Embeddings and Vector Databases for Fast RAG (100% Local) | Ollama, Supabase (PostgreSQL + pgvector)

Complete tutorial and source code (requires MLExpert Pro): https://www.mlexpert.io/academy/v2/context-engineering/embeddings-and-vector-databases Simple keyword is not enough for your RAG. If a user asks for "money" but your documents say "revenue," traditional search fails. In this tutorial, we'll build the storage layer of a production-grade RAG system using Vector Embeddings. We will move beyond simple TF-IDF and implement Semantic Search using local open-source models (Qwen3 Embedding), PostgreSQL, and pgvector via Supabase. Qwen3 Embedding: https://qwenlm.github.io/blog/qwen3-embedding…