finalize medical consultation agent backend

backend/app/services/evaluation_service.py

@@ -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。"""

backend/app/services/external_auth_service.py

@@ -100,7 +100,7 @@ class ExternalAuthService:
         username = self._first_present(data, ["username", "account", "mobile", "phone"])
         display_name = self._first_present(data, ["display_name", "name", "nickname", "real_name"])
         role = self._first_present(data, ["role_type", "role", "user_role"])
-        institution_id = self._to_int(data.get("institution"))
+        institution_id = self._to_int(data.get("institution_id") or data.get("institution"))
         tenant_id = str(institution_id) if institution_id is not None else None
         profile = self._build_profile(data)
         return AuthenticatedUser(
@@ -114,7 +114,7 @@ class ExternalAuthService:
             gender=self._to_int(data.get("gender")),
             institution_id=institution_id,
             institution_name=self._to_str(data.get("institution_name")),
-            department_id=self._to_int(data.get("department")),
+            department_id=self._to_int(data.get("department_id") or data.get("department")),
             department_name=self._to_str(data.get("department_name")),
             title_name=self._to_str(data.get("title_name")),
             major=self._to_str(data.get("major")),
@@ -136,8 +136,10 @@ class ExternalAuthService:
             "gender",
             "role_type",
             "institution",
+            "institution_id",
             "institution_name",
             "department",
+            "department_id",
             "department_name",
             "title_name",
             "major",
@@ -151,7 +153,12 @@ class ExternalAuthService:
             "total_case_count",
             "current_level",
             "status",
+            "last_login",
             "last_login_time",
+            "is_superuser",
+            "is_staff",
+            "is_active",
+            "date_joined",
             "created_at",
             "updated_at",
         ]

backend/app/services/pdf_export_service.py

@@ -35,7 +35,8 @@ class PdfExportService:
         timestamp = datetime.utcnow().strftime("%Y%m%d%H%M%S")
         file_name = f"training_record_{record.id}_{record.score_type}_{timestamp}_{uuid.uuid4().hex[:6]}.pdf"
         file_path = output_dir / file_name
-        self._write_pdf(file_path, record, session)
+        score_details = self.repo.list_score_details(record.id)
+        self._write_pdf(file_path, record, session, score_details)
 
         record.pdf_file_path = str(file_path)
         recommendation = dict(record.recommendation_result or {})
@@ -57,7 +58,7 @@ class PdfExportService:
         )
         return self.db.scalar(stmt)
 
-    def _write_pdf(self, file_path: Path, record: TrainingRecord, session: TrainingSession | None) -> None:
+    def _write_pdf(self, file_path: Path, record: TrainingRecord, session: TrainingSession | None, score_details: list | None = None) -> None:
         """PDF 写入：使用 reportlab 生成 Acrobat 可正常打开的标准 PDF。"""
         try:
             from reportlab.lib import colors
@@ -133,7 +134,7 @@ class PdfExportService:
                 bottomMargin=14 * mm,
                 title="医疗问诊 Agent 训练评价报告",
             )
-            doc.build(self._build_story(record, session, context))
+            doc.build(self._build_story(record, session, context, score_details or []))
         except ModuleNotFoundError:
             self._write_minimal_pdf(file_path, record)
         except Exception as exc:
@@ -184,7 +185,7 @@ class PdfExportService:
         )
         file_path.write_bytes(bytes(content))
 
