📖 AI-Powered RAG Document Processing & Chatbot with Google Drive, Supabase, OpenAI
⚡ 13,291 views · 📖 Internal Wiki & Knowledge Base
Description
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
🔗 Nodes Used
Google Drive, Google Drive Trigger, AI Agent, Basic LLM Chain, Embeddings OpenAI, OpenAI Chat Model
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File