刘金宝
291 lines
12 KiB
Python
291 lines
12 KiB
Python
from __future__ import annotations
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import sys
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from pathlib import Path
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from sqlalchemy import select
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from app.db.base import Base
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from app.db.session import SessionLocal, engine
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from app.models import (
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CaseBase,
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CaseExamItem,
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Department,
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KnowledgeChunk,
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KnowledgeDocument,
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KnowledgeSource,
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PromptTemplate,
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ScoringRule,
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TeachingCase,
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TraditionalCase,
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)
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def init_database() -> None:
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"""数据库初始化：创建当前新表体系并写入第一版 Demo 种子数据。"""
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Base.metadata.create_all(bind=engine)
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with SessionLocal() as db:
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seed_demo_data(db)
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db.commit()
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def seed_demo_data(db) -> None:
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"""Demo 数据初始化：仅为本地/测试库写入儿科病例和基础训练数据。"""
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department = _get_or_create_department(db)
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case = _get_or_create_case_base(db, department.id)
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_seed_traditional_case(db, case.id)
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_seed_teaching_case(db, case.id)
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_seed_exam_items(db, case.id)
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_seed_scoring_rules(db, case.id)
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_seed_knowledge(db, department.id)
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_seed_prompts(db)
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def _get_or_create_department(db) -> Department:
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"""科室种子：写入儿科科室。"""
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department = db.scalar(select(Department).where(Department.name == "儿科"))
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if department:
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return department
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department = Department(name="儿科", category="clinical", institution_id=1)
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db.add(department)
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db.flush()
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return department
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def _get_or_create_case_base(db, department_id: int) -> CaseBase:
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"""病例主表种子：以 case_base 作为病例唯一主表。"""
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case = db.scalar(select(CaseBase).where(CaseBase.title == "支气管肺炎 - 6岁男性患儿"))
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if case:
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return case
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case = CaseBase(
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title="支气管肺炎 - 6岁男性患儿",
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case_type="diagnosis_treatment",
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difficulty="medium",
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difficulty_score=2,
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chief_complaint="发热、咳嗽4天，喘息1天",
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description=(
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"患儿4天前无明显诱因出现发热，最高体温39.2℃，伴阵发性咳嗽，后有少量白色黏痰。"
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"1天前出现喘息，夜间明显，活动后加重。精神较差，食欲下降，小便略少。"
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),
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patient_age=6,
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patient_gender="male",
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tags="pediatrics,pneumonia,demo",
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symptom_tags=["发热", "咳嗽", "喘息", "精神食纳差"],
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disease_tags=["支气管肺炎"],
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competency_tags=["问诊完整性", "儿科查体规范", "关键症状识别", "诊断准确性", "治疗计划合理性"],
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guideline_tags=["CAP_2019", "HUMANISTIC_CARE"],
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knowledge_points=["血常规", "CRP", "胸片", "血氧饱和度", "肺部湿啰音"],
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icd_codes="",
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estimated_minutes=20,
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osce_enabled=False,
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rag_enabled=True,
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ai_prompt_template="app/prompts/patient/practice.md",
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multimodal_assets=[],
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vector_status=0,
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publish_status=1,
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status=1,
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created_by_id=None,
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department_id=department_id,
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)
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db.add(case)
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db.flush()
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return case
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def _seed_traditional_case(db, case_id: int) -> None:
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"""传统病例种子：练习模式读取 case_base + traditional_case。"""
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if db.scalar(select(TraditionalCase).where(TraditionalCase.case_id == case_id)):
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return
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db.add(
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TraditionalCase(
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case_id=case_id,
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standard_diagnosis="支气管肺炎",
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standard_treatment=(
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"抗感染、止咳平喘、改善氧合、严密观察病情变化；必要时雾化吸入缓解喘息，"
