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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>
326 lines
13 KiB
Python
326 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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run_scheduler.py — momo-scheduler 容器入口點
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排程任務清單（對齊 app.py init_scheduler + scheduler.py 全任務）：
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每 30 分鐘：auto_import、whitepage_check
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每 1 小時：momo、edm、festival
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每 4 小時：competitor_price_feeder、icaim_analysis
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每 6 小時：quality_rescore
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每 12 小時：dedup_batch
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每 1 天 ：db_backup（03:00）、cleanup_agent_context（03:30）、backup_monitor（04:00）、daily_report（09:00）、ai_smoke_summary（09:10）、pchome_match_backfill（10:30）、openclaw_meta_analysis（12:00, Phase 4 降頻）、daily_token_report（23:55）
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每 1 週 ：weekly_strategy（週一 06:00）
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每 1 月 ：monthly_report（每月1日 07:00）
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"""
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import asyncio
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import logging
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import threading
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import time
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import schedule
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# 匯入全部排程任務函式
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from scheduler import (
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run_momo_task,
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run_edm_task,
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run_festival_task,
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run_promo_event_task,
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run_auto_import_task,
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run_whitepage_check,
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run_competitor_price_feeder_task,
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run_pchome_match_backfill_task,
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run_icaim_analysis_task,
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run_weekly_strategy_task,
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run_db_backup_task,
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run_backup_monitor_task,
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run_openclaw_meta_analysis_task,
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run_dedup_batch_task,
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run_quality_rescore_task,
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run_daily_report_task,
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run_ai_smoke_daily_summary_task,
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run_monthly_report_task,
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)
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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)
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logger = logging.getLogger(__name__)
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def _register_schedules():
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schedule.every(30).minutes.do(run_auto_import_task)
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logger.info("📅 每 30 分鐘：auto_import")
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schedule.every(30).minutes.do(run_whitepage_check)
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logger.info("📅 每 30 分鐘：whitepage_check")
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schedule.every(1).hours.do(run_momo_task)
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logger.info("📅 每 1 小時：momo_task")
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schedule.every(1).hours.do(run_edm_task)
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logger.info("📅 每 1 小時：edm_task")
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schedule.every(1).hours.do(run_festival_task)
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logger.info("📅 每 1 小時：festival_task")
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# 動態註冊促銷活動爬蟲（根據配置）
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from services.crawler_config_loader import get_enabled_crawlers
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enabled_crawlers = get_enabled_crawlers()
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promo_event_configs = {
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'mothers_day_2026': {'lpn': '', 'page_type': 'mothers_day', 'name': '母親節超值限時購'},
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'valentine_520_2026': {'lpn': '', 'page_type': 'valentine_520', 'name': '520情人節限定購物'},
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'labor_day_2026': {'lpn': '', 'page_type': 'labor_day', 'name': '勞動節購物優惠'}
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}
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for crawler_key, config in enabled_crawlers.items():
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if crawler_key in promo_event_configs:
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event_config = promo_event_configs[crawler_key]
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lpn_code = config.get('lpn_code', '')
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if lpn_code:
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schedule_hours = config.get('schedule_hours', 4)
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schedule.every(schedule_hours).hours.do(
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lambda lpn=lpn_code, pt=event_config['page_type'], an=event_config['name']:
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run_promo_event_task(lpn, pt, an)
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)
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logger.info(f"📅 每 {schedule_hours} 小時：{event_config['name']} ({event_config['page_type']})")
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else:
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logger.warning(f"⚠️ {event_config['name']} 未配置 LPN 代碼，跳過排程")
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schedule.every(4).hours.do(run_competitor_price_feeder_task)
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logger.info("📅 每 4 小時：competitor_price_feeder")
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schedule.every(4).hours.do(run_icaim_analysis_task)
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logger.info("📅 每 4 小時：icaim_analysis")
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# Operation Ollama-First v5.0 Phase 4：Meta 自審降頻 6h → 每日 12:00（月省 ~1.875M Gemini tokens）
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# icaim_analysis 內原本 line 2233/2253 的額外觸發已同步移除（避免重複呼叫）
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schedule.every().day.at("12:00").do(run_openclaw_meta_analysis_task)
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logger.info("📅 每日 12:00：openclaw_meta_analysis（Phase 4 降頻：原 6h）")
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schedule.every(6).hours.do(run_quality_rescore_task)
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logger.info("📅 每 6 小時：quality_rescore")
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schedule.every(12).hours.do(run_dedup_batch_task)
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logger.info("📅 每 12 小時：dedup_batch")
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# Operation Ollama-First v5.0 Phase 11+ — RAG 學習迴圈 worker（Phase 12 收尾）
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# 預設 RAG_ENABLED=false 時，learning_episodes 不會有資料，worker 跑空 loop（無害）
