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).