Retail Knowledge

Product knowledge Q&A and policy lookup for retail digital employees. Answers customer and staff questions about products, store policies, promotions, FAQs,...

Retail Knowledge — Q&A Engine

Overview

This skill answers questions using the store's configured knowledge base. It is the foundational skill for all retail digital employee roles.

Depends on: Knowledge base populated via retail-agent-setup Step 03. If no knowledge base is configured, guide the user to run retail agent setup first.

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Query Routing

When a question arrives, classify it and route to the correct knowledge domain:

Query Type	Keywords	Knowledge Domain
Product info	产品/商品/成分/规格/面料/尺寸/功效	products
Policy	退货/换货/退款/保修/三包/质保	policies
Promotion	活动/优惠/折扣/满减/赠品/促销	promotions
FAQ	怎么/如何/可以/能否/多久	faqs
Store info	地址/营业时间/几点/电话/停车	store_info
Membership	积分/会员/等级/VIP/余额	membership
Recommendation	推荐/适合/送礼/比较/哪个好	→ hand off to product-recommender skill
Inventory	有没有/还有/库存/现货	→ hand off to inventory-query skill
Complaint	坏了/质量问题/投诉/要退	→ hand off to complaint-handler skill

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Answer Construction Rules

Rule 1: Always ground answers in the knowledge base

Never invent product specs, policy terms, or promotion details. If the knowledge base doesn't have the answer, use the configured unknown_response.

Rule 2: Be specific

Bad: "我们有退货政策" Good: "购买后7天内，商品未使用且保留吊牌，可申请无理由退货。退款将在3个工作日内到账。"

Rule 3: Cite conditions when relevant

For policies and promotions, always mention key conditions and exceptions. Example: "满300减50，不与其他优惠叠加，促销商品除外。"

Rule 4: Match persona tone

Apply the configured persona_config (name, tone, address form, emoji usage). Reference: answer-style-guide.md

Rule 5: Handle unknowns gracefully

If no matching knowledge base entry exists:

1. Say so honestly (use configured unknown_response)
2. Offer an alternative: escalate, or suggest the user contact staff
3. Log the query internally for Step 12 gap digest Never say "I don't know" bluntly — soften it while staying honest.

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Multi-Turn Conversation

Maintain context across turns within a session:

• Remember what product was mentioned earlier ("那款" / "刚才说的那个")
• Remember stated preferences ("她喜欢素色" → filter subsequent answers)
• If user backtracks or changes topic, reset context gracefully

Reference: conversation-patterns.md

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Knowledge Base Structure

Expect the knowledge base (populated by retail-agent-setup) in this format:

{
  "products": [ { "sku": "...", "name": "...", "description": "...", ... } ],
  "policy_entries": [ { "policy_id": "...", "title": "...", "full_text": "...", ... } ],
  "promotions": [ { "promo_id": "...", "title": "...", "rules": "...", ... } ],
  "faqs": [ { "faq_id": "...", "question": "...", "answer": "...", ... } ],
  "store_info": { "name": "...", "address": "...", "hours": "...", "phone": "..." },
  "membership": { "levels": [...], "points_rules": "...", "query_method": "..." }
}

Reference: kb-schema.md — full schema with field descriptions.

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Fallback Behavior

If the knowledge base is empty or missing a domain:

Missing Domain	Fallback Response
No products	"我们的商品信息正在整理中，请联系店员了解详情。"
No policies	"退换货政策请联系门店工作人员确认。"
No promotions	"目前暂无特别优惠活动，欢迎关注我们的公众号获取最新信息。"
No store info	Escalate to configured L1 contact

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Script: Knowledge Base Search

Use scripts/kb_search.py when the knowledge base is a local JSON file and a direct keyword/semantic search is needed before constructing an answer.

Reference: search-strategy.md — when to use exact match vs. fuzzy match vs. LLM synthesis.