Vercel AI SDK 的可观测性与追踪

import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { LangfuseClient } from "@langfuse/client"; // 添加这一行 import       
 
const langfuse = new LangfuseClient();
 
// 获取当前的 `production` 版本
const prompt = await langfuse.prompt.get("movie-critic");
 
// 将变量代入 prompt 模板
const compiledPrompt = prompt.compile({
  someVariable: "example-variable",
});
 
const { text } = await generateText({
  model: openai("gpt-5"),
  prompt: compiledPrompt,
  experimental_telemetry: { 
    isEnabled: true,
    metadata: {
      langfusePrompt: prompt.toJSON() // 这会将 Generation 与你在 Langfuse 中的 prompt 关联
    },
  },
});

// instrumentation.ts
 
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
 
// 默认的智能过滤会导出 Langfuse + GenAI/LLM 的 span
export const langfuseSpanProcessor = new LangfuseSpanProcessor();
 
const tracerProvider = new NodeTracerProvider({
  spanProcessors: [langfuseSpanProcessor],
});
 
tracerProvider.register();

// app/api/chat/route.ts
 
import { streamText } from "ai";
import { after } from "next/server";
 
import { openai } from "@ai-sdk/openai";
import {
  observe,
  propagateAttributes,
  setActiveTraceIO,
} from "@langfuse/tracing";
import { trace } from "@opentelemetry/api";
 
import { langfuseSpanProcessor } from "@/src/instrumentation";
 
const handler = async (req: Request) => {
  const {
    messages,
    chatId,
    userId,
  }: { messages: UIMessage[]; chatId: string; userId: string } =
    await req.json();
 
  // 在 active trace 上设置 session id 和 user id
  const inputText = messages[messages.length - 1].parts.find(
    (part) => part.type === "text"
  )?.text;
 
  // 在 active trace 上设置 input
  setActiveTraceIO({
    input: inputText,
  });
 
  // 为 trace 添加会话和用户上下文
  await propagateAttributes(
    {
      traceName: "chat-message",
      sessionId: chatId,  // 将相关消息组合到一起
      userId,             // 记录是哪位用户发起的请求
    },
    async () => {
      const result = streamText({
        model: openai("gpt-5.1"),
        messages,
        experimental_telemetry: {
          isEnabled: true,
        },
        onFinish: async (result) => {
          // 流结束后用最终输出更新 trace
          setActiveTraceIO({
            output: result.content,
          });
 
          // 流结束后手动结束 span
          trace.getActiveSpan().end();
        },
        onError: async (error) => {
          setActiveTraceIO({
            output: error,
          });
 
          // 由于是流式输出，需要手动结束 span
          trace.getActiveSpan()?.end();
        },
      });
 
      // 对 serverless 至关重要：在函数终止前刷新 trace
      after(async () => await langfuseSpanProcessor.forceFlush());
 
      return result.toUIMessageStreamResponse();
    }
  );
};
 
// 用 observe() 包装 handler 来创建 Langfuse trace
export const POST = observe(handler, {
  name: "handle-chat-message",
  endOnExit: false, // 在流完成之前不要结束 observation
});