feat(kb+apm): KB Phase 2-A 自動萃取 + KB-D Markdown 詳情面板 + APM 趨勢圖

- KB-A: 新增 knowledge_extractor_service.py (Ollama llama3.2:3b 本地推理)
- KB-A: incident_service.py resolve hook (fire-and-forget asyncio.create_task)
- KB-D: 引入 react-markdown + remark-gfm，知識庫詳情面板 Markdown 渲染
- KB-D: 批准/封存按鈕串接 API (POST /knowledge/{id}/approve, PATCH status)
- KB-D: i18n 新增 approving/archiving 載入狀態文字
- APM: apm/page.tsx 整合 TimeSeriesChart sparkline (使用 trend[] 欄位)

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

apps/api/src/services/incident_service.py

@@ -682,6 +682,16 @@ class IncidentService:
                 error=str(e),
             )
 
+        # KB Phase 2-A: 自動萃取 KB 草稿 (fire-and-forget, 2026-04-03 ogt)
+        try:
+            import asyncio
+            from src.services.knowledge_extractor_service import get_knowledge_extractor
+            asyncio.create_task(
+                get_knowledge_extractor().extract_from_incident(incident)
+            )
+        except Exception:
+            logger.exception("kb_extract_task_create_failed", incident_id=incident_id)
+
         return incident
 
     async def find_by_proposal_id(self, proposal_id: str) -> Incident | None:

