""" GovernanceDispatcher 決策融合適配器 ====================================== 將 decision_fusion / playbook_service / Ollama 的既有能力 組合成「給治理事件用的三維融合介面」。 設計原則： - 不修改任何 Tier 3 檔（decision_manager / learning_service / trust_engine） - 只 consume 公開 API（read-only） - 三維融合：LLM × Playbook trust × MCP 情報 - Exception 隔離：任一維度失敗 → 中立值 0.5，不阻塞主流程 融合公式（起始權重，TODO 移到 settings 由 AI 自學調整）： confidence = w_llm * llm_score + w_playbook * playbook_trust + w_mcp * mcp_score w_llm=0.4, w_playbook=0.3, w_mcp=0.3 決策分支（閾值 TODO 移到 settings）： confidence >= 0.85 → auto_dispatch 0.65 <= conf < 0.85 → pending_approval conf < 0.65 → skip 2026-05-03 ogt + Claude Sonnet 4.6（亞太）: GovernanceDispatcher Wave 2E 實作 """ from __future__ import annotations import asyncio import re from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, Literal import httpx import structlog from src.core.config import get_settings if TYPE_CHECKING: from src.db.models import AiGovernanceEvent logger = structlog.get_logger(__name__) # ============================================================================= # 常數 # TODO: 移到 settings（ADR-P2E-FUTURE），屆時可讓 AI 自學調整 # ============================================================================= # 三維融合權重（0.4 / 0.3 / 0.3） _W_LLM: float = 0.4 # TODO: 由 AI 自學調整，初始值 0.4 _W_PLAYBOOK: float = 0.3 # TODO: 由 AI 自學調整，初始值 0.3 _W_MCP: float = 0.3 # TODO: 由 AI 自學調整，初始值 0.3 # 決策分支閾值 # TODO: 移到 settings，未來由 AI 根據 false-positive rate 動態調整 _AUTO_DISPATCH_THRESHOLD: float = 0.85 # >= 此值 → auto_dispatch _PENDING_APPROVAL_THRESHOLD: float = 0.65 # >= 此值 < AUTO → pending_approval # # < 此值 → skip # Ollama 推理超時（秒） _LLM_TIMEOUT_SEC: float = 30.0 # Prometheus 查詢超時（秒） _PROM_TIMEOUT_SEC: float = 5.0 # ============================================================================= # FusedDecision 資料結構 # ============================================================================= @dataclass class FusedDecision: """三維融合決策輸出。 所有分數均為 0.0-1.0（0.5 為中立值，任一維度失敗時使用）。 decision_path 決定 GovernanceDispatcher 寫入哪種 dispatch。 Attributes: confidence: 三維加權融合分數（0.0-1.0） recommended_action: LLM 推薦的修復動作摘要（≤200 字） matched_playbook_id: 最高相似度的 Playbook ID（可 None） playbook_trust: matched_playbook 的 trust_score（可 None） llm_reasoning: LLM 原始輸出摘要（dict，供 decision_context JSONB 記錄） mcp_snapshot: MCP 情報快照（dict，供 decision_context JSONB 記錄） decision_path: auto_dispatch / pending_approval / skip llm_score: LLM 分數（0.0-1.0） playbook_score: Playbook 信任分數（0.0-1.0，無 playbook 時 0.3） mcp_score: MCP 感官品質分數（0.0-1.0） """ confidence: float recommended_action: str matched_playbook_id: str | None playbook_trust: float | None llm_reasoning: dict[str, Any] mcp_snapshot: dict[str, Any] decision_path: Literal["auto_dispatch", "pending_approval", "skip"] llm_score: float playbook_score: float mcp_score: float # ============================================================================= # DecisionFusionAdapter # ============================================================================= class DecisionFusionAdapter: """治理事件決策融合適配器。 將 decision_fusion / playbook_service / MCP 的既有能力組合成 「給治理事件用的三維融合介面」。本類不修改任何 Tier 3 檔，只 consume。 不注入 Tier 3 class： - DecisionManager — 有 incident 中心的複雜狀態機，不適合治理事件 - TrustEngine — 只管理 incident 信任分數 - LearningService — 只管理 KM 寫入路徑 本 Adapter 直接呼叫： - Ollama（仿 decision_fusion._score_hermes 模式）→ LLM 推理 - playbook_service.get_recommendations → Playbook trust - Prometheus provider → MCP 情報 """ def __init__(self) -> None: self._settings = get_settings() # ========================================================================= # Public API # ========================================================================= async def fuse_decision(self, event: "AiGovernanceEvent") -> FusedDecision: """三維融合：LLM × Playbook × MCP → FusedDecision。 