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schedule.every(5).minutes.do(run_promotion_gate_worker)
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logger.info("📅 每 5 分鐘：promotion_gate_worker（pending → promote/reject/await）")
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schedule.every(30).minutes.do(run_awaiting_review_push)
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logger.info("📅 每 30 分鐘：awaiting_review_push（推 Telegram 等 👍/👎）")
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schedule.every(4).hours.do(run_expire_stale_reviews)
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logger.info("📅 每 4 小時：expire_stale_reviews（24h 無回應降權 0.5）")
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schedule.every().day.at("03:00").do(run_db_backup_task)
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logger.info("📅 每日 03:00：db_backup")
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schedule.every().day.at("03:30").do(run_cleanup_agent_context)
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logger.info("📅 每日 03:30：cleanup_agent_context")
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schedule.every().day.at("04:00").do(run_backup_monitor_task)
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logger.info("📅 每日 04:00：backup_monitor")
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schedule.every().monday.at("06:00").do(run_weekly_strategy_task)
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logger.info("📅 每週一 06:00：weekly_strategy")
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schedule.every().day.at("09:00").do(run_daily_report_task)
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logger.info("📅 每日 09:00：daily_report")
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schedule.every().day.at("09:10").do(run_ai_smoke_daily_summary_task)
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logger.info("📅 每日 09:10：ai_smoke_daily_summary")
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schedule.every().day.at("10:30").do(run_pchome_match_backfill_task)
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logger.info("📅 每日 10:30：pchome_match_backfill")
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# Operation Ollama-First v5.0 — Phase 1 收尾：每日 23:55 LLM Token 日報
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schedule.every().day.at("23:55").do(run_daily_token_report_task)
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logger.info("📅 每日 23:55：daily_token_report")
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# 每月1日 07:00 月報（schedule 不支援 every().month，用每日 07:00 + 日期判斷）
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def _monthly_report_gate():
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from datetime import datetime as _dt
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if _dt.now().day == 1:
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run_monthly_report_task()
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schedule.every().day.at("07:00").do(_monthly_report_gate)
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logger.info("📅 每月1日 07:00：monthly_report")
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def run_daily_token_report_task():
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"""每日 23:55 — Operation Ollama-First v5.0 Phase 1 收尾：LLM Token 日報。
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任務:
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1. 查 ai_calls 過去 24h 統計（總覽 / 供應商 / TOP caller / 成本 / 趨勢 / 告警）
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2. 推 Telegram + 寫 ai_insights（type='daily_token_report'）
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紀律:
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- 失敗安全：DB 查不到資料 → 推「⚠️ 報表生成失敗」訊息但不爆 scheduler
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- 不影響其他排程：例外完全吞掉，僅 log error
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"""
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try:
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from services.token_report_service import send_daily_report
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result = send_daily_report()
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logger.info(
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"[TokenReport] sent=%s failed=%s chars=%s ok=%s",
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result.get('sent'), result.get('failed'),
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result.get('chars'), result.get('ok'),
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)
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except Exception as e:
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logger.error(f"[TokenReport] task failed: {e}", exc_info=True)
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# ─────────────────────────────────────────────────────────────────────────────
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# Operation Ollama-First v5.0 Phase 11+ — RAG 學習迴圈 worker（Phase 12 收尾）
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# ─────────────────────────────────────────────────────────────────────────────
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def run_promotion_gate_worker():
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"""每 5 分鐘 — 批次處理 learning_episodes pending → can_promote → promote/reject/await。
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依 ADR-032 PromotionGate 4 階段，不主動跑 LLM（Distiller 純規則引擎）。
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RAG_ENABLED=false 時 learning_episodes 為空，worker 跑空 loop（無害）。
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"""
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try:
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from services.learning_pipeline import process_pending_episodes
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stats = process_pending_episodes()
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if stats.get('pending_seen', 0) > 0:
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logger.info(
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"[PromotionWorker] pending=%d promoted=%d rejected=%d awaiting=%d errors=%d",
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stats['pending_seen'], stats['promoted'], stats['rejected'],
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stats['awaiting'], stats['errors'],
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)
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except Exception as e:
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logger.error(f"[PromotionWorker] task failed: {e}", exc_info=True)
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def run_awaiting_review_push():
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"""每 30 分鐘 — 推 awaiting_review episode 到 Telegram 等 👍/👎。
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限制：TELEGRAM_ADMIN_CHAT_ID 未設則跳過（fail-safe）。
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"""
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try:
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from services.learning_pipeline import push_awaiting_reviews_to_telegram
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pushed = push_awaiting_reviews_to_telegram()
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if pushed > 0:
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logger.info("[AwaitingReviewPush] pushed=%d episodes", pushed)
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except Exception as e:
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logger.error(f"[AwaitingReviewPush] task failed: {e}", exc_info=True)
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def run_expire_stale_reviews():
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"""每 4 小時 — 24h 無回應 awaiting_review → expired（weight=0.5）。
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依 ADR-033 護欄 #1 Stage 4 規則。
