Retrieval-Augmented Generation (RAG) significantly enhances the performance of large language models (LLMs) in downstream tasks by integrating external knowledge. To facilitate researchers in deploying RAG systems, various RAG toolkits have been introduced. However, many existing RAG toolkits lack support for knowledge adaptation tailored to specific application scenarios. To address this limitation, we propose UltraRAG, a RAG toolkit that automates knowledge adaptation throughout the entire workflow, from data construction and training to evaluation, while ensuring ease of use. UltraRAG features a user-friendly WebUI that streamlines the RAG process, allowing users to build and optimize systems without coding expertise. It supports multimodal input and provides comprehensive tools for managing the knowledge base. With its highly modular architecture, UltraRAG delivers an end-to-end development solution, enabling seamless knowledge adaptation across diverse user scenarios. The code, demonstration videos, and installable package for UltraRAG are publicly available atthis https URL.
View on arXiv@article{chen2025_2504.08761,
title={ UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation },
author={ Yuxuan Chen and Dewen Guo and Sen Mei and Xinze Li and Hao Chen and Yishan Li and Yixuan Wang and Chaoyue Tang and Ruobing Wang and Dingjun Wu and Yukun Yan and Zhenghao Liu and Shi Yu and Zhiyuan Liu and Maosong Sun },
journal={arXiv preprint arXiv:2504.08761},
year={ 2025 }
}