feat(adr-081): Phase 1 感官縱深 — 8D 情報蒐集 + 執行後驗證

成品：
- IncidentEvidence DB model（8D 感官 + pre/post 執行狀態）
- EvidenceSnapshot dataclass（build_summary → LLM 上下文）
- SanitizationService（Prompt Injection 0-tolerance，12 pattern）
- MCPToolRegistry（動態工具登記，suggest_tools 不寫死告警類型）
- PreDecisionInvestigator（8D 並行感官，P99 < 8s，Redis 30s 快取）
- PostExecutionVerifier（warmup 10s → 後狀態評估 success/degraded/failed）
- decision_manager + approval_execution 接線（feature flag 守衛）

Gate 1 修復：D4/D5/D7/D8 補 sanitize_dict_values；移除裸 "error" failure
signal 防 error_rate key 誤判；evidence_snapshot rowcount 零行警告。

測試：130 passed（+111 新增）

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

apps/api/src/services/evidence_snapshot.py

@@ -0,0 +1,322 @@
+"""
+AWOOOI AIOps Phase 1 — 不可變事件證據快照
+==========================================
+EvidenceSnapshot：PreDecisionInvestigator 的輸出契約。
+
+設計原則：
+1. 不可變（Immutable）— 建立後只讀；執行後補填 post_execution_state
+2. 版本化（Versioned）— schema_version 確保 fine-tune pipeline 可過濾
+3. 安全（Sanitized）— 所有感官文字必須過 SanitizationService
+4. 降級友好（Graceful Degradation）— 部分感官失敗不阻塞決策
+
+資料流：
+  PreDecisionInvestigator
+    → EvidenceSnapshot（Pydantic model）
+    → save() 寫入 incident_evidence 表
+    → 傳給 decision_manager._dual_engine_analyze()
+
+  PostExecutionVerifier
+    → update_post_execution() 補填 post_execution_state
+
+ADR-081: PreDecisionInvestigator + EvidenceSnapshot
+2026-04-15 ogt + Claude Sonnet 4.6 (亞太): Phase 1 初始建立
+"""
+
+from __future__ import annotations
+
+import uuid
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Any
+
+import structlog
+from sqlalchemy import update
+
+from src.db.base import get_db_context
+from src.db.models import IncidentEvidence
+from src.utils.timezone import now_taipei
+
+logger = structlog.get_logger(__name__)
+
+# EvidenceSnapshot schema 版本
+SCHEMA_VERSION = "v1"
+
+# Evidence summary 最大長度（防止超出 LLM token budget）
+MAX_SUMMARY_CHARS = 32_000  # ≈ 8K tokens（UTF-8 中文 1 字 ≈ 4 chars）
+
+
+@dataclass
+class EvidenceSnapshot:
+    """
+    AI 決策前的不可變情報快照。
+
+    8D 感官維度：
+      D1 k8s_state        — kubectl describe pod + events
+      D2 recent_logs      — container stderr tail-50（已 sanitize）
+      D3 metrics_snapshot — Prometheus 5min vs 1h baseline
+      D4 recent_deployments — ArgoCD/Gitea 過去 1h 部署 diff
+      D5 business_metrics — 訂單量 / 登入成功率 / P0 SLI
+      D6 historical_context — 過去 30 天同 alertname 處置歷史
+      D7 peer_health      — 同 Deployment 其他 replica 健康度
+      D8 dependency_topology — Istio/Service Mesh 上下游 latency
+
+    品質指標：
+      mcp_health          — 各工具呼叫成敗 {tool_name: bool}
+      sensors_attempted / sensors_succeeded — 感官覆蓋率
+
+    Usage:
+        snapshot = EvidenceSnapshot(incident_id="INC-001")
+        snapshot.k8s_state = {"phase": "CrashLoopBackOff", ...}
+        snapshot_id = await snapshot.save()
+    """
+
+    incident_id: str
+
+    # Identifiers
+    snapshot_id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    schema_version: str = SCHEMA_VERSION
+    collected_at: datetime = field(default_factory=now_taipei)
+
+    # 8D 感官數據
+    k8s_state: dict[str, Any] | None = None          # D1
+    recent_logs: str | None = None                   # D2 (sanitized)
+    metrics_snapshot: dict[str, Any] | None = None   # D3
+    recent_deployments: list[dict] | None = None     # D4
+    business_metrics: dict[str, Any] | None = None   # D5
+    historical_context: str | None = None            # D6
+    peer_health: dict[str, Any] | None = None        # D7
+    dependency_topology: dict[str, Any] | None = None  # D8
+
+    # 感官品質
+    mcp_health: dict[str, bool] = field(default_factory=dict)
+    collection_duration_ms: int | None = None
+    sensors_attempted: int = 0
+    sensors_succeeded: int = 0
+
+    # LLM 輸入摘要（由 Investigator 組裝）
+    evidence_summary: str | None = None
+
+    # 執行前後 State
+    pre_execution_state: dict[str, Any] | None = None
+    post_execution_state: dict[str, Any] | None = None
+    verification_result: str | None = None
+
+    # Phase 3 填充（目前永 null）
+    matched_playbook_id: str | None = None
+
+    # ─────────────────────────────────────────────────────────────