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"""
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try:
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from services.learning_pipeline import expire_stale_reviews
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n = expire_stale_reviews()
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if n > 0:
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logger.info("[ExpireStale] expired %d awaiting_review episodes (24h timeout)", n)
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except Exception as e:
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logger.error(f"[ExpireStale] task failed: {e}", exc_info=True)
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def run_cleanup_agent_context():
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"""每日 03:30 — 清理 agent_context 表中已過期的 TTL 記錄（migration 018 定義）"""
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from database.manager import get_session
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from sqlalchemy import text
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session = get_session()
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try:
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session.execute(text("SELECT cleanup_expired_agent_context()"))
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session.commit()
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logger.info("[Cleanup] agent_context TTL 清理完成")
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except Exception as e:
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logger.error(f"[Cleanup] agent_context 清理失敗: {e}")
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try:
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from services.event_router import notify_failure
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notify_failure(
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task_name="run_cleanup_agent_context",
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error=e,
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source="Scheduler.Cleanup",
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event_type="agent_context_cleanup_failure",
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priority="P2",
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title="agent_context TTL 清理失敗",
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dedup_ttl_sec=3600,
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)
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except Exception as _router_e:
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logger.error(f"[Cleanup] event_router 失敗: {_router_e}")
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finally:
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session.close()
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def _run_elephant_alpha_engine():
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"""Daemon thread: ElephantAlpha 自主監控引擎（獨立 asyncio loop）"""
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loop = None
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try:
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from services.elephant_alpha_autonomous_engine import autonomous_engine
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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logger.info("🐘 [ElephantAlpha] Autonomous engine thread started")
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loop.run_until_complete(autonomous_engine.start_autonomous_monitoring())
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except Exception as e:
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logger.error(f"🐘 [ElephantAlpha] Engine crashed: {e}")
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finally:
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if loop is not None:
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loop.close()
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if __name__ == "__main__":
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logger.info("🚀 momo-scheduler 啟動中...")
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_register_schedules()
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logger.info("✅ 全部排程任務已註冊")
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_ea_thread = threading.Thread(
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target=_run_elephant_alpha_engine,
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daemon=True,
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name="elephant-alpha-engine",
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)
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_ea_thread.start()
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logger.info("🐘 [ElephantAlpha] Autonomous engine thread launched")
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logger.info("⏰ 排程主迴圈啟動，等待任務觸發...")
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_ea_watchdog_counter = 0 # 每 60 秒（60 次 sleep(1)）做一次存活檢查
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while True:
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try:
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schedule.run_pending()
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time.sleep(1)
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# 每 60 秒檢查 ElephantAlpha 執行緒是否還活著
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_ea_watchdog_counter += 1
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if _ea_watchdog_counter >= 60:
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_ea_watchdog_counter = 0
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if not _ea_thread.is_alive():
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logger.error("[ElephantAlpha] 監控執行緒已死亡，嘗試重啟")
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try:
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from services.event_router import dispatch_sync as _dispatch_sync
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_dispatch_sync({
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"source": "Scheduler.ElephantAlpha",
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"event_type": "thread_crashed",
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"severity": "alert",
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"title": "ElephantAlpha 執行緒死亡",
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"status": "自動重啟中",
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"impact": "P2 - 自主監控引擎暫停",
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"summary": "ElephantAlphaEngine daemon thread 意外終止，排程主迴圈已偵測並觸發重啟",
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})
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except Exception as _alert_err:
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logger.error(f"[ElephantAlpha] 無法發送告警: {_alert_err}")
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_ea_thread = threading.Thread(
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target=_run_elephant_alpha_engine,
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daemon=True,
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name="ElephantAlphaEngine",
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)
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_ea_thread.start()
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logger.info("[ElephantAlpha] 執行緒已重啟")
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except KeyboardInterrupt:
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logger.info("⛔ Scheduler stopped.")
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break
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except Exception as e:
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logger.error(f"Scheduler error: {e}")
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time.sleep(5)
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