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