Build a RAG document chatbot with Supabase vector search and OpenRouter

What this workflow does

This workflow builds a Retrieval-Augmented Generation (RAG) document chat assistant inside n8n using Supabase Vector Store and AI models.

The workflow allows users to upload documents, convert them into embeddings, store them inside Supabase pgvector, and query them through an AI chat interface using semantic search.

When a user sends a question through the webhook endpoint, the workflow retrieves the most relevant document chunks from Supabase and uses an AI model to generate a grounded response based on the uploaded documents.

This template includes:

Document ingestion pipeline Recursive text chunking AI embeddings generation Supabase vector storage Semantic retrieval AI-powered document question answering Webhook API integration for frontend apps

How it works

The workflow is split into two main parts: