Building a RAG Pipeline with Claude API and Supabase

Building a RAG Pipeline with Claude API and Supabase Tags: claude supabase rag ai Retrieval-Augmented Generation (RAG) is one of those patterns that sounds academic until you actually build one — then you realize it's just smart plumbing. You store knowledge somewhere searchable, retrieve the relevant bits at query time, and feed them to an LLM as context. The LLM stops hallucinating because it's working from your data, not just its training weights. In this article, I'll walk you through building a production-ready RAG pipeline using: Claude API (Anthropic) — for generation and embeddings Supabase — for vector storage via pgvector Node.js — the glue By the end, you'll have a pipeline that ingests documents, embeds them, stores them in Supabase, and answers questions grounded in that knowledge base. Architecture Overview [Documents] → [Chunker] → [Embedder] → [Supabase pgvector] ↓ [User Query] → [Embed Query] → [Similarity Search] → [Top-K Chunks] ↓ [Claude API + Context] → [Answer] Tw