The KEY to Infinitely Scale Your n8n RAG Agents

Video Description

👉 Get our State-of-the-Art n8n RAG Systems and learn how to customize them, in our community https://www.theaiautomators.com/?utm_...    • n8n RAG Masterclass - Build AI Agents + Sy...   Chapters: 0:00 - Overview 0:41 - The Scaling Challenge 2:25 - The Approach 3:06 - Demo and High-Level Overview 8:15 - Building the Orchestrator 17:33 - Webhooks vs. Subworkflows 20:16 - Adapting the Ingestion Workflow 22:30 - Error handler workflow 24:17 - Key Scaling Lessons 29:51 - Managing Supabase Infrastructure 30:12 - Next Steps Importing a small amount of files into your n8n RAG system is easy, but scaling it to 1,000s of files is challenging. In this tutorial, you'll learn how to scale your n8n workflows to reliably process thousands of files per hour using an orchestrator pattern. From handling parallel executions to retrying failed jobs and preventing crashes, we cover it all. We start by breaking down common scaling issues: slow imports, memory overloads, API rate limits (e.g. OpenAI, Supabase), and system instability. Then we show you how to build an orchestrator workflow that: Executes ingestion flows in parallel batches Uses Supabase to track parent/child executions Handles errors with automated retries Avoids overloading your n8n instance or external APIs Delivers massive throughput at high reliability You'll see a full demo of this in action, processing documents in batches of 50, moving files with SFTP, and coordinating via webhook calls rather than sub-workflows (which significantly improves performance, while making tracking executions possible). You’ll also learn: How to restructure your RAG ingestion workflow to receive input via webhook triggers Why we're using webhooks instead of Execute Workflow nodes in this workflow How to prevent database bloat by disabling execution logging for successful runs The importance of layered error handling Key Supabase configuration tips for scaling compute and storage The top 5 lessons learned from scaling n8n to 5,000 file ingestions per hour