257 lines
12 KiB
Python
257 lines
12 KiB
Python
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import json
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from datetime import datetime
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from sqlalchemy.orm import Session
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from app.agents.orchestrator import MedicalConsultationOrchestrator
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from app.core.context import UserContext
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from app.core.exceptions import AppError
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from app.models.training_record import TrainingRecord
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from app.models.user import UserLearningProfile
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from app.repositories.case_repository import CaseRepository
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from app.repositories.evaluation_repository import EvaluationRepository
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from app.repositories.profile_repository import UserLearningProfileRepository
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from app.repositories.session_repository import SessionRepository
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from app.repositories.source_case_repository import SourceCaseRepository
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from app.schemas.evaluation import (
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CreateEvaluationRequest,
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DimensionScore,
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EvaluationDetailResponse,
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EvaluationListItem,
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EvaluationListResponse,
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EvaluationResponse,
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)
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from app.services.audit_service import AuditService
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from app.services.knowledge_service import KnowledgeService
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from app.services.runtime_memory import runtime_memory
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class EvaluationService:
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"""评价服务：基于新源库表和 training_record 完成评分、历史和学习档案更新。"""
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def __init__(self, db: Session) -> None:
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self.db = db
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self.session_repo = SessionRepository(db)
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self.case_repo = CaseRepository(db)
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self.eval_repo = EvaluationRepository(db)
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self.source_repo = SourceCaseRepository(db)
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self.profile_repo = UserLearningProfileRepository(db)
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self.knowledge = KnowledgeService(db)
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self.audit = AuditService(db)
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self.orchestrator = MedicalConsultationOrchestrator()
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async def create_evaluation(self, ctx: UserContext, session_id: int, payload: CreateEvaluationRequest) -> EvaluationResponse:
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"""评价生成：读取会话短期 memory、提交内容、评分规则和指南后写入 training_record。"""
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session = self.session_repo.get_owned_session(session_id, ctx.user_id)
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if not session:
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raise AppError("SESSION_NOT_FOUND", "session not found or not owned by current user", 404)
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if session.status not in {"evaluating", "completed"}:
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raise AppError("SESSION_STATUS_INVALID", "evaluation requires treatment submission", 400)
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existed = self.eval_repo.get_by_session(session.id, ctx.user_id)
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if existed:
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return self._to_response(existed)
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case = self.case_repo.get_active_case(session.case_id)
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if not case:
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raise AppError("CASE_NOT_FOUND", "case not found or inactive", 404)
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submission = self.session_repo.get_submission(session.id)
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if not submission or not submission.treatment_submitted_at:
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raise AppError("TREATMENT_REQUIRED", "treatment submission is required", 400)
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session.score_type = payload.score_type
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memory_messages = runtime_memory.get_messages(session.memory_key)
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keyword_seed = (case.key_symptoms or []) + (case.key_exams or []) + [case.diagnosis_primary or ""]
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guideline_result = self.knowledge.search_guidelines(case.department_id, session.training_type, keyword_seed)
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guideline_refs = guideline_result["source_refs"]
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scoring_rules = self.source_repo.get_scoring_rules(case.id)
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report = await self.orchestrator.evaluate(
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session=session,
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case=case,
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memory_messages=memory_messages,
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orders=session.orders,
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submission=submission,
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rubric=None,
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guideline_refs=guideline_refs,
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scoring_rules=scoring_rules,
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)
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record = self._build_training_record(ctx, session, case, submission, report, scoring_rules, guideline_result)
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self.eval_repo.create_record(record)
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self.session_repo.update_status(session, "completed")
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runtime_memory.release(session.memory_key)
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self._update_learning_profile(ctx, record)
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self.audit.log(ctx, "evaluation.generate", "training_record", str(record.id), session.id)
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return self._to_response(record)
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def _build_training_record(
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self,
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ctx: UserContext,
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session,
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case,
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submission,
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report: dict,
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scoring_rules: list,
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guideline_result: dict,
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) -> TrainingRecord:
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"""训练记录写入：完整流程结束后把评分结果沉淀到 training_record。"""
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end_time = datetime.utcnow()
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start_time = session.started_at or session.created_at or end_time
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duration_seconds = int((end_time - start_time).total_seconds()) if start_time else None
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total_score = float(report.get("total_score") or 0)
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structured = {
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"score_type": report.get("score_type", session.score_type),
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"total_score": total_score,
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"dimension_scores": report.get("dimension_scores") or [],
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"errors": report.get("errors") or [],
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"improvement_plan": report.get("improvement_plan") or [],
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"evidence_summary": report.get("evidence_summary") or [],
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"guideline_refs": report.get("guideline_refs") or [],
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"overall_comment": report.get("overall_comment") or "",
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"llm_model": report.get("_llm_model"),
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"latency_metrics": report.get("_latency_metrics") or {},
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}
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return TrainingRecord(
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training_mode=session.mode,
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case_type=session.training_type,
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start_time=start_time,
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end_time=end_time,
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duration_seconds=duration_seconds,
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total_score=total_score,
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ai_score=total_score,
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teacher_score=None,
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evaluation_level=self._evaluation_level(total_score, report.get("score_type", session.score_type)),
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status="completed",
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feedback=structured["overall_comment"],
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thinking_chain=json.dumps(
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{
