https://github.com/tpoisonooo/rograg
ROGRAG: A Robustly Optimized GraphRAG Framework
https://github.com/tpoisonooo/rograg
knowledge-base knowledge-graph knownledge-augmented-generation llm precision retrieval-augmented-generation
Last synced: 2 months ago
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ROGRAG: A Robustly Optimized GraphRAG Framework
- Host: GitHub
- URL: https://github.com/tpoisonooo/rograg
- Owner: tpoisonooo
- License: bsd-3-clause
- Created: 2024-12-18T08:48:58.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-07T03:47:20.000Z (2 months ago)
- Last Synced: 2025-04-07T04:29:51.119Z (2 months ago)
- Topics: knowledge-base, knowledge-graph, knownledge-augmented-generation, llm, precision, retrieval-augmented-generation
- Language: Python
- Homepage:
- Size: 1.87 MB
- Stars: 102
- Watchers: 1
- Forks: 9
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
English | [Simplified Chinese](./README_zh_cn.md)
## 🔥 Introduction
ROGRAG enhances LLM performance on specialized topics using a robust GraphRAG approach. It features a two-stage (dual-level and logic form methods) retrieval mechanism to improve accuracy without extra computation costs. ROGRAG achieves a 15% score boost on [SeedBench](https://github.com/open-sciencelab/SeedBench), outperforming mainstream methods.
**Key Highlights**:
- Two-stage retrieval for robustness
- Incremental database construction
- Enhanced fuzzy matching and structured reasoning
| Method | QA-1 (Accuracy) | QA-2 (F1) | QA-3 (Rouge) | QA-4 (Rouge) |
|-----------------|-----------------|-----------|--------------|--------------|
| vanilla (w/o RAG) | 0.57 | 0.71 | 0.16 | 0.35 |
| LangChain | 0.68 | 0.68 | 0.15 | 0.04 |
| BM25 | 0.65 | 0.69 | 0.23 | 0.03 |
| RQ-RAG | 0.59 | 0.62 | 0.17 | 0.33 |
| ROGRAG (Ours) | **0.75** | **0.79** | **0.36** | **0.38** |
Deployed on an online research platform, ROGRAG is ready for integration. [Here](https://arxiv.org/abs/2503.06474) is the technical report.
If it is useful to you, please star it ⭐
## 📖 Documentation
- [1. Run from Docker (CMD / Swagger Server API / Gradio)](docs/en/doc_how_to_run_from_docker.md)
- [2. Run from Source](docs/en/doc_how_to_run.md)
- [3. Directory Structure and Function](docs/en/doc_architecture.md)
- [**FAQ** about environment and error](https://github.com/tpoisonooo/HuixiangDou2/issues/8)
## 🔆 Version Description
Compared to [HuixiangDou](https://github.com/internlm/huixiangdou), this repo improves accuracy:
1. **Graph Schema**. Dense retrieval is only for querying similar entities and relationships.
2. Ported/merged multiple open-source implementations, with code differences of nearly 18k lines:
- **Data**. Organized a set of real domain knowledge that LLM has not fully seen for testing (gpt accuracy < 0.6)
- **Ablation**. Confirmed the impact of different stages and parameters on accuracy
3. API remains compatible. That means Wechat/Lark/Web in v1 is also accessible.
```text
# v1 API https://github.com/InternLM/HuixiangDou/blob/main/huixiangdou/service/parallel_pipeline.py#L290
async def generate(self,
query: Union[Query, str],
history: List[Tuple[str]]=[],
language: str='zh',
enable_web_search: bool=True,
enable_code_search: bool=True):
# v2 API https://github.com/tpoisonooo/HuixiangDou2/blob/main/huixiangdou/pipeline/parallel.py#L135
async def generate(self,
query: Union[Query, str],
history: List[Pair] = [],
request_id: str = 'default',
language: str = 'zh_cn'):
```
## 🍀 Acknowledgements
- [SiliconCloud](https://siliconflow.cn) Abundant LLM API, some models are free
- [KAG](https://github.com/OpenSPG/KAG) Graph retrieval based on reasoning
- [DB-GPT](https://github.com/eosphoros-ai/DB-GPT) LLM tool collection
- [LightRAG](https://github.com/HKUDS/LightRAG) Simple and efficient graph retrieval solution
- [SeedBench](https://github.com/open-sciencelab/SeedBench) A multi-task benchmark for evaluating LLMs in seed science
## 📝 Citation
!!! The impact of open-source on different fields/industries varies. Since licensing restriction, we can **only give the code and test conclusions, and the test data cannot be provided**.
```text
@misc{kong2024huixiangdou,
title={HuiXiangDou: Overcoming Group Chat Scenarios with LLM-based Technical Assistance},
author={Huanjun Kong and Songyang Zhang and Jiaying Li and Min Xiao and Jun Xu and Kai Chen},
year={2024},
eprint={2401.08772},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{kong2024labelingsupervisedfinetuningdata,
title={Labeling supervised fine-tuning data with the scaling law},
author={Huanjun Kong},
year={2024},
eprint={2405.02817},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2405.02817},
}
@misc{kong2025huixiangdou2robustlyoptimizedgraphrag,
title={HuixiangDou2: A Robustly Optimized GraphRAG Approach},
author={Huanjun Kong and Zhefan Wang and Chenyang Wang and Zhe Ma and Nanqing Dong},
year={2025},
eprint={2503.06474},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2503.06474},
}
```