fix(heartbeat): KM向量化改用raw SQL + 格式優化去除空格對齊

- KM vectorized 改用 raw SQL (ORM 無 embedding 欄位)
- 移除 {display:<18} 空格對齊（非等寬字體Telegram會錯位）
- 格式: Name: value 每行一項，清楚易讀
- KM向量化加狀態icon (✅ ≥90% / ⚠️ <90%)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

apps/api/src/services/heartbeat_report_service.py

@@ -340,12 +340,12 @@ class HeartbeatReportService:
                 stats.km_total = km_total or 0
 
                 # KM 向量化數（embedding IS NOT NULL）
-                # 注意：knowledge_entries 無 vectorized 欄位，用 embedding 判斷
-                vec_count = await db.scalar(
-                    select(func.count()).select_from(KnowledgeEntryRecord)
-                    .where(KnowledgeEntryRecord.embedding.isnot(None))
+                # KnowledgeEntryRecord ORM 無 embedding 欄位，改用 raw SQL
+                from sqlalchemy import text as sa_text
+                vec_result = await db.execute(
+                    sa_text("SELECT COUNT(*) FROM knowledge_entries WHERE embedding IS NOT NULL")
                 )
-                stats.km_vectorized = vec_count or 0
+                stats.km_vectorized = vec_result.scalar() or 0
 
                 # 24h 修復統計
                 since = datetime.utcnow() - timedelta(hours=24)
@@ -441,50 +441,42 @@ def report_to_telegram_html(report: HeartbeatReport) -> str:
     # --- AI 服務 ---
     lines.append("🤖 <b>AI 服務</b>")
     ollama_probe = report.ai_services.get("ollama", ProbeResult(False, "❌ 無回應"))
-    latency_str = f" ({ollama_probe.latency_ms:.0f}ms)" if ollama_probe.latency_ms else ""
-    lines.append(f"  Ollama:          {ollama_probe.status}{latency_str}")
-
-    # 各模型狀態（縮排顯示）
+    lat = f" ({ollama_probe.latency_ms:.0f}ms)" if ollama_probe.latency_ms else ""
+    lines.append(f"Ollama: {ollama_probe.status}{lat}")
     for model, loaded in report.ollama_models.items():
         icon = "✅" if loaded else "❌"
-        short = model.split(":")[0]
-        lines.append(f"    {icon} {html.escape(short)}")
+        lines.append(f"  {icon} {html.escape(model.split(':')[0])}")
 
     for svc_name, display in [("nemotron", "Nemotron NIM"), ("gemini", "Gemini API"), ("claude", "Claude API")]:
         probe = report.ai_services.get(svc_name, ProbeResult(False, "❌ 無回應"))
-        latency_str = f" ({probe.latency_ms:.0f}ms)" if probe.latency_ms else ""
-        lines.append(f"  {display:<18}{probe.status}{latency_str}")
+        lat = f" ({probe.latency_ms:.0f}ms)" if probe.latency_ms else ""
+        lines.append(f"{display}: {probe.status}{lat}")
 
     lines.append("")
 
     # --- MCP Provider ---
     lines.append("🔌 <b>MCP Provider</b>")
-    mcp_display = {
-        "k8s": "K8s MCP",
-        "ssh": "SSH MCP",
-        "argocd": "ArgoCD MCP",
-        "sentry": "Sentry MCP",
-    }
-    for key, display in mcp_display.items():
+    for key, display in [("k8s", "K8s MCP"), ("ssh", "SSH MCP"), ("argocd", "ArgoCD MCP"), ("sentry", "Sentry MCP")]:
         probe = report.mcp_providers.get(key, ProbeResult(False, "❌ 無回應"))
-        lines.append(f"  {display:<18}{probe.status}")
+        lines.append(f"{display}: {probe.status}")
 
     lines.append("")
 
     # --- 飛輪狀態 ---
     fw = report.flywheel
     lines.append("🔄 <b>飛輪狀態（24h）</b>")
-    lines.append(f"  Playbooks:       {fw.playbook_count} 個")
+    lines.append(f"Playbooks: {fw.playbook_count} 個")
     if fw.attempt_24h > 0:
         rate = int(fw.success_24h / fw.attempt_24h * 100)
-        lines.append(f"  今日修復:        {fw.success_24h}/{fw.attempt_24h} 次 ({rate}%)")
+        lines.append(f"今日修復: {fw.success_24h}/{fw.attempt_24h} 次 ({rate}%)")
     else:
-        lines.append(f"  今日修復:        0 次")
+        lines.append("今日修復: 0 次")
     if fw.km_total > 0:
         vec_rate = int(fw.km_vectorized / fw.km_total * 100)
-        lines.append(f"  KM 向量化:       {fw.km_vectorized}/{fw.km_total} ({vec_rate}%)")
+        icon = "✅" if vec_rate >= 90 else "⚠️"
+        lines.append(f"KM 向量化: {icon} {fw.km_vectorized}/{fw.km_total} ({vec_rate}%)")
     if fw.last_learning_at:
-        lines.append(f"  最後學習固化:    {fw.last_learning_at.strftime('%H:%M')}")
+        lines.append(f"最後學習: {fw.last_learning_at.strftime('%H:%M')}")
 
     lines.append("")
 
@@ -492,15 +484,15 @@ def report_to_telegram_html(report: HeartbeatReport) -> str:
     lines.append("🚀 <b>基礎設施</b>")
     argocd = report.infra.get("argocd_sync", ProbeResult(False, "❌ 無回應"))
     velero = report.infra.get("velero", ProbeResult(False, "❌ 無回應"))
-    lines.append(f"  ArgoCD:          {argocd.status}")
-    lines.append(f"  Velero 備份:     {velero.status}")
+    lines.append(f"ArgoCD: {argocd.status}")
+    lines.append(f"Velero 備份: {velero.status}")
 
     # --- Warnings ---
     if report.warnings:
         lines.append("")
         lines.append(f"⚠️ <b>需關注（{len(report.warnings)} 項）</b>")
         for w in report.warnings:
-            lines.append(f"  - {html.escape(w)}")
+            lines.append(f"• {html.escape(w)}")
     else:
         lines.append("")
         lines.append("✅ <b>全部正常</b>")