A step-by-step guide to building a production-grade Retrieval-Augmented Generation (RAG) pipeline with full observability using Langfuse, vLLM, and FAISS. Covers instrumenting every pipeline stage — document embedding with SentenceTransformers, vector search with FAISS IndexFlatL2, LLM inference via a vLLM OpenAI-compatible server, and automatic evaluation of relevancy and hallucination risk. Each component is wrapped with Langfuse @observe decorators to produce hierarchical traces with nested spans, token usage, latency metrics, and quality scores. The entire stack runs locally via Docker Compose with no cloud dependencies.
Table of contents
RAG Observability with Langfuse, vLLM, and FAISSIntroduction to Production-Grade RAG and LLM ObservabilityRAG Observability Architecture with Langfuse, vLLM, and FAISSProject SetupBuilding a Langfuse-Traced Retriever with FAISSBuilding a Traced LLM Wrapper for vLLM and LangfuseBuilding a Fully Traced RAG Pipeline with LangfuseImplementing LLM Evaluation Metrics for RAG: Relevancy and Hallucination RiskRunning and Inspecting the RAG Pipeline End-to-EndViewing RAG Traces, Spans, and Scores in LangfuseSummary28 Impressions