| 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.

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📖 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_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.

• Installation
• Quickstart
• Tutorial

🏗️ 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:

• Faiss
• FlashRAG
• LanceDB
• ANN Benchmarks
• Chonkie
• rerankers