Stop Using RAG for Spreadsheets — Use This Instead (n8n)

👉 Upgrade your n8n AI Agents with our Advanced RAG workflows https://www.theaiautomators.com/?utm_source=youtube&utm_medium=video&utm_campaign=tutorial&utm_content=agentic_sheets RAG Masterclass - https://www.youtube.com/watch?v=75lwkzFxyLs Hybrid Search Video - https://www.youtube.com/watch?v=2-6ckhW3Hmo Cole's video on Agentic RAG: https://www.youtube.com/watch?v=mQt1hOjBH9o 0:00 - Overview 0:50 - Storing & Querying Data 1:30 - Demo: Querying Spreadsheet Data 3:00 - How the Agent Works 4:25 - RAG System Approaches 5:52 - Data Ingestion Pipeline 11:17 - Record Management with Superbase 13:44 - Handling Tabular Data 18:18 - AI Agent Setup & Tools 22:30 - Final Demo & Resources In this video, we dive deep into how to build a powerful AI agent capable of querying structured data like spreadsheets and databases using a hybrid approach that combines relational databases and vector stores. Traditional vector search often falls short for structured queries—like summing orders or filtering data by fields—because semantic search retrieves imprecise context. Instead, this tutorial introduces a more effective method: using natural language queries (NLQ) on structured data stored in Supabase. You'll learn how to ingest data from various sources like CSVs, Excel files, and Google Sheets into a unified relational structure using a JSONB column format. This flexible architecture eliminates the need for separate tables per data schema and enables fast, accurate querying with SQL—generated dynamically by your AI agent. The agent can smartly choose between querying vector embeddings for unstructured data or executing SQL for structured insights. We walk through a live demo showing the agent breaking down orders by country, summing values, and identifying top-performing ad platforms. The video also details the complete data ingestion pipeline, showing how to manage new, updated, and deleted files in Google Drive, extract schema and data, and update Supabase accordingly. You'll see how the AI agent is configured with specific tools and prompts to ensure robust and contextually accurate responses. Plus, learn about schema-based query constraints, view definitions for complex setups, and when to use vector vs. relational queries effectively.