RAG Chatbot over 100,000+ Posts
A retrieval-augmented chatbot embedded in a professional content platform, indexing over 100,000 posts in a Supabase vector database and answering strictly from stored content. An n8n workflow adds new posts to the index automatically, keeping the knowledge base current in real time.
RAG Chatbot over 100,000+ Posts
Project showcase
The challenge
A professional content platform wanted readers to ask questions and get answers drawn strictly from its own library of more than 100,000 posts, with no invented material and no manual re-indexing as new posts were published.
What we did
Built a Django application using LangChain and OpenAI, with Supabase Vector DB indexing the full corpus. Retrieval is constrained so the model answers only from stored content. An n8n workflow watches for new posts and adds them to the vector index automatically, keeping the knowledge base current in real time.
The outcome
A chatbot embedded in the platform that answers from 100,000+ posts, stays current without operator intervention, and does not stray from the source material.
By the numbers
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Technology stack
Technologies that powered this project.
Frontend
Backend
Database & Tools
Development process
Our proven methodology for delivering exceptional results.
Discovery & Planning
Understanding requirements, analyzing scope, and creating a detailed roadmap
1-2 weeks
Design & Architecture
System architecture, UI/UX designs, and technical specifications
2-3 weeks
Development & Testing
Agile development with continuous testing, code reviews, and QA
4-8 weeks
Deployment & Launch
Production deployment, performance optimization, and go-live support
1 week
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