Build a Complete Langfuse Observability and Evaluation Pipeline for Tracing, Prompt Management, Scoring, and Experiments

This tutorial demonstrates how to implement a comprehensive Langfuse pipeline for observing and evaluating LLM applications. Langfuse is an open-source platform that enables tracing, prompt management, scoring, and experimentation. The workflow supports both real OpenAI API access and a deterministic mock LLM, allowing users to explore all major features without paid model dependencies. We begin by configuring credentials and establishing a connection to Langfuse. We then trace function calls, instrument a small RAG pipeline, manage prompts centrally, and attach evaluation scores. We also perform dataset-based experiments. Langfuse helps in systematically observing, evaluating, and enhancing LLM applications to be production-ready. We start by installing the necessary Langfuse and OpenAI packages. Then, we gather Langfuse credentials, specify the region or a self-hosted URL, and optionally provide an OpenAI API key. We initialize the Langfuse client, verify authentication, and confirm whether an OpenAI model or the built-in mock LLM is being used. An LLM helper function is defined to support both real OpenAI generations and mock responses, ensuring traceability even without an OpenAI key. Basic decorator-based tracing is shown by wrapping a simple story-generation pipeline with @observe. A small manual RAG pipeline is constructed using an in-memory knowledge base. The retrieval step is traced separately, and user IDs, session IDs, and tags are propagated across the entire trace using propagate_attributes. Finally, we demonstrate running a refund query through this RAG pipeline.