| Introduction Video | README (chinese) | Documentation | Retrievers | Examples |
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
- 2025-03-24: 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_retrieverand 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}
}
❤️ Acknowledgements
This project benefits from the following open-source projects:
1 maintainer
Metadata
1 maintainer
Release files for flexrag 0.3.0
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