mcp-translation-server

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,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。

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Added on 3/28/2025

MCP Translation Server

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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

git clone https://github.com/yourusername/mcp-translation-server.git
cd mcp-translation-server

2. 环境设置

# 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

# 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

# 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

POST /api/v1/translate
Content-Type: application/json

{
    "text": "bi bithe arambi",
    "source_lang": "manchu",
    "target_lang": "chinese"
}

形态分析

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 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

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 for details.

License

This project is licensed under the MIT License - see the 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
    • Analysis (C^a)
    • Structure (C^a+s)