+    # Derived helpers
+    # ─────────────────────────────────────────────────────────────
+
+    @property
+    def sensor_coverage_ratio(self) -> float:
+        """感官覆蓋率（0.0 ~ 1.0）"""
+        if self.sensors_attempted == 0:
+            return 0.0
+        return self.sensors_succeeded / self.sensors_attempted
+
+    @property
+    def has_k8s_context(self) -> bool:
+        return self.k8s_state is not None
+
+    @property
+    def has_log_context(self) -> bool:
+        return self.recent_logs is not None and len(self.recent_logs) > 0
+
+    def build_summary(self) -> str:
+        """
+        組裝 LLM-ready 情報摘要（< MAX_SUMMARY_CHARS）。
+
+        格式採用 <raw_evidence> 區塊隔離，防止 Prompt Injection。
+        """
+        parts: list[str] = []
+
+        if self.k8s_state:
+            parts.append(f"[K8s狀態] {self.k8s_state}")
+        if self.recent_logs:
+            parts.append(f"[近期日誌]\n{self.recent_logs[:2000]}")
+        if self.metrics_snapshot:
+            parts.append(f"[指標快照] {self.metrics_snapshot}")
+        if self.recent_deployments:
+            dep_str = "; ".join(
+                d.get("summary", str(d)) for d in self.recent_deployments[:3]
+            )
+            parts.append(f"[近期部署] {dep_str}")
+        if self.business_metrics:
+            parts.append(f"[業務指標] {self.business_metrics}")
+        if self.historical_context:
+            parts.append(f"[歷史脈絡] {self.historical_context[:500]}")
+        if self.peer_health:
+            parts.append(f"[同級副本健康度] {self.peer_health}")
+        if self.dependency_topology:
+            parts.append(f"[依賴拓撲] {self.dependency_topology}")
+
+        # 感官品質報告
+        failed_tools = [t for t, ok in self.mcp_health.items() if not ok]
+        if failed_tools:
+            parts.append(f"[感官警告] 以下工具呼叫失敗，情報可能不完整: {failed_tools}")
+
+        raw = "\n\n".join(parts)
+        summary = f"<raw_evidence>\n{raw}\n</raw_evidence>"
+
+        # Token budget 保護
+        if len(summary) > MAX_SUMMARY_CHARS:
+            summary = summary[:MAX_SUMMARY_CHARS] + "\n[...已截斷，超出 token budget]</raw_evidence>"
+
+        return summary
+
+    # ─────────────────────────────────────────────────────────────
+    # Persistence
+    # ─────────────────────────────────────────────────────────────
+
+    async def save(self) -> str:
+        """
+        將快照持久化到 incident_evidence 表。
+
+        Returns:
+            str: snapshot_id（UUID）
+        """
+        if self.evidence_summary is None:
+            self.evidence_summary = self.build_summary()
+
+        try:
+            async with get_db_context() as db:
+                record = IncidentEvidence(
+                    id=self.snapshot_id,
+                    incident_id=self.incident_id,
+                    matched_playbook_id=self.matched_playbook_id,
+                    schema_version=self.schema_version,
+                    k8s_state=self.k8s_state,
+                    recent_logs=self.recent_logs,
+                    metrics_snapshot=self.metrics_snapshot,
+                    recent_deployments=self.recent_deployments,
+                    business_metrics=self.business_metrics,
+                    historical_context=self.historical_context,
+                    peer_health=self.peer_health,
+                    dependency_topology=self.dependency_topology,
+                    mcp_health=self.mcp_health,
+                    collection_duration_ms=self.collection_duration_ms,
+                    sensors_attempted=self.sensors_attempted,
+                    sensors_succeeded=self.sensors_succeeded,
+                    evidence_summary=self.evidence_summary,
+                    pre_execution_state=self.pre_execution_state,
+                    post_execution_state=self.post_execution_state,
+                    verification_result=self.verification_result,
+                    collected_at=self.collected_at,
+                )
+                db.add(record)
+                await db.flush()
+
+            logger.info(
+                "evidence_snapshot_saved",
