A fully integrated workflow that sets up a Retrieval-Augmented Generation (RAG) agent using a relational database and a vector store. How to configure Postgres as chat memory and Supabase as a vector database using PG Vector for similarity search. The system is implemented within n8n by establishing credentials, configuring both database and tool nodes, and leveraging recursive text splitting for document ingestion. Detailed technical steps include setting up Supabase, connecting to Postgres, and embedding documents for AI-driven query responses.
You'll bring your own API credentials (so the workflow runs under your accounts) — I'll walk you through wiring them up on our setup call, which is included with your purchase.
Schedule a free call and I'll help you identify the highest-impact automation opportunities for your business.
Book a Call