---
title: "FlashRAG"
type: "tool"
slug: "ruc-nlpir-flashrag"
canonical_url: "https://www.graphcanon.com/tools/ruc-nlpir-flashrag"
github_url: "https://github.com/RUC-NLPIR/FlashRAG"
homepage_url: "https://arxiv.org/abs/2405.13576"
stars: 3514
forks: 306
primary_language: "Python"
license: "MIT"
categories: ["model-training", "llm-frameworks"]
tags: ["benchmark", "datasets", "large-language-models", "retrieval-augmented-generation"]
updated_at: "2026-07-07T18:42:26.877209+00:00"
---

# FlashRAG

> FlashRAG: A Python Toolkit for Efficient RAG Research

A toolkit providing pre-processed benchmark datasets and state-of-the-art algorithms for Retrieval Augmented Generation (RAG) research.

## Facts

- Repository: https://github.com/RUC-NLPIR/FlashRAG
- Homepage: https://arxiv.org/abs/2405.13576
- Stars: 3,514 · Forks: 306 · Open issues: 37 · Watchers: 20
- Primary language: Python
- License: MIT
- Last pushed: 2026-04-10T03:37:48+00:00

## Categories

- [Model Training](/categories/model-training.md)
- [LLM Frameworks](/categories/llm-frameworks.md)

## Tags

benchmark, datasets, large language models, retrieval-augmented-generation

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## README (excerpt)

```text
# <div align="center">⚡FlashRAG: A Python Toolkit for Efficient RAG Research<div>
\[ English | [中文](README_zh.md) \]
<div align="center">
<a href="https://arxiv.org/abs/2405.13576" target="_blank"><img src=https://img.shields.io/badge/arXiv-b5212f.svg?logo=arxiv></a>
<a href="https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets/" target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace%20Datasets-27b3b4.svg></a>
<a href="https://www.modelscope.cn/datasets/hhjinjiajie/FlashRAG_Dataset" target="_blank"><img src=https://custom-icon-badges.demolab.com/badge/ModelScope%20Datasets-624aff?style=flat&logo=modelscope&logoColor=white></a>
<a href="https://deepwiki.com/RUC-NLPIR/FlashRAG"><img src="https://devin.ai/assets/deepwiki-badge.png" alt="DeepWiki Document" height="20"/></a>
<a href="https://github.com/RUC-NLPIR/FlashRAG/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/LICENSE-MIT-green"></a>
<a><img alt="Static Badge" src="https://img.shields.io/badge/made_with-Python-blue"></a>
</div>

<h4 align="center">

<p>
<a href="#wrench-installation">Installation</a> |
<a href="#sparkles-features">Features</a> |
<a href="#rocket-quick-start">Quick-Start</a> |
<a href="#gear-components"> Components</a> |
<a href="#art-flashrag-ui"> FlashRAG-UI</a> |
<a href="#robot-supporting-methods"> Supporting Methods</a> |
<a href="#notebook-supporting-datasets--document-corpus"> Supporting Datasets</a> |
<a href="#raised_hands-additional-faqs"> FAQs</a>
</p>

</h4>


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.

<p align="center">
<img src="asset/framework.jpg">
</p>

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

<p>
<a href="https://trendshift.io/repositories/10454" target="_blank"><img src="https://trendshift.io/api/badge/repositories/10454" alt="RUC-NLPIR%2FFlashRAG | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</p>

## :link: Navigation
- [Features](#sparkles-features)
- [Roadmap](#mag_right-roadmap)
- [Changelog](#page_with_curl-changelog)
- [Installation](#wrench-installation)
- [Quick Start](#rocket-quick-start)
- [Components](#gear-components)
- [FlashRAG-UI](#art-flashrag-ui)
- [Supporting Methods](#robot-supporting-methods)
- [Supporting Datasets & Document Corpus](#notebook-supporting-datasets--document-corpus)
- [Additional FAQs](#raised_hands-additional-faqs)
- [License](#bookmark-license)
- [Citation](#star2-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
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/ruc-nlpir-flashrag`](/api/graphcanon/tools/ruc-nlpir-flashrag)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_