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Tutorial: Build a Natural-Language Database Assistant Using PostgreSQL and pgvector

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A new developer tutorial published on DEV Community walks through building a RAG-powered assistant that lets users query a PostgreSQL database using plain English questions. The system uses pgvector to store and retrieve schema metadata embeddings, allowing an LLM to receive relevant table and column context before generating SQL. Unlike sending raw questions to a general-purpose AI, this approach keeps the model grounded in the actual database schema, reducing hallucinations and improving accuracy. Query examples are also embedded alongside schema data to teach the model a team's preferred SQL style. The entire retrieval and operational stack runs within a single PostgreSQL instance, simplifying infrastructure management.

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