What the documents look like
Price and display auditing in a chain-store setting takes place during ordinary trading periods. The focus is on whether published standards continue to be followed after opening and training have finished. The main data sources are the supervision system, POS price lists, in-store cameras and the head office promotion calendar. Day-to-day documents are store visit photographs, price tag lists, display standards and rectification notices. Material is refreshed weekly or monthly, so price tag lists and display standards from more than one promotion window can sit in the same retrieval library at the same time.
The contract and transaction material comparison task requires store visit photographs, price tag lists and rectification notices to be checked against contracts or transaction records on the same subject, the same period and the same field. The retrieval step only brings candidate material forward. It does not decide authorization status or any business conclusion.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Libraries built on different vector models cannot be associated together. The vector model identifier is checked first, non-matching libraries are excluded, and the actual observation is recorded. No conclusion about missed recall or redundancy follows from that check.
Retrieval modes are compared against the test question: semantic search, full-text search and hybrid search. Semantic search depends on vector distance. Full-text search depends on keyword hits. Hybrid search merges the two paths through RRF. RRF is a fusion process, not a switch that is turned on. Similarity scores and the minimum relevance value are relevance filters. They do not create permission or entity isolation, and retrieval itself performs no authorization decision.
The reference limit is measured in tokens, not in item count. Minimum relevance takes effect only under semantic search or when result re-ranking is used. Question optimization performs coreference resolution and question expansion, and it adds an extra model call. Comparing the original question with the optimized question allows the subject to be checked for distortion. Reference content is output as an array whose length can be zero, and the downstream path still runs when nothing is found.
Image retrieval requires image input and the corresponding model capability. Re-ranking requires a model and must be enabled through its setting. The optional paths shown in the overall document flow diagram do not mean that every request executes all of them.
Hypothetical paper exercise sample: a chain brand operations department, during the March 2025 promotion window audit, holds a price tag list whose text reads "Store A applies a promotional price of 9.9 yuan from 1 March 2025 to 31 March 2025, corresponding contract number H-2025-031, signing status signed by both parties, effective date 1 March 2025". A store visit photograph record states "Store A displayed a price tag of 12.9 yuan on 15 March 2025". The draft sentence to be assessed is "During March 2025, Store A's price tag matched the contract promotional price and the rectification was closed". That draft sentence touches a price field and a rectification status, and it must be checked on the same subject, the same period and the same field.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason for the choice and comparison method |
|---|---|---|
| Associated knowledge bases | Price tag list library and display standard library, associated after the vector model identifiers are checked as identical | Both libraries must use the same vector model; compare identifiers one by one and exclude non-matching ones |
| Search mode | Test the same question with semantic search and full-text search separately, then compare hybrid search results | Price tag numbers are exact queries and promotion descriptions are natural language; choose the mode by observed behaviour |
| Reference limit | Set to 2000 tokens as the starting point for this exercise | Test with price tag lists and rectification notices of different lengths, compare whether content is truncated against the full paragraph length, then adjust |
| Minimum relevance | Label relevant and irrelevant samples first, then set 0.3 as the starting point for this exercise | This value takes effect only under semantic search or result re-ranking; compare candidates before and after filtering |
| Question optimization | Off first, tested with a complete question; then on, comparing the subject in the optimized question | Used for coreference resolution and question expansion; check whether the store number and promotion window were altered |
| Result re-ranking | Compare ordering only after a re-ranking model is confirmed as configured and enabled through its setting | Re-ranking requires a model; compare candidate order before and after enabling, without assuming the first hit |
Three common errors
- Forcing knowledge bases built on different vector models into one association. The input is two libraries with different vector model identifiers. Compare whether the identifiers match. The observable result is that the association is excluded or recorded as non-matching. No runtime outcome such as missed recall or redundancy is inferred from this.
- Treating minimum relevance as a permission or entity filter. The input is the same question run against material for different stores. Compare the score with the candidate range. The observable result is that material for an unrelated store can still be recalled, and permission decisions require a separate downstream implementation.
- Treating a rectification notice under review as closed. The input is the rectification notice text. Compare whether it states a completion status and a completion date. The observable result is that wording indicating review in progress is judged unsupported, and it is not called a conflict or a denial.
How to verify the setup
- Input the price tag list text and the store visit photograph record from the hypothetical sample. Compare the price field value for the same store and the same period. The observable result is that the two prices differ, so the draft sentence "the price tag matched the contract promotional price" is unsupported. Rectification closure requires a separate check of whether the rectification notice text states a completion status.
- Input contract number H-2025-031 from the hypothetical sample and query it with full-text search and semantic search separately. Compare whether the recalled candidates include the material containing that number. The observable result is that full-text search hits the number, while the semantic search result is recorded as actually observed, with no guarantee of a first hit or full recall.
- Input an explicitly constructed empty-result check case, in which the retrieval reference content array is []. Compare whether the downstream design runs the path with an empty array. The observable result is that the path does not stop because of the empty result. That [] is a paper input, and no claim is made that a fictional rule retrieval necessarily returns [].
This page addresses only chain-store price and display auditing, contract and transaction material comparison, and knowledge base retrieval and recall. Which materials, operations and requests sit in which authorization state during an operations handover is not decided by retrieval or by a form.