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**