From fb1bbd0e202efddfeaa9ae34dab622ed704e975e Mon Sep 17 00:00:00 2001 From: OG T Date: Wed, 15 Apr 2026 20:57:33 +0800 Subject: [PATCH] =?UTF-8?q?feat(Phase=203):=20=E5=AD=B8=E7=BF=92=E9=96=89?= =?UTF-8?q?=E7=92=B0=E8=A3=9C=E5=AE=8C=20=E2=80=94=20Root=20cause=203=20+?= =?UTF-8?q?=20=E8=A8=BA=E6=96=B7=20feedback=20+=20=E7=9F=A5=E8=AD=98?= =?UTF-8?q?=E9=81=BA=E5=BF=98=20+=20Fine-tune=20=E7=AE=A1=E7=B7=9A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - approval_execution.py: _run_post_execution_verify() 補接 record_verification_result() Root cause 3 終結：環境驗證結果（success/degraded/failed/timeout）不再孤立 - learning_service.py: 新增 record_verification_result() — 驗證結果 → Redis + Playbook EWMA - learning_service.py: 新增 record_diagnosis_outcome() — 誤診負向訊號回寫（L3×D4） - jobs/knowledge_decay_job.py: 新建 30d 知識遺忘 Job（未引用 draft/review → archived） - services/finetune_exporter.py: 新建每週 JSONL 匯出（EvidenceSnapshot × AgentSession） - main.py: 掛載 knowledge_decay_loop（24h）+ finetune_export_loop（7d） - MASTER §8: Phase 3 核心改造項全部落地記錄 Co-Authored-By: Claude Sonnet 4.6 --- apps/api/src/jobs/knowledge_decay_job.py | 180 +++++++++++++ apps/api/src/main.py | 18 ++ apps/api/src/services/approval_execution.py | 19 ++ apps/api/src/services/finetune_exporter.py | 247 ++++++++++++++++++ apps/api/src/services/learning_service.py | 104 ++++++++ ...-04-15-MASTER-ai-autonomous-flywheel-v2.md | 30 +++ 6 files changed, 598 insertions(+) create mode 100644 apps/api/src/jobs/knowledge_decay_job.py create mode 100644 apps/api/src/services/finetune_exporter.py diff --git a/apps/api/src/jobs/knowledge_decay_job.py b/apps/api/src/jobs/knowledge_decay_job.py new file mode 100644 index 00000000..f002074e --- /dev/null +++ b/apps/api/src/jobs/knowledge_decay_job.py @@ -0,0 +1,180 @@ +""" +AWOOOI AIOps Phase 3 — 知識遺忘 Job +===================================== +職責：每日掃描知識庫（knowledge_entries）中 30 天未被引用（view_count = 0 +且 updated_at < now-30d）的草稿/審核條目，標記為 archived（知識遺忘）。 + +為什麼需要知識遺忘？ + 短期學習偏差：AI 早期案例學習的修復模式可能已過時（K8s 版本、服務名稱改變）。 + 若不遺忘，舊的 zero-evidence 條目會持續污染 RAG 檢索， + 拉低 Playbook 匹配精度，增加誤診率。 + +遺忘策略： + - 對象：status in (draft, review) 且 view_count = 0 且 updated_at < now-30d + - 動作：status → archived，tags 追加 'kb_decay_30d' + - 豁免：status = approved（需人工封存）；status = archived（已封存） + - 每次執行記錄摘要到 structlog（不寫 governance event，避免雜訊） + +設計原則： + 1. 只標記，不刪除（符合 archive_not_delete 鐵律） + 2. 批次操作，每次最多 200 筆（避免長事務） + 3. Job 失敗只記錄 error，不影響主路徑 + +ADR-083 Phase 3: 知識遺忘（L7×D4） +2026-04-15 ogt + Claude Sonnet 4.6（亞太）: Phase 3 初始建立 +""" + +from __future__ import annotations + +import asyncio +from dataclasses import dataclass, field +from datetime import timedelta + +import structlog +from sqlalchemy import and_, select, update + +from src.db.base import get_session_factory +from src.db.models import KnowledgeEntryRecord +from src.models.knowledge import EntryStatus +from src.utils.timezone import now_taipei + +logger = structlog.get_logger(__name__) + +# ───────────────────────────────────────────────────────────────────────────── +# 常數 +# ───────────────────────────────────────────────────────────────────────────── + +DECAY_AGE_DAYS = 30 +DECAY_TAG = "kb_decay_30d" +BATCH_LIMIT = 200 +DAILY_INTERVAL_SEC = 86_400 # 24h + + +# ───────────────────────────────────────────────────────────────────────────── +# Data Types +# ───────────────────────────────────────────────────────────────────────────── + +@dataclass +class DecayScanResult: + """知識遺忘掃描結果""" + total_scanned: int + decayed_ids: list[str] = field(default_factory=list) + scanned_at: str = field(default_factory=lambda: now_taipei().isoformat()) + + @property + def decayed_count(self) -> int: + return len(self.decayed_ids) + + def to_dict(self) -> dict: + return { + "total_scanned": self.total_scanned, + "decayed_count": self.decayed_count, + "decayed_ids_sample": self.decayed_ids[:20], + "scanned_at": self.scanned_at, + } + + +# ───────────────────────────────────────────────────────────────────────────── +# Main Job +# ───────────────────────────────────────────────────────────────────────────── + +class KnowledgeDecayJob: + """ + 知識遺忘 Job（每日執行） + + Usage: + job = KnowledgeDecayJob() + result = await job.run() + """ + + async def run(self) -> DecayScanResult: + """ + 完整執行：掃描 → 標記 archived（知識遺忘）。 + + Returns: + DecayScanResult + """ + from src.core.feature_flags import aiops_flags + if not aiops_flags.AIOPS_P3_ENABLED: + logger.debug("knowledge_decay_job_skipped_feature_flag") + return DecayScanResult(total_scanned=0) + + try: + return await self._run_scan() + except Exception as e: + logger.error( + "knowledge_decay_job_error", + error=str(e), + ) + return DecayScanResult(total_scanned=0) + + async def _run_scan(self) -> DecayScanResult: + cutoff = now_taipei() - timedelta(days=DECAY_AGE_DAYS) + decayable_statuses = [EntryStatus.DRAFT.value, EntryStatus.REVIEW.value] + + session_factory = get_session_factory() + async with session_factory() as db: + # 查：30 天未引用（view_count=0）且 updated_at < cutoff 的 draft/review 條目 + stmt = select(KnowledgeEntryRecord).where( + and_( + KnowledgeEntryRecord.status.in_(decayable_statuses), + KnowledgeEntryRecord.view_count == 0, + KnowledgeEntryRecord.updated_at < cutoff, + ) + ).limit(BATCH_LIMIT) + + result = await db.execute(stmt) + entries = result.scalars().all() + total_scanned = len(entries) + + if not entries: + logger.debug("knowledge_decay_nothing_to_decay") + return DecayScanResult(total_scanned=0) + + decayed_ids = [] + for entry in entries: + # 追加 decay tag（不重複） + current_tags: list[str] = list(entry.tags or []) + if DECAY_TAG not in current_tags: + current_tags.append(DECAY_TAG) + + entry.status = EntryStatus.ARCHIVED.value + entry.tags = current_tags + decayed_ids.append(entry.id) + + await db.commit() + + result = DecayScanResult( + total_scanned=total_scanned, + decayed_ids=decayed_ids, + ) + + logger.info( + "knowledge_decay_job_done", + **result.to_dict(), + ) + return result + + +# ───────────────────────────────────────────────────────────────────────────── +# Loop（掛載到 main.py） +# ───────────────────────────────────────────────────────────────────────────── + +async def run_knowledge_decay_loop() -> None: + """ + 無限迴圈：每 24h 執行一次知識遺忘掃描。 + 在 main.py startup 以 asyncio.create_task 掛載。 + """ + job = KnowledgeDecayJob() + while True: + try: + result = await job.run() + if result.decayed_count > 0: + logger.info( + "knowledge_decay_loop_tick", + decayed=result.decayed_count, + ) + except Exception as e: + logger.error("knowledge_decay_loop_error", error=str(e)) + + await asyncio.sleep(DAILY_INTERVAL_SEC) diff --git a/apps/api/src/main.py b/apps/api/src/main.py index be36be1c..f576a65d 100644 --- a/apps/api/src/main.py +++ b/apps/api/src/main.py @@ -345,6 +345,24 @@ async def lifespan(_app: FastAPI) -> AsyncGenerator[None, None]: except Exception as e: logger.warning("approval_timeout_resolver_schedule_failed", error=str(e)) + # ADR-083 Phase 3: 知識遺忘 Job（每日）— 30d 未引用 KB entry 標記 archived + # 2026-04-15 ogt + Claude Sonnet 4.6（亞太）: Phase 3 初始建立 + try: + from src.jobs.knowledge_decay_job import run_knowledge_decay_loop + asyncio.create_task(run_knowledge_decay_loop()) + logger.info("knowledge_decay_loop_scheduled", interval_sec=86400) + except Exception as e: + logger.warning("knowledge_decay_loop_schedule_failed", error=str(e)) + + # ADR-083 Phase 3: Fine-tune JSONL 匯出（每週）— EvidenceSnapshot × AgentSession → JSONL + # 2026-04-15 ogt + Claude Sonnet 4.6（亞太）: Phase 3 初始建立 + try: + from src.services.finetune_exporter import run_finetune_export_loop + asyncio.create_task(run_finetune_export_loop()) + logger.info("finetune_export_loop_scheduled", interval_sec=604800) + except Exception as e: + logger.warning("finetune_export_loop_schedule_failed", error=str(e)) + # Phase 4 ADR-084: 主動巡檢每 5 分鐘執行一次 # 協調 DynamicBaselineService + LogAnomalyDetector + TrendPredictor # Shadow Mode 控制：AIOPS_P4_SHADOW_MODE=True 時只記錄，不觸發 Alert diff --git a/apps/api/src/services/approval_execution.py b/apps/api/src/services/approval_execution.py index 58936831..4a45f362 100644 --- a/apps/api/src/services/approval_execution.py +++ b/apps/api/src/services/approval_execution.py @@ -568,6 +568,25 @@ class ApprovalExecutionService: action=action_taken, ) + # ADR-083 Phase 3 Root cause 3: 驗證結果接線到學習服務 + # 環境驗證（Pod Running / 指標恢復）是比執行 exit code 更精確的學習訊號 + # 2026-04-15 ogt + Claude Sonnet 4.6（亞太） + try: + from src.services.learning_service import get_learning_service + _matched_pb_id = getattr(approval, "matched_playbook_id", None) + await get_learning_service().record_verification_result( + incident_id=approval.incident_id, + action_taken=action_taken, + verification_result=verification_result, + matched_playbook_id=_matched_pb_id, + ) + except Exception as _lerr: + logger.warning( + "post_verify_learning_failed", + approval_id=str(approval.id), + error=str(_lerr), + ) + except Exception as _e: # 驗證失敗不影響執行結果 logger.warning( diff --git a/apps/api/src/services/finetune_exporter.py b/apps/api/src/services/finetune_exporter.py new file mode 100644 index 00000000..47ae8abb --- /dev/null +++ b/apps/api/src/services/finetune_exporter.py @@ -0,0 +1,247 @@ +""" +AWOOOI AIOps Phase 3 — Fine-tune JSONL 匯出器 +============================================= +職責：每週將（EvidenceSnapshot × AgentSession × AutoRepairExecution） + 組合成訓練對（instruction, input, output），匯出為 JSONL 檔案供 LLM 微調。 + +為什麼需要 fine-tune 管線？ + EWMA Playbook trust 只調整「選哪個 Playbook」， + 但 LLM 本身的推理模式（症狀識別、根因分析、行動描述格式）無法從 EWMA 學習。 + Fine-tune 資料管線讓 AI 從真實成功案例中學習「如何推理」， + 不只學習「信任哪個 Playbook」。 + +匯出策略： + - 查詢 incident_evidence 中 verification_result = 'success' 且有 evidence_summary 的記錄 + - 聯結同 incident_id 的 AgentSession（coordinator turn）取得推理決策 + - 聯結 auto_repair_executions 取得實際執行動作 + - 組合成 JSONL 格式（Alpaca instruction-input-output 格式） + - 輸出到 FINETUNE_EXPORT_PATH（預設 /tmp/finetune/）；MinIO 支援待設定 + +JSONL 格式（每行 1 個 JSON 物件）： + { + "instruction": "根據 AIOps 情報摘要，分析告警根因並提出修復建議", + "input": "", + "output": "", + "metadata": { + "incident_id": "...", + "alertname": "...", + "verification_result": "success", + "collected_at": "...", + "schema_version": "v1" + } + } + +設計原則： + 1. 只匯出 verification_result = 'success' 的記錄（負向案例不入訓練集，避免強化錯誤模式） + 2. 每次匯出加時間戳前綴（不覆蓋舊檔） + 3. 每批最多 500 筆（大規模訓練集需分批） + 4. 失敗只記錄 error，不影響主路徑 + +ADR-083 Phase 3: Fine-tune 管線（L7×D4） +2026-04-15 ogt + Claude Sonnet 4.6（亞太）: Phase 3 初始建立 +""" + +from __future__ import annotations + +import asyncio +import json +import os +from datetime import timedelta +from pathlib import Path + +import structlog +from sqlalchemy import and_, select + +from src.db.base import get_session_factory +from src.db.models import AgentSession, AutoRepairExecution, IncidentEvidence +from src.utils.timezone import now_taipei + +logger = structlog.get_logger(__name__) + +# ───────────────────────────────────────────────────────────────────────────── +# 常數 +# ───────────────────────────────────────────────────────────────────────────── + +FINETUNE_EXPORT_PATH = os.getenv("FINETUNE_EXPORT_PATH", "/tmp/finetune") +BATCH_LIMIT = 500 +EXPORT_LOOKBACK_DAYS = 7 # 只匯出過去 N 天的資料 +WEEKLY_INTERVAL_SEC = 7 * 86_400 + +INSTRUCTION = ( + "根據以下 AIOps 情報摘要（EvidenceSnapshot），" + "分析告警根因並提出具體的修復建議，說明修復動作的理由。" +) + + +# ───────────────────────────────────────────────────────────────────────────── +# Fine-tune Exporter +# ───────────────────────────────────────────────────────────────────────────── + +class FineTuneExporter: + """ + Fine-tune JSONL 匯出器（每週執行） + + Usage: + exporter = FineTuneExporter() + path, count = await exporter.export() + """ + + async def export(self) -> tuple[str | None, int]: + """ + 匯出訓練資料。 + + Returns: + (output_file_path, row_count) + 若無資料或功能關閉，返回 (None, 0) + """ + from src.core.feature_flags import aiops_flags + if not aiops_flags.AIOPS_P3_FINETUNE_EXPORT: + logger.debug("finetune_exporter_skipped_feature_flag") + return None, 0 + + try: + return await self._run_export() + except Exception as e: + logger.error("finetune_exporter_error", error=str(e)) + return None, 0 + + async def _run_export(self) -> tuple[str | None, int]: + cutoff = now_taipei() - timedelta(days=EXPORT_LOOKBACK_DAYS) + session_factory = get_session_factory() + + async with session_factory() as db: + # 1. 