fix(api): include auto repair executions in AI stats

apps/api/src/api/v1/stats.py

@@ -122,6 +122,11 @@ class AIPerformance(BaseModel):
     effectiveness_distribution: dict[int, int] = Field(
         description="有效性評分分佈 {1: count, 2: count, ...}"
     )
+    outcome_proposal_count: int = Field(default=0, description="Incident outcome 舊提案數")
+    outcome_executed_count: int = Field(default=0, description="Incident outcome 舊執行數")
+    auto_repair_total: int = Field(default=0, description="自動修復執行紀錄數")
+    auto_repair_success: int = Field(default=0, description="自動修復成功紀錄數")
+    source: str = Field(default="incident_outcome", description="AI 效能資料來源")
 
 
 class ServiceImpact(BaseModel):

apps/api/src/services/stats_service.py

@@ -27,7 +27,7 @@ from sqlalchemy import func, select
 
 from src.core.redis_client import get_redis
 from src.db.base import get_db_context
-from src.db.models import IncidentRecord
+from src.db.models import AutoRepairExecution, IncidentRecord
 from src.models.incident import IncidentStatus
 
 logger = structlog.get_logger(__name__)
@@ -343,12 +343,26 @@ class StatsService:
                 )
                 outcomes = [row[0] for row in result.all() if row[0]]
 
-                total = len(outcomes)
-                executed = sum(1 for o in outcomes if o.get("proposal_executed"))
-                success = sum(
+                outcome_total = len(outcomes)
+                outcome_executed = sum(1 for o in outcomes if o.get("proposal_executed"))
+                outcome_success = sum(
                     1 for o in outcomes if o.get("proposal_executed") and o.get("execution_success")
                 )
 
+                auto_result = await db.execute(
+                    select(
+                        AutoRepairExecution.success,
+                        AutoRepairExecution.execution_time_ms,
+                    ).where(AutoRepairExecution.created_at >= since)
+                )
+                auto_rows = auto_result.all()
+                auto_total = len(auto_rows)
+                auto_success = sum(1 for row in auto_rows if row[0] is True)
+
+                total = max(outcome_total, auto_total)
+                executed = max(outcome_executed, auto_total)
+                success = auto_success if auto_total > 0 else outcome_success
+
                 effectiveness_dist: dict[int, int] = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0}
                 scores = []
                 for o in outcomes:
@@ -367,6 +381,13 @@ class StatsService:
                     "success_rate": round((success / executed * 100) if executed > 0 else 0, 2),
                     "avg_effectiveness": round(avg_effectiveness, 2) if avg_effectiveness else None,
                     "effectiveness_distribution": effectiveness_dist,
+                    "outcome_proposal_count": outcome_total,
+                    "outcome_executed_count": outcome_executed,
+                    "auto_repair_total": auto_total,
+                    "auto_repair_success": auto_success,
+                    "source": "auto_repair_executions+incident_outcome"
+                    if auto_total > 0
+                    else "incident_outcome",
                 }
 
         return await self.get_cached_or_compute(cache_key, compute)