4 pgvector Mistakes That Silently Break Your RAG Pipeline in Production

pgvector is the fastest way to add vector search to an existing PostgreSQL database. One extension, a few SQL commands, and you have similarity search running alongside your relational data. No new infrastructure. No new SDK. No vendor lock-in. That simplicity is also its trap. Most teams add pgvector in a day and spend the next six months debugging performance issues that have nothing to do with the extension itself. The problems are almost always configuration mistakes that tutorials skip over. Here are four I have seen break RAG pipelines in production, and how to fix each one before your team starts debating a migration to Pinecone. No HNSW Index Means Full Table Scans By default, pgvector performs exact nearest neighbor search. That means it scans every single row in the table on every query. For a prototype with 10,000 vectors, this is invisible. At 500,000 vectors, queries start crossing 800 milliseconds. At a million, you are looking at multi-second response times that make you