TermsAI是一个基于AI技术的知识图谱生成工具，只需输入学习主题和概念数量，即可自动生成结构化的知识图谱。该工具帮助用户快速梳理特定领域的关键概念及其之间的关系，是学习新知识、备课教学和研究工作的得力助手。
演示地址：https://termsai.shuaifox.com/
- 智能图谱生成：输入主题和所需概念数量，AI自动生成相关概念及其关系
- 可视化呈现：通过交互式网络图直观展示概念之间的关联
- 概念数量调整：灵活设置5-20个概念的生成量
- 图谱保存：一键下载生成的知识图谱为图片
- 图谱搜索：通过图谱ID查找之前生成的图谱
- 反馈优化：提供满意度反馈，不满意可重新生成
- 概念扩展：支持向已有图谱添加新概念
- 后端：Python、Flask
- 前端：HTML、CSS、JavaScript
- 可视化：vis-network.js
- 数据存储：SQLAlchemy
- AI生成：基于先进的AI大语言模型
- Python 3.7+
- 所需Python包：Flask, SQLAlchemy等
- 克隆仓库到本地
git clone https://github.com/raphaelxiao/termsai.git
cd termsai- 安装依赖
pip install -r requirements.txt- 设置
- 在.env文件填写大模型API KEY
- 在configs/client.py设置模型（目前是设置了OPENAI的GPT-4o/4o-mini、SilconFlow的DeepSeek-V3/Qwen2.5-72B-Instruct以及阿里云百炼的deepseek-v3）
- 在configs/model_configs.py选择模型
- 启动应用
python app.py- 访问应用
浏览器打开
http://localhost:5000即可使用
termsai/
├── app.py # 主应用入口和API
├── utils.py # 工具函数
├── database.py # 数据库管理
├── models.py # 数据模型
├── templates/ # HTML模板
│ └── index.html # 主页面
└── static/ # 静态资源
├── css/ # 样式文件
├── js/ # JavaScript文件
└── images/ # 图片资源
- 应用内容由AI生成，仅供参考
- 对于复杂主题，生成过程可能需要几分钟时间
- 建议使用现代浏览器以获得最佳体验
- 支持多语言知识图谱生成
- 添加图谱比较功能
- 优化生成速度和质量
- 增加用户自定义样式
MIT
欢迎贡献代码，提出建议或报告问题！
TermsAI is an AI-powered knowledge graph generation tool that automatically creates structured knowledge graphs by simply inputting a learning topic and the desired number of concepts. This tool helps users quickly organize key concepts and their relationships in a specific field, making it an excellent assistant for learning new topics, preparing lessons, and conducting research.
Demo: https://termsai.shuaifox.com/
- Intelligent Graph Generation: Input a topic and the number of concepts needed, and the AI will automatically generate related concepts and their relationships.
- Visualization: Interactive network diagrams intuitively display the relationships between concepts.
- Concept Quantity Adjustment: Flexibly set the number of concepts to generate (5-20).
- Graph Saving: Download the generated knowledge graph as an image with one click.
- Graph Search: Find previously generated graphs using a graph ID.
- Feedback Optimization: Provide satisfaction feedback, and regenerate if unsatisfied.
- Concept Expansion: Support adding new concepts to existing graphs.
- Backend: Python, Flask
- Frontend: HTML, CSS, JavaScript
- Visualization: vis-network.js
- Data Storage: SQLAlchemy
- AI Generation: Based on advanced large language models
- Python 3.7+
- Required Python packages: Flask, SQLAlchemy, etc.
- Clone the repository to your local machine.
git clone https://github.com/your-username/termsai.git
cd termsai- Install dependencies.
pip install -r requirements.txt- Start the application.
python app.py- Access the application.
Open your browser and visit
http://localhost:5000to use the tool.
termsai/
├── app.py # Main application entry and API
├── utils.py # Utility functions
├── database.py # Database management
├── models.py # Data models
├── templates/ # HTML templates
│ └── index.html # Main page
└── static/ # Static resources
├── css/ # Style files
├── js/ # JavaScript files
└── images/ # Image resources
- The content generated by the application is for reference only.
- For complex topics, the generation process may take a few minutes.
- It is recommended to use modern browsers for the best experience.
- Support multi-language knowledge graph generation.
- Add graph comparison functionality.
- Optimize generation speed and quality.
- Allow user-defined styles.
MIT
Contributions are welcome! Feel free to contribute code, suggest ideas, or report issues!