取得成功驗證的 EvidenceSnapshot（有 evidence_summary + verification_result='success'） + stmt = select(IncidentEvidence).where( + and_( + IncidentEvidence.verification_result == "success", + IncidentEvidence.evidence_summary.isnot(None), + IncidentEvidence.collected_at >= cutoff, + ) + ).limit(BATCH_LIMIT) + + result = await db.execute(stmt) + evidences = result.scalars().all() + + if not evidences: + logger.info("finetune_exporter_no_data", lookback_days=EXPORT_LOOKBACK_DAYS) + return None, 0 + + # 2. 為每筆 evidence 取對應的 coordinator AgentSession + AutoRepairExecution + rows: list[dict] = [] + for ev in evidences: + row = await self._build_row(db, ev) + if row: + rows.append(row) + + if not rows: + return None, 0 + + # 3. 寫出 JSONL + output_path = await self._write_jsonl(rows) + logger.info( + "finetune_export_done", + row_count=len(rows), + path=output_path, + ) + return output_path, len(rows) + + async def _build_row(self, db, ev: IncidentEvidence) -> dict | None: + """組合單筆訓練對。""" + # 取 coordinator Agent turn（若有） + agent_stmt = select(AgentSession).where( + and_( + AgentSession.incident_id == ev.incident_id, + AgentSession.agent_role == "coordinator", + ) + ).order_by(AgentSession.created_at.desc()).limit(1) + agent_result = await db.execute(agent_stmt) + coordinator = agent_result.scalar_one_or_none() + + # 取最新執行記錄 + exec_stmt = select(AutoRepairExecution).where( + AutoRepairExecution.incident_id == ev.incident_id, + ).order_by(AutoRepairExecution.created_at.desc()).limit(1) + exec_result = await db.execute(exec_stmt) + execution = exec_result.scalar_one_or_none() + + # 組合 output 文字 + output_parts: list[str] = [] + if coordinator and coordinator.output_json: + coord_out = coordinator.output_json + if isinstance(coord_out, dict): + # 取 reasoning 或 decision 字段 + reasoning = ( + coord_out.get("reasoning") + or coord_out.get("decision") + or str(coord_out)[:500] + ) + output_parts.append(f"[AI 決策]\n{reasoning}") + + if execution: + action_desc = execution.playbook_name or "未知" + if execution.executed_steps: + steps = execution.executed_steps + if isinstance(steps, list) and steps: + first = steps[0] + if isinstance(first, dict): + action_desc = first.get("action") or first.get("step") or action_desc + output_parts.append(f"[執行動作]\n{action_desc}") + output_parts.append( + f"[執行結果] {'成功' if execution.success else '失敗'}" + ) + + if not output_parts: + return None # 無 output 資料，跳過 + + # 取 alertname（優先從 ev 關聯 incident 的 signal labels） + alertname = ev.incident_id # fallback to incident_id + + return { + "instruction": INSTRUCTION, + "input": (ev.evidence_summary or "").strip(), + "output": "\n\n".join(output_parts), + "metadata": { + "incident_id": ev.incident_id, + "alertname": alertname, + "verification_result": ev.verification_result, + "collected_at": ev.collected_at.isoformat() if ev.collected_at else None, + "schema_version": ev.schema_version, + "matched_playbook_id": ev.matched_playbook_id, + }, + } + + async def _write_jsonl(self, rows: list[dict]) -> str: + """寫出 JSONL 到 FINETUNE_EXPORT_PATH。""" + export_dir = Path(FINETUNE_EXPORT_PATH) + export_dir.mkdir(parents=True, exist_ok=True) + + ts = now_taipei().strftime("%Y%m%d-%H%M%S") + filename = f"finetune-{ts}.jsonl" + output_path = export_dir / filename + + with open(output_path, "w", encoding="utf-8") as f: + for row in rows: + f.write(json.dumps(row, ensure_ascii=False) + "\n") + + return str(output_path) + + +# ───────────────────────────────────────────────────────────────────────────── +# Loop（掛載到 main.py） +# ───────────────────────────────────────────────────────────────────────────── + +async def run_finetune_export_loop() -> None: + """ + 無限迴圈：每 7 天執行一次 fine-tune 資料匯出。 + 在 main.py startup 以 asyncio.create_task 掛載。 + """ + exporter = FineTuneExporter() + while True: + try: + path, count = await exporter.export() + if count > 0: + logger.info("finetune_export_loop_tick", rows=count, path=path) + except Exception as e: + logger.error("finetune_export_loop_error", error=str(e)) + + await asyncio.sleep(WEEKLY_INTERVAL_SEC) diff --git a/apps/api/src/services/learning_service.py b/apps/api/src/services/learning_service.py index c2938e6a..5603283a 100644 --- a/apps/api/src/services/learning_service.py +++ b/apps/api/src/services/learning_service.py @@ -645,6 +645,110 @@ class LearningService: ) return False + async def record_diagnosis_outcome( + self, + incident_id: str, + matched_playbook_id: str | None, + was_correct: bool, + actual_fix: str | None = None, + ) -> None: + """ + 記錄 AI 診斷結果的正確性。 + + ADR-083 Phase 3: 誤診回寫 playbook_diagnosis_feedback（L3×D4）。 + 當 AI 提議被人工拒絕、或執行後驗證失敗，代表診斷可能有誤； + 此時回寫負向信號，讓對應 Playbook trust_score EWMA 收縮。 + + Args: + incident_id: 關聯 Incident ID + matched_playbook_id: 此次診斷使用的 Playbook ID（若有） + was_correct: 診斷是否正確（False = 誤診） + actual_fix: 實際有效的修復動作（可供 Evolver 學習） + + 2026-04-15 ogt + Claude Sonnet 4.6（亞太）: Phase 3 誤診回饋接線 + """ + # 1. 記錄到 Repository（diag: 前綴與 exec: / verify: 區分） + try: + await self._repository.record_repair( + anomaly_key=f"diag:{incident_id}", + repair_action=actual_fix or "unknown", + success=was_correct, + fix_description=f"diagnosis_correct={was_correct}", + ) + except Exception as e: + logger.warning( + "record_diagnosis_to_repo_failed", + incident_id=incident_id, + error=str(e), + ) + + # 2. 誤診時強化 Playbook 負向學習（已有 2x EWMA 衰減係數） + if matched_playbook_id and not was_correct: + await self._update_playbook_stats( + playbook_id=matched_playbook_id, + success=False, + ) + + logger.info( + "diagnosis_outcome_recorded", + incident_id=incident_id, + was_correct=was_correct, + matched_playbook_id=matched_playbook_id, + ) + + async def record_verification_result( + self, + incident_id: str, + action_taken: str, + verification_result: str, + matched_playbook_id: str | None = None, + ) -> None: + """ + 記錄環境驗證結果到學習系統。 + + ADR-083 Phase 3 Root cause 3: post_execution_verifier → learning 接線修復。 + 環境驗證（Pod Running / 指標恢復）比執行指令 exit code 更精確， + 單獨存一條 verify: 前綴記錄，並更新 Playbook EWMA stats。 + + Args: + incident_id: 關聯 Incident ID + action_taken: 執行的動作描述（例如 "restart_service:awoooi-api"） + verification_result: "success" | "degraded" | "failed" | "timeout" + matched_playbook_id: 匹配的 Playbook ID（有則更新 EWMA stats） + + 2026-04-15 ogt + Claude Sonnet 4.6（亞太）: Phase 3 Root cause 3 修復 + """ + success = (verification_result == "success") + + # 1. 