Grocery Comparison

文化口味偏好

根据用户的文化背景，理解他们可能的口味偏好：

推荐策略

当用户说 "我想买排骨" 时，不要直接比价，先问/判断：

1. 做什么菜？ → 决定推荐哪个部位
2. 几个人吃？ → 决定推荐多少量
3. 有时间处理吗？ → 决定推荐整块还是切好的

示例对话：

用户: 我想买排骨
Agent: 你是想做什么菜呢？
- 如果是糖醋小排、红烧排骨，推荐买肋排 (spare ribs)，Weee 和 168 都有切好的小块
- 如果是煲汤，推荐买筒骨/大骨 (soup bones)，168 的更便宜而且新鲜

你住在安娜堡对吧？168 Asian Mart 开车约 25 分钟，他们的排骨 $3.99/lb 是附近最便宜的。

美式产品避雷

中国用户常见的"踩坑"产品，可以主动提醒：

Data Source Availability Status (Tested 2026-04-08)

Cookies 文件位置 (Staging/Production)

~/server/DoWhiz/DoWhiz_service/.samsclub_cookies.json  # Sam's Club 会员登录
~/server/DoWhiz/DoWhiz_service/.notion_cookies_liuxt.json  # Notion (测试用)

小红书访问方案 (TODO)

小红书对非中国 IP 有严格限制。解决方案：

方案 1: Azure Asia VM (推荐)

# 在 Azure East Asia / China North 创建小型 VM
# 专门用于抓取小红书
# 通过 API 调用或 SSH tunnel 访问

方案 2: 代理服务

• 使用中国代理 IP 池
• 或用户自己的 VPN 账号

方案 3: 用户自助

• 让用户自己搜索小红书，提供关键词
• 用户粘贴相关笔记链接，agent 解析

推荐抓取策略：

1. 首选: Weee + Yami + Google Maps (直接 browser-use)
2. 需 API: Kroger (grocery_cli kroger search)
3. 需 cookies: Sam's Club (有 cookies，但需处理 CAPTCHA)
4. 需特殊处理: 小红书 (Azure Asia VM 或用户自助)

UI Questionnaire Component

问卷 UI 组件位于 website/src/components/intake/GroceryPreferencesQuestionnaire.jsx

功能特性：

• 6步向导式流程
• 单选/多选按钮组
• 滑块控件（甜度、辣度）
• 拖拽排序（优先级）
• 响应式设计

使用方式：

import GroceryPreferencesQuestionnaire from './components/intake/GroceryPreferencesQuestionnaire';

<GroceryPreferencesQuestionnaire
  onComplete={(formData) => {
    // 保存到用户memory
    console.log('User preferences:', formData);
  }}
  onCancel={() => {
    // 取消问卷
  }}
  initialData={{}} // 可传入已有偏好数据
/>

Quick Start: Check User Preferences First

ALWAYS check user preferences before giving grocery recommendations:

# Get user's saved preferences (zip code, stores, dietary restrictions, etc.)
grocery_cli preferences get <user_id>

If preferences not found or incomplete, you have two options:

1. Ask in conversation - If you need critical info (like location), ask directly
2. Point to questionnaire - For comprehensive setup: https://dowhiz.com/grocery/onboarding

Use your judgment: if user's question can be answered with partial info, answer first and suggest completing preferences later.

Response Requirements

0. Price Accuracy (CRITICAL)

价格准确性是用户信任的基础。 错误的价格会让用户失去对我们的信任。

实时验证流程 (REQUIRED)

在给出价格推荐之前，必须使用 browser-use 实时验证价格：

# 1. 搜索产品
IN_DOCKER=true browser-use open "https://www.sayweee.com/en/search?keyword=orion+choco+pie"
browser-use state  # 获取搜索结果

# 2. 点击具体产品页面获取准确价格
browser-use click <product_index>
browser-use state  # 获取产品详情页的准确价格

# 3. 截图作为证据（可选但推荐）
browser-use screenshot /tmp/price_verification.png

验证要点：

• 不要只依赖 web search 返回的价格（可能是缓存/过期数据）
• 必须进入产品详情页确认当前价格
• 注意区分原价和促销价
• 检查是否有会员专属价格

