Hey everyone,

After spending months building RAG pipelines and fighting with pgvector configs, I ended up writing everything down. It turned into a book called "PostgreSQL for AI - Building Intelligent Applications"

It covers pgvector (HNSW vs IVFFlat, hybrid search), RAG pipelines, collaborative filtering, feature engineering, in-database ML with PostgresML, and production topics like CDC with Debezium.

The whole thing is built around a product recommendation app (RecSys) that you build chapter by chapter. Think e-commerce: 1000 products, semantic search, a chatbot that answers product questions, personalized recommendations. There's also a bonus project called "Ask the Book" where you build a RAG tool that can query the book itself. You end up using what you learned to query what you learned from.

Everything runs locally on Docker (Postgres 17, pgvector, TimescaleDB, Ollama). No GPU needed.

Free sample chapter: https://book.zeybek.dev

There's also a pro tier with access to the full source code repo if you want to dig into the working projects.

Happy to answer pgvector/RAG questions.