Add AI Agent Integration Support

[FerdinandSu]

Description / 功能描述

English:
Enable AI agents (OpenAI, Claude, LangChain, etc.) to automatically discover and execute MobileSuit commands. This will allow building AI-powered assistants that can interact with your console applications through natural language.

中文:
使AI代理（OpenAI、Claude、LangChain等）能够自动发现并执行MobileSuit命令。这将允许构建能够通过自然语言与控制台应用程序交互的AI助手。

Features / 功能特性

English:

• Export command metadata to OpenAI Function Calling format
• Export command metadata to Anthropic Claude Tool Use format
• Create LangChain tools from MobileSuit commands
• Automatic schema generation from command signatures
• Natural language to command execution
• Support for argument validation and type conversion
• Error handling and result formatting for AI responses

中文:

• 将命令元数据导出为OpenAI函数调用格式
• 将命令元数据导出为Anthropic Claude工具使用格式
• 从MobileSuit命令创建LangChain工具
• 从命令签名自动生成模式
• 自然语言到命令执行
• 支持参数验证和类型转换
• AI响应的错误处理和结果格式化

Use Cases / 使用场景

English:

from ReFreSH.MobileSuit.AI import AIToolkit

# Export to OpenAI
functions = AIToolkit.ExportToOpenAI(MyApp)

# Use with OpenAI
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Send email to john@example.com"}],
    functions=functions
)

# Execute command from AI response
result = AIToolkit.ExecuteFromOpenAI(MyApp, response)

中文:

from ReFreSH.MobileSuit.AI import AIToolkit

# 导出到OpenAI
functions = AIToolkit.ExportToOpenAI(MyApp)

# 与OpenAI一起使用
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "向john@example.com发送邮件"}],
    functions=functions
)

# 从AI响应执行命令
result = AIToolkit.ExecuteFromOpenAI(MyApp, response)

Tech Stack / 技术栈

• Use reflection to extract command metadata / 使用反射提取命令元数据
• JSON schema generation for function definitions / 为函数定义生成JSON模式
• Integration with OpenAI, Anthropic, LangChain APIs / 与OpenAI、Anthropic、LangChain API集成
• Optional: pydantic for schema validation / 可选：使用 pydantic 进行模式验证

Difficulty & Time / 难度与时间

Difficulty / 难度: 🟡 Medium / 中等
Estimated Time / 预估时间: 1-2 weeks / 1-2周

Requirements / 要求

English:

• MUST support OpenAI Function Calling format
• MUST support Anthropic Claude Tool Use format
• MUST support LangChain tool creation
• SHOULD include example AI agents for common use cases
• PR MUST include:
  • Unit tests for schema generation
  • Integration tests with mock AI responses
  • Example applications demonstrating AI integration
  • Comprehensive documentation with tutorials
  • Security considerations documented

中文:

• 必须 支持OpenAI函数调用格式
• 必须 支持Anthropic Claude工具使用格式
• 必须 支持LangChain工具创建
• 应该 包含常见用例的示例AI代理
• PR 必须 包含：
  • 模式生成的单元测试
  • 使用模拟AI响应的集成测试
  • 演示AI集成的示例应用
  • 包含教程的全面文档
  • 记录安全考虑因素

Acceptance Criteria / 验收标准

• Create MobileSuit/AI/ module / 创建AI模块
• Implement AIToolkit.ExportToOpenAI(app_class) / 实现OpenAI导出
• Implement AIToolkit.ExportToClaude(app_class) / 实现Claude导出
• Implement AIToolkit.CreateLangChainTools(app_class) / 实现LangChain工具
• Generate JSON schemas from type hints / 从类型提示生成JSON模式
• Handle parameter descriptions from @SuitInfo / 从装饰器处理参数描述
• Implement command execution from AI function calls / 实现从AI函数调用执行命令
• Add error handling and validation / 添加错误处理和验证
• Create example: OpenAI assistant / 创建示例：OpenAI助手
• Create example: Claude assistant / 创建示例：Claude助手
• Create example: LangChain agent / 创建示例：LangChain代理
• Add security documentation (command whitelisting, etc.) / 添加安全文档
• Add comprehensive unit tests (80%+ coverage) / 添加全面的单元测试
• Update main documentation / 更新主文档

Before Starting / 开始之前

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请在此issue下评论，让我们知道你正在处理！

[gx-bit]

选择小组：宫玺，钱毅哲