查询时间戳 (REQUIRED)

所有价格推荐必须包含查询时间：

## 价格比较 (查询时间: 2026-04-08 13:30 EST)

| 渠道 | 价格 | 链接 |
|------|------|------|
| Weee | $4.49 | [购买](url) |

免责声明 (REQUIRED)

每次价格推荐的结尾必须添加免责声明：

---
⚠️ **价格说明**: 以上价格为查询时的实时价格，可能随时变动。最终价格以实际页面为准。
促销价可能需要满足特定条件（如会员、满减等）。

处理价格不确定性

如果无法实时验证价格（如实体店），明确标注数据来源和时效：

| 店铺 | 价格 | 数据来源 | 可信度 |
|------|------|---------|--------|
| 168 Asian Mart | ~$3.99/lb | 小红书@用户 (2026-03-15) | ⚠️ 可能有变化 |
| Weee | $4.49 | 实时查询 (2026-04-08) | ✅ 已验证 |

1. Purchase Links (REQUIRED)

Every product recommendation MUST include clickable purchase links:

示例输出:

## 五花肉比价 (查询时间: 2026-04-08 13:30 EST)

| 渠道 | 价格 | 数据来源 | 购买链接 |
|------|------|---------|---------|
| [168 Asian Mart](https://maps.google.com/?q=168+Asian+Mart+Madison+Heights+MI) | ~$3.99/lb | 小红书 (3/15) | 实体店 |
| [Weee](https://www.sayweee.com/en/product/12345) | $5.49/lb | ✅ 实时验证 | [立即购买](https://www.sayweee.com/en/product/12345) |
| [Costco](https://maps.google.com/?q=Costco+Ann+Arbor) | $3.29/lb | 用户报价 (3/20) | 需会员 |

---
⚠️ **价格说明**: 以上价格为查询时的实时价格，可能随时变动。最终价格以实际页面为准。

2. Questionnaire Link (回复结尾)

在回复结尾附上问卷链接，让用户选择是否完善偏好:

---
💡 完成偏好问卷可获得更精准推荐: https://dowhiz.com/grocery/onboarding

3. Preference Updates (Agent Judgment)

当用户在对话中透露偏好信息时，自主判断是否值得保存:

• 明确的偏好 (如 "我不吃猪肉") → 保存到 memory
• 一次性需求 (如 "今天想吃辣的") → 不需要保存
• 位置信息 (如 "我住安娜堡") → 保存到 memory

保存格式: 更新用户 memory 文件的 ## Grocery Preferences 部分

Conversational Preference Collection

如果用户没有完整偏好且你需要关键信息才能回答，可以在对话中自然地询问。

原则:

• 不要一次问太多问题
• 只问回答当前问题必需的信息
• 让用户选择是回答还是跳过

示例:

用户: 排骨哪里买便宜？
Agent: 为了给你推荐最近的店，请问你住在哪个城市/zip code？
       (或者直接告诉我你方便去的超市，我帮你比价)

Legacy: Detailed Questionnaire Reference

以下问题仅供参考，用于理解完整的偏好维度。实际收集通过 UI 问卷完成。

可收集的偏好维度:

• 位置: city, zip_code
• 交通: has_car, max_drive_minutes
• 购物方式: online/offline/both
• 会员: costco, sams_club, kroger_plus
• 常买品类: meat, seafood, vegetables, snacks, hotpot...
• 口味: sweetness_tolerance, spice_tolerance, american_sweets_opinion
• 饮食限制: vegetarian, no_pork, lactose_intolerant, gluten_free
• 预算: price_first, value_focused, quality_first

详见 website/src/components/intake/GroceryPreferencesQuestionnaire.jsx

Shopping Habits Reference

以下内容描述用户可能的购物习惯，用于理解上下文:

交通选项:

• 没有车，靠公共交通/走路
• 有车，15分钟内
• 有车，30分钟内
• 有车，1小时内也可以

User Preferences Schema

偏好数据由 UI 问卷收集并存储在后端。Agent 通过 grocery_cli preferences get <user_id> 读取。

Memory 文件格式示例:

## Grocery Preferences

### Profile
- background: chinese_mainland
- city: Ann Arbor, MI
- zip_code: 48109
- household_size: 2

### Transportation
- has_car: true
- max_drive_minutes: 30
- prefers: both

### Memberships
- costco: true
- sams_club: false
- kroger_plus: true

### Main Categories
- primary: [meat, vegetables, snacks, hotpot]
- secondary: [condiments, instant_noodles]

### Taste Profile
- sweetness_tolerance: 2  # 1-5，美式甜品太甜
- spice_tolerance: 4  # 能吃辣
- american_sweets: too_sweet

### Exclusions
- dietary: []  # 无饮食限制

### Budget & Priorities
- budget_mindset: value_focused  # price_first/value_focused/quality_first
- priority_order: [price, quality, convenience, variety]

### Known Baselines (user-reported prices)
| Item | Store | Price | Unit |
|------|-------|-------|------|
| 五花肉 | 168 Asian Mart | $3.99 | /lb |
| 老干妈 | 168 Asian Mart | $3.29 | 280g |

Data Sources (Priority Order)

Tier 1: API/Automated (Most Reliable)

Kroger API (via grocery_cli)

IMPORTANT: Use grocery_cli for Kroger queries - it handles OAuth automatically.

# Search for products (with prices at nearest store)
grocery_cli kroger search "pork belly" --location 48109 --limit 5

# Find nearby Kroger stores
grocery_cli kroger stores 48109 --radius 15 --limit 5

# Get user's saved preferences
grocery_cli preferences get <user_id>

Example output:

Kroger Products for "pork belly":

1. Kroger® Fresh Natural Pork Belly Boneless
   Brand: Kroger
   Size: 1.5 lb   Price: $12.99

2. Smithfield Fresh Pork Belly
   Brand: Smithfield
   Size: 2 lb   Price: $15.49 (Sale: $12.99)

Available data: Product name, brand, price, sale price, size, store location

Weee (via browser-use)

# Weee doesn't have public API, use browser automation
IN_DOCKER=true browser-use open "https://www.sayweee.com/en/search?keyword=pork%20belly"
browser-use state  # Get product listings
browser-use screenshot /tmp/weee_search.png

Parsing Weee results: Look for price patterns like $X.XX and unit patterns like /lb, /ea, /pack

Tier 2: Browser Scraping (Medium Reliability)

H Mart Online

IN_DOCKER=true browser-use open "https://www.hmart.com/search?q=pork+belly"
browser-use state

Yami (Yamibuy)

IN_DOCKER=true browser-use open "https://www.yamibuy.com/en/search?keywords=pork+belly"
browser-use state

Costco (Requires login)

# Check if user has saved Costco cookies
if [ -f ~/.costco_cookies.json ]; then
  IN_DOCKER=true browser-use cookies import ~/.costco_cookies.json
fi
IN_DOCKER=true browser-use open "https://www.costco.com/CatalogSearch?keyword=pork+belly"
browser-use state

Tier 3: Social Media & Reviews Mining (For Local Asian Markets)

For stores without online presence (168 Asian Mart, Way1/Huaxing, local markets), use social media and review platforms to gather price intelligence:

Google Maps Reviews

# Search for store and read reviews mentioning prices
IN_DOCKER=true browser-use open "https://www.google.com/maps/search/168+Asian+Mart+Madison+Heights+MI"
browser-use state  # Find the store listing
browser-use click <store_listing_index>  # Click to open details
browser-use scroll down  # Scroll to reviews
browser-use state  # Look for reviews mentioning prices

# Search pattern in reviews:
# - "prices are good/cheap/expensive"
# - "$X.XX" patterns
# - "cheaper than H Mart"
# - Comparisons with other stores

What to extract from Google Maps:

• Overall price sentiment (cheap/moderate/expensive)
• Specific price mentions in reviews
• Comparisons with other stores
• Recent review dates (prioritize recent ones)

