llm, tools })（API 文档）构造函数实现类似功能。如果您习惯于LangChain的 AgentExecutor 类，这可能更合适。

设置¶

本指南将使用 OpenAI 的 GPT-4o 模型。我们可以选择设置用于 LangSmith 追踪的 API 密钥，这将为我们提供一流的可观察性。

// process.env.OPENAI_API_KEY = "sk_...";

// Optional, add tracing in LangSmith
// process.env.LANGCHAIN_API_KEY = "ls__...";
// process.env.LANGCHAIN_CALLBACKS_BACKGROUND = "true";
// process.env.LANGCHAIN_TRACING = "true";
// process.env.LANGCHAIN_PROJECT = "Stream Tokens: LangGraphJS";

定义状态¶

状态是我们图中所有节点的接口。

import { Annotation } from "@langchain/langgraph";
import type { BaseMessageLike } from "@langchain/core/messages";

const StateAnnotation = Annotation.Root({
  messages: Annotation<BaseMessageLike[]>({
    reducer: (x, y) => x.concat(y),
  }),
});

设置工具¶

首先定义您要使用的工具。对于这个简单的示例，我们将创建一个占位符搜索引擎，但有关如何创建自己的自定义工具，请参阅此处的文档。

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

const searchTool = tool((_) => {
  // This is a placeholder for the actual implementation
  return "Cold, with a low of 3℃";
}, {
  name: "search",
  description:
    "Use to surf the web, fetch current information, check the weather, and retrieve other information.",
  schema: z.object({
    query: z.string().describe("The query to use in your search."),
  }),
});

await searchTool.invoke({ query: "What's the weather like?" });

const tools = [searchTool];

现在我们可以将这些工具封装在一个预构建的 ToolNode 中。每当我们的LLM调用这些工具（函数）时，此对象将实际运行它们。

import { ToolNode } from "@langchain/langgraph/prebuilt";

const toolNode = new ToolNode(tools);

设置模型¶

现在加载聊天模型。

它应该能与消息配合使用。我们将所有代理状态都以消息的形式表示，因此它需要能够很好地与消息配合使用。
它应该能与工具调用配合使用，这意味着它可以在响应中返回函数参数。

注意

这些模型要求并非 LangGraph 的通用要求——它们仅是此示例的要求。

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

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

完成此操作后，我们应该确保模型知道它有这些可供调用的工具。我们可以通过调用 bindTools 来实现这一点。

const boundModel = model.bindTools(tools);

定义图¶

现在我们可以将所有内容整合在一起。

import { StateGraph, END } from "@langchain/langgraph";
import { AIMessage } from "@langchain/core/messages";

const routeMessage = (state: typeof StateAnnotation.State) => {
  const { messages } = state;
  const lastMessage = messages[messages.length - 1] as AIMessage;
  // If no tools are called, we can finish (respond to the user)
  if (!lastMessage?.tool_calls?.length) {
    return END;
  }
  // Otherwise if there is, we continue and call the tools
  return "tools";
};

const callModel = async (
  state: typeof StateAnnotation.State,
) => {
  // For versions of @langchain/core < 0.2.3, you must call `.stream()`
  // and aggregate the message from chunks instead of calling `.invoke()`.
  const { messages } = state;
  const responseMessage = await boundModel.invoke(messages);
  return { messages: [responseMessage] };
};

const workflow = new StateGraph(StateAnnotation)
  .addNode("agent", callModel)
  .addNode("tools", toolNode)
  .addEdge("__start__", "agent")
  .addConditionalEdges("agent", routeMessage)
  .addEdge("tools", "agent");

const agent = workflow.compile();

import * as tslab from "tslab";

const runnableGraph = agent.getGraph();
const image = await runnableGraph.drawMermaidPng();
const arrayBuffer = await image.arrayBuffer();

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

流式传输LLM Token¶

您可以使用两种方法访问每个节点生成的LLM token

stream 方法以及 streamMode: "messages"
streamEvents 方法

stream 方法¶

兼容性

本节需要 @langchain/langgraph>=0.2.20。有关升级帮助，请参阅此指南。

对于此方法，您还必须使用支持流式传输的LLM（例如 new ChatOpenAI({ model: "gpt-4o-mini" })）或在内部LLM调用上调用 .stream。

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

const stream = await agent.stream(
  { messages: [{ role: "user", content: "What's the current weather in Nepal?" }] },
  { streamMode: "messages" },
);

for await (const [message, _metadata] of stream) {
  if (isAIMessageChunk(message) && message.tool_call_chunks?.length) {
    console.log(`${message.getType()} MESSAGE TOOL CALL CHUNK: ${message.tool_call_chunks[0].args}`);
  } else {
    console.log(`${message.getType()} MESSAGE CONTENT: ${message.content}`);
  }
}

ai MESSAGE TOOL CALL CHUNK: 
ai MESSAGE TOOL CALL CHUNK: {"
ai MESSAGE TOOL CALL CHUNK: query
ai MESSAGE TOOL CALL CHUNK: ":"
ai MESSAGE TOOL CALL CHUNK: current
ai MESSAGE TOOL CALL CHUNK:  weather
ai MESSAGE TOOL CALL CHUNK:  in
ai MESSAGE TOOL CALL CHUNK:  Nepal
ai MESSAGE TOOL CALL CHUNK: "}
ai MESSAGE CONTENT: 
tool MESSAGE CONTENT: Cold, with a low of 3℃
ai MESSAGE CONTENT: 
ai MESSAGE CONTENT: The
ai MESSAGE CONTENT:  current
ai MESSAGE CONTENT:  weather
ai MESSAGE CONTENT:  in
ai MESSAGE CONTENT:  Nepal
ai MESSAGE CONTENT:  is
ai MESSAGE CONTENT:  cold
ai MESSAGE CONTENT: ,
ai MESSAGE CONTENT:  with
ai MESSAGE CONTENT:  a
ai MESSAGE CONTENT:  low
ai MESSAGE CONTENT:  temperature
ai MESSAGE CONTENT:  of
ai MESSAGE CONTENT:  
ai MESSAGE CONTENT: 3
ai MESSAGE CONTENT: ℃
ai MESSAGE CONTENT: .
ai MESSAGE CONTENT:

