Embeddings and Vector Databases for Fast RAG - 100% Local with Ollama, Supabase, PostgreSQL and pgvector

Learn to build a production-grade RAG system storage layer using vector embeddings and semantic search with completely local, open-source tools. Discover how to move beyond simple keyword matching by implementing vector embeddings that understand semantic meaning - enabling your system to connect queries about "money" with documents containing "revenue." Set up a local PostgreSQL database with pgvector extension through Supabase, generate embeddings using Ollama with the Qwen3 Embedding model, and store vector data in SQL for efficient retrieval. Compare the limitations of traditional full-text search with the power of vector semantic search, and understand why persistence is crucial for production RAG systems. Master the complete workflow from database setup and Docker initialization to embedding generation and vector storage, building the foundation for advanced retrieval-augmented generation applications.