mcp-translation-server

# MCP Translation Server [](LICENSE) [](https://www.python.org/downloads/) [](docs/operations.md) ## Overview MCP Translation Server 是一个专门用于满-汉双向翻译的高性能机器翻译系统。它基于先进的语言学处理和深度学习技术，为低资源语言翻译提供全面的解决方案。 ## 主要特性 ### 1. 增强型形态分析 - 🔍 完整的满语语言规则支持 - 🎯 精确的元音和谐分析 - 📊 智能词形变化预测 - ✨ 自动错误检测和纠正 ### 2. 高级翻译引擎 - 🚀 多级翻译策略 - 📚 智能语料库匹配 - 🔄 形态分析集成 - 📊 详细翻译元数据 ### 3. 丰富的语言资源 - 📖 完整的语言规则系统 - 💾 扩展的平行语料库 - 📚 优化的词典结构 - 🔍 上下文感知分析 ## 快速开始 ### 1. 克隆仓库 ```bash git clone https://github.com/yourusername/mcp-translation-server.git cd mcp-translation-server ``` ### 2. 环境设置 ```bash # 创建虚拟环境 python -m venv venv # 激活虚拟环境 source venv/bin/activate # Linux/Mac # 或 venv\Scripts\activate # Windows # 安装依赖 pip install -r requirements.txt ``` ### 3. 配置 ```bash # 复制配置模板 cp config/config.example.json config/config.json # 编辑配置文件 vim config/config.json # 或使用其他编辑器 ``` ### 4. 运行演示 ```bash # 运行综合演示 python demo/comprehensive_demo.py # 运行翻译服务器 python server.py ``` ## 系统架构 ### 核心组件 1. **形态分析器** (`enhanced_morphology.py`) - 词形分析和生成 - 元音和谐处理 - 错误检测和纠正 2. **翻译引擎** (`enhanced_translation.py`) - 多级翻译策略 - 语料库匹配 - 形态分析集成 3. **语言资源** - 语言规则 (`manchu_rules.json`) - 平行语料库 (`parallel_corpus.json`) - 词典系统 (`dictionary.json`) ## API 文档 ### 基本翻译 ```python POST /api/v1/translate Content-Type: application/json { "text": "bi bithe arambi", "source_lang": "manchu", "target_lang": "chinese" } ``` ### 形态分析 ```python POST /api/v1/analyze Content-Type: application/json { "text": "arambi", "type": "morphology" } ``` ## 贡献指南 1. Fork 本仓库 2. 创建特性分支 (`git checkout -b feature/AmazingFeature`) 3. 提交更改 (`git commit -m 'Add some AmazingFeature'`) 4. 推送到分支 (`git push origin feature/AmazingFeature`) 5. 开启 Pull Request ## 许可证 本项目采用 MIT 许可证。详见 [LICENSE](LICENSE) 文件。 ## 致谢 - 感谢所有为满语研究做出贡献的学者 - 感谢开源社区的支持 - 特别感谢为本项目提供语料和建议的专家们 # Copy example configuration file cp config.example.py config.py # Edit config.py with your settings vim config.py # or use your preferred editor # Set required environment variables export MCP_SECRET_KEY="your-secure-random-string" # Required export MCP_API_TOKEN="your-api-token" # Required export MCP_REDIS_PASSWORD="your-redis-password" # Optional export MCP_SMTP_PASSWORD="your-smtp-password" # Optional ``` 4. Run the server: ```bash python server.py ``` ### Configuration #### Environment Variables The following environment variables are supported: | Variable | Required | Description | Example | |----------|----------|-------------|----------| | `MCP_SECRET_KEY` | Yes | Secret key for session encryption | `openssl rand -hex 32` | | `MCP_API_TOKEN` | Yes | API authentication token | `openssl rand -hex 32` | | `MCP_REDIS_PASSWORD` | No | Redis server password | `your-redis-password` | | `MCP_SMTP_PASSWORD` | No | SMTP server password | `your-smtp-password` | #### Configuration File The server can be configured by copying `config.example.py` to `config.py` and editing the values. The configuration file supports: - API settings (host, port, debug mode) - Security settings (secret key, API token) - Rate limiting rules - Cache configuration - Model settings - Resource paths - Monitoring options - Logging configuration - Email notifications **Important Security Notes:** 1. Never commit `config.py` to version control 2. Use strong, random values for `SECRET_KEY` and `API_TOKEN` 3. Store sensitive credentials in environment variables 4. Keep your `.env` file secure and never commit it 5. Regularly rotate security credentials ## Documentation - [API Documentation](docs/api.md) - [Usage Examples](docs/examples.md) - [Operations Guide](docs/operations.md) - [Contributing Guide](CONTRIBUTING.md) ## Architecture ### Core Components 1. Translation Engine - MT5-based neural translation - Context-aware processing - Batch processing support 2. Language Resources - Comprehensive dictionary - Grammar rule engine - Morphological analyzer - Parallel corpus 3. System Features - Efficient caching - Performance monitoring - Resource management - Error handling ## Performance - Average translation latency: < 1s - 95th percentile latency: < 2s - Concurrent request handling: 100+ req/s - Cache hit rate: > 80% ## Monitoring - Real-time metrics via Prometheus - Visualizations through Grafana - Automated alerting system - Performance tracking ## Contributing We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details. ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Acknowledgments - Research paper authors - Open-source community - Contributors and maintainers ## Contact - GitHub Issues: For bug reports and feature requests - Email: your.email@example.com - Documentation: [Full Documentation](docs/) - Analysis (C^a) - Structure (C^a+s)