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🔴 Critical - auto_heal_service: 補 import re + sqlalchemy.text + 修正 orchestrator 變數名 + autoheal_playbook→playbooks 表名 + _alert_and_store cooldown 修復 - aider_heal_executor: shell injection 改 shell=False + list 參數 - docker-compose: DISABLE_LOGIN 改 env var + 移除密碼 fallback + POSTGRES_HOST 修正 - app.py: /api/backup /api/run_task 等 6 個管理 API 加 @login_required - config.py + pg_sync + e2e_test: 移除 wooo_pg_2026 hardcoded 密碼 fallback - pg_backup.sh: 移除 TELEGRAM_TOKEN= 中間變數，直接用 $TELEGRAM_BOT_TOKEN - migration 014: trigger_pattern→match_pattern + 補 error_type NOT NULL 欄位 🟡 High - telegram_bot_service: str(e) 改通用訊息 + session try/finally + 移除 pa:/pr: 舊 callback - run_scheduler: ElephantAlpha thread 死亡監控 + 自動重啟 + Telegram 告警 + agent_context 03:30 TTL 定時清理任務 - openclaw_learning_service: build_rag_context 兩路徑加 .limit(200) - hooks: commit-quality + momo-prod-guard 空 catch 改 stderr+exit(1) - scripts/code_review: auto_yes 預設改 false - db_backup_service: PGPASSWORD 透過 env dict 傳遞 📦 Migrations - 013_autoheal: 修正建表順序 playbooks→incidents（外鍵前向引用） - 018_add_missing_indexes: heal_logs/incidents 外鍵索引 + cleanup_expired_agent_context() 🟢 Infrastructure - requirements.txt: 加版本下界 Flask>=2.3 SQLAlchemy>=1.4 等 - cd.yaml: 新增 run_scheduler.py + run_telegram_bot.py 監聽路徑 - .gitignore: insert_playbook_local.py 加入忽略 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
544 lines
22 KiB
Python
544 lines
22 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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AI 競價情報模組 E2E 整合測試
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通關清單（三道關卡）：
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關卡一：Feeder 語意萃取 — PChome 搜尋 + 模糊比對 + tags 生成
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關卡二：Hermes 情境認知 — competitor_tags 注入後 JSON 品質
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關卡三：NemoTron + Telegram — DB 寫入 + 語意化排版 + 運算足跡
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測試策略：使用 SQLite in-memory DB + 真實假資料，不依賴 Docker/PostgreSQL。
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NIM 與 Telegram 可選跳過（設 SKIP_NIM=1 / SKIP_TELEGRAM=1 環境變數）。
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執行：
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cd /Users/ogt/momo-pro-system
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python3 scripts/e2e_test_ai_pipeline.py
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# 跳過真實 API（純本地邏輯驗證）
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SKIP_NIM=1 SKIP_TELEGRAM=1 python3 scripts/e2e_test_ai_pipeline.py
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# 啟用 Hermes（需 192.168.0.111 可達）
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SKIP_NIM=1 python3 scripts/e2e_test_ai_pipeline.py
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"""
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import json
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import logging
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import os
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import sys
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import time
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from dataclasses import dataclass
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from datetime import datetime, timedelta
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# ── 確保從 project root 執行 ──────────────────────────
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PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, PROJECT_ROOT)
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from dotenv import load_dotenv
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load_dotenv(os.path.join(PROJECT_ROOT, ".env"))
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s %(levelname)s %(message)s",
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datefmt="%H:%M:%S",
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)
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logger = logging.getLogger("e2e")
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SKIP_NIM = os.getenv("SKIP_NIM", "0") == "1"
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SKIP_TELEGRAM = os.getenv("SKIP_TELEGRAM", "0") == "1"
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SKIP_HERMES = os.getenv("SKIP_HERMES", "0") == "1"
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SKIP_FEEDER = os.getenv("SKIP_FEEDER", "0") == "1"
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USE_POSTGRESQL = os.getenv("USE_POSTGRESQL", "0") == "1"
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# PostgreSQL 連線參數（從 env 讀，對齊 docker-compose.yml 預設值）
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PG_HOST = os.getenv("POSTGRES_HOST", "127.0.0.1")
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PG_PORT = os.getenv("POSTGRES_PORT", "5432")
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PG_USER = os.getenv("POSTGRES_USER", "momo")
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PG_PASSWORD = os.getenv("POSTGRES_PASSWORD")
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PG_DB = os.getenv("POSTGRES_DB", "momo_analytics")
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# ═══════════════════════════════════════════════════════════
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# 測試基礎設施：SQLite in-memory + 建表 + 假資料
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# ═══════════════════════════════════════════════════════════
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def _build_test_engine():
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"""
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建立測試 engine：
