Supabase Vector Store Template with OpenAI Embeddings (Insert, Upsert, Retrieve)

This n8n template is a complete reference workflow for working with a Supabase pgvector database using OpenAI embeddings, covering the three core operations every RAG builder needs: inserting new documents, upserting updates, and retrieving relevant matches through similarity search. It includes the exact Supabase SQL setup, pgvector extension, table schema, and the match_documents function, needed to get a 1536-dimension vector store running correctly with OpenAI's Text-Embedding-3-Small model. Ideal for developers building AI knowledge bases or chatbots who want a proven, working starting point instead of assembling Supabase vector search from scratch.

Category: RAG & Knowledge Base