FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation
This provides a flexible tool for researchers to prototype and deploy RAG systems, but it is incremental as it builds on existing frameworks.
The paper tackles challenges in existing Retrieval-Augmented Generation (RAG) frameworks, such as algorithm reproduction issues and high overhead, by introducing FlexRAG, an open-source framework that supports text-based, multimodal, and network-based RAG with efficient processing and caching, enabling rapid development and sharing of advanced systems.
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}.