FlexRAG提供灵活高效的RAG框架，支持多模态与网络检索，助力快速研发共享。
FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation
- 支持文本、多模态和网络检索的统一框架，可灵活扩展新算法。
- 采用异步处理与持久缓存，显著降低系统开销，提升推理效率。
- 专为研究设计，适合需要快速原型开发与成果共享的AI团队。
检索增强生成（RAG）在现代大语言模型应用中扮演关键角色，已有多种框架提供了丰富的功能以支持RAG系统开发。然而，现有框架仍存在算法复现困难、技术更新滞后及系统开销高等问题。为此，我们提出开源框架FlexRAG，专为研究与原型开发设计。该框架支持基于文本、多模态及网络的RAG，提供全生命周期支持，并具备高效异步处理与持久化缓存能力。通过提供强大且灵活的解决方案，FlexRAG使研究人员能够快速构建、部署并共享先进的RAG系统。相关工具与资源已开放于https://github.com/ictnlp/FlexRAG。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) plays a pivotal role in modern large language model applications, with numerous existing frameworks offering a wide range of functionalities to facilitate the development of RAG systems. However, we have identified several persistent challenges in these frameworks, including difficulties in algorithm reproduction and sharing, lack of new techniques, and high system overhead. To address these limitations, we introduce \textbf{FlexRAG}, an open-source framework specifically designed for research and prototyping. FlexRAG supports text-based, multimodal, and network-based RAG, providing comprehensive lifecycle support alongside efficient asynchronous processing and persistent caching capabilities. By offering a robust and flexible solution, FlexRAG enables researchers to rapidly develop, deploy, and share advanced RAG systems. Our toolkit and resources are available at \href{https://github.com/ictnlp/FlexRAG}{https://github.com/ictnlp/FlexRAG}.
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