If you want to learn:
How do you map data from one format to another using n8n's Edit Fields node?
What's the best way to structure content for vector embeddings in a RAG system?
How do you set up a Supabase project for building AI-powered chatbots?
What are the essential steps to prepare data for retrieval-augmented generation workflows?
How can you transform product data into LLM-ready content using n8n workflow automation?
Then this lecture is for you!
In this hands-on tutorial, you'll master data mapping with n8n's Edit Fields node and set up your first Supabase project for building a RAG system. You'll learn how to transform raw product data into structured content optimized for vector embeddings and retrieval-augmented generation. The lecture walks you through manual mapping techniques, showing you how to create content fields that combine product names, categories, SKUs, prices, and descriptions into LLM-ready text. You'll discover how to use expressions like $JSON to dynamically pull data and structure it for AI agents and chatbots. The tutorial then guides you through creating a Supabase account, setting up an organization, and launching your first database project with pgvector support. You'll learn best practices for metadata tagging, including how to add category fields for filtering search results. By the end, you'll understand how to build data pipelines that prepare information for vector stores, execute workflow automation steps in n8n, and configure Supabase as your vector database backend. This practical session covers essential n8n workflow template patterns, database setup with PostgreSQL, and the foundational architecture needed for building agentic RAG systems with searchable knowledge bases.