-    def _build_story(self, record: TrainingRecord, session: TrainingSession | None, context: dict[str, Any]) -> list:
+    def _build_story(self, record: TrainingRecord, session: TrainingSession | None, context: dict[str, Any], score_details: list | None = None) -> list:
         """报告模板：按基本信息、病例、提交、检查、评分细则和改进计划组织内容。"""
         Paragraph = context["Paragraph"]
         Spacer = context["Spacer"]
@@ -220,7 +221,7 @@ class PdfExportService:
         self._append_case_section(story, case, context)
         self._append_submission_section(story, submission, context)
         self._append_order_section(story, orders, context)
-        self._append_dimension_section(story, structured.get("dimension_scores") or [], context)
+        self._append_dimension_section(story, score_details or structured.get("dimension_scores") or [], context)
         self._append_error_section(story, structured.get("errors") or record.wrong_points or [], context)
         self._append_list_section(story, "七、改进计划", structured.get("improvement_plan") or (record.recommendation_result or {}).get("improvement_plan") or [], context)
         self._append_list_section(story, "八、证据摘要", structured.get("evidence_summary") or [], context)
@@ -284,17 +285,49 @@ class PdfExportService:
         if not dimensions:
             story.append(context["Paragraph"]("暂无维度评分。", context["styles"]["body"]))
             return
-        rows = [["维度", "得分", "评价"]]
+        rows = [["维度", "得分", "扣分原因", "置信度", "评语"]]
         for item in dimensions:
-            rows.append([item.get("dimension", "未命名维度"), f"{item.get('score', 0)} / {item.get('max_score', '-')}", item.get("comment", "")])
-        self._append_table(story, rows, [88, 58, 339], context)
+            payload = self._score_detail_payload(item)
+            rows.append([
+                payload["dimension"],
+                payload["score_text"],
+                payload["deducted_reason"],
+                payload["ai_confidence"],
+                payload["comment"],
+            ])
+        self._append_table(story, rows, [70, 45, 160, 45, 165], context)
         for index, item in enumerate(dimensions, start=1):
-            title = f"{index}. {item.get('dimension', '未命名维度')}：{item.get('score', 0)} / {item.get('max_score', '-')}"
+            payload = self._score_detail_payload(item)
+            title = f"{index}. {payload['dimension']}：{payload['score_text']}"
             story.append(context["Paragraph"](self._safe_text(title), context["styles"]["body"]))
-            self._append_list_section(story, "证据", item.get("evidence") or [], context, compact=True)
-            self._append_list_section(story, "扣分原因", item.get("deductions") or [], context, compact=True)
-            if item.get("improvement"):
-                story.append(context["Paragraph"](f"改进动作：{self._safe_text(item.get('improvement'))}", context["styles"]["small"]))
+            self._append_list_section(story, "证据", payload["evidence"], context, compact=True)
+            self._append_list_section(story, "扣分原因", [payload["deducted_reason"]] if payload["deducted_reason"] else [], context, compact=True)
+            if payload["comment"]:
+                story.append(context["Paragraph"](f"评语：{self._safe_text(payload['comment'])}", context["styles"]["small"]))
+
+    def _score_detail_payload(self, item: Any) -> dict[str, Any]:
+        """评分明细展示：兼容 training_score_detail ORM 和旧 dimension_scores 字典。"""
+        if isinstance(item, dict):
+            score = item.get("score", 0)
+            max_score = item.get("max_score")
+            return {
+                "dimension": item.get("dimension", "未命名维度"),
+                "score_text": f"{score} / {max_score}" if max_score is not None else str(score),
+                "deducted_reason": item.get("deducted_reason") or "；".join(str(value) for value in item.get("deductions", []) if value),
+                "ai_confidence": item.get("ai_confidence") or "未记录",
+                "comment": item.get("comment") or item.get("improvement") or "",
+                "evidence": item.get("evidence_message_ids") or item.get("evidence") or [],
+            }
+        score = getattr(item, "score", None)
+        confidence = getattr(item, "ai_confidence", None)
+        return {
+            "dimension": getattr(item, "dimension", "未命名维度"),
+            "score_text": f"{float(score):g}" if score is not None else "未记录",
+            "deducted_reason": getattr(item, "deducted_reason", "") or "",
+            "ai_confidence": f"{float(confidence):g}" if confidence is not None else "未记录",
+            "comment": getattr(item, "comment", "") or "",
+            "evidence": getattr(item, "evidence_message_ids", None) or [],
+        }
 
     def _append_error_section(self, story: list, errors: list, context: dict[str, Any]) -> None:
         """扣分问题分节：展示模型识别出的关键问题和严重程度。"""