W-03 · n8n · Supabase
AI Answers Grounded in Your Own Documents
n8n + Supabase Vector Store RAG pipeline that auto-syncs Google Drive into an AI agent's knowledge base.
Problem
Businesses often store important documents in Google Drive, but finding accurate information quickly becomes difficult as files grow. Teams waste time manually searching documents, while AI chatbots without retrieval systems return inaccurate or outdated answers.
Approach
Built an AI-powered Retrieval-Augmented Generation (RAG) system using n8n, Supabase Vector Store, OpenAI Embeddings, Google Drive, and Google Gemini. The workflow automatically monitors Google Drive for file changes, processes documents into vector embeddings, stores them in Supabase, and enables an AI agent to retrieve relevant knowledge before generating responses. The system includes: Automated Google Drive sync, AI document ingestion pipeline, vector embeddings with semantic search, AI agent with contextual memory, and automatic update/delete handling for document changes.
Outcome
Created a centralized AI knowledge system capable of retrieving accurate contextual information from uploaded business documents automatically. Eliminates manual document searching, improves response accuracy, and enables scalable AI-powered knowledge retrieval for operational workflows.
n8n / Supabase / OpenAI Embeddings / Google Gemini / Google Drive / RAG Architecture
Carlos Chiu, AI Automation & Enablement Specialist: all work