66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
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from typing import List
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from pydantic import BaseModel, Field
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from agent_system.base import BaseResponseModel
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class EvaluationDimension(BaseModel):
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"""单个评价维度"""
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score: float = Field(
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...,
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description="该维度的评分(0-5分，0分最差，5分最好)",
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ge=0.0,
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le=5.0
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)
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comment: str = Field(
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...,
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description="该维度的详细评价和分析"
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)
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class EvaluatorResult(BaseResponseModel):
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"""评价器评价结果"""
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# 基础评价维度（5个）
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clinical_inquiry: EvaluationDimension = Field(
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default=EvaluationDimension(score=0.0, comment="评价失败：临床问诊能力评价缺失"),
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description="临床问诊能力评价"
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)
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diagnostic_reasoning: EvaluationDimension = Field(
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default=EvaluationDimension(score=0.0, comment="评价失败：诊断推理能力评价缺失"),
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description="诊断推理能力评价"
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)
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communication_quality: EvaluationDimension = Field(
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default=EvaluationDimension(score=0.0, comment="评价失败：沟通表达能力评价缺失"),
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description="沟通表达能力评价"
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)
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multi_round_consistency: EvaluationDimension = Field(
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default=EvaluationDimension(score=0.0, comment="评价失败：多轮一致性评价缺失"),
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description="多轮一致性评价"
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)
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overall_professionalism: EvaluationDimension = Field(
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default=EvaluationDimension(score=0.0, comment="评价失败：整体专业性评价缺失"),
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description="整体专业性评价"
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)
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# 相似度评价维度（3个）
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present_illness_similarity: EvaluationDimension = Field(
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default=EvaluationDimension(score=0.0, comment="评价失败：现病史相似度评价缺失"),
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description="现病史相似度评价"
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)
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past_history_similarity: EvaluationDimension = Field(
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default=EvaluationDimension(score=0.0, comment="评价失败：既往史相似度评价缺失"),
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description="既往史相似度评价"
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)
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chief_complaint_similarity: EvaluationDimension = Field(
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default=EvaluationDimension(score=0.0, comment="评价失败：主述相似度评价缺失"),
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description="主述相似度评价"
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)
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# 总结和建议
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summary: str = Field(
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default="评价失败：整体评价总结缺失",
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description="整体评价总结"
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)
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key_suggestions: List[str] = Field(
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default=["评价失败：关键改进建议缺失"],
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description="关键改进建议列表"
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)
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