Building a RAG System from Scratch with pgvector and Gemini — Implementation (opens in new tab)

In the , we covered the three core concepts behind RAG. Now let's build it. By the end of this article you'll have a working RAG pipeline: documents stored as vectors in pgvector, semantic search retrieving the right context, and Gemini generating grounded answers. Environment Setup Prerequisites Python 3.12 (pyenv recommended) Docker Google Gemini API key — get one free at aistudio.google.com Project setup mkdir pgvector-tutorial && cd pgvector-tutorial pyenv local 3.12.0 python -m venv .ven...