What the documents look like
Seller ratings management for platform stores tracks consumer feedback that arrives after fulfillment. The main data sources named for this category are Douyin Shop experience score, Tmall service indicators, JD after-sales reviews, and the customer service system. The documents handled day to day are negative review handling records, experience score details, service diagnostic reports, and rectification ledgers. The public brief marks confidence as roughly accurate, so platform field names and thresholds should be checked against the actual back office.
For operating reports and exception reviews, citation tracing determines whether each conclusion in a report can be returned to its source text. Three features of this material shape that work. First, the records mix quantitative indicators with process states: a reply rate sits next to a ticket status. Second, statuses such as pending review are intermediate, so a report sentence that describes completion needs a completion field behind it. Third, the same subject and the same period must be matched before two statements can be compared as consistent or conflicting. Update frequency does not change the tracing step itself, so it is not treated here as a configuration concern.
Constraints at the citation tracing step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Its citation output is an array, and the length may be zero, which means the downstream path still runs when nothing is retrieved. Results are passed to the answering step, where each answer sentence is checked against the cited source text.
The citation limit is measured in tokens, not in a count of chunks. The minimum relevance value is a 0-1 number that filters low-relevance results, and it takes effect only in semantic retrieval or when result reranking is used. That threshold is a filter condition; it does not carry permission or entity isolation. RRF is the fusion process that merges results from hybrid retrieval, not a switch. Full-text retrieval, image retrieval, and reranking are optional paths: each runs only when the corresponding input and model capability are present.
A hypothetical paper exercise sample is used below. It is not a real customer record and no test was performed.
Material source text: "From March 1 to March 31, 2025, the experience score details for Store A record an after-sales review reply rate of 92%. The negative review handling record shows that on March 12 a ticket was submitted with supplementary evidence, and its status is pending review."
Draft sentence to be judged: "Store A met the standard for after-sales review reply rate in March, and the negative review rectification has been completed."
The draft sentence turns a pending-review status into completed rectification. The word "met the standard" has no corresponding threshold field in the source text.
Configuration and implementation checks
| Configuration item | Hypothetical starting value for this exercise | How to check |
|---|---|---|
| Associated knowledge bases | Only the ratings maintenance library using the same vector model | Compare library identifiers and vector model identifiers; exclude libraries on a different model |
| Search mode | Hybrid retrieval | Compare against semantic retrieval and full-text retrieval results separately |
| Citation limit | Set to a starting value for this exercise | Compare the number of cited items across different token values |
| Minimum relevance | Set to a starting value for this exercise | Observe the filtering effect only under semantic retrieval or reranking |
| Question optimization | Off at the start | Compare the retrieval terms and results with the setting on |
| Result reranking | Not enabled at the start | Confirm the rerank model is available before comparing ordering |
The numeric starting values are chosen for this hypothetical exercise. They are not official defaults, and observations should be recorded after comparing adjacent values.
Three common errors
- Treating minimum relevance as a permission filter, so that excluded low-score records are read as records the operator is not allowed to see. The threshold only filters low-relevance results; permission requires a separate system implementation.
- Writing a pending-review ticket as completed rectification. Pending review and completed rectification are different facts, and the draft sentence must be checked against the status field in the source text.
- Associating knowledge bases that use different vector models. The same vector model is a joint association condition, so identifiers are checked first and non-matching libraries are excluded. A cross-model retrieval comparison is not run.
How to verify the setup
- Input the material source text and the draft sentence above. Compare "the negative review rectification has been completed" in the draft against "its status is pending review" in the source. When the source does not state that rectification is complete, the draft sentence is judged unsupported.
- Input the phrase "met the standard" from the draft sentence. Compare it against the source text to see whether a reply rate threshold field exists. When the source has no such field, the phrase is judged to have no basis.
- Use a paper input of an empty array to check the downstream handling design, confirming that an empty citation does not interrupt the flow. Whether retrieval actually returns an empty array must be recorded from observation and is not presumed.
These three checks state expected conditions. No test, deployment, approval, or business action is claimed to have occurred.