刘金宝
85 lines
2.1 KiB
Python
85 lines
2.1 KiB
Python
from datetime import datetime
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from pydantic import BaseModel, Field
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class CreateEvaluationRequest(BaseModel):
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"""评价生成入参：指定输出分数类型。"""
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score_type: str = Field(default="percentage", pattern="^(percentage|five_point)$")
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class DimensionScore(BaseModel):
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"""维度评分：保存单个评分维度的分数、满分和评价。"""
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dimension: str
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score: float
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max_score: float
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comment: str
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evidence: list[str] = Field(default_factory=list)
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deductions: list[str] = Field(default_factory=list)
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improvement: str = ""
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class ScoreDetailItem(BaseModel):
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"""评分明细：对应 training_score_detail 的单条评分细则。"""
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id: int | None = None
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record_id: int | None = None
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rule_id: int | None = None
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dimension: str
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score: float | None = None
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deducted_reason: str | None = None
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evidence_message_ids: list = Field(default_factory=list)
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ai_confidence: float | None = None
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comment: str | None = None
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class EvaluationResponse(BaseModel):
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"""评价报告响应：返回结构化 AI 评价报告。"""
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evaluation_id: int
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score_type: str
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total_score: float
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dimension_scores: list[DimensionScore]
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score_details: list[ScoreDetailItem] = Field(default_factory=list)
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errors: list[dict]
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improvement_plan: list[str]
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evidence_summary: list[str]
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guideline_refs: list[dict]
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overall_comment: str
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class EvaluationListItem(BaseModel):
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"""历史评价列表项：按 user_id 查询完整训练后的评价记录。"""
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evaluation_id: int
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case_title: str
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score_type: str
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total_score: float
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created_at: datetime
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pdf_exported: bool
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class EvaluationListResponse(BaseModel):
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"""历史评价列表响应。"""
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items: list[EvaluationListItem]
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class ExportPdfResponse(BaseModel):
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"""PDF 导出响应：返回导出记录和本地文件路径。"""
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export_id: int
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file_path: str
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class EvaluationDetailResponse(EvaluationResponse):
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"""评价详情响应：在报告详情页使用。"""
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session_id: int
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case_id: int
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case_title: str
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created_at: datetime
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pdf_file_path: str | None = None
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