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- 預設：SQLite in-memory（不需任何外部服務）
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- USE_POSTGRESQL=1：連接本地 Docker PostgreSQL（需先跑 migrations）
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"""
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from sqlalchemy import create_engine, text
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if USE_POSTGRESQL:
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dsn = f"postgresql+psycopg2://{PG_USER}:{PG_PASSWORD}@{PG_HOST}:{PG_PORT}/{PG_DB}"
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logger.info(f"[Engine] 使用 PostgreSQL: {PG_HOST}:{PG_PORT}/{PG_DB}")
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engine = create_engine(dsn, echo=False)
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# PostgreSQL 模式：表已由 migrations 建立，直接回傳
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return engine
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# SQLite in-memory：自建所有表
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engine = create_engine("sqlite:///:memory:", echo=False)
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with engine.begin() as conn:
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# products
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conn.execute(text("""
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CREATE TABLE products (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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i_code TEXT UNIQUE NOT NULL,
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name TEXT NOT NULL,
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category TEXT,
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status TEXT DEFAULT 'ACTIVE',
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created_at TEXT DEFAULT CURRENT_TIMESTAMP,
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updated_at TEXT DEFAULT CURRENT_TIMESTAMP
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)
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"""))
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# price_records
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conn.execute(text("""
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CREATE TABLE price_records (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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product_id INTEGER NOT NULL,
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price REAL NOT NULL,
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timestamp TEXT DEFAULT CURRENT_TIMESTAMP
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)
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"""))
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# daily_sales_snapshot（動態表，欄位用實際 MOMO Excel 名稱）
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conn.execute(text("""
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CREATE TABLE daily_sales_snapshot (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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"商品ID" TEXT,
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"商品名稱" TEXT,
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"銷售金額" REAL,
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snapshot_date TEXT
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)
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"""))
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# competitor_prices
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conn.execute(text("""
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CREATE TABLE competitor_prices (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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sku TEXT NOT NULL,
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source TEXT NOT NULL DEFAULT 'pchome',
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price REAL NOT NULL,
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original_price REAL,
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discount_pct INTEGER,
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competitor_product_id TEXT,
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competitor_product_name TEXT,
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match_score REAL,
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tags TEXT DEFAULT '[]',
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crawled_at TEXT DEFAULT CURRENT_TIMESTAMP,
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expires_at TEXT,
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UNIQUE(sku, source)
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)
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"""))
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# ai_price_recommendations
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conn.execute(text("""
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CREATE TABLE ai_price_recommendations (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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sku TEXT NOT NULL UNIQUE,
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name TEXT NOT NULL,
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reason TEXT,
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strategy TEXT DEFAULT 'promote',
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confidence REAL,
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momo_price REAL,
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pchome_price REAL,
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gap_pct REAL,
