Date: 04/14/2025
Alright, this video is gold for us devs diving into the AI revolution! It walks you through building a real-deal AI Agent with RAG (Retrieval Augmented Generation) using n8n, a no-code automation platform, and Supabase for chat memory and vector storage. Forget those toy examples you see online. This is about creating something production-ready that can actually handle document updates and persistent data, something a developer can feel good about.
Why is this valuable? Well, instead of hand-coding everything, you’re leveraging n8n to orchestrate the workflow, connecting your LLM to a proper vector database in Supabase. This means you can build sophisticated applications like AI-powered customer support, internal knowledge bases, or even dynamic content generation engines, all without drowning in code. It shows you how to build a legitimate agent instead of duct-taping together a simple workflow that quickly breaks down with real-world usage. The agent properly handles upserts (updates and inserts) to the vector store, has solid memory management and is fast.
I’m definitely experimenting with this! Seeing how Supabase integrates with n8n for RAG is a game-changer. Imagine automating the process of keeping your AI agent up-to-date with the latest documentation or product information. Plus, the provided n8n workflow template means you can get started quickly and customize it to your specific needs. It is a fantastic way to abstract away a lot of the underlying vector DB and memory management boilerplate so you can focus on building the business logic that the agent will provide.