ictnlp/FlexRAG

FlexRAG: A RAG Framework for Information Retrieval and Generation.

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Python

primary language

Aug 31, 2026

updated

flexrag.readthedocs.io/en/latest

llms

nlp

rag

README

FlexRAG is an innovative open-source framework designed to streamline the rapid reproduction, development, and evaluation of Retrieval-Augmented Generation (RAG) systems. It provides comprehensive support for various RAG scenarios, including text-based, multimodal, and web-accessible applications. With an end-to-end pipeline covering everything from data preparation to system evaluation, FlexRAG empowers researchers to efficiently share their work with the community and swiftly develop demonstrative prototypes based on their algorithms.

📖 Table of Contents

📖 Table of Contents
✨ Key Features
📢 News
🚀 Getting Started
🏗️ Architecture
📊 Benchmarks
🏷️ License
🖋️ Citation
❤️ Acknowledgements

✨ Key Features

📢 News

2025-06-17: Our publication FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation is now available!
2025-03-24: The Chinese documentation is now available! Please visit the documentation for more details.
2025-02-25: FlexRAG's LocalRetriever now supports loading from the HuggingFace Hub.
2025-01-22: A new entrypoint run_retriever and four new information retrieval metrics (e.g., RetrievalMAP) are now available. Check out the documentation for more details.
2025-01-08: We provide Windows wheels for FlexRAG. You can install FlexRAG via pip on Windows now.
2025-01-08: The benchmark of FlexRAG on Single-hop QA tasks is now available. Check out the benchmarks for more details.
2025-01-05: Documentation for FlexRAG is now available. Check out the documentation for more details.

🚀 Getting Started

To install FlexRAG via pip:

pip install flexrag

Visit our documentation to learn more.

🏗️ Architecture

FlexRAG is designed with a modular architecture, allowing you to easily customize and extend the framework to meet your specific needs. The following diagram illustrates the architecture of FlexRAG:

📊 Benchmarks

We have conducted extensive benchmarks using the FlexRAG framework. For more details, please refer to the benchmarks page.

🏷️ License

This repository is licensed under the MIT License. See the LICENSE file for details.

🖋️ Citation

If you use FlexRAG in your research, please cite our project:

@software{Zhang_FlexRAG_2025,
author = {Zhang, Zhuocheng and Feng, Yang and Zhang, Min},
doi = {10.5281/zenodo.14593327},
month = jan,
title = {{FlexRAG}},
url = {https://github.com/ictnlp/FlexRAG},
year = {2025}
}
@misc{zhang2025flexragflexiblecomprehensiveframework,
      title={FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation}, 
      author={Zhuocheng Zhang and Yang Feng and Min Zhang},
      year={2025},
      eprint={2506.12494},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.12494}, 
}