小红书 (Xiaohongshu) Price Discovery

⚠️ CURRENT STATUS: Direct scraping blocked

• Xiaohongshu blocks datacenter IPs (Azure, AWS, etc.) with error 300012 "IP存在风险"
• Fallback: Use web search (Google/Bing) to find Xiaohongshu content indexed by search engines
• Alternative: Use Zhihu (知乎) or other review platforms for similar user-generated content

# BLOCKED - Direct access no longer works from cloud IPs
# IN_DOCKER=true browser-use open "https://www.xiaohongshu.com/search_result?keyword=168亚洲超市+密歇根+价格"

# ALTERNATIVE: Use web search to find indexed Xiaohongshu content
# Search engines have limited Xiaohongshu indexing, but some content is available

# Alternative searches:
# "密歇根 华人超市 价格"
# "Ann Arbor 买菜 攻略"
# "168超市 什么便宜"
# "美国亚超 比价"

小红书搜索关键词模板：

小红书内容解析要点 - 不只是价格！

小红书"问一问"AI功能：

# 小红书有AI问答功能，可以直接提问
IN_DOCKER=true browser-use open "https://www.xiaohongshu.com/search_result?keyword=山姆+五花肉"
browser-use state
# 找到"问一问"入口或搜索结果中的AI总结
# 可以直接问："山姆的五花肉怎么样？骨头多吗？"

产品评价综合报告示例：

## 山姆 五花肉 详细评价

### 基本信息
- 价格: $3.29/lb (会员价)
- 规格: 整块约5-6lb，需要自己切
- 产地: 美国

### 用户评价摘要 (来自小红书 12篇笔记)
- **优点**: 
  - 价格便宜，性价比高
  - 肉质新鲜，脂肪层分明
- **缺点**:
  - 骨头比较大，需要自己处理
  - 整块太大，1-2人家庭吃不完
  - 需要自己切片，不如168切好的方便
  
### 适合人群
- ✅ 3人以上家庭
- ✅ 会做饭、有切肉工具的人
- ✅ 喜欢批量买然后冷冻的人
- ❌ 1-2人小家庭（分量太大）
- ❌ 不想自己处理的人

### 加工建议
- 买回来先切成小块分装冷冻
- 或者让山姆肉柜帮忙切（部分店提供）

### 综合推荐
如果你是3人以上家庭，有时间处理，山姆的五花肉性价比最高。
如果你是1-2人，建议去168买已切好的，虽然贵$0.70/lb但省事很多。

WeChat Articles & Groups (微信公众号/群聊截图)

# Search for WeChat articles about local grocery shopping
IN_DOCKER=true browser-use open "https://www.google.com/search?q=site:mp.weixin.qq.com+密歇根+华人超市+价格"
browser-use state

微信渠道信息来源：

• 当地华人公众号的超市攻略
• 留学生群里分享的价格截图
• 团购群的拼单信息

Reddit & Local Forums

# Search Reddit for local grocery discussions
IN_DOCKER=true browser-use open "https://www.reddit.com/r/AnnArbor/search/?q=asian+grocery+prices"
browser-use state

# Alternative: Google search with site filter
IN_DOCKER=true browser-use open "https://www.google.com/search?q=site:reddit.com+Ann+Arbor+168+Asian+Mart+prices"

Yelp Reviews

IN_DOCKER=true browser-use open "https://www.yelp.com/biz/168-asian-mart-madison-heights"
browser-use scroll down  # Scroll to reviews
browser-use state

Tier 4: Web Search (Supplementary Channel)

When direct platform access is blocked or data sources are insufficient, use web search as a supplementary channel:

# General product reviews and comparisons
# Use search queries like:
# - "山姆 排骨 评价" (Sam's Club ribs reviews)
# - "Costco meat quality reviews 2026"
# - "[product] 测评 知乎" (product review on Zhihu)
# - "site:zhihu.com [product] 推荐" (search Zhihu specifically)

Best search queries by intent:

Useful sources accessible via web search:

• 知乎 (Zhihu): site:zhihu.com - Chinese Q&A platform with detailed product discussions
• Reddit: site:reddit.com - English-language discussions on r/Costco, r/samsclub, local city subreddits
• Food blogs: General queries often surface recipe sites with ingredient recommendations
• 腾讯新闻/网易: Aggregated content sometimes includes 小红书 summaries

Note: Web search provides indexed snapshots - content may be outdated. Prioritize recent results (check dates).

