# MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation

**Authors:** Tianyu Fan, Jingyuan Wang, Xubin Ren, Chao Huang  
**arXiv:** [2501.06713](https://arxiv.org/abs/2501.06713)  
**Date:** 2025-01-12  
**Summary page:** https://www.summarizepaper.com/en/paper/2501.06713v2/

## Key points

- Researchers Tianyu Fan, Jingyuan Wang, Xubin Ren, and Chao Huang have introduced an innovative RAG system to address the demand for efficient and lightweight solutions.
- The RAG system focuses on extreme simplicity and efficiency when deploying in existing frameworks.
- It merges text chunks and named entities into a unified structure to reduce reliance on complex semantic comprehension.
- It utilizes graph structures for efficient knowledge discovery without requiring advanced language capabilities.
- Extensive experiments show that the system achieves comparable performance to Large Language Model (LLM)-based methods while using only 25% of the storage space.
- The team has provided a benchmark dataset for evaluating lightweight RAG systems under realistic scenarios and made their implementation and datasets open-source at https://github.com/HKUDS/MiniRAG.

## Summary

In response to the growing demand for efficient and lightweight , researchers Tianyu Fan, Jingyuan Wang, Xubin Ren, and Chao Huang have introduced . This innovative RAG system addresses the challenges faced when deploying  in existing frameworks by focusing on extreme simplicity and efficiency. incorporates two key technical advancements to overcome limitations associated with SLMs' limited semantic understanding and text processing capabilities. Firstly, it introduces a  that merges text chunks and named entities into a unified structure. This approach reduces the system's reliance on complex semantic comprehension, thereby enhancing performance. Secondly,  implements a  that utilizes graph structures for efficient knowledge discovery without necessitating advanced language capabilities. Extensive experiments conducted by the researchers demonstrate that  achieves comparable performance to Large Language Model (LLM)-based methods even when utilizing SLMs. Remarkably,  accomplishes this while requiring only 25% of the storage space typically needed by other systems. Additionally, the team has contributed a comprehensive benchmark dataset specifically designed for evaluating lightweight RAG systems under realistic on-device scenarios with complex queries. To facilitate further research and development in this area, the researchers have made their implementation and datasets fully open-source at https://github.com/HKUDS/MiniRAG. The introduction of <kd> MiniRAG</hd represents a significant step towards simplifying and optimizing RAG systems for enhanced efficiency in resource-constrained environments.