flexrag 0.1.9

A RAG Framework for Information Retrieval and Generation.

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FlexRAG is a flexible and high-performance framework designed for Retrieval-Augmented Generation (RAG) tasks, offering support for multimodal data, seamless configuration management, and out-of-the-box performance for both research and prototyping.

https://github.com/user-attachments/assets/4dfc0ec9-686b-40e2-b1f0-daa2b918e093

📖 Table of Contents

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

✨ Key Features

• Multimodal RAG Support: FlexRAG isn't limited to just text-based Retrieval-Augmented Generation (RAG). It also supports multimodal RAG, opening up a wide range of application possibilities across different media types.
• Diverse Data Types: FlexRAG enables seamless integration of multiple data formats, including text (e.g., CSV, JSONL), images, documents, web snapshots, and more, giving you flexibility in working with varied data sources.
• Unified Configuration Management: Leveraging python dataclass and hydra-core, FlexRAG simplifies configuration management, making it easier to handle complex setups and customize your workflow.
• Out-of-the-Box: With carefully optimized default configurations, FlexRAG delivers solid performance without the need for extensive parameter tuning.
• High Performance: Built with persistent cache system and asynchronous methods to significantly improve speed and reduce latency in RAG workflows.
• Research & Development Friendly: Supports multiple development modes and includes a companion repository, flexrag_examples, to help you reproduce various RAG algorithms with ease.
• Lightweight: Designed with minimal overhead, FlexRAG is efficient and easy to integrate into your project.

📢 News

• 2025-2-25: 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
• Entrypoints

🏗️ 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.

❤️ Acknowledgements

This project benefits from the following open-source projects:

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