Tier 5: User-Reported Data (Baseline Prices)

For stores where social media mining doesn't yield results:

1. Check user's baseline prices in their memory file
2. Ask user to report if data is stale (>7 days)
3. Offer to help track via receipt scanning (future feature)

Social Media Price Discovery Workflow

When a user asks about a store without online pricing (like 168 Asian Mart), follow this discovery workflow:

Step 1: Quick Google Maps Check

# Fast sentiment check from reviews
IN_DOCKER=true browser-use open "https://www.google.com/maps/search/{store_name}+{city}+{state}"
browser-use state
# Click on store → scroll to reviews → extract price mentions

Step 2: Web Search for Reviews (Fallback for 小红书)

# Direct 小红书 access is BLOCKED from cloud IPs
# Use web search to find indexed content instead:

# Search Zhihu (知乎) - often has similar user-generated content
# Search pattern: site:zhihu.com {store_name} {product} 评价

# Or general web search for Chinese grocery reviews:
# Query: "{store_name} {city} 评价 买菜"

Parsing 小红书 Results:

1. Look for posts with 价格/价签/账单 in title
2. Check images for price tag photos
3. Note the post date (ignore posts >6 months old)
4. Read comments for updated prices

Step 3: Synthesize Price Intelligence

After gathering data, create a summary:

## Price Intelligence: 168 Asian Mart (Madison Heights, MI)

### Data Sources
- Google Maps: 47 reviews mentioning prices (avg sentiment: "affordable")
- 小红书: 12 relevant posts (most recent: 2026-03-15)
- Yelp: 23 price mentions

### Price Findings
| Item | Reported Price | Source | Date |
|------|---------------|--------|------|
| 五花肉 | $3.99/lb | 小红书@密歇根吃货 | 2026-03-15 |
| 老干妈 | $3.29 | Google review | 2026-02-20 |
| 整体印象 | "比H Mart便宜20-30%" | Multiple sources | - |

### Confidence Level
- High: 多个来源一致的价格
- Medium: 单一来源但近期
- Low: 老旧数据或模糊描述

### Recommendation
基于社媒数据，168的价格整体比H Mart便宜，尤其是中国特色商品。
建议将此信息保存到你的memory作为baseline。

Step 4: Update User Memory

If useful price data is found, offer to save to user's memory:

我从小红书和Google Maps找到了168的一些价格信息：
- 五花肉 ~$3.99/lb
- 老干妈 ~$3.29
- 整体比H Mart便宜20-30%

要我帮你保存到memory作为baseline吗？下次比价可以直接用。

Comparison Workflow

Step 1: Understand the Request

Parse user query to extract:

• Product: What they want to buy (e.g., "五花肉", "pork belly", "老干妈")
• Quantity: How much (optional, affects bulk store recommendations)
• Urgency: Need it today vs can wait for delivery

Example queries:

• "帮我比一下五花肉哪里便宜"
• "Where should I buy pork belly?"
• "Weee上的旺旺和168比哪个划算"

Step 2: Gather Price Data

# Pseudocode for data gathering
async def gather_prices(product: str, user_prefs: dict):
    results = []
    
    # 1. Check user's baseline data first
    baseline = check_user_baselines(product, user_prefs)
    if baseline:
        results.extend(baseline)
    
    # 2. Query online sources
    if "Weee" in user_prefs.preferred_stores:
        weee_price = await scrape_weee(product)
        results.append(weee_price)
    
    if "Kroger" in user_prefs.preferred_stores:
        kroger_price = await query_kroger_api(product, user_prefs.zip_code)
        results.append(kroger_price)
    
    # 3. Browser scrape other online stores
    for store in ["H Mart", "Yami"]:
        if store in user_prefs.preferred_stores:
            price = await browser_scrape(store, product)
            results.append(price)
    
    return results

Step 3: Normalize and Compare

Unit normalization is critical:

def normalize_price(price: float, unit: str) -> float:
    """Convert to $/kg for comparison"""
    conversions = {
        "lb": 2.20462,  # 1kg = 2.20462 lb
        "oz": 35.274,   # 1kg = 35.274 oz
        "g": 1000,      # 1kg = 1000g
        "kg": 1,
    }
    # Parse unit and convert
    ...