記錄環境驗證結果到 Repository（anomaly_key 加 verify: 前綴與執行記錄區分） + try: + await self._repository.record_repair( + anomaly_key=f"verify:{incident_id}", + repair_action=action_taken, + success=success, + fix_description=verification_result, + ) + except Exception as e: + logger.warning( + "record_verification_to_repo_failed", + incident_id=incident_id, + error=str(e), + ) + + # 2. 更新 Playbook EWMA stats（比執行 exit code 更精確的訊號） + if matched_playbook_id: + await self._update_playbook_stats( + playbook_id=matched_playbook_id, + success=success, + ) + + logger.info( + "verification_result_recorded", + incident_id=incident_id, + verification_result=verification_result, + matched_playbook_id=matched_playbook_id, + ) + async def get_recommended_fix(self, anomaly_key: str) -> dict: """ 根據歷史學習，推薦最佳修復方案 diff --git a/docs/superpowers/specs/2026-04-15-MASTER-ai-autonomous-flywheel-v2.md b/docs/superpowers/specs/2026-04-15-MASTER-ai-autonomous-flywheel-v2.md index 5020ea6c..f5e4a76c 100644 --- a/docs/superpowers/specs/2026-04-15-MASTER-ai-autonomous-flywheel-v2.md +++ b/docs/superpowers/specs/2026-04-15-MASTER-ai-autonomous-flywheel-v2.md @@ -1529,3 +1529,33 @@ Phase 6 完成後 - [ ] 生產驗證（等 3ce5025 / Phase 6 image 部署後觀察） **commit chain：** fab65e7 → f31b4e3 → f045506 → f9ba200 → 3ce5025 → (Phase 6 REST API commit) + +--- + +### 2026-04-15 深夜 (台北) — Phase 3 學習閉環補完 — Root cause 3 + 診斷 feedback + 知識遺忘 + Fine-tune 管線 + +**本次完成：Phase 3「核心改造項」全部落地** + +| 檔案 | 修改內容 | 對應項目 | +|------|---------|---------| +| `services/approval_execution.py` | `_run_post_execution_verify()` 補接 `record_verification_result()` 呼叫，Root cause 3 終結 | 驗證結果 → 學習接線 | +| `services/learning_service.py` | 新增 `record_verification_result()`：環境驗證結果（success/degraded/failed/timeout）回寫 Redis + 更新 Playbook EWMA | Root cause 3 接線 | +| `services/learning_service.py` | 新增 `record_diagnosis_outcome()`：誤診（人工拒絕/驗證失敗）回寫負向 Playbook 訊號 | 診斷 feedback（L3×D4） | +| `jobs/knowledge_decay_job.py` | **新建** 每日 Job：30 天未引用（view_count=0）的 draft/review KB 條目標 archived + tag `kb_decay_30d` | 知識遺忘（L7×D4） | +| `services/finetune_exporter.py` | **新建** 每週 Job：verification_result='success' 的 EvidenceSnapshot × AgentSession × AutoRepairExecution → Alpaca JSONL → `/tmp/finetune/` | Fine-tune 管線（L7×D4） | +| `main.py` | 掛載 `run_knowledge_decay_loop`（每 24h）+ `run_finetune_export_loop`（每 7d）| Job 調度 | + +**Phase 3 退出條件更新：** +- [x] Root cause 1：fire-and-forget → await（7da64ea） +- [x] Root cause 2：matched_playbook_id 永不填充（7da64ea） +- [x] Root cause 3：驗證結果未傳學習（本次） +- [x] 2x EWMA 負向衰減（7da64ea，playbook_repository.py） +- [x] Evolver Agent（7da64ea，playbook_evolver.py） +- [x] 診斷 feedback（本次，record_diagnosis_outcome） +- [x] 知識遺忘 Job（本次，knowledge_decay_job.py） +- [x] Fine-tune 管線（本次，finetune_exporter.py） +- [ ] `matched_playbook_id` null 率 = 0（生產驗證需 7 天監控） +- [ ] Playbook trust_score 有 ≥ 1 筆 24h 動態更新（生產驗證） +- [ ] Fine-tune JSONL ≥ 10 條（待 EvidenceSnapshot 累積 7 天後驗證） + +**下一步：** 推 Gitea → CD 部署 → 7 天生產觀察 Phase 3 退出條件