From 3920701e1a323a18df1b093451bbf792d29bc1f6 Mon Sep 17 00:00:00 2001 From: OoO Date: Fri, 1 May 2026 16:01:52 +0800 Subject: [PATCH] feat(dashboard): optimize cache and AI pick confidence --- CONSTITUTION.md | 2 +- app.py | 4 +- config.py | 2 +- docs/AI_INTELLIGENCE_MODULE_SOT.md | 6 +- routes/dashboard_routes.py | 95 ++++++++++++++++++++++++++++-- services/ai_product_pick_agent.py | 92 +++++++++++++++++++++++++---- services/cache_manager.py | 10 ++++ tests/test_cache_manager.py | 7 ++- tests/test_frontend_v2_assets.py | 7 +++ 9 files changed, 198 insertions(+), 27 deletions(-) diff --git a/CONSTITUTION.md b/CONSTITUTION.md index d8a76ec..d9f09de 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**: V10.59 (Dashboard AI pick list can export 50-item action workbook) +> **當前版本**: V10.61 (Dashboard speed cache and evidence-based AI pick confidence) > **最後更新**: 2026-05-01 --- diff --git a/app.py b/app.py index d0b2198..34251f0 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 V10.59: Dashboard AI pick list can export 50-item action workbook -SYSTEM_VERSION = "V10.59" +# 🚩 2026-05-01 V10.61: Dashboard speed cache and evidence-based AI pick confidence +SYSTEM_VERSION = "V10.61" # ========================================== # 🔒 SQL Injection 防護函數 diff --git a/config.py b/config.py index 9314aba..38430ad 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 = "V10.59" +SYSTEM_VERSION = "V10.61" 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 688ba28..8211288 100644 --- a/docs/AI_INTELLIGENCE_MODULE_SOT.md +++ b/docs/AI_INTELLIGENCE_MODULE_SOT.md @@ -30,14 +30,14 @@ SQL漏斗(~300筆) `services/ai_product_pick_agent.py` 新增 PChome 銷售用挑品 Agent： -- 只讀真實資料表：`products`、`price_records`、`competitor_prices`、`competitor_price_history`，若 `daily_sales_snapshot` 可用則納入近 7 天銷售額與數量。 -- 將 PChome 比 MOMO 有價格優勢、比對信心足夠、且有歷史快照或銷售動能的品項寫入 `ai_price_recommendations`。 +- 只讀真實資料表：`products`、`price_records`、`competitor_prices`、`competitor_price_history`，若 `daily_sales_snapshot` 可用則納入近 7 天銷售額、數量、毛利或成本推算毛利率。 +- 將 PChome 比 MOMO 有價格優勢、比對信心足夠、且有歷史快照或銷售動能的品項寫入 `ai_price_recommendations`。信心度不以固定倍率灌高，而是由商機分數與證據完整度共同決定，證據包含 PChome match score、歷史快照、銷售/毛利、PChome 商品 ID/名稱、抓取時間與促銷/評價/庫存標籤。 - 寫入策略使用 `strategy='product_pick'`，保留在既有 AI 決策表，不新增假頁面或暫存 JSON。 - 後台入口：`POST /api/ai/product-picks/generate`，`/ai_intelligence` 可手動產生清單。 - 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；所有資料異動仍透過 `clear_dashboard_cache()` 同步清除記憶體與共享快取。 | 角色 | 模型 | 主機 | 成本 | 每日限額 | |------|------|------|------|---------| diff --git a/routes/dashboard_routes.py b/routes/dashboard_routes.py index 9a55c9f..3f8ef21 100644 --- a/routes/dashboard_routes.py +++ b/routes/dashboard_routes.py @@ -10,6 +10,7 @@ import json import math import time import hashlib +import pickle from datetime import datetime, timezone, timedelta from flask import Blueprint, request, render_template from sqlalchemy import func, and_, text, bindparam @@ -20,7 +21,11 @@ from config import BASE_DIR, SYSTEM_VERSION, public_url from database.manager import DatabaseManager from database.models