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sales_7d_delta REAL,
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model_footprint TEXT DEFAULT '{}',
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status TEXT DEFAULT 'pending',
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reviewed_by TEXT,
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reviewed_at TEXT,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP,
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updated_at TEXT DEFAULT CURRENT_TIMESTAMP
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)
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"""))
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return engine
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def _seed_test_data(engine):
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"""填充測試假資料"""
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from sqlalchemy import text
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today = datetime.now().strftime("%Y-%m-%d")
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week_ago = (datetime.now() - timedelta(days=8)).strftime("%Y-%m-%d")
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with engine.begin() as conn:
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# 三支監控商品
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products = [
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("A003", "舒特膚AD乳液200ml", "美妝保養"),
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("A001", "玻尿酸面膜10片裝", "美妝保養"),
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("A009", "美白化妝水150ml", "美妝保養"),
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]
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for i, (i_code, name, cat) in enumerate(products, 1):
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conn.execute(text("""
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INSERT INTO products (i_code, name, category, status)
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VALUES (:i_code, :name, :cat, 'ACTIVE')
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ON CONFLICT (i_code) DO NOTHING
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"""), {"i_code": i_code, "name": name, "cat": cat})
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# 最新 MOMO 售價（price_records，透過 i_code subquery 取 product_id）
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prices = [
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("A003", 1200.0),
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("A001", 320.0),
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("A009", 420.0),
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]
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for i_code, price in prices:
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conn.execute(text("""
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INSERT INTO price_records (product_id, price, timestamp)
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SELECT id, :price, CURRENT_TIMESTAMP
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FROM products WHERE i_code = :i_code
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LIMIT 1
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"""), {"i_code": i_code, "price": price})
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# daily_sales_snapshot：近7天銷量下滑 > 10%（漏斗條件）
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sales_data = [
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# sku, name, curr, prev, date
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("A003", "舒特膚AD乳液200ml", 65000, 100000, today),
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("A001", "玻尿酸面膜10片裝", 58000, 100000, today),
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("A009", "美白化妝水150ml", 78000, 100000, today),
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# 前一週資料（用 week_ago 日期）
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("A003", "舒特膚AD乳液200ml", 100000, 0, week_ago),
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("A001", "玻尿酸面膜10片裝", 100000, 0, week_ago),
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("A009", "美白化妝水150ml", 100000, 0, week_ago),
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]
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for sku, name, amount, _, date in sales_data:
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conn.execute(text("""
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INSERT INTO daily_sales_snapshot ("商品ID", "商品名稱", "銷售金額", snapshot_date)
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VALUES (:sku, :name, :amount, :date)
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"""), {"sku": sku, "name": name, "amount": amount, "date": date})
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# competitor_prices：預填 PChome 競品資料（模擬 Feeder 已跑過）
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expires = (datetime.now() + timedelta(hours=6)).strftime("%Y-%m-%d %H:%M:%S")
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comp_data = [
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("A003", 980.0, None, None, "DDABSD-A003FAKE", "舒特膚 AD 乳液 200ml",
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0.82, '["on_sale","discount_10pct"]'),
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("A001", 280.0, 320.0, 12, "DDABSD-A001FAKE", "玻尿酸保濕面膜10入",
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0.76, '["discount_10pct"]'),
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("A009", 350.0, 400.0, 12, "DDABSD-A009FAKE", "美白化妝水 150ml",
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0.71, '["on_sale"]'),
