Index n8n workflows and enable semantic AI search with OpenAI and Supabase

n8n Workflow Intelligence (RAG): Auto Indexing & Semantic AI Search with Supabase Vector DB

This workflow automatically indexes your n8n workflows every 24 hours, converts them into vector embeddings using OpenAI and stores them in Supabase. It exposes a webhook that lets you query your workflows in natural language. The AI agent uses Retrieval-Augmented Generation (RAG) to fetch relevant workflow data and generate contextual answers—making it easy to understand, debug and reuse automation logic.

Quick Implementation Steps

1. Enable n8n API and configure authentication (header-based). 2. Set up Supabase with pgvector and create the required table and function. 3. Add OpenAI credentials (for embeddings and chat model). 4. Import and activate the workflow in n8n. 5. Send a POST request to /ask-workflows: json { "query": "How does my webhook workflow work?" } 6. Receive AI-powered answers based on your workflows.

What It Does

This workflow creates an intelligent knowledge layer on top of your n8n automations. It automatically fetches workflows from your n8n instance, processes each node and converts them into structured text chunks. These chunks are transformed into vector embeddings using OpenAI and stored in Supabase for semantic search.

Once indexed, users can query workflows through a webhook endpoint using natural language. The AI agent retrieves relevant workflow data using vector similarity search and generates meaningful responses. It can also guide users directly to workflows using links.

In short, it transforms your workflows into a searchable, AI-powered system.