316 lines
8.9 KiB
Python
316 lines
8.9 KiB
Python
"""
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完整的数据模型和 API，实现 Skills 与 LLM 的深度结合
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"""
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from pydantic import BaseModel, Field
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from typing import List, Optional, Dict, Any, Union
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from datetime import datetime
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from enum import Enum
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# ============================================================================
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# Skill 与 LLM 结合的核心模型
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# ============================================================================
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class SkillTriggerType(str, Enum):
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"""Skill 触发类型"""
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# LLM 根据 description 自动选择
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AUTO = "auto"
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# 用户/Agent 显式指定
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EXPLICIT = "explicit"
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# Agent 工作流固定使用
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FIXED = "fixed"
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class SkillMetadata(BaseModel):
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"""Skill 元数据（用于触发和选择）"""
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name: str
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# 关键：description 用于 LLM 理解何时使用此 Skill
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description: str # 应包含：功能、触发条件、使用场景
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category: str
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tags: List[str]
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trigger_type: SkillTriggerType
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# 可选的触发关键词（用于精确匹配）
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keywords: List[str] = Field(default_factory=list)
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# 适用场景（用于 AI 匹配）
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use_cases: List[str] = Field(default_factory=list)
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# 依赖的其他 Skills
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dependencies: List[str] = Field(default_factory=list)
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# 权重（用于多 Skill 选择时的排序）
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priority: float = 1.0
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class SkillReferenceFile(BaseModel):
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"""参考文件定义"""
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filename: str
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filetype: str # md, txt, json, yaml, py, sh
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content: str
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description: Optional[str] = None
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size_bytes: int
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class SkillScript(BaseModel):
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"""脚本定义"""
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filename: str
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language: str # python, bash, javascript
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code: str
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description: str
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parameters: Dict[str, Any] = Field(default_factory=dict)
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class SkillAsset(BaseModel):
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"""资产定义（模板、图标等）"""
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filename: str
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filetype: str
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content: str # base64 或文本
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description: str
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class SkillStructure(BaseModel):
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"""完整的 Skill 结构（符合 skill-creator 标准）"""
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# SKILL.md
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frontmatter: Dict[str, Any] # YAML frontmatter: name, description
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behavior_guide: str # Markdown 内容
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# 可选资源
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scripts: List[SkillScript] = Field(default_factory=list)
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references: List[SkillReferenceFile] = Field(default_factory=list)
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assets: List[SkillAsset] = Field(default_factory=list)
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# ============================================================================
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# AI 生成 Skill 的完整流程
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# ============================================================================
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class SkillGenerationRequest(BaseModel):
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"""
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AI 生成 Skill 请求
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按照 skill-creator 的完整流程：
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Step 1: Understanding - 理解需求
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Step 2: Planning - 规划资源
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Step 3: Initialize - 生成结构
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Step 4: Edit - 用户调整
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Step 5: Package - 打包
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"""
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# Step 1: 用户意图
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user_intent: str = Field(
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...,
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description="用户想要创建的 Skill 的意图描述"
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)
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use_cases: List[str] = Field(
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default_factory=list,
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description="具体用例示例，如 ['用户说：帮我写一段古风对话', '用户说：审核这段内容的一致性']"
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)
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# 可选约束
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preferred_category: Optional[str] = None
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preferred_tags: Optional[List[str]] = None
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trigger_type: SkillTriggerType = SkillTriggerType.AUTO
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# 生成选项
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include_scripts: bool = False # 是否生成脚本
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include_references: bool = False # 是否生成参考文档模板
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include_assets: bool = False # 是否生成资产模板
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temperature: float = Field(0.7, ge=0, le=1)
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class SkillAnalysis(BaseModel):
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"""Step 1: 需求分析结果"""
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skill_type: str # 如 "对话创作", "内容审核", "文件处理"
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core_functionality: str
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intended_users: List[str]
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key_features: List[str]
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suggested_complexity: str # "simple", "medium", "complex"
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class SkillPlanning(BaseModel):
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"""Step 2: 资源规划结果"""
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needs_scripts: bool
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suggested_scripts: List[Dict[str, str]] # [{filename, purpose}]
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needs_references: bool
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suggested_references: List[Dict[str, str]]
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needs_assets: bool
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suggested_assets: List[Dict[str, str]]
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# Degree of freedom 建议
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suggested_freedom: str # "high", "medium", "low"
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class SkillGenerationResponse(BaseModel):
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"""
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AI 生成 Skill 的完整响应
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包含完整的 skill-creator 流程结果
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"""
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# Step 1: 分析结果
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analysis: SkillAnalysis
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# Step 2: 规划结果
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planning: SkillPlanning
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# Step 3: 完整的 Skill 结构