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]
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for sku, price, orig, disc, comp_id, comp_name, score, tags in comp_data:
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conn.execute(text("""
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INSERT INTO competitor_prices
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(sku, source, price, original_price, discount_pct,
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competitor_product_id, competitor_product_name,
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match_score, tags, crawled_at, expires_at)
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VALUES
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(:sku, 'pchome', :price, :orig, :disc,
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:comp_id, :comp_name,
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:score, :tags, CURRENT_TIMESTAMP, :expires)
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ON CONFLICT(sku, source) DO NOTHING
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"""), {
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"sku": sku, "price": price, "orig": orig, "disc": disc,
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"comp_id": comp_id, "comp_name": comp_name,
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"score": score, "tags": tags, "expires": expires,
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})
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logger.info("✅ 測試資料填充完成")
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# ═══════════════════════════════════════════════════════════
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# 關卡一：Feeder 語意萃取（CLI 模式，不需 DB）
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# ═══════════════════════════════════════════════════════════
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def gate1_feeder():
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print("\n" + "═"*60)
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print("【關卡一】Feeder 語意萃取 — PChome 搜尋 + 比對 + tags")
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print("═"*60)
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if SKIP_FEEDER:
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print("⏭️ SKIP_FEEDER=1，跳過（假設 competitor_prices 已由 seed 填入）")
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return True
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from services.pchome_crawler import PChomeCrawler
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from services.competitor_price_feeder import _find_best_match, _extract_tags, MIN_MATCH_SCORE
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test_skus = [
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{"sku": "A003", "name": "舒特膚AD乳液200ml"},
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{"sku": "A001", "name": "玻尿酸面膜10片裝"},
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]
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crawler = PChomeCrawler(delay=0.8)
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ok_count = 0
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for item in test_skus:
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keyword = item["name"][:20]
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success, _, products = crawler.search_products(keyword, limit=10)
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if not success or not products:
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print(f" ⚠️ [{item['sku']}] PChome 搜尋無結果（網路問題或無此商品）")
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continue
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result = _find_best_match(item["name"], products)
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if not result:
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print(f" ⚠️ [{item['sku']}] 無法找到比對結果")
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continue
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best, score = result
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tags = _extract_tags(best)
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status = "✅" if score >= MIN_MATCH_SCORE else "⚠️ 低分"
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print(f" {status} [{item['sku']}] {item['name'][:25]}")
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print(f" PChome: {best.name[:40]}")
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print(f" 售價 ${best.price} | 分數 {score:.3f} | 標籤 {tags}")
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if score >= MIN_MATCH_SCORE:
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ok_count += 1
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print(f"\n → 關卡一結果：{ok_count}/{len(test_skus)} 支成功比對")
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return ok_count > 0
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# ═══════════════════════════════════════════════════════════
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# 關卡二：Hermes 情境認知
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# ═══════════════════════════════════════════════════════════
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def gate2_hermes(engine):
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print("\n" + "═"*60)
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print("【關卡二】Hermes 情境認知 — competitor_tags 注入後 JSON 品質")
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print("═"*60)
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if SKIP_HERMES:
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print("⏭️ SKIP_HERMES=1，跳過")
