UltraRAG自动化适配外部知识，让非编程用户也能轻松构建RAG系统。
UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation
- 模块化设计支持从数据到评估全流程自动化
- 提供无需编码的WebUI和多模态输入支持
- 适合希望快速部署RAG系统的研究人员和开发者
检索增强生成（RAG）通过整合外部知识显著提升大语言模型在下游任务中的表现。为帮助研究者部署RAG系统，已有多种工具包问世。然而，许多现有工具包缺乏针对特定应用场景的知识自适应支持。为此，我们提出UltraRAG，一个可在整个工作流中自动实现知识适配的RAG工具包，涵盖数据构建、训练到评估全过程，并确保易用性。UltraRAG配备友好的WebUI，使用户无需编程即可构建和优化系统；支持多模态输入，提供全面的知识库管理工具。其高度模块化架构提供了端到端解决方案，实现跨多样场景的无缝知识适配。代码、演示视频及可安装包已公开于https://github.com/OpenBMB/UltraRAG。
原文摘要 · Abstract (English)
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.
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