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finalize medical consultation agent backend

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This commit is contained in:
刘金宝
2026-06-03 15:51:46 +08:00
parent 93d9e1c6a5
commit eb43573a44
33 changed files with 1063 additions and 281 deletions
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backend/app/services/evaluation_service.py
+97 -28
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@@ -1,16 +1,15 @@
import json
from datetime import datetime
from decimal import Decimal
from sqlalchemy.orm import Session
from app.agents.orchestrator import MedicalConsultationOrchestrator
from app.core.context import UserContext
from app.core.exceptions import AppError
from app.models.training_record import TrainingRecord
from app.models.user import UserLearningProfile
from app.models.training_record import TrainingRecord, TrainingScoreDetail
from app.repositories.case_repository import CaseRepository
from app.repositories.evaluation_repository import EvaluationRepository
from app.repositories.profile_repository import UserLearningProfileRepository
from app.repositories.session_repository import SessionRepository
from app.repositories.source_case_repository import SourceCaseRepository
from app.schemas.evaluation import (
@@ -20,6 +19,7 @@ from app.schemas.evaluation import (
EvaluationListItem,
EvaluationListResponse,
EvaluationResponse,
ScoreDetailItem,
)
from app.services.audit_service import AuditService
from app.services.knowledge_service import KnowledgeService
@@ -27,7 +27,7 @@ from app.services.runtime_memory import runtime_memory
class EvaluationService:
"""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。"""
"""评价服务：基于病例、评分规则和作答过程生成 training_record 与评分明细。"""
def __init__(self, db: Session) -> None:
self.db = db
@@ -35,7 +35,6 @@ class EvaluationService:
self.case_repo = CaseRepository(db)
self.eval_repo = EvaluationRepository(db)
self.source_repo = SourceCaseRepository(db)
self.profile_repo = UserLearningProfileRepository(db)
self.knowledge = KnowledgeService(db)
self.audit = AuditService(db)
self.orchestrator = MedicalConsultationOrchestrator()
@@ -79,9 +78,9 @@ class EvaluationService:
record = self._build_training_record(ctx, session, case, submission, report, scoring_rules, guideline_result)
self.eval_repo.create_record(record)
self.eval_repo.replace_score_details(record.id, self._build_score_details(record.id, report, scoring_rules))
self.session_repo.update_status(session, "completed")
runtime_memory.release(session.memory_key)
self._update_learning_profile(ctx, record)
self.audit.log(ctx, "evaluation.generate", "training_record", str(record.id), session.id)
return self._to_response(record)
@@ -104,6 +103,7 @@ class EvaluationService:
"score_type": report.get("score_type", session.score_type),
"total_score": total_score,
"dimension_scores": report.get("dimension_scores") or [],
"score_details": report.get("score_details") or [],
"errors": report.get("errors") or [],
"improvement_plan": report.get("improvement_plan") or [],
"evidence_summary": report.get("evidence_summary") or [],
@@ -160,6 +160,61 @@ class EvaluationService:
pdf_file_path=None,
)
def _build_score_details(self, record_id: int, report: dict, scoring_rules: list) -> list[TrainingScoreDetail]:
"""评分明细写入：把 LLM 结构化评分结果映射到 training_score_detail。"""
raw_items = report.get("score_details") or report.get("dimension_scores") or []
rule_map = self._rule_map(scoring_rules)
details: list[TrainingScoreDetail] = []
for item in raw_items:
if not isinstance(item, dict):
continue
dimension = str(item.get("dimension") or "综合表现")
matched_rule = self._match_rule(item, dimension, rule_map)
deducted_reason = item.get("deducted_reason")
if not deducted_reason:
deducted_reason = "；".join(str(value) for value in (item.get("deductions") or []) if value)
evidence = item.get("evidence_message_ids")
if evidence is None:
evidence = item.get("evidence") or []
details.append(
TrainingScoreDetail(
record_id=record_id,
rule_id=int(item.get("rule_id") or matched_rule.id) if matched_rule else None,
dimension=dimension[:50],
score=self._decimal_or_none(item.get("score")),
deducted_reason=deducted_reason or "",
evidence_message_ids=evidence if isinstance(evidence, list) else [evidence],
ai_confidence=self._decimal_or_none(item.get("ai_confidence") or 0.85),
comment=item.get("comment") or item.get("improvement") or "",
)
)
return details
def _rule_map(self, scoring_rules: list) -> dict[str, object]:
"""评分规则映射：按维度和能力维度建立匹配索引。"""
result = {}
for rule in scoring_rules:
for key in (getattr(rule, "dimension", ""), getattr(rule, "competency_dimension", "")):
if key:
