langfuse-core-workflow-a

# Langfuse Core Workflow A: Tracing LLM Calls ## Overview End-to-end tracing of LLM calls, chains, and agents. Covers the OpenAI drop-in wrapper, manual tracing with `startActiveObservation`, RAG pipeline instrumentation, streaming response tracking, and LangChain integration. ## Prerequisites - Completed `langfuse-install-auth` setup - OpenAI SDK installed (`npm install openai`) - For v4+: `@langfuse/openai`, `@langfuse/tracing`, `@langfuse/otel`, `@opentelemetry/sdk-node` ## Instructions ### Step 1: OpenAI Drop-In Wrapper (Zero-Code Tracing) ```typescript import OpenAI from "openai"; import { observeOpenAI } from "@langfuse/openai"; // Wrap the OpenAI client -- all calls are now traced automatically const openai = observeOpenAI(new OpenAI()); // Every call captures: model, input, output, tokens, latency, cost const response = await openai.chat.completions.create({ model: "gpt-4o", messages: [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: "What is Langfuse?" }, ], }); // Add metadata to traces const res = await observeOpenAI(new OpenAI(), { generationName: "product-description", generationMetadata: { feature: "onboarding" }, sessionId: "session-abc", userId: "user-123", tags: ["production", "onboarding"], }).chat.completions.create({ model: "gpt-4o-mini", messages: [{ role: "user", content: "Describe this product" }], }); ``` ### Step 2: Manual Tracing -- RAG Pipeline (v4+ SDK) ```typescript import...