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README.md

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<p align="center">
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<img src="resources/images/logo.png"/>
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</p>
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<!-- icon -->
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[![stars](https://img.shields.io/github/stars/open-sciencelab/GraphGen.svg)](https://github.com/open-sciencelab/GraphGen)
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[![forks](https://img.shields.io/github/forks/open-sciencelab/GraphGen.svg)](https://github.com/open-sciencelab/GraphGen)
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[![open issues](https://img.shields.io/github/issues-raw/open-sciencelab/GraphGen)](https://github.com/open-sciencelab/GraphGen/issues)
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[![issue resolution](https://img.shields.io/github/issues-closed-raw/open-sciencelab/GraphGen)](https://github.com/open-sciencelab/GraphGen/issues)
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<p align="center">
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<a href="README.md">English</a> | <a href="README_zh.md">简体中文</a>
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</p>
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- 🏗️ [System Architecture](#-system-architecture)
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- ⚙️ [Configurations](#-configurations)
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- 📅 [Roadmap](#-roadmap)
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- 💰 [Cost Analysis](#-cost-analysis)
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</details>
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## 📝 What is GraphGen?
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GraphGen is a framework for synthetic data generation guided by knowledge graphs.
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GraphGen is a framework for synthetic data generation guided by knowledge graphs.
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It begins by constructing a fine-grained knowledge graph from the source text,then identifies knowledge gaps in LLMs using the expected calibration error metric, prioritizing the generation of QA pairs that target high-value, long-tail knowledge.
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Furthermore, GraphGen incorporates multi-hop neighborhood sampling to capture complex relational information and employs style-controlled generation to diversify the resulting QA data.
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## 🚀 Quick Start
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[//]: # (TODO)
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### Command Line
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### Gradio Demo
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### User Interface
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![ui](resources/images/interface.jpg)
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### Run from Source
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1. Install dependencies
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```bash
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pip install -r requirements.txt
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```
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2. Configure the environment
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```bash
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cp configs/llm_config.yaml.example configs/llm_config.yaml
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```
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## 📌 Latest Updates
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## 🌟 Key Features
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## ⚙️ Configurations
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## 📅 Roadmap
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## 💰 Cost Analysis

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