If you want to learn:
How do I build a RAG pipeline using n8n and Supabase?
What's the best way to automate data ingestion from Google Sheets into a vector database?
How can I create an AI-powered expert system for my business using workflow automation?
What are the steps to set up Supabase vector store with n8n for retrieval-augmented generation?
How do I transform and load data into a vector database for AI chatbot applications?
What's the difference between basic chatbots and RAG systems for business automation?
Then this lecture is for you!
In this hands-on lecture, you'll build the data ingestion pipeline for a complete RAG system using n8n workflow automation and Supabase vector store. You'll learn how to extract product data from Google Sheets, transform it using the Edit Fields node in n8n, and load it into a Supabase PostgreSQL vector database for AI-powered question answering. This lecture covers the essential ETL process for RAG applications, showing you how to integrate n8n with Supabase using proper API credentials and authentication. You'll discover why Supabase is an enterprise-grade, scalable solution for vector storage and how to work with vector embeddings for retrieval-augmented generation. The workflow you build will handle 60 product records but is designed to scale to thousands of entries, making it perfect for real-world business applications. You'll also learn best practices for data transformation, understand the architecture of agentic RAG systems, and prepare your infrastructure for adding AI chat functionality. This practical tutorial focuses on building production-ready automation workflows that can be immediately implemented for clients, with clear explanations of each node configuration and integration setup in the n8n interface.