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skill_structure: SkillStructure
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# 元数据建议
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suggested_metadata: SkillMetadata
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# 实现建议
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implementation_steps: List[str]
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best_practices: List[str]
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# 验证结果
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validation_passed: bool
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validation_warnings: List[str] = Field(default_factory=list)
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validation_errors: List[str] = Field(default_factory=list)
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# ============================================================================
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# Skill 调整/优化请求
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# ============================================================================
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class SkillRefinementRequest(BaseModel):
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"""对已生成的 Skill 进行调整"""
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skill_id: str
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current_content: str
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# 调整需求
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refinement_type: str # "optimize", "extend", "fix", "adapt"
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refinement_description: str
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# 上下文
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user_feedback: Optional[str] = None
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use_case_update: Optional[str] = None
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temperature: float = Field(0.7, ge=0, le=1)
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class SkillRefinementResponse(BaseModel):
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"""调整后的 Skill"""
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updated_structure: SkillStructure
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changes_made: List[str]
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improvement_suggestions: List[str]
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# ============================================================================
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# Skill 选择和路由（LLM 与 Skills 结合）
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# ============================================================================
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class SkillSelectionCriteria(BaseModel):
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"""Skill 选择条件"""
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# 用户意图
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user_intent: str
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# 上下文信息
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context: Dict[str, Any] = Field(default_factory=dict)
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# 约束条件
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required_category: Optional[str] = None
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required_tags: Optional[List[str]] = None
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exclude_skills: List[str] = Field(default_factory=list)
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# 选择模式
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selection_mode: str = "auto" # "auto", "fixed", "hybrid"
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class SkillSelectionResult(BaseModel):
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"""Skill 选择结果"""
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selected_skills: List[SkillMetadata]
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selection_reason: str
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confidence_scores: Dict[str, float]
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alternative_skills: List[SkillMetadata] = Field(default_factory=list)
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class SkillExecutionContext(BaseModel):
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"""Skill 执行上下文"""
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skill_id: str
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skill_metadata: SkillMetadata
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execution_mode: str # "integrated", "standalone", "chained"
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# 上下文数据
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user_input: str
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context_data: Dict[str, Any] = Field(default_factory=dict)
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# 参考文件（已加载）
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loaded_references: Dict[str, str] = Field(default_factory=dict)
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# 执行参数
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temperature: float = 0.7
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max_tokens: Optional[int] = None
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class SkillExecutionResponse(BaseModel):
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"""Skill 执行结果"""
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skill_id: str
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execution_success: bool
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result: str
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# 使用的信息
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references_used: List[str]
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scripts_executed: List[str]
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# 反馈信息
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execution_time_ms: int
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tokens_used: Dict[str, int]
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# 建议
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improvement_suggestions: List[str] = Field(default_factory=list)
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# ============================================================================
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# Agent 工作流中的 Skill 配置
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# ============================================================================
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class AgentWorkflowStep(BaseModel):
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"""Agent 工作流步骤"""
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step_name: str
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step_number: int
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# Skill 配置
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skill_id: Optional[str] = None # None 表示不使用 Skill
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skill_mode: str = "required" # "required", "optional", "conditional"
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# 条件（用于 conditional 模式）
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condition: Optional[str] = None
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# 回退行为
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fallback_skill_id: Optional[str] = None
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fallback_behavior: Optional[str] = None
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class AgentWorkflowConfig(BaseModel):
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"""Agent 工作流配置（定义哪些步骤使用哪些 Skills）"""
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agent_id: str
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agent_name: str
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# 工作流步骤
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steps: List[AgentWorkflowStep]
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# 全局配置
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default_temperature: float = 0.7
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allow_skill_substitution: bool = False
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require_all_skills: bool = True
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# ============================================================================
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# 批量 Skill 操作
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# ============================================================================
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class BatchSkillOperation(BaseModel):
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"""批量 Skill 操作"""
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operation: str # "validate", "package", "deploy"
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skill_ids: List[str]
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options: Dict[str, Any] = Field(default_factory=dict)
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class BatchSkillOperationResponse(BaseModel):
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"""批量操作结果"""
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operation: str
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total_skills: int
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successful_skills: List[str]
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failed_skills: List[Dict[str, str]]
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summary: Dict[str, Any]
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