import Product, PriceRecord from services.logger_manager import SystemLogger -from services.cache_manager import _DASHBOARD_DATA_CACHE, _DASHBOARD_CACHE_TTL +from services.cache_manager import ( + _DASHBOARD_DATA_CACHE, + _DASHBOARD_CACHE_TTL, + _DASHBOARD_SHARED_CACHE_FILE, +) # 時區設定 TAIPEI_TZ = timezone(timedelta(hours=8)) @@ -515,6 +520,62 @@ class FileLock: _DASHBOARD_FILE_LOCK = FileLock(_DASHBOARD_LOCK_FILE) + +def _load_shared_full_dashboard_cache(now): + """讀取跨 worker 共享的商品看板深度快取。""" + cache_file = str(_DASHBOARD_SHARED_CACHE_FILE) + if not os.path.exists(cache_file): + return None + + try: + with open(cache_file, 'rb') as f: + payload = pickle.load(f) + + full_timestamp = payload.get('full_timestamp') + full_data = payload.get('full_data') + if not full_timestamp or not full_data: + return None + + age = now.timestamp() - full_timestamp + if age >= _DASHBOARD_CACHE_TTL: + return None + + _DASHBOARD_DATA_CACHE['full_data'] = full_data + _DASHBOARD_DATA_CACHE['full_timestamp'] = full_timestamp + _DASHBOARD_DATA_CACHE['consolidated_data'] = payload.get('consolidated_data') + _DASHBOARD_DATA_CACHE['consolidated_timestamp'] = payload.get('consolidated_timestamp') + _DASHBOARD_DATA_CACHE['today_start'] = payload.get('today_start') + sys_log.debug(f"[Dashboard] [Cache] ✅ 使用共享完整看板快取 | 快取年齡: {age:.0f}秒") + return full_data + except Exception as exc: + sys_log.warning(f"[Dashboard] [Cache] 共享快取讀取失敗，改走資料庫重建: {exc}") + return None + + +def _write_shared_full_dashboard_cache(full_data): + """原子寫入跨 worker 共享的商品看板深度快取。""" + cache_file = str(_DASHBOARD_SHARED_CACHE_FILE) + tmp_file = f"{cache_file}.{os.getpid()}.tmp" + payload = { + 'full_data': full_data, + 'full_timestamp': _DASHBOARD_DATA_CACHE.get('full_timestamp'), + 'consolidated_data': _DASHBOARD_DATA_CACHE.get('consolidated_data'), + 'consolidated_timestamp': _DASHBOARD_DATA_CACHE.get('consolidated_timestamp'), + 'today_start': _DASHBOARD_DATA_CACHE.get('today_start'), + } + try: + os.makedirs(os.path.dirname(cache_file), exist_ok=True) + with open(tmp_file, 'wb') as f: + pickle.dump(payload, f, protocol=pickle.HIGHEST_PROTOCOL) + os.replace(tmp_file, cache_file) + except Exception as exc: + sys_log.warning(f"[Dashboard] [Cache] 共享快取寫入失敗，仍保留記憶體快取: {exc}") + try: + if os.path.exists(tmp_file): + os.remove(tmp_file) + except OSError: + pass + # 慢查詢監控 _SLOW_QUERY_STATS = { 'total_queries': 0, @@ -744,15 +805,31 @@ def get_full_dashboard_data(): sys_log.debug(f"[Dashboard] [Cache] ✅ 使用完整看板快取 | 快取年齡: {age:.0f}秒") return _DASHBOARD_DATA_CACHE['full_data'] + shared_full_data = _load_shared_full_dashboard_cache(now) + if shared_full_data: + return shared_full_data + # V-Opt: 使用檔案鎖避免多 gunicorn worker 同時計算 - if not _DASHBOARD_FILE_LOCK.acquire(blocking=False): + lock_acquired = _DASHBOARD_FILE_LOCK.acquire(blocking=False) + if not lock_acquired: # 如果無法取得鎖，表示其他 worker 正在重建，等待並使用更新後的快取 sys_log.debug("[Dashboard] [Cache] ⏳ 等待其他 worker 重建快取...") _DASHBOARD_FILE_LOCK.acquire() # 等待取得鎖 _DASHBOARD_FILE_LOCK.release() # 立即釋放 - # 返回更新後的快取 - if _DASHBOARD_DATA_CACHE.get('full_data'): - return _DASHBOARD_DATA_CACHE['full_data'] + shared_full_data = _load_shared_full_dashboard_cache(now) + if shared_full_data: + return shared_full_data + if _DASHBOARD_DATA_CACHE.get('full_data') and _DASHBOARD_DATA_CACHE.get('full_timestamp'): + age = now.timestamp() - _DASHBOARD_DATA_CACHE['full_timestamp'] + if age < _DASHBOARD_CACHE_TTL: + return _DASHBOARD_DATA_CACHE['full_data'] + lock_acquired = _DASHBOARD_FILE_LOCK.acquire() + if not lock_acquired: + sys_log.warning("[Dashboard] [Cache] 共享鎖取得失敗，改用無鎖重建") + + shared_full_data = _load_shared_full_dashboard_cache(now) + if shared_full_data: + return shared_full_data try: # 再次檢查快取（可能其他 worker 已經更新） @@ -762,6 +839,10 @@ def get_full_dashboard_data(): sys_log.debug(f"[Dashboard] [Cache] ✅ 使用完整看板快取 (其他 worker 已更新) | 快取年齡: {age:.0f}秒") return _DASHBOARD_DATA_CACHE['full_data'] + shared_full_data = _load_shared_full_dashboard_cache(now) + if shared_full_data: + return shared_full_data + sys_log.info("[Dashboard] [Cache] 🔄 完整快取過期，重新計算所有 KPIs 與統計數據...") query_start_time = time.time() @@ -883,6 +964,7 @@ def get_full_dashboard_data(): # 更新快取 _DASHBOARD_DATA_CACHE['full_data'] = full_data _DASHBOARD_DATA_CACHE['full_timestamp'] = now.timestamp() + _write_shared_full_dashboard_cache(full_data) query_duration_ms = (time.time() - query_start_time) * 1000 track_query_time('get_full_dashboard_data', query_duration_ms) @@ -898,7 +980,8 @@ def get_full_dashboard_data(): session.close() finally: # V-Opt: 確保釋放檔案鎖 - _DASHBOARD_FILE_LOCK.release() + if lock_acquired: + _DASHBOARD_FILE_LOCK.release() def get_dashboard_stats(): diff --git a/services/ai_product_pick_agent.py b/services/ai_product_pick_agent.py index 7bc6d9e..6aa1c7e 100644 --- a/services/ai_product_pick_agent.py +++ b/services/ai_product_pick_agent.py @@ -85,6 +85,8 @@ def _daily_sales_columns(conn) -> Dict[str, str]: "date": first_available(["snapshot_date", "日期", "訂單日期", "交易日期", "Date"]), "revenue": first_available(["總業績", "銷售金額", "業績", "金額", "Amount", "Sales", "Total"]), "qty": first_available(["數量", "銷售數量", "銷量", "Qty", "Quantity"]), + "profit": first_available(["毛利", "Profit", "利潤"]), + "cost": first_available(["總成本", "成本", "Cost", "進價"]), } @@ -96,7 +98,7 @@ def _fetch_candidates(conn, limit: int) -> List[Dict[str, Any]]: from sqlalchemy import text sales_join = "" - sales_select = "0 AS sales_7d, 0 AS sales_prev_7d, 0 AS qty_7d" + sales_select = "0 AS sales_7d, 0 AS sales_prev_7d, 0 AS qty_7d, 0 AS profit_7d, 0 AS cost_7d" sales_cols = {} if _has_daily_sales_snapshot(conn): sales_cols = _daily_sales_columns(conn) @@ -108,6 +110,10 @@ def _fetch_candidates(conn, limit: int) -> List[Dict[str, Any]]: date_col = _quote_identifier(sales_cols["date"]) revenue_col = _quote_identifier(sales_cols["revenue"]) qty_col = _quote_identifier(sales_cols["qty"]) + profit_col = _quote_identifier(sales_cols["profit"]) if sales_cols.get("profit") else None + cost_col = _quote_identifier(sales_cols["cost"]) if sales_cols.get("cost") else None + profit_expr = f"COALESCE({profit_col}::numeric, 0)" if profit_col else "0" + cost_expr = f"COALESCE({cost_col}::numeric, 0)" if cost_col else "0" sales_join = """ LEFT JOIN ( SELECT @@ -118,7 +124,11 @@ def _fetch_candidates(conn, limit: int) -> List[Dict[str, Any]]: AND {date_col}::date < CURRENT_DATE - 7 THEN COALESCE({revenue_col}::numeric, 0) ELSE 0 END) AS sales_prev_7d, SUM(CASE WHEN {date_col}::date >= CURRENT_DATE - 7 - THEN COALESCE({qty_col}::numeric, 0) ELSE 0 END) AS qty_7d + THEN COALESCE({qty_col}::numeric, 0) ELSE 0 END) AS qty_7d, + SUM(CASE WHEN {date_col}::date >= CURRENT_DATE - 7 + THEN {profit_expr} ELSE 0 END) AS profit_7d, + SUM(CASE WHEN {date_col}::date >= CURRENT_DATE - 7 + THEN {cost_expr} ELSE 0 END) AS cost_7d FROM daily_sales_snapshot GROUP BY {sku_col} ) sales ON sales.sku = lm.sku @@ -127,11 +137,15 @@ def _fetch_candidates(conn, limit: int) -> List[Dict[str, Any]]: date_col=date_col, revenue_col=revenue_col, qty_col=qty_col, + profit_expr=profit_expr, + cost_expr=cost_expr, ) sales_select = """ COALESCE(sales.sales_7d, 0) AS sales_7d, COALESCE(sales.sales_prev_7d, 0) AS sales_prev_7d, - COALESCE(sales.qty_7d, 0) AS qty_7d + COALESCE(sales.qty_7d, 0) AS qty_7d, + COALESCE(sales.profit_7d, 0) AS profit_7d, + COALESCE(sales.cost_7d, 0) AS cost_7d """ sql = text(f""" @@ -236,7 +250,9 @@ def _fetch_candidates(conn, limit: int) -> List[Dict[str, Any]]: NULL AS max_pchome_price, 0 AS sales_7d, 0 AS sales_prev_7d, - 0 AS qty_7d + 0 AS qty_7d, + 0 AS profit_7d, + 0 AS cost_7d FROM latest_momo lm JOIN competitor_prices cp ON cp.sku = lm.sku @@ -257,24 +273,60 @@ def _score_candidate(row: Dict[str, Any]) -> Dict[str, Any]: sales_7d = _to_float(row.get("sales_7d")) sales_prev_7d = _to_float(row.get("sales_prev_7d")) qty_7d = _to_float(row.get("qty_7d")) + profit_7d = _to_float(row.get("profit_7d")) + cost_7d = _to_float(row.get("cost_7d")) history_points = int(_to_float(row.get("history_points"))) + min_pchome_price = _to_float(row.get("min_pchome_price")) tags = _load_json_tags(row.get("tags")) gap_pct = ((momo_price - pchome_price) / pchome_price * 100) if pchome_price else 0 sales_delta = ((sales_7d - sales_prev_7d) / sales_prev_7d * 100) if sales_prev_7d else None + if not profit_7d and cost_7d and sales_7d: + profit_7d = sales_7d - cost_7d + margin_rate = (profit_7d / sales_7d * 100) if sales_7d and profit_7d else None - price_score = max(0, min(38, gap_pct * 1.8 + 8)) - match_component = max(0, min(24, match_score * 24)) + price_score = max(0, min(40, gap_pct * 1.9 + 8)) + match_component = max(0, min(30, match_score * 30)) sales_component = 0 if sales_7d > 0: - sales_component += min(10, sales_7d / 30000 * 10) + sales_component += min(9, sales_7d / 30000 * 9) if qty_7d > 0: - sales_component += min(5, qty_7d / 20 * 5) + sales_component += min(4, qty_7d / 20 * 4) if sales_delta is not None and sales_delta > 0: - sales_component += min(8, sales_delta / 40 * 8) - history_component = min(10, history_points * 2) - promo_component = 5 if any(tag in tags for tag in ["on_sale", "discount_10pct", "discount_20pct", "discount_30pct"]) else 0 - score = round(min(100, price_score + match_component + sales_component + history_component + promo_component), 1) + sales_component += min(7, sales_delta / 40 * 7) + margin_component = 0 + if margin_rate is not None: + margin_component = max(0, min(10, margin_rate / 35 * 10)) + history_component = min(12, history_points * 2.4) + promo_component = 0 + if any(tag in tags for tag in ["on_sale", "discount_10pct", "discount_20pct", "discount_30pct"]): + promo_component += 5 + if "high_rating" in tags: + promo_component += 3 + if "low_stock" in tags: + promo_component -= 4 + price_position_component = 0 + if min_pchome_price and pchome_price: + if pchome_price <= min_pchome_price * 1.03: + price_position_component = 6 + elif pchome_price <= min_pchome_price * 1.08: + price_position_component = 3 + + opportunity_score = min( + 100, + price_score + sales_component + margin_component + promo_component + price_position_component, + ) + evidence_quality = min( + 100, + match_component + + history_component + + (12 if sales_7d > 0 else 0) + + (8 if margin_rate is not None else 0) + + (8 if row.get("competitor_product_id") and row.get("competitor_product_name") else 0) + + (6 if row.get("crawled_at") else 0), + ) + score = round(min(100, opportunity_score + evidence_quality * 0.35), 1) + confidence = round(max(0.45, min(0.98, (score * 0.65 + evidence_quality * 0.35) / 100)), 3) if gap_pct >= 10: angle = "PChome 價格優勢明顯" @@ -292,15 +344,26 @@ def _score_candidate(row: Dict[str, Any]) -> Dict[str, Any]: ] if sales_7d > 0: reason_parts.append(f"近 7 天銷售額 ${sales_7d:,.0f}") + if margin_rate is not None: + reason_parts.append(f"近 7 天毛利率 {margin_rate:.1f}%") if history_points: reason_parts.append(f"已有 {history_points} 筆 PChome 歷史快照") + if price_position_component: + reason_parts.append("目前 PChome 價格接近 30 天低點") + if "high_rating" in tags: + reason_parts.append("PChome 商品評價訊號佳") + if "low_stock" in tags: + reason_parts.append("PChome 庫存偏低，需留意供貨") return { **row, "gap_pct": round(gap_pct, 1), "sales_7d_delta": round(sales_delta, 1) if sales_delta is not None else 0, "pick_score": score, - "confidence": round(max(0.45, min(0.98, score / 100)), 3), + "confidence": confidence, + "evidence_quality": round(evidence_quality, 1), + "opportunity_score": round(opportunity_score, 1), + "margin_rate": round(margin_rate, 1) if margin_rate is not None else None, "reason": "；".join(reason_parts), } @@ -315,6 +378,9 @@ def _write_pick(conn, pick: Dict[str, Any]) -> None: "generated_at": datetime.now().isoformat(timespec="seconds"), "inputs": ["products", "price_records", "competitor_prices", "competitor_price_history", "daily_sales_snapshot"], "score": pick["pick_score"], + "opportunity_score": pick.get("opportunity_score"), + "evidence_quality": pick.get("evidence_quality"), + "margin_rate": pick.get("margin_rate"), }, "competitor": { "source": "pchome", diff --git a/services/cache_manager.py b/services/cache_manager.py index 723699e..8ae36cd 100644 --- a/services/cache_manager.py +++ b/services/cache_manager.py @@ -7,7 +7,9 @@ ADR-017 Phase 3f-2: 將 sales/import/export/daily 會共同碰到的 module-level cache 收斂到這裡，避免各 route 各自持有一份 dict。 """ +import os import time +from pathlib import Path class FingerprintCache: @@ -59,6 +61,8 @@ _DASHBOARD_DATA_CACHE = { 'full_timestamp': None, } _DASHBOARD_CACHE_TTL = 1800 +_BASE_DIR = Path(__file__).resolve().parents[1] +_DASHBOARD_SHARED_CACHE_FILE = _BASE_DIR / "data" / "dashboard_full_cache.pkl" def cleanup_sales_cache(): @@ -123,3 +127,9 @@ def clear_dashboard_cache(): 'full_data': None, 'full_timestamp': None, }) + try: + os.remove(_DASHBOARD_SHARED_CACHE_FILE) + except FileNotFoundError: + pass + except OSError: + pass diff --git a/tests/test_cache_manager.py b/tests/test_cache_manager.py index 5cbdefe..0cab97a 100644 --- a/tests/test_cache_manager.py +++ b/tests/test_cache_manager.py @@ -68,9 +68,13 @@ def test_set_sales_processed_cache_adds_timestamp_and_aliases(): assert cache_manager._SALES_PROCESSED_CACHE["realtime_sales_monthly"] is entry -def test_dashboard_cache_clear_restores_expected_shape(): +def test_dashboard_cache_clear_restores_expected_shape(tmp_path, monkeypatch): from services import cache_manager + shared_cache = tmp_path / "dashboard_full_cache.pkl" + shared_cache.write_bytes(b"stale") + monkeypatch.setattr(cache_manager, "_DASHBOARD_SHARED_CACHE_FILE", shared_cache) + cache_manager._DASHBOARD_DATA_CACHE["consolidated_data"] = ["stale"] cache_manager._DASHBOARD_DATA_CACHE["full_data"] = ["stale"] @@ -83,6 +87,7 @@ def test_dashboard_cache_clear_restores_expected_shape(): "full_data": None, "full_timestamp": None, } + assert not shared_cache.exists() def test_cache_dicts_are_only_defined_in_cache_manager(): diff --git a/tests/test_frontend_v2_assets.py b/tests/test_frontend_v2_assets.py index 0ebaf1a..75a5f7e 100644 --- a/tests/test_frontend_v2_assets.py +++ b/tests/test_frontend_v2_assets.py @@ -47,6 +47,8 @@ def test_dashboard_v2_is_production_default_and_uses_real_dashboard_data(): assert "request.args.get('ui') == 'legacy'" in route_source assert "template_name = 'dashboard.html' if request.args.get('ui') == 'legacy' else 'dashboard_v2.html'" in route_source assert "get_full_dashboard_data()" in route_source + assert "_load_shared_full_dashboard_cache(now)" in route_source + assert "_write_shared_full_dashboard_cache(full_data)" in route_source assert "_load_competitor_decision_overview(session)" in route_source assert "ai_price_recommendations" in route_source assert "pending_match_count" in route_source @@ -187,6 +189,11 @@ def test_ai_product_pick_agent_uses_real_competitor_data_and_dashboard_action(): assert "PChome 價格優勢" in agent_source assert "_daily_sales_columns" in agent_source assert '"總業績"' in agent_source + assert '"毛利"' in agent_source + assert '"總成本"' in agent_source + assert "evidence_quality" in agent_source + assert "opportunity_score" in agent_source + assert "margin_rate" in agent_source assert "{date_col}::date" in agent_source assert "conn.rollback()" in agent_source