If you want to learn:
- How do I run a complete data ingest pipeline using n8n and Supabase?
- What are the steps to load and vectorize data into a Supabase vector database?
- How can I build a RAG system pipeline that processes and stores embeddings automatically?
- How do I configure the Supabase vector store node in n8n workflows?
- What's the easiest way to transform data and create a knowledge base for retrieval-augmented generation?
- How do I troubleshoot and rerun my n8n workflow when fixing data errors?
Then this lecture is for you!
In this hands-on session, you'll execute a complete data ingest pipeline using n8n with Supabase to build a functional RAG system. You'll configure the Supabase vector store node by connecting it to your knowledge_base table, then run the workflow to process 60 items through extraction, transformation, chunking, and vectorization stages. Watch as your n8n workflow automatically generates embeddings using OpenAI and stores them in your Supabase vector database with proper metadata configuration. Learn to verify your data in the Supabase SQL editor, troubleshoot common issues like missing table parameters, and quickly rerun your pipeline to fix data errors. This practical guide to building a rag pipeline demonstrates the complete flow from empty database to populated vector store, showing you how to transform spreadsheet data into AI-ready embeddings. By the end, you'll have a working data ingest workflow that loads vectorized content into PostgreSQL, setting the foundation for your retrieval-augmented generation system. Perfect for anyone building AI agents, chatbots, or agentic RAG systems who wants step-by-step instruction on n8n and Supabase integration.