apps/api/src/services/knowledge_extractor_service.py

@@ -0,0 +1,228 @@
+"""
+Knowledge Extractor Service — KB Phase 2-A
+==========================================
+Incident resolved 後自動萃取 KB 草稿。
+
+設計原則:
+- 強制使用 Ollama llama3.2:3b（本地推理，符合 Phase 24 D7 隱私規則）
+- fire-and-forget：失敗不影響 resolve 主流程
+- logger.exception 保留完整 Stack Trace 供 Prompt 調優
+
+2026-04-03 ogt: KB Phase 2-A 初始實作
+"""
+
+import structlog
+
+logger = structlog.get_logger(__name__)
+
+_OLLAMA_BASE = "http://192.168.0.188:11434"
+_EXTRACT_MODEL = "llama3.2:3b"
+_EXTRACT_TIMEOUT = 30.0  # 秒，容忍慢速
+
+# Linear / Nothing.tech 風格的 SRE KB Prompt
+_PROMPT_TEMPLATE = """你是一位資深 SRE 工程師，請用**繁體中文**撰寫一份知識庫條目（Markdown 格式）。
+
+## 事件資訊
+- 事件 ID：{incident_id}
+- 嚴重度：{severity}
+- 發生時間：{created_at}
+- 解決時間：{resolved_at}
+
+## 觸發信號
+{signals}
+
+## 請輸出以下結構的 Markdown（只輸出 Markdown，不要其他說明文字）：
+
+# [一句話摘要標題]
+
+## 問題描述
+（簡述發生了什麼問題，2-3 句）
+
+## 根本原因
+（分析可能的根本原因，條列式）
+
+## 解決方法
+（列出實際採取的解決步驟，條列式）
+
+## 預防措施
+（如何避免未來再發生，條列式）
+
+## 相關標籤
+`{severity}` `ai_extracted`
+"""
+
+# 信號關鍵字 → KB 分類映射
+_CATEGORY_KEYWORDS: dict[str, list[str]] = {
+    "infrastructure": ["k8s", "pod", "node", "deploy", "container", "namespace", "kubectl",
+                       "memory", "cpu", "disk", "oom", "evict", "crashloop"],
+    "application":   ["api", "http", "latency", "5xx", "4xx", "error rate", "timeout",
+                      "connection", "database", "redis", "postgres", "slow"],
+    "ai_system":     ["ai", "llm", "openclaw", "nemo", "ollama", "gemini", "claude",
+                      "router", "provider", "inference", "token"],
+    "security":      ["ssl", "cert", "auth", "permission", "scan", "vuln", "exploit",
+                      "unauthorized", "403", "401"],
+}
+
+
+class KnowledgeExtractorService:
+    """
+    Incident → KB 草稿自動萃取器
+
+    使用 Ollama llama3.2:3b 本地推理，產生 Markdown 格式的 SRE 知識條目。
+    """
+
+    async def extract_from_incident(self, incident) -> bool:
+        """
+        從已解決的 Incident 萃取 KB 草稿。
+
+        Args:
+            incident: Incident 物件（需有 incident_id, severity, signals, created_at）
+
+        Returns:
+            True = 萃取成功，False = 失敗（已記錄 Stack Trace）
+        """
+        try:
+            # 1. 組 Prompt
+            signals_text = "\n".join(
+                f"- {s.description}" for s in (incident.signals or [])
+            ) or "（無信號記錄）"
+
+            prompt = _PROMPT_TEMPLATE.format(
+                incident_id=incident.incident_id,
+                severity=incident.severity.value,
+                created_at=str(getattr(incident, "created_at", "未知"))[:19],
+                resolved_at=str(getattr(incident, "resolved_at", "未知"))[:19],
+                signals=signals_text,
+            )
+
+            # 2. 呼叫 Ollama（直接 HTTP，不走 AIRouter 避免路由邏輯開銷）
+            markdown_content = await self._call_ollama(prompt)
+            if not markdown_content:
+                logger.warning(
+                    "kb_extract_empty_response",
+                    incident_id=incident.incident_id,
+                    model=_EXTRACT_MODEL,
+                )
+                return False
+
+            # 3. 萃取標題（第一行 `# 標題`）
+            title = self._extract_title(markdown_content, incident)
+
+            # 4. 推斷分類
+            category = self._infer_category(incident)
+
+            # 5. 建立 KB 條目
+            from src.models.knowledge import EntrySource, EntryType, KnowledgeEntryCreate
+            from src.services.knowledge_service import get_knowledge_service
+
+            entry_data = KnowledgeEntryCreate(
+                title=title,
+                content=markdown_content,
+                entry_type=EntryType.INCIDENT_CASE,
+                category=category,
+                tags=[incident.severity.value, "ai_extracted", category],
+                source=EntrySource.AI_EXTRACTED,
+                related_incident_id=incident.incident_id,
+                created_by="openclaw_ai",
+            )
+            await get_knowledge_service().create_entry(entry_data)
+
+            logger.info(
+                "kb_extract_success",
+                incident_id=incident.incident_id,
+                title=title,
+                category=category,
+                model=_EXTRACT_MODEL,
+            )
+            return True
+
+        except Exception:
+            # 統帥指示：保留完整 Stack Trace 供初期 Prompt 調優
+            logger.exception(
+                "kb_extract_failed",
+                incident_id=getattr(incident, "incident_id", "unknown"),
+            )
+            return False
+
+    async def _call_ollama(self, prompt: str) -> str | None:
+        """
+        直接呼叫 Ollama REST API。
+
+        不走 AIRouter 是刻意設計：
+        - KB 萃取是背景工作，不需要完整的路由/閘門/Cache 邏輯
+        - 強制本地，不允許 fallback 到 cloud provider
+        """
+        import httpx
+
+        try:
+            async with httpx.AsyncClient(timeout=_EXTRACT_TIMEOUT) as client:
+                r = await client.post(
+                    f"{_OLLAMA_BASE}/api/generate",
+                    json={
+                        "model": _EXTRACT_MODEL,
+                        "prompt": prompt,
+                        "stream": False,
+                        "options": {
+                            "temperature": 0.3,   # 低溫：減少幻覺
+                            "num_predict": 800,   # 控制長度
+                            "stop": ["\n\n\n"],   # 防止無限生成
+                        },
+                    },
+                )
+                r.raise_for_status()
+                text = r.json().get("response", "").strip()
+                return text or None
+
+        except Exception:
+            logger.exception(
+                "kb_ollama_call_failed",
+                model=_EXTRACT_MODEL,
+                base=_OLLAMA_BASE,
+            )
+            return None
+
+    def _extract_title(self, markdown: str, incident) -> str:
+        """
+        從 Markdown 第一行 `# 標題` 萃取標題。
+        Fallback：使用 incident_id + 第一個 signal 描述。
+        """
+        for line in markdown.splitlines():
+            stripped = line.strip()
+            if stripped.startswith("# "):
+                title = stripped[2:].strip()
+                if title:
+                    return title[:200]  # DB column max 255
+
+        # Fallback
+        signals = incident.signals or []
+        desc = signals[0].description[:60] if signals else "未知事件"
+        return f"[AI 萃取] {incident.incident_id}: {desc}"
+
+    def _infer_category(self, incident) -> str:
+        """
+        依 signals 關鍵字推斷 KB 分類。
+        依序比對，第一個匹配的分類獲勝。
+        """
+        text = " ".join(
+            s.description.lower() for s in (incident.signals or [])
+        )
+        for category, keywords in _CATEGORY_KEYWORDS.items():
+            if any(k in text for k in keywords):
+                return category
+
+        # 保守 fallback
+        return "infrastructure"
+
+
+# =============================================================================
+# Singleton
+# =============================================================================
+
+_extractor: KnowledgeExtractorService | None = None
+
+
+def get_knowledge_extractor() -> KnowledgeExtractorService:
+    global _extractor
+    if _extractor is None:
+        _extractor = KnowledgeExtractorService()
+    return _extractor