What the documents look like
Price and display auditing in chain stores takes place during day-to-day operations, and its central concern is sustained execution of standards. Materials come from the supervision system, POS price lists, store cameras and head office promotion schedules, and the recurring documents are store visit photographs, price tag lists, display standards and rectification notices, refreshed weekly or monthly. In cross-border and multilingual settings, one price tag list may exist in both Chinese and a foreign language, and the tag text captured in a store visit photograph has to be matched line by line against the standard list. The material forms include text lists, on-site photographs and rectification documents, and a retrieval request may be posed in Chinese while the target knowledge point is foreign language text, which is where the cross-language understanding of semantic retrieval becomes relevant. Whether an image can be retrieved depends on whether a corresponding index was built at ingestion time; uploading an image on the query side is only one of the conditions.
Constraints at the vector indexes step
Knowledge bases linked for retrieval must use the same vector model. Bases built on different vector models cannot be linked together; the model identifier is checked first and non-matching bases are excluded. One source data block can be associated with several indexes, and one set of data can correspond to several vectors, so the indexes and the source content are verified separately. Index content is best kept complete and focused, and retrieval coverage against index length is weighed against the sample at hand. Image retrieval further depends on whether image descriptions or image vectors were produced at ingestion. Semantic recall and relevance scores carry no responsibility for permission or entity isolation; the minimum relevance value is a filtering threshold, and retrieval itself does not constitute authorization. RRF is the fusion process for mixed results, not a switch that can be turned on. Image retrieval requires the corresponding image input and model capability, and reranking requires the model and its configured activation; the optional paths shown in the overall document flow diagram do not mean every path runs on every request.
Configuration and implementation checks
Hypothetical drill sample: the operations department of a chain brand prepares a foreign-language price tag verification file. The source material reads: "Store A, during the March 2025 promotion period, displayed a shelf tag unit price of 12.5 yuan, the register receipt recorded a unit price of 12.5 yuan, and the display standard required this item to sit on the second-tier end shelf." The draft sentence under review is: "Store A executed price consistently during this promotion period, the display position met the standard, and the rectification notice can be closed." The material does not state the current status of the rectification notice or the authority to close it.
| Configuration item | Hypothetical starting point for this drill | Reason for the choice and comparison method |
|---|---|---|
| Linked knowledge bases | Link only knowledge bases on the same vector model | Check the model identifier of each base and exclude those that do not match before linking |
| Index content | Build separate indexes for the price tag, the receipt and the display segment | Compare recall coverage against merging the three into a single index |
| Image index | On ingestion of store visit photographs, confirm whether image descriptions or image vectors were generated | Compare against images with no index to see whether an image query can reach them |
| Search mode | Hybrid retrieval, with full-text retrieval for identifier-type queries | Compare hit differences on a price tag identifier against semantic retrieval alone |
| Minimum relevance | Set to 0.4 as the starting point of this drill | Compare the relevance of returned reference content against 0.3 and 0.5 |
| Reference limit | Set to 2000 tokens as the starting point of this drill | Compare reference completeness and noise against 1000 and 3000 |
These values are hypothetical starting points for this drill and are not official defaults. Retrieval and forms only collect and return content; business verification, approval, database writes and external synchronization require separately connected system implementation.
Three common errors
- Linking knowledge bases on different vector models. The input is two bases whose model identifiers differ; the comparison is whether the identifiers match; the observable result is that the non-matching base is excluded and no cross-model retrieval is run to compare missed recalls.
- Treating the minimum relevance value as a permission or entity filter. The input is a retrieval request spanning stores; the comparison is whether returned content is constrained by store scope; the observable result is that relevance affects ordering and filtering only, does not exclude unauthorized records, and retrieval does not constitute authorization.
- Treating a rectification notice awaiting review as closed. The input is the draft sentence and the source material; the comparison is the status field of the rectification notice; the observable result is that the material states neither the status nor the closing authority, so the conclusion is unsupported and remains to be verified.
How to verify the setup
- Take the hypothetical source material and the draft sentence as input. Compare "executed price consistently" against the price tag and receipt unit prices in the source for the same subject, the same period and the same field; the observable result is that the unit price agreement is supported. Compare "the display position met the standard" against the second-tier end shelf in the source; that is supported. "The rectification notice can be closed" is unsupported because the material states neither status nor authority.
- Take a price tag identifier query as input. Compare the returned content under semantic retrieval against full-text retrieval; the observable result is that identifier-type queries hit more reliably under full-text retrieval. A first-position hit or complete recall is not guaranteed.
- Take an explicit paper input of an empty result array []. Compare how the downstream design handles a reference length of zero; the observable result is that the output path still completes. No claim is made that a fictional rule retrieval necessarily returns [].
After any model or index adjustment, compare the referenced source text using the same set of questions, and do not promise an outcome on the basis of a vector score alone.