result[str(key).strip()] = rule
return result
def _match_rule(self, item: dict, dimension: str, rule_map: dict[str, object]):
"""评分规则匹配：优先按 rule_id，其次按维度文本匹配 scoring_rule。"""
rule_id = item.get("rule_id")
if rule_id:
for rule in rule_map.values():
if getattr(rule, "id", None) == rule_id:
return rule
return rule_map.get(dimension) or rule_map.get(str(item.get("competency_dimension") or "").strip())
def _decimal_or_none(self, value: object) -> Decimal | None:
"""分数转换：将 LLM 返回值转换为 Decimal，异常时置空。"""
try:
return Decimal(str(value))
except Exception:
return None
def _evaluation_level(self, score: float, score_type: str) -> str:
"""评价等级：根据百分制或五分制总分生成训练记录等级。"""
normalized = score * 20 if score_type == "five_point" else score
@@ -177,11 +232,11 @@ class EvaluationService:
return f"knowledge_chunks:{len(matched)}" if matched else "none"
def _numeric_user_id(self, user_id: str) -> int | None:
"""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。"""
"""用户 ID 兼容：Django 返回的 id 写入 external_user_id，纯数字时同步写入源库 user_id。"""
return int(user_id) if str(user_id).isdigit() else None
def list_history(self, user_id: str) -> EvaluationListResponse:
"""历史评价：按外部 user_id 查询完整训练后的 training_record。"""
"""历史评价：按 Django 用户中心 ID 查询完整训练后的 training_record。"""
records = self.eval_repo.list_by_user(user_id)
return EvaluationListResponse(
items=[
@@ -198,7 +253,7 @@ class EvaluationService:
)
def get_detail(self, evaluation_id: int, user_id: str) -> EvaluationDetailResponse:
"""评价详情：按 user_id 校验归属并返回完整报告。"""
"""评价详情：按 Django 用户中心 ID 校验归属并返回完整报告。"""
record = self.eval_repo.get_owned_record(evaluation_id, user_id)
if not record:
raise AppError("EVALUATION_NOT_FOUND", "evaluation not found or not owned by current user", 404)
@@ -221,6 +276,7 @@ class EvaluationService:
score_type=record.score_type,
total_score=float(record.total_score or structured.get("total_score") or 0),
dimension_scores=[DimensionScore(**item) for item in dimension_scores],
score_details=self._score_detail_response(record),
errors=structured.get("errors") or record.wrong_points or [],
improvement_plan=structured.get("improvement_plan") or (record.recommendation_result or {}).get("improvement_plan") or [],
evidence_summary=structured.get("evidence_summary") or [],
@@ -228,25 +284,38 @@ class EvaluationService:
overall_comment=structured.get("overall_comment") or record.feedback or "",
)
def _update_learning_profile(self, ctx: UserContext, record: TrainingRecord) -> None:
"""学习档案：根据完整训练记录更新用户平均分和薄弱维度。"""
profile = self.profile_repo.get_profile(ctx.user_id, ctx.tenant_id)
if not profile:
profile = UserLearningProfile(user_id=ctx.user_id, tenant_id=ctx.tenant_id)
records = self.eval_repo.list_by_user(ctx.user_id)
percentage_scores = [float(item.total_score or 0) for item in records if item.score_type == "percentage"]
five_point_scores = [float(item.total_score or 0) for item in records if item.score_type == "five_point"]
dimensions = (record.ai_feedback_structured or {}).get("dimension_scores") or []
weak_dimensions = sorted(dimensions, key=lambda item: float(item.get("score", 0)))[:2]
profile.total_evaluations = len(records)
profile.avg_score_percentage = round(sum(percentage_scores) / len(percentage_scores), 2) if percentage_scores else None
profile.avg_score_five_point = round(sum(five_point_scores) / len(five_point_scores), 2) if five_point_scores else None
profile.weak_dimensions = weak_dimensions
profile.last_evaluation_id = record.id
profile.last_trained_at = datetime.utcnow()
self.profile_repo.save(profile)
def _score_detail_response(self, record: TrainingRecord) -> list[ScoreDetailItem]:
"""评分明细响应：优先读取 training_score_detail，旧记录回退到结构化维度评分。"""
details = self.eval_repo.list_score_details(record.id)
if details:
return [
ScoreDetailItem(
id=item.id,
record_id=item.record_id,
rule_id=item.rule_id,
dimension=item.dimension,
score=float(item.score) if item.score is not None else None,
deducted_reason=item.deducted_reason,
evidence_message_ids=item.evidence_message_ids or [],
ai_confidence=float(item.ai_confidence) if item.ai_confidence is not None else None,
comment=item.comment,
)
for item in details
]
structured = record.ai_feedback_structured or {}
return [
ScoreDetailItem(
record_id=record.id,
dimension=item.get("dimension", "综合表现"),
score=float(item.get("score") or 0),
deducted_reason="；".join(str(value) for value in item.get("deductions", []) if value),
evidence_message_ids=item.get("evidence") or [],
ai_confidence=None,
comment=item.get("comment") or "",
)
for item in structured.get("dimension_scores") or []
if isinstance(item, dict)
]
def _case_title(self, case_id: int | None) -> str:
"""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。"""