In my previous blog post, we built the simplest possible Retrieval Augmented Generation (RAG) pipeline inside PostgreSQL. We embedded our manuals, stored those vectors in a table, ran a similarity search, and handed the top 5 results straight to a Large Language Model. The result was encouraging, we could already see the model drawing on our content rather than inventing information. But as with...
