diff --git a/CONSTITUTION.md b/CONSTITUTION.md index 3c1b81a..ced71da 100644 --- a/CONSTITUTION.md +++ b/CONSTITUTION.md @@ -2,7 +2,7 @@ > 本文件定義專案開發的核心準則與不可違反的規範 > **建立日期**: 2026-01-12 -> **當前版本**: V10.62 (Product pick regeneration clears dashboard cache) +> **當前版本**: V10.63 (Warm dashboard cache after AI pick regeneration) > **最後更新**: 2026-05-01 --- diff --git a/app.py b/app.py index fd73db4..14cbaeb 100644 --- a/app.py +++ b/app.py @@ -95,8 +95,8 @@ except Exception as e: sys_log.error(f"無法檢測磁碟空間: {e}") # 🚩 系統版本定義 (備份與顯示用) -# 🚩 2026-05-01 V10.62: Product pick regeneration clears dashboard cache -SYSTEM_VERSION = "V10.62" +# 🚩 2026-05-01 V10.63: Warm dashboard cache after AI pick regeneration +SYSTEM_VERSION = "V10.63" # ========================================== # 🔒 SQL Injection 防護函數 diff --git a/config.py b/config.py index 5df719b..6dc308a 100644 --- a/config.py +++ b/config.py @@ -254,7 +254,7 @@ YOUTUBE_API_KEY = os.getenv('YOUTUBE_API_KEY', '') # ========================================== # 系統版本與路徑 # ========================================== -SYSTEM_VERSION = "V10.62" +SYSTEM_VERSION = "V10.63" LOG_FILE_PATH = os.path.join(BASE_DIR, 'logs/system.log') public_url = PUBLIC_URL # 用於模板顯示 diff --git a/docs/AI_INTELLIGENCE_MODULE_SOT.md b/docs/AI_INTELLIGENCE_MODULE_SOT.md index b045e68..7809d86 100644 --- a/docs/AI_INTELLIGENCE_MODULE_SOT.md +++ b/docs/AI_INTELLIGENCE_MODULE_SOT.md @@ -37,7 +37,7 @@ SQL漏斗(~300筆) - 配對來源仍以 PChome crawler 真實搜尋結果為準；無競品資料時不生成挑品。 - 比對覆蓋率補強入口：`POST /api/ai/pchome-match/backfill`，優先補抓仍無有效 PChome 配對的高價 ACTIVE 商品，完成後自動重算 AI 挑品清單。 - 排程閉環：`run_pchome_match_backfill_task` 每日 10:30 執行，補抓 PChome 待比對商品、寫入歷史價格，再重算 `strategy='product_pick'` 清單。 -- 商品看板第一屏：`/` 的 V2 看板直接以 `products`、`price_records`、`competitor_prices`、`ai_price_recommendations` 顯示比對覆蓋率、PChome 優勢、MOMO 威脅、AI 挑品與待比對優先清單；`filter=ai_picks` 可查看 50 品 AI 挑品列表，並在列表上方顯示平均信心、平均價差、最大價差與估算總價差空間，列表列內顯示 AI 排名與建議理由，且可透過 `/api/export/excel/ai-picks` 匯出 50 品 Excel 操作清單。商品看板深度快取同時寫入 `data/dashboard_full_cache.pkl`，供多個 Gunicorn worker 共用，避免部署後各 worker 重複重建 7,000+ 商品統計造成開頁變慢；所有資料異動與 AI 挑品重算都透過 `clear_dashboard_cache()` 同步清除記憶體與共享快取。 +- 商品看板第一屏：`/` 的 V2 看板直接以 `products`、`price_records`、`competitor_prices`、`ai_price_recommendations` 顯示比對覆蓋率、PChome 優勢、MOMO 威脅、AI 挑品與待比對優先清單；`filter=ai_picks` 可查看 50 品 AI 挑品列表，並在列表上方顯示平均信心、平均價差、最大價差與估算總價差空間，列表列內顯示 AI 排名與建議理由，且可透過 `/api/export/excel/ai-picks` 匯出 50 品 Excel 操作清單。商品看板深度快取同時寫入 `data/dashboard_full_cache.pkl`，供多個 Gunicorn worker 共用，避免部署後各 worker 重複重建 7,000+ 商品統計造成開頁變慢；所有資料異動與 AI 挑品重算都透過 `clear_dashboard_cache()` 同步清除記憶體與共享快取，手動重算 API 會立即預熱商品看板快取，避免第一位使用者承擔重建成本。 | 角色 | 模型 | 主機 | 成本 | 每日限額 | |------|------|------|------|---------| diff --git a/routes/ai_routes.py b/routes/ai_routes.py index f38dee2..a8c4338 100644 --- a/routes/ai_routes.py +++ b/routes/ai_routes.py @@ -1625,6 +1625,7 @@ def api_generate_product_picks(): from sqlalchemy import create_engine from services.ai_product_pick_agent import generate_product_pick_list from services.cache_manager import clear_dashboard_cache + from routes.dashboard_routes import get_full_dashboard_data payload = request.get_json(silent=True) or {} limit = int(payload.get('limit', 50)) @@ -1633,6 +1634,11 @@ def api_generate_product_picks(): engine = create_engine(DATABASE_PATH) result = generate_product_pick_list(engine, limit=limit) clear_dashboard_cache() + dashboard_cache_warmed = False + try: + dashboard_cache_warmed = bool(get_full_dashboard_data()) + except Exception as warm_err: + logger.warning(f"[ProductPickAgent] 商品看板快取預熱失敗: {warm_err}") return jsonify({ 'success': True, @@ -1641,6 +1647,7 @@ def api_generate_product_picks(): 'candidates': result.candidates, 'written': result.written, 'generated_at': result.generated_at, + 'dashboard_cache_warmed': dashboard_cache_warmed, 'picks': result.picks[:50], } }) @@ -1668,6 +1675,8 @@ def api_pchome_match_backfill(): engine = create_engine(DATABASE_PATH) result = CompetitorPriceFeeder(engine=engine).run_unmatched_priority(limit=limit) pick_result = generate_product_pick_list(engine, limit=50) + from services.cache_manager import clear_dashboard_cache + clear_dashboard_cache() logger.info( "[PChomeBackfill] done total=%s matched=%s no=%s low=%s errors=%s history=%s duration=%ss pick_written=%s", result.total_skus, diff --git a/scheduler.py b/scheduler.py index b4a250f..84b5fcd 100644 --- a/scheduler.py +++ b/scheduler.py @@ -2071,6 +2071,7 @@ def run_pchome_match_backfill_task(): from config import DATABASE_PATH from sqlalchemy import create_engine from services.ai_product_pick_agent import generate_product_pick_list + from services.cache_manager import clear_dashboard_cache from services.competitor_price_feeder import CompetitorPriceFeeder now_str = datetime.now(TAIPEI_TZ).strftime('%Y-%m-%d %H:%M') @@ -2079,6 +2080,7 @@ def run_pchome_match_backfill_task(): engine = create_engine(DATABASE_PATH) feeder_result = CompetitorPriceFeeder(engine=engine).run_unmatched_priority(limit=120) pick_result = generate_product_pick_list(engine, limit=50) + clear_dashboard_cache() stats = { "total_skus": feeder_result.total_skus, diff --git a/tests/test_frontend_v2_assets.py b/tests/test_frontend_v2_assets.py index 2c3cfee..97f10c1 100644 --- a/tests/test_frontend_v2_assets.py +++ b/tests/test_frontend_v2_assets.py @@ -181,6 +181,8 @@ def test_ai_product_pick_agent_uses_real_competitor_data_and_dashboard_action(): assert "generate_product_pick_list" in agent_source assert "clear_dashboard_cache()" in route_source + assert "get_full_dashboard_data()" in route_source + assert "dashboard_cache_warmed" in route_source assert "competitor_prices" in agent_source assert "competitor_price_history" in agent_source assert "daily_sales_snapshot" in agent_source