Building a Powerful RAG System with n8n, Supabase, and Postgres

Learn to build an advanced Retrieval-Augmented Generation (RAG) system that automatically manages document uploads and updates in your vector database. Discover how to integrate n8n, Supabase, and Postgres to create a system that intelligently processes files based on their type and seamlessly updates records when changes occur. Master the setup of file watchers that monitor for newly created documents, implement automatic document processing workflows, and configure update mechanisms that keep your AI knowledge base current. Explore practical testing scenarios with different file types including PDFs, understand the execution flow for updated files, and gain insights into streamlining data management for enhanced AI applications. The tutorial covers the complete workflow from initial file detection through database integration, providing hands-on experience with no-code automation tools for building robust RAG implementations.