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6/27/2026
Build a RAG system from scratch with pgvector and Gemini

Build a RAG system from scratch with pgvector and Gemini

Original: Building a RAG System from Scratch with pgvector and Gemini — Introduction

Short summary

Learn to build Retrieval-Augmented Generation (RAG) systems using PostgreSQL's pgvector extension and Google Gemini. This introduction to a comprehensive series explains embeddings, vector-based semantic search, and the RAG pattern itself—then outlines a step-by-step learning path from local implementation with Docker to cloud deployment on Render and Supabase. Ideal for Python engineers new to AI application development.

  • •RAG pattern retrieves relevant documents to augment LLM context with real-time knowledge
  • •Embeddings convert text to numeric vectors for semantic similarity search without keyword matching
  • •Hands-on series progression: local implementation → Tool Use → AI Agents → MCP server → cloud deployment

Generated with AI, which can make mistakes.

#ai-tools#ai-agents#certification-education#open-source
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