+                snapshot_id=self.snapshot_id,
+                incident_id=self.incident_id,
+                sensors_succeeded=self.sensors_succeeded,
+                collection_ms=self.collection_duration_ms,
+            )
+            return self.snapshot_id
+
+        except Exception:
+            logger.exception(
+                "evidence_snapshot_save_error",
+                snapshot_id=self.snapshot_id,
+                incident_id=self.incident_id,
+            )
+            raise
+
+    async def update_post_execution(
+        self,
+        post_state: dict[str, Any],
+        verification_result: str,
+    ) -> None:
+        """
+        PostExecutionVerifier 執行後補填 post_execution_state。
+
+        Args:
+            post_state: 執行後環境狀態
+            verification_result: "success" / "degraded" / "failed" / "timeout"
+        """
+        self.post_execution_state = post_state
+        self.verification_result = verification_result
+
+        try:
+            async with get_db_context() as db:
+                stmt_result = await db.execute(
+                    update(IncidentEvidence)
+                    .where(IncidentEvidence.id == self.snapshot_id)
+                    .values(
+                        post_execution_state=post_state,
+                        verification_result=verification_result,
+                    )
+                )
+
+            # Gate 1 fix: 零行更新代表 snapshot 從未持久化（save() 失敗）→ 學習數據將靜默丟失
+            if stmt_result.rowcount < 1:
+                logger.warning(
+                    "evidence_snapshot_post_update_no_rows",
+                    snapshot_id=self.snapshot_id,
+                    verification_result=verification_result,
+                )
+            else:
+                logger.info(
+                    "evidence_snapshot_post_execution_updated",
+                    snapshot_id=self.snapshot_id,
+                    verification_result=verification_result,
+                )
+        except Exception:
+            logger.exception(
+                "evidence_snapshot_post_update_error",
+                snapshot_id=self.snapshot_id,
+            )
+            raise
+
+
+async def get_latest_snapshot(incident_id: str) -> EvidenceSnapshot | None:
+    """
+    查詢某 Incident 最新的 EvidenceSnapshot（由 snapshot_id 識別）。
+
+    主要供測試和 Phase 3 learning pipeline 使用。
+    """
+    from sqlalchemy import desc, select
+
+    try:
+        async with get_db_context() as db:
+            result = await db.execute(
+                select(IncidentEvidence)
+                .where(IncidentEvidence.incident_id == incident_id)
+                .order_by(desc(IncidentEvidence.collected_at))
+                .limit(1)
+            )
+            row = result.scalar_one_or_none()
+
+        if row is None:
+            return None
+
+        snap = EvidenceSnapshot(
+            incident_id=row.incident_id,
+            snapshot_id=row.id,
+            schema_version=row.schema_version,
+            collected_at=row.collected_at,
+            k8s_state=row.k8s_state,
+            recent_logs=row.recent_logs,
+            metrics_snapshot=row.metrics_snapshot,
+            recent_deployments=row.recent_deployments,
+            business_metrics=row.business_metrics,
+            historical_context=row.historical_context,
+            peer_health=row.peer_health,
+            dependency_topology=row.dependency_topology,
+            mcp_health=row.mcp_health or {},
+            collection_duration_ms=row.collection_duration_ms,
+            sensors_attempted=row.sensors_attempted or 0,
+            sensors_succeeded=row.sensors_succeeded or 0,
+            evidence_summary=row.evidence_summary,
+            pre_execution_state=row.pre_execution_state,
+            post_execution_state=row.post_execution_state,
+            verification_result=row.verification_result,
+            matched_playbook_id=row.matched_playbook_id,
+        )
+        return snap
+
+    except Exception:
+        logger.exception("evidence_snapshot_get_error", incident_id=incident_id)
+        return None