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"监测体温、呼吸、血氧、精神反应和饮水尿量，出现低氧或呼吸困难加重时及时升级处理。"
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),
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guideline_reference=(
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"诊断依据：发热、咳嗽、喘息，肺部湿啰音，炎症指标升高，胸片提示右下肺片状模糊影，"
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"符合儿童社区获得性肺炎/支气管肺炎诊断思路。严重程度需结合呼吸频率、SpO2、意识、循环和进食饮水情况。"
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),
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)
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)
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def _seed_teaching_case(db, case_id: int) -> None:
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"""教学互动病例种子：教学互动模式读取 case_base + teaching_case。"""
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if db.scalar(select(TeachingCase).where(TeachingCase.case_id == case_id)):
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return
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db.add(
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TeachingCase(
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case_id=case_id,
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teaching_goal="围绕儿科肺炎问诊、检查选择、诊断依据、治疗决策和医患沟通完成互动训练。",
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discussion_questions="如何判断病情严重程度？哪些检查是关键检查？治疗方案如何兼顾抗感染、平喘和氧合监测？",
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teacher_guide="观察学生是否完整追问发热、咳嗽、喘息、既往史、接触史，并能解释胸片、炎症指标和血氧。",
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scoring_focus="问诊完整性、检查合理性、诊断准确性、治疗计划、风险预案、人文沟通。",
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)
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)
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def _seed_exam_items(db, case_id: int) -> None:
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"""检查项目种子：写入病例可申请检查和固定返回结果。"""
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if db.scalar(select(CaseExamItem).where(CaseExamItem.case_id == case_id)):
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return
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items = [
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("blood_routine", "血常规", "lab", "WBC 12.5×10^9/L，中性粒细胞比例72%，提示感染及炎症反应。", {"wbc": "12.5×10^9/L", "neutrophil": "72%"}, True, True, 1),
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("crp", "CRP", "lab", "CRP 28 mg/L，提示炎症反应升高。", {"crp": "28 mg/L"}, True, True, 2),
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("chest_xray", "胸片", "imaging", "双下肺纹理增多，右下肺片状模糊影，支持肺部感染。", {"finding": "右下肺片状模糊影"}, True, True, 3),
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("spo2", "血氧饱和度", "vital_sign", "室内空气 SpO2 94%，处于临界偏低范围。", {"spo2": "94%"}, True, True, 4),
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("mp_igm", "肺炎支原体IgM", "lab", "肺炎支原体IgM阴性。", {"mp_igm": "negative"}, False, False, 5),
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]
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for code, name, item_type, result_text, structured, is_key, abnormal, order in items:
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db.add(
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CaseExamItem(
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case_id=case_id,
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item_code=code,
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item_name=name,
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item_type=item_type,
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category=item_type,
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result_text=result_text,
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result_structured=structured,
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is_key=is_key,
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is_abnormal=abnormal,
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score_weight=5.0 if is_key else 1.0,
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display_order=order,
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)
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)
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def _seed_scoring_rules(db, case_id: int) -> None:
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"""评分规则种子：写入 scoring_rule，评价时作为基础评分细则。"""
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if db.scalar(select(ScoringRule).where(ScoringRule.case_id == case_id)):
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return
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rules = [
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("信息获取", "问诊完整性", 25, "覆盖现病史、既往史、个人史、家族史、儿科特异性症状与家属担忧。"),
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("分析推理", "诊断与鉴别诊断", 25, "结合症状、体征、胸片、炎症指标和血氧支持支气管肺炎诊断，并列出合理鉴别诊断。"),
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("处置决策", "检查与治疗方案", 20, "检查申请合理，治疗原则覆盖抗感染、止咳平喘、改善氧合、风险预案和随访。"),
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("沟通人文", "家属沟通", 15, "向家属说明病情、用药注意事项、危险信号、复诊或住院指征，并回应焦虑。"),
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("临床整合", "流程与整体思维", 15, "流程连贯，把问诊、检查、诊断、治疗和沟通整合成完整临床决策。"),
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]
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for dimension, competency, weight, standard in rules:
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db.add(
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ScoringRule(
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case_id=case_id,
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dimension=dimension,
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competency_dimension=competency,
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score_weight=weight,
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ai_auto_score=True,
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osce_dimension=False,
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scoring_standard=standard,
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rubric_json={"max_score": weight, "criteria": standard},
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)
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)
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def _seed_knowledge(db, department_id: int) -> None:
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"""知识库种子：写入评分参考指南和人文沟通片段。"""
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if db.scalar(select(KnowledgeSource).where(KnowledgeSource.source_code == "CAP_2019")):
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return
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source = KnowledgeSource(
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source_code="CAP_2019",
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source_name="儿童社区获得性肺炎诊疗规范（2019年版）",
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source_type="clinical_guideline",
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authority_level=5,
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is_active=True,
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)
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human = KnowledgeSource(