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return _mock_threats()
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from services.hermes_analyst_service import HermesAnalystService
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# 用 SQLite 版的 fetch_candidates（需調整 SQL）
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# SQLite 不支援 PostgreSQL 特有語法，改為帶資料的直接查詢
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service = HermesAnalystService(engine=engine)
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from sqlalchemy import text
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with engine.connect() as conn:
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rows = conn.execute(text("""
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SELECT
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p.i_code AS sku, p.name, p.category,
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pr.price AS momo_price,
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(SELECT SUM(CAST("銷售金額" AS REAL))
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FROM daily_sales_snapshot WHERE "商品ID" = p.i_code
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AND date(snapshot_date) = date('now')) AS sales_7d_curr,
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(SELECT SUM(CAST("銷售金額" AS REAL))
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FROM daily_sales_snapshot WHERE "商品ID" = p.i_code
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AND date(snapshot_date) < date('now')) AS sales_7d_prev,
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cp.price AS pchome_price,
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cp.tags AS competitor_tags
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FROM products p
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JOIN price_records pr ON pr.product_id = p.id
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LEFT JOIN competitor_prices cp ON cp.sku = p.i_code AND cp.source = 'pchome'
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WHERE p.status = 'ACTIVE'
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""")).fetchall()
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candidates = [dict(r._mapping) for r in rows]
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logger.info(f"[關卡二] 候選商品 {len(candidates)} 筆")
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if not candidates:
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print(" ❌ 沒有候選商品，測試資料可能有誤")
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return []
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# 顯示 candidates 驗證
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for c in candidates:
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tags = c.get("competitor_tags") or "無"
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print(f" ✅ {c['sku']} | MOMO ${c['momo_price']} | PChome ${c.get('pchome_price','?')} "
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f"| tags={tags}")
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# 呼叫 Hermes
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start = time.time()
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raw = service._batch_analyze(candidates)
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duration = round(time.time() - start, 2)
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if not raw:
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print(" ⚠️ Hermes 回傳空結果（Ollama 未啟動或逾時）")
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return _mock_threats()
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print(f"\n ✅ Hermes 推理完成 ({duration}s)，回傳 {len(raw)} 筆")
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for item in raw[:3]:
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tags_mention = "competitor_tags" in str(item.get("recommended_action", ""))
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print(f" [{item['sku']}] {item.get('risk','?')} | "
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f"confidence={item.get('confidence','?')} | "
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f"action={item.get('recommended_action','')[:50]}")
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if tags_mention:
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print(" 🎯 AI 洞察中包含競品標籤資訊")
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from services.hermes_analyst_service import PriceThreat
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threats = [
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PriceThreat(
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sku=t["sku"], name=t["name"],
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category=t.get("category", ""),
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momo_price=t.get("momo_price", 0),
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pchome_price=t.get("pchome_price", 0),
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gap_pct=t.get("gap_pct", 0),
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sales_7d_delta_pct=t.get("sales_7d_delta_pct", 0),
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risk=t.get("risk", "LOW"),
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recommended_action=t.get("recommended_action", ""),
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confidence=t.get("confidence", 0.5),
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)
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for t in raw
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]
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return threats, {"duration_sec": duration, "tokens": 512}
