Year: 2025
Type: conference-paper
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 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 https://github.com/ictnlp/FlexRAG.
Authors: Zhang Zhuocheng, Yang Feng, Min Zhang
Institution: Harbin Institute of Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412886893