三個維度並行評估（asyncio.gather），任一失敗靜默降為 0.5。 依 confidence 決定 decision_path。 Args: event: AiGovernanceEvent ORM 物件（不修改此物件） Returns: FusedDecision 含完整三維快照，供 dispatcher 寫入 decision_context """ # 並行取三維分數 results = await asyncio.gather( self._score_llm(event), self._score_playbook(event), self._score_mcp(event), return_exceptions=True, ) # 安全解包（Exception → 中立值 0.5） llm_result = results[0] playbook_result = results[1] mcp_result = results[2] if isinstance(llm_result, Exception): logger.warning( "fusion_llm_score_failed", event_id=event.id, event_type=event.event_type, error=str(llm_result), ) llm_result = (0.5, "（LLM 評估失敗，使用中立值）", {}) if isinstance(playbook_result, Exception): logger.warning( "fusion_playbook_score_failed", event_id=event.id, error=str(playbook_result), ) playbook_result = (0.3, None, None) if isinstance(mcp_result, Exception): logger.warning( "fusion_mcp_score_failed", event_id=event.id, error=str(mcp_result), ) mcp_result = (0.5, {}) llm_score, recommended_action, llm_reasoning = llm_result playbook_score, matched_playbook_id, playbook_trust = playbook_result mcp_score, mcp_snapshot = mcp_result # 三維加權融合 # TODO: 移到 settings，未來由 AI 自學調整 _W_LLM / _W_PLAYBOOK / _W_MCP confidence = ( _W_LLM * llm_score + _W_PLAYBOOK * playbook_score + _W_MCP * mcp_score ) confidence = max(0.0, min(1.0, confidence)) # 決策分支 # TODO: 閾值移到 settings，未來由 AI 根據 false-positive rate 動態調整 if confidence >= _AUTO_DISPATCH_THRESHOLD: decision_path: Literal["auto_dispatch", "pending_approval", "skip"] = "auto_dispatch" elif confidence >= _PENDING_APPROVAL_THRESHOLD: decision_path = "pending_approval" else: decision_path = "skip" logger.info( "governance_fusion_complete", event_id=event.id, event_type=event.event_type, llm_score=round(llm_score, 4), playbook_score=round(playbook_score, 4), mcp_score=round(mcp_score, 4), confidence=round(confidence, 4), decision_path=decision_path, ) return FusedDecision( confidence=confidence, recommended_action=recommended_action, matched_playbook_id=matched_playbook_id, playbook_trust=playbook_trust, llm_reasoning=llm_reasoning, mcp_snapshot=mcp_snapshot, decision_path=decision_path, llm_score=llm_score, playbook_score=playbook_score, mcp_score=mcp_score, ) # ========================================================================= # 維度 1：LLM 推理（Ollama qwen3:8b — 仿 decision_fusion._score_hermes） # ========================================================================= async def _score_llm( self, event: "AiGovernanceEvent" ) -> tuple[float, str, dict[str, Any]]: """Ollama LLM 推理：治理事件情境 → 建議動作 + 信心度。 Prompt 設計： - 提供 event_type + details 摘要（sanitize 後） - 要求輸出「信心度（0-1）+ 建議動作」 Returns: (llm_score, recommended_action, llm_reasoning_dict) """ event_type = str(event.event_type or "unknown") details_summary = self._summarize_details(event.details or {}) prompt = ( "你是 AIOps 治理分析員。根據以下治理事件，評估自動修復的可行性與建議動作。\n\n" f"【事件類型】{event_type}\n" f"【事件摘要】{details_summary}\n\n" "請以以下格式回應（不超過 200 字）：\n" "CONFIDENCE: [0.0-1.0 的數字]\n" "ACTION: [具體建議修復動作，≤100字]\n\n" "注意：\n" "- CONFIDENCE 越高表示越適合自動執行\n" "- 若事件模糊或影響範圍不明，給低分（0.3-0.5）\n" "- 若有明確、低風險的修復路徑，可給高分（0.7-0.9）\n" "只輸出 CONFIDENCE 和 ACTION 兩行，不要其他解釋。" ) ollama_url = getattr(self._settings, "OLLAMA_URL", "http://34.143.170.20:11434") # 2026-05-03 ogt: ADR-110 GCP-A Primary try: async with httpx.AsyncClient( timeout=httpx.Timeout(_LLM_TIMEOUT_SEC, connect=5.0) ) as client: resp = await client.post( f"{ollama_url}/api/generate", json={ "model": "qwen3:8b", "prompt": prompt, "stream": False, "options": {"num_predict": 128, "temperature": 0.1}, }, ) if resp.status_code != 200: logger.warning( "fusion_llm_http_error", status=resp.status_code, event_id=event.id, ) return 0.5, "（LLM 不可用，使用中立值）", {"error": f"http_{resp.status_code}"} raw_text = resp.json().get("response", "").strip() except Exception as exc: logger.warning("fusion_llm_request_failed", event_id=event.id, error=str(exc)) return 0.5, "（LLM 連線失敗，使用中立值）", {"error": str(exc)} # 移除 標籤（qwen3 CoT 輸出） clean = re.sub(r".