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"evidence_summary": structured["evidence_summary"],
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"guideline_refs": structured["guideline_refs"],
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"scoring_rule_count": len(scoring_rules),
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},
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ensure_ascii=False,
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),
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diagnosis_path=json.dumps(
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{
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"primary_diagnosis": submission.primary_diagnosis,
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"differential_diagnoses": submission.differential_diagnoses or [],
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"diagnosis_basis": submission.diagnosis_basis,
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"standard_diagnosis": case.diagnosis_primary,
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},
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ensure_ascii=False,
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),
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wrong_points=structured["errors"],
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missed_questions=[],
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recommendation_result={"improvement_plan": structured["improvement_plan"]},
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ai_feedback_structured=structured,
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osce_station_score={},
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interruption_count=0,
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emotion_analysis={},
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prompt_version="v1",
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rag_context_version=self._rag_context_version(guideline_result),
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case_id=case.id,
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teacher_id=None,
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user_id=self._numeric_user_id(ctx.user_id),
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external_user_id=ctx.user_id,
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session_id=session.id,
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evaluation_record_id=None,
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score_type=structured["score_type"],
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pdf_file_path=None,
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)
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def _evaluation_level(self, score: float, score_type: str) -> str:
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"""评价等级：根据百分制或五分制总分生成训练记录等级。"""
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normalized = score * 20 if score_type == "five_point" else score
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if normalized >= 90:
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return "excellent"
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if normalized >= 80:
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return "good"
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if normalized >= 60:
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return "pass"
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return "needs_improvement"
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def _rag_context_version(self, guideline_result: dict) -> str:
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"""RAG 版本：记录评分时是否命中指南片段。"""
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matched = guideline_result.get("matched_chunks") or []
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return f"knowledge_chunks:{len(matched)}" if matched else "none"
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def _numeric_user_id(self, user_id: str) -> int | None:
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"""用户 ID 兼容：宿主传字符串 user_id 时写入 external_user_id，数字 ID 同步写入 user_id。"""
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return int(user_id) if str(user_id).isdigit() else None
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def list_history(self, user_id: str) -> EvaluationListResponse:
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"""历史评价：按外部 user_id 查询完整训练后的 training_record。"""
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records = self.eval_repo.list_by_user(user_id)
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return EvaluationListResponse(
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items=[
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EvaluationListItem(
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evaluation_id=record.id,
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case_title=self._case_title(record.case_id),
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score_type=record.score_type,
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total_score=float(record.total_score or 0),
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created_at=record.created_at,
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pdf_exported=bool(record.pdf_file_path),
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)
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for record in records
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]
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)
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def get_detail(self, evaluation_id: int, user_id: str) -> EvaluationDetailResponse:
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"""评价详情：按 user_id 校验归属并返回完整报告。"""
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record = self.eval_repo.get_owned_record(evaluation_id, user_id)
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if not record:
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raise AppError("EVALUATION_NOT_FOUND", "evaluation not found or not owned by current user", 404)
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base = self._to_response(record)
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return EvaluationDetailResponse(
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**base.model_dump(),
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session_id=record.session_id or 0,
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case_id=record.case_id,
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case_title=self._case_title(record.case_id),
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created_at=record.created_at,
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pdf_file_path=record.pdf_file_path,
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)
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def _to_response(self, record: TrainingRecord) -> EvaluationResponse:
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"""评价转换：把 training_record 转换为接口响应结构。"""
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structured = record.ai_feedback_structured or {}
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dimension_scores = structured.get("dimension_scores") or []
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return EvaluationResponse(
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evaluation_id=record.id,
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score_type=record.score_type,
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total_score=float(record.total_score or structured.get("total_score") or 0),
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dimension_scores=[DimensionScore(**item) for item in dimension_scores],
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errors=structured.get("errors") or record.wrong_points or [],
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improvement_plan=structured.get("improvement_plan") or (record.recommendation_result or {}).get("improvement_plan") or [],
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evidence_summary=structured.get("evidence_summary") or [],
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guideline_refs=structured.get("guideline_refs") or [],
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overall_comment=structured.get("overall_comment") or record.feedback or "",
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)
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def _update_learning_profile(self, ctx: UserContext, record: TrainingRecord) -> None:
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"""学习档案：根据完整训练记录更新用户平均分和薄弱维度。"""
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profile = self.profile_repo.get_profile(ctx.user_id, ctx.tenant_id)
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if not profile:
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profile = UserLearningProfile(user_id=ctx.user_id, tenant_id=ctx.tenant_id)
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records = self.eval_repo.list_by_user(ctx.user_id)
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percentage_scores = [float(item.total_score or 0) for item in records if item.score_type == "percentage"]
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five_point_scores = [float(item.total_score or 0) for item in records if item.score_type == "five_point"]
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dimensions = (record.ai_feedback_structured or {}).get("dimension_scores") or []
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weak_dimensions = sorted(dimensions, key=lambda item: float(item.get("score", 0)))[:2]
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profile.total_evaluations = len(records)
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profile.avg_score_percentage = round(sum(percentage_scores) / len(percentage_scores), 2) if percentage_scores else None
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profile.avg_score_five_point = round(sum(five_point_scores) / len(five_point_scores), 2) if five_point_scores else None
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profile.weak_dimensions = weak_dimensions
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profile.last_evaluation_id = record.id
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profile.last_trained_at = datetime.utcnow()
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self.profile_repo.save(profile)
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def _case_title(self, case_id: int | None) -> str:
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"""病例标题：历史记录只保存 case_id，展示时按新病例主表读取标题。"""
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if not case_id:
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return ""
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case = self.case_repo.get_active_case(case_id)
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return case.title if case else ""
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