UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation

TL;DR

The paper tackles the challenge of domain-specific knowledge adaptation in Retrieval-Augmented Generation (RAG) systems, which often suffer from hallucinations and misalignment with real-world tasks. It introduces UltraRAG, a modular toolkit that automates knowledge adaptation across data construction, model fine-tuning, and evaluation, anchored by a user-friendly WebUI and multimodal support. The framework comprises two global settings (Model Management, Knowledge Management) and three functional modules (Data Construction, Training, Evaluation & Inference), plus multiple inference workflows and a unified data format, enabling end-to-end development and deployment. A legal-domain case study using LawBench demonstrates that knowledge adaptation via UltraRAG—through embedding fine-tuning and generation strategies like UltraRAG-DDR and UltraRAG-KBAlign—improves retrieval and generation performance, validating the practical impact for domain-specific RAG applications and enabling more reproducible comparisons across baselines.

Abstract

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 at https://github.com/OpenBMB/UltraRAG.

Figures (2)

• Figure 1: The Overall Architecture of UltraRAG Framework.
• Figure 2: Screenshots of UltraRAG.