How to Build a Production RAG Chatbot Using Claude, pgvector, and FastAPI

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A practical guide outlines how developers can build a retrieval-augmented generation (RAG) chatbot over a weekend using three core tools: PostgreSQL with the pgvector extension, FastAPI, and Anthropic's Claude model. The architecture works by embedding a user's query into a vector, retrieving the most relevant document chunks from the database, and passing that context to Claude to generate a grounded answer. A key point of confusion for beginners is that Claude does not provide an embeddings endpoint, so a separate embedding provider such as Voyage AI or OpenAI must be used. The pgvector column dimensions must match the chosen embedding model exactly, making the embedding provider a critical dependency of the database schema. The guide includes working code for chunking documents, generating embeddings, storing them in Postgres, and exposing the full retrieve-then-generate flow via a FastAPI endpoint.

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