langfuse-deploy-integration

# Langfuse Deploy Integration ## Overview Deploy Langfuse LLM observability alongside your application. Covers integrating the SDK for serverless (Vercel/Lambda), Docker, Cloud Run, and self-hosting the Langfuse server itself. ## Prerequisites - Langfuse API keys (cloud or self-hosted) - Application using Langfuse SDK - Target platform CLI installed ## Instructions ### Step 1: Vercel / Next.js Deployment ```bash set -euo pipefail # Add secrets to Vercel vercel env add LANGFUSE_PUBLIC_KEY production vercel env add LANGFUSE_SECRET_KEY production vercel env add LANGFUSE_BASE_URL production ``` ```typescript // app/api/chat/route.ts (Next.js App Router) import { NextRequest, NextResponse } from "next/server"; import { LangfuseClient } from "@langfuse/client"; import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing"; import OpenAI from "openai"; const langfuse = new LangfuseClient(); const openai = new OpenAI(); export async function POST(req: NextRequest) { const { messages } = await req.json(); const response = await startActiveObservation( { name: "chat-api", asType: "generation" }, async () => { updateActiveObservation({ model: "gpt-4o", input: messages, metadata: { endpoint: "/api/chat" }, }); const result = await openai.chat.completions.create({ model: "gpt-4o", messages, }); updateActiveObservation({ output: result.choices[0].message, usage...