⚡FlashRAG: A Python Toolkit for Efficient RAG Research

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Installation | Features | Quick-Start | Components | FlashRAG-UI | Supporting Methods | Supporting Datasets | FAQs

FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 23 state-of-the-art RAG algorithms, including 7 reasoning-based methods that combine reasoning ability with retrieval.

With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components. Besides, we provide an easy-to-use UI:

https://github.com/user-attachments/assets/8ca00873-5df2-48a7-b853-89e7b18bc6e9

:link: Navigation

• Features
• Roadmap
• Changelog
• Installation
• Quick Start
• Components
• FlashRAG-UI
• Supporting Methods
• Supporting Datasets & Document Corpus
• Additional FAQs
• License
• Citation

:sparkles: Features

• Extensive and Customizable Framework: Includes essential components for RAG scenarios such as retrievers, rerankers, generators, and compressors, allowing for flexible assembly of complex pipelines.

• Comprehensive Benchmark Datasets: A collection of 36 pre-processed RAG benchmark datasets to test and validate RAG models' performances.

• Pre-implemented Advanced RAG Algorithms: Features 23 advancing RAG algorithms with reported results, based on our framework. Easily reproducing results under different settings.

• 🚀 Reasoning-based Methods: NEW! We now support 7 reasoning-based methods that combine reasoning ability with retrieval, achieving superior performance on complex multi-hop tasks.

• Efficient Preprocessing Stage: Simplifies the RAG workflow preparation by providing various scripts like corpus processing for retrieval, retrieval index building, and pre-retrieval of documents.

• Optimized Execution: The library's efficiency is enhanced with tools like vLLM, FastChat for L