AI-Powered RAG Document Processing & Chatbot with Google Drive, Supabase, OpenAI

Who is this for? This workflow is perfect for: Businesses and teams who need an automated solution to organize, analyze, and retrieve insights from their internal documents. Researchers who want to quickly analyze and query large collections of research papers, reports, or datasets. Customer support teams looking to streamline access to product documentation and support resources. Legal and compliance professionals needing to reference and query legal documents with confidence. AI enthusiasts and developers wanting to implement Retrieval-Augmented Generation (RAG) systems without starting from scratch. What problem is this workflow solving? Manually organizing, processing, and searching through documents can be time-consuming, error-prone, and inefficient. This workflow solves that by: Automating document processing from Google Drive, supporting multiple formats like PDFs, CSVs, and Google Docs. Extracting, chunking, and enhancing document text, preserving context and improving AI comprehension. Storing vector embeddings in a secure, scalable Supabase vector database, enabling semantic search and retrieval. Providing an interactive AI chat interface that allows users to ask natural language questions and get precise, document-based answers. This means teams can quickly access relevant insights from their document repositories—boosting productivity and ensuring accurate information retrieval. Key Features 🚀 End-to-End Document Processing: From Google Drive upload detection to vector embedding and storage. 🔍 Semantic Search & Retrieval: Users can ask complex, natural-language questions and receive contextually relevant answers. 🤖 AI-Powered Summaries & Metadata: Automatically generates document titles and summaries using Google Gemini AI. 📝 Smart Chunking & Contextual Enhancement: Breaks documents into smart chunks with overlap, preserving context and table integrity. 🔐 Secure & Scalable Vector Database: Stores and retrieves embeddings in a Supabase vector store for fast, reliable searches. 💬 Conversational AI Interface: Uses OpenAI to power natural, accurate, and cost-effective AI chat interactions. How does this workflow work? Monitors Google Drive for new files Extracts text from PDFs and CSVs (or Google Docs auto-converted) Splits text into context-preserving chunks Enhances chunk quality and stores embeddings in Supabase Enables natural language search and AI-powered chat interactions with the stored documents Typical Use Cases 📚 Corporate Knowledge Base 🔬 Research Paper Analysis 📞 Customer Support Document Query ⚖️ Legal Document Review and Analysis 🔍 Internal Team Documentation Search Why You’ll Love It This workflow lets you build a scalable, searchable, and AI-powered document system—without needing to write complex code or manage multiple systems. With this, you can: Stay organized with automated document processing. Deliver faster, more accurate answers to user queries. Reduce manual work and improve productivity. Gain a competitive edge with cutting-edge AI search capabilities. Setup Requirements An n8n instance with Google Drive, Supabase, OpenAI, and Gemini credentials configured. Access to a Supabase vector store for storing document embeddings. Configurable chunk size, overlap, and processing limits (default: 1000 characters per chunk, 20 chunks max). Contact me for consulting and support: 📧 billychartanto@gmail.com