Step 4: Apply User Preferences

Scoring formula:

score = base_price_score 
      + taste_penalty      # -100 if American-style and user avoids
      + distance_penalty   # -10 per 10 min drive over threshold
      + delivery_bonus     # +20 if delivers to user's zip
      + freshness_bonus    # +15 for fresh vs frozen

Step 5: Generate Recommendation

Output format:

## 五花肉 (Pork Belly) 比价结果

### 推荐: 168 Asian Mart
- 价格: $3.99/lb ($8.80/kg)
- 距离: 15分钟车程
- 优点: 最便宜，品质好，新鲜
- 缺点: 需要开车

### 其他选项:

| 渠道 | 价格 | 单价 | 配送 | 备注 |
|------|------|------|------|------|
| Weee | $5.49/lb | $12.10/kg | 免费送货 | 方便但贵$1.50/lb |
| H Mart | $4.49/lb | $9.90/kg | 需自取 | 20分钟车程 |
| Costco | $3.29/lb | $7.25/kg | 需自取 | 需买整块5lb+，会员价 |

### 建议
考虑到你住在Ann Arbor，168开车15分钟可以接受。如果你这周不想出门，Weee虽然贵$1.50/lb但免费送货，5lb以内差价不到$8，可能值得。

如果你要买大量(>5lb)，Costco最划算，但需要会员卡。

Common Products (Chinese/English Mapping)

Store Profiles

Online Delivery

Physical Stores (Michigan Ann Arbor Area)

Handling Edge Cases

Product not found online

我在Weee上没找到这个产品。根据你之前报告的价格，168有卖$X.XX。
你要我帮你记录一下其他店的价格吗？下次可以直接比较。

Price data is stale

你上次记录168的价格是2周前了。这些价格可能有变化：
- 五花肉 $3.99/lb (记录于 2026-03-18)

你最近去过168吗？可以帮我更新一下价格。

User asks about non-Asian product

鸡蛋在这些店的价格大概是：
- Costco: $X.XX/18个 (需会员)
- Aldi: $X.XX/12个 (最便宜非会员选项)
- Kroger: $X.XX/12个 (查看本周flyer可能有折扣)

注意：亚洲超市的鸡蛋通常比Costco/Aldi贵，除非你需要特定品种（如皮蛋、咸蛋）。

Weekly Deals Integration

Parsing Promotional Emails

If user forwards promotional emails to DoWhiz:

# Extract deals from email body
def parse_promo_email(email_body: str, store: str):
    # Look for patterns like:
    # "Pork Belly $2.99/lb (reg $3.99)"
    # "五花肉 特价 $2.99/磅"
    deals = extract_deals(email_body)
    
    # Store in user's deal tracker
    for deal in deals:
        save_deal(user_id, store, deal.product, deal.sale_price, deal.valid_until)

Proactive Recommendations

If user has subscribed to weekly recommendations:

## 本周值得买 (Week of 2026-04-01)

基于你的偏好和各店促销:

1. **五花肉** - 168特价 $2.99/lb (原价$3.99，省25%)
   - 比Weee便宜$2.50/lb
   - 建议：多买点冻起来

2. **旺旺仙贝** - Weee买二送一
   - 相当于$3.33/包，比168便宜
   
3. **老干妈** - Yami有8折码 SPRING20
   - 但算上运费不如168划算，除非你要买其他东西凑$49免邮

Receipt OCR Integration (Future)

When receipt scanning is available:

