1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
|
# 🌟 **LinearRAG**
> *LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora*
> A lightweight GraphRAG framework that eliminates LLM token cost during graph construction – making GraphRAG faster and more efficient than ever.
<p align="center">
<a href="https://arxiv.org/abs/2510.10114" target="_blank">
<img src="https://img.shields.io/badge/Paper-Arxiv-red?logo=arxiv&style=flat-square" alt="arXiv:2506.08938">
</a>
<a href="https://huggingface.co/datasets/Zly0523/linear-rag/tree/main" target="_blank">
<img src="https://img.shields.io/badge/HuggingFace-Model-yellow?logo=huggingface&style=flat-square" alt="HuggingFace">
</a>
<a href="https://github.com/LuyaoZhuang/linear-rag" target="_blank">
<img src="https://img.shields.io/badge/GitHub-Project-181717?logo=github&style=flat-square" alt="GitHub">
</a>
</p>
---
## 🚀 **Highlights**
- ✅ **Fact-Level Faithfulness**: Eliminates unstable relation extraction, using only lightweight entity recognition to graph.
- 🔥 **Zero Token Consumption**: Complete graph construction and retrieval without any LLM calls.
- 📊 **Strong Results**: Outperforms previous RAG methods on widely-used benchmarks.
<p align="center">
<img src="figure/main_figure.png" width="80%" alt="Framework Overview">
</p>
---
## 🛠️ **Usage**
### 1️⃣ Install Dependencies
**Step 1: Install Python packages**
```bash
pip install -r requirements.txt
```
**Step 2: Download Spacy language model**
```bash
python -m spacy download en_core_web_trf
```
> **Note:** For the `medical` dataset, you need to install the scientific/biomedical Spacy model:
```bash
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_scibert-0.5.3.tar.gz
```
**Step 3: Set up your OpenAI API key**
```bash
export OPENAI_API_KEY="your-api-key-here"
export OPENAI_BASE_URL="your-base-url-here"
```
**Step 4: Download Datasets**
Download the datasets from HuggingFace and place them in the `dataset/` folder:
```bash
git clone https://huggingface.co/datasets/Zly0523/linear-rag
cp -r linear-rag/dataset/* dataset/
```
**Step 5: Prepare Embedding Model**
Make sure the embedding model is available at:
```
model/all-mpnet-base-v2/
```
### 2️⃣ Quick Start Example
```bash
SPACY_MODEL="en_core_web_trf"
EMBEDDING_MODEL="model/bge-large-en-v1.5"
DATASET_NAME="2wikimultihop"
LLM_MODEL="gpt-4o-mini"
MAX_WORKERS=16
python run.py \
--spacy_model ${SPACY_MODEL} \
--embedding_model ${EMBEDDING_MODEL} \
--dataset_name ${DATASET_NAME} \
--llm_model ${LLM_MODEL} \
--max_workers ${MAX_WORKERS}
```
## 🎯 **Performance**
<div align="center">
<img src="figure/generation_results.png" alt="framework" width="1000">
**Main results of end-to-end performance**
</div>
<div align="center">
<img src="figure/efficiency_result.png" alt="framework" width="1000">
**Efficiency and performance comparison.**
</div>
## 📖 Citation
If you find this work helpful, please consider citing us:
```bibtex
@article{zhuang2025linearrag,
title={LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora},
author={Zhuang, Luyao and Chen, Shengyuan and Xiao, Yilin and Zhou, Huachi and Zhang, Yujing and Chen, Hao and Zhang, Qinggang and Huang, Xiao},
journal={arXiv preprint arXiv:2510.10114},
year={2025}
}
```
## 📬 Contact
✉️ Email: zhuangluyao523@gmail.com
|