禁用流式传输¶

如果您希望禁用给定节点或模型调用的流式传输，可以添加一个 "nostream" 标签。这是一个示例，我们添加了一个初始节点，其中包含一个LLM调用，该调用不会在最终输出中进行流式传输

import { RunnableLambda } from "@langchain/core/runnables";

const unstreamed = async (_: typeof StateAnnotation.State) => {
  const model = new ChatOpenAI({
    model: "gpt-4o-mini",
    temperature: 0,
  });
  const res = await model.invoke("How are you?");
  console.log("LOGGED UNSTREAMED MESSAGE", res.content);
  // Don't update the state, this is just to show a call that won't be streamed
  return {};
}

const agentWithNoStream = new StateGraph(StateAnnotation)
  .addNode("unstreamed",
    // Add a "nostream" tag to the entire node
    RunnableLambda.from(unstreamed).withConfig({
      tags: ["nostream"]
    })
  )
  .addNode("agent", callModel)
  .addNode("tools", toolNode)
  // Run the unstreamed node before the agent
  .addEdge("__start__", "unstreamed")
  .addEdge("unstreamed", "agent")
  .addConditionalEdges("agent", routeMessage)
  .addEdge("tools", "agent")
  .compile();

const stream = await agentWithNoStream.stream(
  { messages: [{ role: "user", content: "What's the current weather in Nepal?" }] },
  { streamMode: "messages" },
);

for await (const [message, _metadata] of stream) {
  if (isAIMessageChunk(message) && message.tool_call_chunks?.length) {
    console.log(`${message.getType()} MESSAGE TOOL CALL CHUNK: ${message.tool_call_chunks[0].args}`);
  } else {
    console.log(`${message.getType()} MESSAGE CONTENT: ${message.content}`);
  }
}

LOGGED UNSTREAMED MESSAGE I'm just a computer program, so I don't have feelings, but I'm here and ready to help you! How can I assist you today?
ai MESSAGE TOOL CALL CHUNK: 
ai MESSAGE TOOL CALL CHUNK: {"
ai MESSAGE TOOL CALL CHUNK: query
ai MESSAGE TOOL CALL CHUNK: ":"
ai MESSAGE TOOL CALL CHUNK: current
ai MESSAGE TOOL CALL CHUNK:  weather
ai MESSAGE TOOL CALL CHUNK:  in
ai MESSAGE TOOL CALL CHUNK:  Nepal
ai MESSAGE TOOL CALL CHUNK: "}
ai MESSAGE CONTENT: 
tool MESSAGE CONTENT: Cold, with a low of 3℃
ai MESSAGE CONTENT: 
ai MESSAGE CONTENT: The
ai MESSAGE CONTENT:  current
ai MESSAGE CONTENT:  weather
ai MESSAGE CONTENT:  in
ai MESSAGE CONTENT:  Nepal
ai MESSAGE CONTENT:  is
ai MESSAGE CONTENT:  cold
ai MESSAGE CONTENT: ,
ai MESSAGE CONTENT:  with
ai MESSAGE CONTENT:  a
ai MESSAGE CONTENT:  low
ai MESSAGE CONTENT:  temperature
ai MESSAGE CONTENT:  of
ai MESSAGE CONTENT:  
ai MESSAGE CONTENT: 3
ai MESSAGE CONTENT: ℃
ai MESSAGE CONTENT: .
ai MESSAGE CONTENT:

如果您从 "unstreamed" 节点中删除了标签，那么其中模型调用的结果也将包含在最终流中。

streamEvents 方法¶

您也可以这样使用 streamEvents 方法

const eventStream = agent.streamEvents(
  { messages: [{ role: "user", content: "What's the weather like today?" }] },
  { version: "v2" },
);

for await (const { event, data } of eventStream) {
  if (event === "on_chat_model_stream" && isAIMessageChunk(data.chunk)) {
    if (
      data.chunk.tool_call_chunks !== undefined &&
      data.chunk.tool_call_chunks.length > 0
    ) {
      console.log(data.chunk.tool_call_chunks);
    }
  }
}

[
  {
    name: 'search',
    args: '',
    id: 'call_Qpd6frHt0yUYWynRbZEXF3le',
    index: 0,
    type: 'tool_call_chunk'
  }
]
[
  {
    name: undefined,
    args: '{"',
    id: undefined,
    index: 0,
    type: 'tool_call_chunk'
  }
]
[
  {
    name: undefined,
    args: 'query',
    id: undefined,
    index: 0,
    type: 'tool_call_chunk'
  }
]
[
  {
    name: undefined,
    args: '":"',
    id: undefined,
    index: 0,
    type: 'tool_call_chunk'
  }
]
[
  {
    name: undefined,
    args: 'current',
    id: undefined,
    index: 0,
    type: 'tool_call_chunk'
  }
]
[
  {
    name: undefined,
    args: ' weather',
    id: undefined,
    index: 0,
    type: 'tool_call_chunk'
  }
]
[
  {
    name: undefined,
    args: ' today',
    id: undefined,
    index: 0,
    type: 'tool_call_chunk'
  }
]
[
  {
    name: undefined,
    args: '"}',
    id: undefined,
    index: 0,
    type: 'tool_call_chunk'
  }
]