# User uploads receipt image
receipt_ocr --image /tmp/receipt.jpg --output json > /tmp/receipt_data.json

# Parse and update price database
update_prices_from_receipt /tmp/receipt_data.json --store "168 Asian Mart"

Search Query Templates by Scenario

Scenario 1: Unknown Store Price Level

Goal: Understand if a store is generally cheap or expensive

Google Maps: "{store_name} {city}" → read reviews for price sentiment
小红书: "{store_name} 价格 贵不贵"
Reddit: "site:reddit.com {city} {store_name} prices cheap expensive"

Scenario 2: Specific Product Price

Goal: Find what a specific item costs at a store

小红书: "{store_name} {product_name_zh} 价格" (e.g., "168超市 五花肉 价格")
Google: "{store_name} {product_name} price reddit OR yelp"

Scenario 3: Store Comparison

Goal: Compare two stores

小红书: "{store_A} vs {store_B}" or "{store_A} {store_B} 比较"
Reddit: "{city} {store_A} vs {store_B}"
Google: "site:reddit.com OR site:yelp.com {store_A} cheaper than {store_B}"

Scenario 4: Best Store for a Category

Goal: Find which store is best for meat/produce/snacks

小红书: "{city} 买肉 哪家便宜" or "{city} 亚洲零食 推荐"
Reddit: "{city} best asian grocery for {category}"

Scenario 5: Current Deals/Sales

Goal: Find current promotions

小红书: "{store_name} 打折 {current_month}" (e.g., "Weee 打折 四月")
Store's Instagram/Facebook (if they have one)
Local WeChat groups (ask user if they're in any)

Data Freshness Guidelines

Rule of thumb:

• Prices can change 5-15% over 6 months
• Sale items can change weekly
• Always note the date when reporting social media prices

Handling No Results

If social media search yields no useful price data:

我在小红书和Google Maps上没找到{store_name}的具体价格信息。

可能的原因：
1. 这家店比较小众，网上讨论少
2. 搜索关键词可能需要调整

建议：
1. 你下次去{store_name}的时候可以拍几张价签，我帮你记录
2. 或者你知道大概价格的话可以告诉我，我保存到你的memory
3. 我可以先用H Mart/Weee的价格作为参考（通常本地小店会便宜10-20%）

需要我用哪种方式帮你？

Tips for Effective Use

1. First time setup: Ask user for zip code and store preferences
2. Build baseline: Encourage user to share prices they see in stores
3. Track over time: Prices fluctuate; historical data helps identify deals
4. Consider total trip: Factor in gas cost for distant stores
5. Batch recommendations: "If you're going to 168 anyway, also grab X, Y, Z"
6. Social media first: For local Asian markets, always check 小红书 first - it's the best source for Chinese grocery price info
7. Save discoveries: When social media yields good price data, offer to save to user's memory

Example Conversations

Basic Price Check

User: 老干妈在哪买便宜
Agent: [Checks Weee, user's 168 baseline, Yami]

老干妈 (280g装) 比价：
- 168 Asian Mart: $3.29 (你上次报告的价格)
- Weee: $3.99 
- Yami: $4.49

建议去168买，便宜$0.70。如果你这周有其他东西要在Weee买，可以顺便加购，差价不大。

Complex Comparison

User: 我想买火锅食材，比较一下168和Weee

Agent: [Comprehensive comparison]

火锅食材比较 (168 vs Weee):

| 食材 | 168 | Weee | 推荐 |
|------|-----|------|------|
| 肥牛卷 | $8.99/lb | $9.49/lb | 168 |
| 羊肉卷 | $12.99/lb | $11.99/lb | Weee |
| 豆腐 | $1.99 | $2.49 | 168 |
| 金针菇 | $1.79 | $1.99 | 168 |
| 海底捞底料 | $5.99 | $5.49 | Weee |

总计 (估算一顿4人份):
- 168: ~$35 + 开车25分钟
- Weee: ~$38 + 免费配送

建议：如果你有时间去168，能省$3+。如果懒得出门，Weee贵不了多少，而且不用自己搬。

Cleanup

Always close browser sessions after scraping:

browser-use close