*?", "", raw_text, flags=re.DOTALL).strip() # 解析 CONFIDENCE 行 llm_score = 0.5 conf_match = re.search(r"CONFIDENCE:\s*([01]?\.\d+|[01])", clean, re.IGNORECASE) if conf_match: try: llm_score = max(0.0, min(1.0, float(conf_match.group(1)))) except ValueError: pass # 解析 ACTION 行 recommended_action = "（LLM 未提供明確建議）" action_match = re.search(r"ACTION:\s*(.+)", clean, re.IGNORECASE) if action_match: recommended_action = action_match.group(1).strip()[:200] llm_reasoning = { "raw_text_preview": raw_text[:300], "parsed_confidence": llm_score, "parsed_action": recommended_action, "event_type": event_type, } logger.debug( "fusion_llm_scored", event_id=event.id, llm_score=llm_score, action_preview=recommended_action[:60], ) return llm_score, recommended_action, llm_reasoning # ========================================================================= # 維度 2：Playbook 比對 + trust_score # ========================================================================= async def _score_playbook( self, event: "AiGovernanceEvent" ) -> tuple[float, str | None, float | None]: """Playbook 相似度比對 → 取最高 trust_score。 治理事件沒有 SymptomPattern，用 event_type 作為 alert_name 搜尋。 無命中時返回保守初始值 (0.3, None, None)。 Returns: (playbook_score, matched_playbook_id, playbook_trust) """ from src.models.playbook import SymptomPattern from src.services.playbook_service import get_playbook_service symptoms = SymptomPattern( alert_names=[event.event_type or "unknown"], affected_services=[], severity_range=["P2"], keywords=self._extract_keywords(event.details or {}), ) try: svc = get_playbook_service() recommendations = await svc.get_recommendations( symptoms=symptoms, top_k=1, use_rag=False, # 治理事件用 Jaccard 精確比對即可 ) except Exception as exc: logger.warning("fusion_playbook_lookup_failed", event_id=event.id, error=str(exc)) return 0.3, None, None if not recommendations: logger.debug("fusion_playbook_no_match", event_id=event.id, event_type=event.event_type) return 0.3, None, None best = recommendations[0] trust = float(best.playbook.trust_score) playbook_id = best.playbook.playbook_id logger.debug( "fusion_playbook_matched", event_id=event.id, playbook_id=playbook_id, trust_score=trust, similarity=round(best.similarity_score, 4), ) return trust, playbook_id, trust # ========================================================================= # 維度 3：MCP 情報（Prometheus） # ========================================================================= async def _score_mcp( self, event: "AiGovernanceEvent" ) -> tuple[float, dict[str, Any]]: """Prometheus 情報採集 → MCP 感官品質分數。 查詢與事件相關的核心指標（autonomy_rate / hallucination_rate）。 MCP 不可用時返回中立值 (0.5, {})。 Returns: (mcp_score, mcp_snapshot_dict) """ prom_url = getattr( self._settings, "PROMETHEUS_URL", "http://prometheus.observability.svc:9090" ) # 依 event_type 選擇查詢指標（治理事件相關） queries: dict[str, str] = self._get_mcp_queries(event.event_type or "unknown") snapshot: dict[str, Any] = {} success_count = 0 total_count = len(queries) if total_count == 0: return 0.5, {"reason": "no_queries_for_event_type"} try: async with httpx.AsyncClient(timeout=_PROM_TIMEOUT_SEC) as client: for metric_name, query in queries.items(): try: resp = await client.get( f"{prom_url}/api/v1/query", params={"query": query}, ) data = resp.json() if data.get("status") == "success": result_list = data.get("data", {}).get("result", []) if result_list: value = float(result_list[0]["value"][1]) snapshot[metric_name] = round(value, 4) success_count += 1 else: snapshot[metric_name] = None # 有回應但無資料 except Exception as exc: snapshot[metric_name] = f"error:{exc!s:.60}" except Exception as exc: logger.warning("fusion_mcp_prometheus_failed", event_id=event.id, error=str(exc)) return 0.5, {"error": str(exc)} # 品質分數：成功取得資料的指標比例（映射到 [0.2, 0.9]） if total_count > 0: ratio = success_count / total_count mcp_score = 0.2 + 0.7 * ratio else: mcp_score = 0.5 snapshot["_meta"] = { "success_count": success_count, "total_queries": total_count, "quality_score": round(mcp_score, 4), } logger.debug( "fusion_mcp_scored", event_id=event.id, mcp_score=round(mcp_score, 4), success=success_count, total=total_count, ) return mcp_score, snapshot # ========================================================================= # Helpers # ========================================================================= @staticmethod def _summarize_details(details: dict[str, Any]) -> str: """從 details dict 提取可讀摘要（≤300 字）。""" if not details: return "（無詳細資訊）" parts: list[str] = [] # 常見欄位優先展示 for key in ("status", "impact", "remediation", "reason"): val = details.get(key) if val is None: continue if isinstance(val, dict): inner = "; ".join(f"{k}={v}" for k, v in list(val.items())[:4]) parts.append(f"{key}: {inner}") elif isinstance(val, (str, int, float)): parts.append(f"{key}: {val!s:.80}") if not parts: # fallback: 取前幾個 top-level k=v parts = [f"{k}={v!s:.40}" for k, v in list(details.items())[:5]] return "; ".join(parts)[:300] @staticmethod def _extract_keywords(details: dict[str, Any]) -> list[str]: """從 details 提取關鍵字供 Playbook 搜尋（最多 5 個）。""" keywords: list[str] = [] for key in ("remediation", "actionable", "impact"): val = details.get(key) if isinstance(val, dict): for sub_key in ("next_action", "items"): sub = val.get(sub_key) if isinstance(sub, str): keywords.append(sub[:50]) elif isinstance(sub, list): keywords.extend(str(x)[:40] for x in sub[:2]) return keywords[:5] @staticmethod def _get_mcp_queries(event_type: str) -> dict[str, str]: """依 event_type 返回相關 Prometheus 查詢指標。 不硬寫 event_type → action 對應規則，僅決定「看哪些指標」。 """ # 通用指標（所有 event_type 都查） base_queries: dict[str, str] = { "autonomy_rate": "sli:autonomy_rate:5m", "decision_accuracy": "sli:decision_accuracy:5m", } # 依 event_type 補充針對性指標 extra: dict[str, str] = {} if event_type in ("trust_drift", "execution_blast_radius"): extra["km_growth_rate"] = "sli:km_growth_rate:24h" elif event_type in ("knowledge_degradation", "kb_stale"): extra["km_growth_rate"] = "sli:km_growth_rate:24h" extra["confidence_calibration"] = "sli:confidence_calibration:1h" elif event_type == "llm_hallucination": extra["confidence_calibration"] = "sli:confidence_calibration:1h" elif event_type == "governance_slo_data_gap": extra["confidence_calibration"] = "sli:confidence_calibration:1h" extra["km_growth_rate"] = "sli:km_growth_rate:24h" return {**base_queries, **extra} # ============================================================================= # Singleton # ============================================================================= _adapter_instance: DecisionFusionAdapter | None = None def get_decision_fusion_adapter() -> DecisionFusionAdapter: """取得 DecisionFusionAdapter 單例（lazy init）。""" global _adapter_instance if _adapter_instance is None: _adapter_instance = DecisionFusionAdapter() return _adapter_instance def reset_decision_fusion_adapter() -> None: """重置 singleton（測試用）。""" global _adapter_instance _adapter_instance = None