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def _mock_threats():
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"""Hermes 跳過時的假威脅清單"""
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from services.hermes_analyst_service import PriceThreat
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threats = [
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PriceThreat("A003", "舒特膚AD乳液200ml", "美妝保養",
|
||
1200, 980, 22.4, -35.0, "HIGH",
|
||
"建議立即降價至 $1,000（競品有 on_sale 促銷標籤）", 0.85),
|
||
PriceThreat("A001", "玻尿酸面膜10片裝", "美妝保養",
|
||
320, 280, 14.3, -42.0, "HIGH",
|
||
"跟進降價至 $285（競品有 discount_10pct）", 0.78),
|
||
PriceThreat("A009", "美白化妝水150ml", "美妝保養",
|
||
420, 350, 20.0, -22.0, "HIGH",
|
||
"價格差距大，建議促銷或捆包", 0.45),
|
||
]
|
||
return threats, {"duration_sec": 0, "tokens": 0}
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════
|
||
# 關卡三：NemoTron + DB 寫入 + Telegram
|
||
# ═══════════════════════════════════════════════════════════
|
||
|
||
def gate3_dispatcher(engine, threats, hermes_stats):
|
||
print("\n" + "═"*60)
|
||
print("【關卡三】NemoTron 決策 + DB 寫入 + Telegram 排版")
|
||
print("═"*60)
|
||
|
||
from services.nemoton_dispatcher_service import NemotronDispatcher
|
||
|
||
# Mock Telegram：不真實發送，只印出訊息
|
||
class MockNotificationManager:
|
||
def _send_telegram_messages(self, msgs):
|
||
for msg in msgs:
|
||
print("\n 📩 [Telegram 預覽]")
|
||
print(" " + "\n ".join(msg.split("\n")))
|
||
|
||
nm = None if not SKIP_TELEGRAM else MockNotificationManager()
|
||
|
||
if SKIP_NIM:
|
||
print(" ⏭️ SKIP_NIM=1，使用 Fallback 路徑直接派發 HIGH 威脅")
|
||
# 手動模擬 dispatch fallback 路徑
|
||
dispatcher = NemotronDispatcher(notification_manager=nm, engine=engine)
|
||
from services.nemoton_dispatcher_service import (
|
||
_build_footprint_block, _build_footprint_json, NIM_DAILY_LIMIT
|
||
)
|
||
fake_nim_stats = {"total_tokens": 185, "quota_used": 1}
|
||
fp_text = _build_footprint_block(hermes_stats, fake_nim_stats)
|
||
fp_data = _build_footprint_json(hermes_stats, fake_nim_stats)
|
||
|
||
dispatched = 0
|
||
for t in threats:
|
||
if t.risk == "HIGH":
|
||
dispatcher._exec_trigger_price_alert(
|
||
t.sku, t.name, t.gap_pct, t.sales_7d_delta_pct,
|
||
t.recommended_action, t.confidence,
|
||
momo_price=t.momo_price, comp_price=t.pchome_price,
|
||
footprint=fp_text,
|
||
)
|
||
dispatched += 1
|
||
# 對 A009 測試 add_to_recommendation
|
||
if t.sku == "A009":
|
||
dispatcher._exec_add_to_recommendation(
|
||
t.sku, t.name,
|
||
"我方價格低於市場，近7天流量上升，建議提升曝光",
|
||
t.confidence,
|
||
footprint=fp_text,
|
||
footprint_data=fp_data,
|
||
threat=t,
|
||
)
|
||
result = {"dispatched": dispatched, "skipped": 0, "errors": [], "nim_stats": fake_nim_stats}
|
||
else:
|
||
dispatcher = NemotronDispatcher(notification_manager=nm, engine=engine)
|
||
result = dispatcher.dispatch(threats, hermes_stats=hermes_stats)
|
||
|
||
print(f"\n → dispatched={result['dispatched']} | "
|
||
f"skipped={result['skipped']} | errors={result['errors']}")
|
||
|
||
# 驗證 DB 寫入
|
||
_verify_db(engine)
|
||
return result
|
||
|
||
|
||
def _verify_db(engine):
|
||
"""驗證 DB 寫入是否正確（關鍵：model_footprint 必須是真實 JSON，不是 {}）"""
|
||
from sqlalchemy import text
|
||
print("\n 🔍 DB 寫入驗證：")
|
||
|
||
with engine.connect() as conn:
|
||
recs = conn.execute(text(
|
||
"SELECT sku, name, confidence, model_footprint, status FROM ai_price_recommendations"
|
||
)).fetchall()
|
||
|
||
if not recs:
|
||
print(" ⚠️ ai_price_recommendations 無資料（add_to_recommendation 未被觸發）")
|
||
return
|
||
|
||
all_ok = True
|
||
for r in recs:
|
||
fp = r._mapping.get("model_footprint", "{}")
|
||
try:
|
||
fp_dict = json.loads(fp) if isinstance(fp, str) else fp
|
||
has_analyst = "analyst" in fp_dict
|
||
has_dispatcher = "dispatcher" in fp_dict
|
||
fp_ok = has_analyst or has_dispatcher
|
||
except Exception:
|
||
fp_ok = False
|
||
|
||
status = "✅" if fp_ok else "❌ model_footprint 為空 {}"
|
||
print(f" {status} [{r._mapping['sku']}] "
|
||
f"confidence={r._mapping['confidence']} "
|
||
f"status={r._mapping['status']} "
|
||
f"footprint_keys={list(json.loads(fp).keys()) if fp_ok else '[]'}")
|
||
|
||
if not fp_ok:
|
||
all_ok = False
|
||
|
||
if all_ok:
|
||
print(" ✅ 所有推薦記錄的 model_footprint 均已正確寫入 DB")
|
||
else:
|
||
print(" ❌ 部分記錄 model_footprint 為空，請檢查 footprint_data 傳遞路徑")
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════
|
||
# 主程式
|
||
# ═══════════════════════════════════════════════════════════
|
||
|
||
def main():
|
||
print("=" * 60)
|
||
print("🚀 AI 競價情報模組 E2E 整合測試")
|
||
print(f" 模式：NIM={'跳過' if SKIP_NIM else '真實'} | "
|
||
f"Hermes={'跳過' if SKIP_HERMES else '真實'} | "
|
||
f"Telegram={'Mock' if SKIP_TELEGRAM else '真實'} | "
|
||
f"Feeder={'跳過' if SKIP_FEEDER else '真實'}")
|
||
print("=" * 60)
|
||
|
||
# 建立測試 DB
|
||
engine = _build_test_engine()
|
||
_seed_test_data(engine)
|
||
|
||
# 關卡一
|
||
gate1_feeder()
|
||
|
||
# 關卡二
|
||
result2 = gate2_hermes(engine)
|
||
if isinstance(result2, tuple):
|
||
threats, hermes_stats = result2
|
||
else:
|
||
threats, hermes_stats = result2, {"duration_sec": 0, "tokens": 0}
|
||
|
||
# 關卡三
|
||
gate3_dispatcher(engine, threats, hermes_stats)
|
||
|
||
print("\n" + "═"*60)
|
||
print("✅ E2E 測試完成")
|
||
print("═"*60)
|
||
print()
|
||
print("下一步：")
|
||
print(" 1. 啟動 Docker Desktop → docker compose -f docker-compose.yml up -d postgres")
|
||
print(" 2. psql -U momo -h 127.0.0.1 -d momo_analytics -f migrations/003_ai_price_recommendations.sql")
|
||
print(" 3. psql -U momo -h 127.0.0.1 -d momo_analytics -f migrations/004_competitor_prices.sql")
|
||
print(" 4. USE_POSTGRESQL=true python3 scripts/e2e_test_ai_pipeline.py")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|