If you want to learn:
How do you connect n8n to Supabase vector store for AI-powered workflows?
What are the steps to configure OpenAI embeddings with Supabase in n8n?
How do you set up API credentials and authentication between n8n and Supabase?
What's the proper way to load and chunk data into a vector database using n8n?
How do you add metadata to your vector store for better semantic search results?
Then this lecture is for you!
This step-by-step tutorial walks you through the complete integration of n8n with Supabase vector store using OpenAI embeddings. You'll learn how to navigate Supabase project settings to locate your database URL and API keys, including the legacy service role key required for n8n authentication. The lecture demonstrates how to add and configure the Supabase vector store node in your n8n workflow, connect it with proper credentials, and set up the embeddings OpenAI node using the text-embedding-3-small model with 1,536 dimensions. You'll discover how to implement the default document loader to properly chunk and ingest text data into your vector database, configure the data loading process to target specific content fields, and add custom metadata properties like categories to enhance your semantic search capabilities. This tutorial provides practical guidance on building your own AI-powered RAG (retrieval-augmented generation) system by connecting these essential AI tools, ensuring your PostgreSQL pgvector setup matches your embedding dimensions, and establishing a solid foundation for workflow automation with vector search functionality.