如何向你的图添加运行时配置

一旦你在 LangGraph 中创建了一个应用，你可能希望允许在运行时进行配置。

例如，你可能希望动态选择 LLM 或提示，配置用户的 user_id 以强制执行行级安全性等等。

在 LangGraph 中，配置和其他 “带外”通信 是通过 RunnableConfig 完成的，它始终是在调用你的应用程序时的第二个位置参数。

下面，我们将逐步介绍一个示例，让你配置用户 ID 并选择要使用的模型。

设置

本指南将使用 Anthropic 的 Claude 3 Haiku 和 OpenAI 的 GPT-4o 模型。我们将可选地为 LangSmith 跟踪 设置我们的 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_V2 = "true";
process.env.LANGCHAIN_PROJECT = "Configuration: LangGraphJS";

Configuration: LangGraphJS

定义图

我们将为本示例创建一个非常简单的消息图。

import { BaseMessage } from "@langchain/core/messages";
import { ChatOpenAI } from "@langchain/openai";
import { ChatAnthropic } from "@langchain/anthropic";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { RunnableConfig } from "@langchain/core/runnables";
import {
  END,
  START,
  StateGraph,
  Annotation,
} from "@langchain/langgraph";

const AgentState = Annotation.Root({
  messages: Annotation<BaseMessage[]>({
    reducer: (x, y) => x.concat(y),
  }),
  userInfo: Annotation<string | undefined>({
    reducer: (x, y) => {
      return y ? y : x ? x : "N/A";
    },
    default: () => "N/A",
  })
});

const promptTemplate = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant.\n\n## User Info:\n{userInfo}"],
  ["placeholder", "{messages}"],
]);

const callModel = async (
  state: typeof AgentState.State,
  config?: RunnableConfig,
) => {
  const { messages, userInfo } = state;
  const modelName = config?.configurable?.model;
  const model = modelName === "claude"
    ? new ChatAnthropic({ model: "claude-3-haiku-20240307" })
    : new ChatOpenAI({ model: "gpt-4o" });
  const chain = promptTemplate.pipe(model);
  const response = await chain.invoke(
    {
      messages,
      userInfo,
    },
    config,
  );
  return { messages: [response] };
};

const fetchUserInformation = async (
  _: typeof AgentState.State,
  config?: RunnableConfig,
) => {
  const userDB = {
    user1: {
      name: "John Doe",
      email: "jod@langchain.ai",
      phone: "+1234567890",
    },
    user2: {
      name: "Jane Doe",
      email: "jad@langchain.ai",
      phone: "+0987654321",
    },
  };
  const userId = config?.configurable?.user;
  if (userId) {
    const user = userDB[userId as keyof typeof userDB];
    if (user) {
      return {
        userInfo:
          `Name: ${user.name}\nEmail: ${user.email}\nPhone: ${user.phone}`,
      };
    }
  }
  return { userInfo: "N/A" };
};

const workflow = new StateGraph(AgentState)
  .addNode("fetchUserInfo", fetchUserInformation)
  .addNode("agent", callModel)
  .addEdge(START, "fetchUserInfo")
  .addEdge("fetchUserInfo", "agent")
  .addEdge("agent", END);

const graph = workflow.compile();

使用配置调用

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

const config = {
  configurable: {
    model: "openai",
    user: "user1",
  },
};
const inputs = {
  messages: [new HumanMessage("Could you remind me of my email??")],
};
for await (
  const { messages } of await graph.stream(inputs, {
    ...config,
    streamMode: "values",
  })
) {
  let msg = messages[messages?.length - 1];
  if (msg?.content) {
    console.log(msg.content);
  } else if (msg?.tool_calls?.length > 0) {
    console.log(msg.tool_calls);
  } else {
    console.log(msg);
  }
  console.log("-----\n");
}

Could you remind me of my email??
-----

Could you remind me of my email??
-----

Your email is jod@langchain.ai.
-----

更改配置

现在让我们尝试使用不同的用户进行相同的输入。

const config2 = {
  configurable: {
    model: "openai",
    user: "user2",
  },
};
const inputs2 = {
  messages: [new HumanMessage("Could you remind me of my email??")],
};
for await (
  const { messages } of await graph.stream(inputs2, {
    ...config2,
    streamMode: "values",
  })
) {
  let msg = messages[messages?.length - 1];
  if (msg?.content) {
    console.log(msg.content);
  } else if (msg?.tool_calls?.length > 0) {
    console.log(msg.tool_calls);
  } else {
    console.log(msg);
  }
  console.log("-----\n");
}

Could you remind me of my email??
-----

Could you remind me of my email??
-----

Your email address is jad@langchain.ai.
-----

查看此运行的 LangSmith 跟踪（链接），以“查看 LLM 所看到的”。

配置架构

你还可以传递一个注释，定义 config.configurable 的形状到你的图中。这目前只会公开编译图上的类型信息，而不会过滤掉键

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

const ConfigurableAnnotation = Annotation.Root({
  expectedField: Annotation<string>,
});

const printNode = async (
  state: typeof MessagesAnnotation.State,
  config: RunnableConfig<typeof ConfigurableAnnotation.State>
) => {
  console.log("Expected", config.configurable?.expectedField);
  // @ts-expect-error This type will be present even though is not in the typing
  console.log("Unexpected", config.configurable?.unexpectedField);
  return {};
};

const graphWithConfigSchema = new StateGraph(MessagesAnnotation, ConfigurableAnnotation)
  .addNode("printNode", printNode)
  .addEdge(START, "printNode")
  .compile();

const result = await graphWithConfigSchema.invoke({
  messages: [{ role: "user", content: "Echo!"} ]
}, { configurable: { expectedField: "I am expected", unexpectedField: "I am unexpected but present" } });

Expected I am expected
Unexpected I am unexpected but present