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source_code="HUMANISTIC_CARE",
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source_name="问诊沟通与人文关怀要求",
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source_type="humanistic_care",
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authority_level=4,
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is_active=True,
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)
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db.add_all([source, human])
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db.flush()
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doc = KnowledgeDocument(
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source_id=source.id,
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department_id=department_id,
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title="儿童社区获得性肺炎诊疗规范摘要",
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task_type="diagnosis_treatment",
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summary="用于肺炎病例诊断、严重程度评估和治疗评分参考。",
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file_path="docs/knowledge/cap_2019.md",
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is_active=True,
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)
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human_doc = KnowledgeDocument(
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source_id=human.id,
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department_id=department_id,
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title="儿科问诊人文关怀要点",
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task_type="diagnosis_treatment",
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summary="用于评价家属沟通、知情告知和健康教育。",
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file_path="docs/knowledge/humanistic_care.md",
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is_active=True,
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)
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db.add_all([doc, human_doc])
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db.flush()
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db.add_all(
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[
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KnowledgeChunk(
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document_id=doc.id,
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department_id=department_id,
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task_type="diagnosis_treatment",
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chunk_text="儿童肺炎诊断需综合发热、咳嗽、喘息、肺部湿啰音、炎症指标和胸部影像学新发浸润影。",
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keywords=["发热", "咳嗽", "喘息", "胸片异常"],
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weight=5.0,
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is_active=True,
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),
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KnowledgeChunk(
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document_id=doc.id,
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department_id=department_id,
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task_type="diagnosis_treatment",
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chunk_text="严重程度评估应关注呼吸频率、血氧饱和度、意识状态、循环状态和进食饮水情况。",
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keywords=["血氧饱和度下降", "呼吸", "严重程度"],
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weight=4.5,
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is_active=True,
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),
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KnowledgeChunk(
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document_id=human_doc.id,
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department_id=department_id,
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task_type="diagnosis_treatment",
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chunk_text="儿科问诊需要向家属说明病情观察指标、用药注意事项、复诊指征，并给予情绪安抚。",
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keywords=["沟通", "健康教育", "家属"],
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weight=4.0,
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is_active=True,
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),
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]
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)
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def _seed_prompts(db) -> None:
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"""提示词种子：写入 Markdown 模板元数据，正文保存在 prompts 目录。"""
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templates = [
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("patient_practice", "patient", "practice", "v1", "fast", "text", "app/prompts/patient/practice.md"),
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("patient_teaching", "patient", "teaching", "v1", "fast", "text", "app/prompts/patient/teaching.md"),
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("novice_case_hint", "hint", "novice", "v1", "fast", "json", "app/prompts/hint/novice_case_hint.md"),
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("scoring_pediatrics_pneumonia", "scoring", "pediatrics_pneumonia", "v1", "fast", "json", "app/prompts/scoring/pediatrics_pneumonia.md"),
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("report_evaluation", "report", "evaluation", "v1", "fast", "json", "app/prompts/report/evaluation_report.md"),
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]
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for code, agent_type, scene, version, model_type, output_format, file_path in templates:
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template = db.scalar(select(PromptTemplate).where(PromptTemplate.template_code == code, PromptTemplate.version_no == version))
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if not template:
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template = PromptTemplate(template_code=code, version_no=version)
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template.agent_type = agent_type
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template.scene = scene
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template.model_type = model_type
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template.output_format = output_format
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template.file_path = file_path
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template.is_active = True
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db.add(template)
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def ensure_storage_dirs() -> None:
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"""目录初始化：创建报告导出目录。"""
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Path("storage/reports").mkdir(parents=True, exist_ok=True)
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if __name__ == "__main__":
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ensure_storage_dirs()
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init_database()
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print("Demo database initialized.")
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