Developer Guide: Building a RAG System from Scratch Using pgvector and Gemini

A new multi-part technical guide published on DEV Community walks developers through building a Retrieval-Augmented Generation (RAG) system from the ground up using pgvector and Google's Gemini API. RAG is a design pattern that retrieves relevant documents at runtime and injects them into an LLM's context, allowing the model to answer questions about data it was never trained on. The guide uses pgvector, a PostgreSQL extension, to store and search text embeddings via cosine similarity, enabling semantic rather than keyword-based document retrieval. Planned across six steps, the series will progress from core RAG implementation to tool use, AI agents, MCP server setup, and cloud deployment on Render and Supabase. The guide targets Python developers new to AI application development who want hands-on experience from local setup through production deployment.