What the documents look like
Seller ratings and experience score maintenance centers on consumer feedback that arrives after fulfillment. The main sources are the Douyin Shop experience score, Tmall service indicators, JD after-sales reviews, and customer service system records. Day-to-day documents include negative review handling records, experience score details, service diagnosis reports, and rectification ledgers. These materials carry attributes such as platform, shop, order, time range, and handling status. In cross-border settings, review originals and translations may appear in more than one language. For retrieval work, model connection and configuration must handle semantic differences across languages and differences in field naming across platforms. Whether a retrieval result is usable depends on whether the document identifier and the vector model identifier are consistent.
Constraints at the model connections step
Cross-border materials and multilingual retrieval require at least one language model and one indexing model before the system can be used normally. AI Proxy is a required dependency. AIPROXY_API_ENDPOINT takes the service root address and does not append /v1; AIPROXY_API_TOKEN takes a non-empty administrator token that matches the server configuration. Knowledge bases can be associated together only when they use the same vector model; knowledge bases on different vector models cannot be associated together, so identifiers must be checked first and non-matching items excluded. Retrieval is a text similarity process. It does not perform authorization decisions, and a similarity score does not indicate that a record belongs to a particular shop or that handling permission has been granted.
Hypothetical exercise sample: a cross-border shop operations team is organizing one negative review handling record. The original text contains three sentences: "Order A-1024, shop S-07, a negative review was received on March 12, 2026; the review language is English and the content concerns logistics delay. Customer service contacted the buyer on March 13 to request supporting evidence; the buyer has not yet replied. The rectification ledger records the status as 'pending follow-up'." One draft sentence is also awaiting judgment: "This negative review has completed rectification and been closed." The materials needed for the check are the record above and the rectification ledger, within the retrieval scope readable by that team.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason for the choice and comparison method |
|---|---|---|
AIPROXY_API_ENDPOINT | Enter the AI Proxy root address without /v1 | Compare against the documented root address format and confirm no extra path is present |
AIPROXY_API_TOKEN | Enter a non-empty token matching the server | Compare against the AI Proxy server configuration and confirm it is consistent and non-empty |
| Language model | Enable one language model that supports multilingual text | Compare against the documented requirement of at least one language model |
| Indexing model | Enable one indexing model | Compare against the documented requirement of at least one indexing model |
| Vector library identifier | Record the vector model identifier used by the retrieval library | Compare against the identifier of the library holding the documents to be retrieved and exclude mismatches |
| Model probe | Enable automatic probing as needed and set the interval | Compare against the documented integer range of 5 to 60 minutes |
Three common errors
- Entering
AIPROXY_API_ENDPOINTas a full model request address with/v1appended. The input is an address carrying/v1, the comparison object is the documented root address format, and the observable result is a startup validation or call path anomaly. - Treating a retrieval similarity score as a permission or entity filter. The input is a retrieval request for one shop, the comparison object is the shop identifier on the returned records, and the observable result is records returned from a different shop.
- Using a draft sentence to conclude directly that a negative review has completed rectification and been closed. The input is the draft sentence together with the rectification ledger original, the comparison object is the "pending follow-up" status in the ledger, and the observable result is that the draft sentence's stated status does not match the original.
How to verify the setup
- Enter the three original sentences from the hypothetical sample and the draft sentence "This negative review has completed rectification and been closed." Compare the draft sentence against the original records stating "the buyer has not yet replied" and "pending follow-up." If the draft sentence claims completed rectification and closure while the original contains only contact and pending follow-up records, the claim is unsupported and the evidence gap must be kept.
- Enter a retrieval request. Compare the shop identifier and vector model identifier on the returned records. If returned records belong to another shop or come from a library on a different vector model, the result does not meet the condition; exclude it and record the actual observation.
- Enter an empty-result scenario using the explicit paper input
[]to check the downstream presentation design. Observe whether the downstream handles it as an empty result. Do not claim that a fabricated rule retrieval necessarily returns an empty array.