FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2506.12494v2
• Date: Mon, 30 Jun 2025 05:45:43 GMT
• Title: FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation
• Authors: Zhuocheng Zhang, Yang Feng, Min Zhang,
• Abstract summary: RAG plays a pivotal role in modern large language model applications, with numerous existing frameworks offering a wide range of functionalities.<n>We have identified several persistent challenges in these frameworks, including difficulties in algorithm reproduction and sharing.<n>To address these limitations, we introduce textbfFlexRAG, an open-source framework specifically designed for research and prototyping.
• Score: 24.01783076521377
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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