Curious about harnessing the power of Retrieval-Augmented Generation (RAG) for your data? Wish setting it up was as simple as plug-and-play? With RAGVerse, it is. We offer a complete, integrated RAG pipeline to transform your datasets into a production-ready, intelligent retrieval system — in just a few easy steps.
We’re already powering ancientexts.com — a live, conversational interface to explore the wisdom of Ancient Hindu Scriptures at your fingertips.
🌐 Live Demo
Explore a real-world implementation of RAGVerse in action at ancientexts.com — combining seamless knowledge retrieval with natural language conversation.
Ragverse is a modular, open-source framework for building and managing end-to-end Retrieval-Augmented Generation (RAG) systems.
It includes:
- A modern chat frontend
- Robust fully integrated backend
- An intuitive control panel for orchestration and observability
Whether you're experimenting with LLMs or building production-grade RAG applications, Ragverse offers a complete ecosystem to help you go from zero to production.
Ragverse builds upon the great work of several open-source projects and contributors:
- Based on smart-chatbot-ui
- Built using patterns from FastAPI + MongoDB Stack
- Powered by React Admin
We welcome all contributions—bug fixes, feature requests, documentation, or ideas!
Please refer to our contribution guidelines to get started.
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