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feat(p11+): RAG worker cron — promotion_gate / awaiting_review / expire
Operation Ollama-First v5.0 / Phase 11+ 收尾（ADR-032/033 落地）
services/learning_pipeline.py 新增 2 個 worker 函數：
- process_pending_episodes(batch=50) — 批次處理 pending → can_promote → promote/reject/await
純規則引擎，不跑 LLM（Distiller 純 Hermes 規則）
- push_awaiting_reviews_to_telegram(batch=5) — 推 Stage 4 awaiting_review 到 Telegram
TELEGRAM_ADMIN_CHAT_ID 未設則跳過（fail-safe）
訊息含 episode_id + weight + quality + 600 字截斷文，附 promotion_review_keyboard 👍/👎
run_scheduler.py 加 3 個 cron + 對應 task wrapper：
- 每 5 分鐘 → run_promotion_gate_worker
- 每 30 分鐘 → run_awaiting_review_push
- 每 4 小時 → run_expire_stale_reviews（24h 無回應 → weight=0.5）
設計安全保證：
- RAG_ENABLED=false 時 learning_episodes 為空，3 個 worker 跑空 loop（無害）
- 所有 worker 例外完全吞掉，僅 log error，不影響其他排程
- promote 成功才回 stats['promoted']++，DB 失敗計 errors
完整 RAG 自主學習迴圈閉環：
LLM 結果 → Distiller → learning_episodes (pending)
↓ 每 5 分鐘 worker
PromotionGate 4 階段
↓ approved → 寫 ai_insights → RAG 可檢索
↓ awaiting_review → 每 30 分鐘推 Telegram
↓ 24h 無回應 → 每 4h expire → weight=0.5
↓ 👍 callback → promote → ai_insights
↓ 👎 callback → rejected_human → 永不晉升
仍待 Phase 12+ 完成：
- learning_episodes.embedding 寫入路徑（Stage 3 dedup 解鎖）
- RAG_ENABLED=true 灰度啟用條件（需 100+ episodes + ANTHROPIC_API_KEY）
regression: 70 unit tests 全綠
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-04 09:11:27 +08:00
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