Tutorial: Build a RAG Pipeline Using pgvector, Gemini, and Python from Scratch

A hands-on implementation guide published on DEV Community walks developers through building a Retrieval-Augmented Generation (RAG) system using PostgreSQL's pgvector extension and Google's Gemini API. The tutorial follows a prior article on RAG concepts and now covers the full build, including database setup, vector indexing, document ingestion, semantic search, and answer generation. The project uses Python 3.12, Docker, and the updated google-genai package, with documents embedded at 768 dimensions to stay within pgvector's HNSW index limit of 2,000 dimensions. Five Python scripts are structured in sequence to handle each stage of the pipeline, from enabling the pgvector extension to running the complete RAG query flow. Developers can obtain a free Gemini API key via Google AI Studio to follow along with the setup.