What the documents look like
Retail checkout and loyalty materials center on promotional price tags, membership point rules, daily checkout reports, and exception refund slips. Sources include the POS system, the member CRM, payment platforms, and the price tag system. Checkout supervisors, CRM operations staff, and finance auditors work from the same set of records. Errors surface as price complaints, cash shortfalls or overages, and disputes over member benefits. Compared with convenience store fresh-food ordering, this category focuses on price and member entitlements and does not handle short-shelf-life inventory.
Product information and selection questions in this category include which stores a promotional price applies to, how membership points accrue, and what state a refund slip is in. These records update daily and per shift, and the same product can carry different values across stores and member tiers. The vector model and index step therefore has to separate semantically close content whose subject differs, such as the same product described under two promotion periods.
Constraints at the vector indexes step
Knowledge bases associated for retrieval must use the same vector model. Confirm the actual model and its input types before deciding whether two knowledge bases can be associated. One source data block can map to several indexes. Check the index and the source text separately; an index hit does not by itself show that the source text supports a conclusion. Index content benefits from being complete and focused, and retrieval coverage against index length needs to be weighed against the sample at hand.
Image retrieval also depends on whether an image description or image vector was generated at ingestion time. Uploading an image on the query side does not by itself create a searchable condition. Semantic recall and the minimum relevance value affect candidate ordering and filtering only; they do not carry permission or entity separation. Retrieval is not authorization, and a form collecting values does not constitute approval, a refund, or a database write.
Hypothetical paper exercise: the source material reads, "From March 1 to March 31, 2025, member-day points at Store A accrue at 2x; Store B does not participate in the same period." A second source reads, "Refund slip R-0312 has status Pending review; the applicant has not signed the termination confirmation." The draft sentence under review is, "Member-day points at both Store A and Store B accrue at 2x in March." The comparison objects are the store scope and the period; the states are stated and not stated; the effective scope is March 2025. This sample is a paper exercise and does not represent any actual store or slip.
Configuration and implementation checks
| Configuration or check item | Purpose on this page | Value and source |
|---|---|---|
| Vector model identifier | Determines which knowledge bases can be associated together | Hypothetical starting point for this exercise is one shared model identifier, because the sample questions need retrieval across promotion and points material; compare against the other model identifier and exclude any that does not match |
| Index-to-source mapping | Checks whether an index hit lands on the correct source text | Based on the indexes field of dataset.datas, matched one by one to the source data |
| Search mode | Chooses semantic search, full-text search, or hybrid search | The sample contains store codes, so semantic and full-text recall are both needed |
| Reference cap | Controls the size of referenced content per search | Hypothetical starting point for this exercise is the tokens unit given in the documentation; no fixed number is set |
| Minimum relevance | Filters low-relevance candidates | Takes effect only under semantic search or result reranking |
| Image index check | Confirms whether an image description or image vector was generated at ingestion | Based on ingestion records; an upload on the query side does not replace an ingestion index |
Three common errors
- Treating an index hit as source-text support. Input is an index reading "Store A, March, 2x points"; the comparison object is the store scope in the source text. If the source text names only Store A, the Store B conclusion is unsupported.
- Treating signature and termination status as one field. Input the source for refund slip R-0312 and check its pending-review state and the applicant's unsigned termination confirmation separately. The source gives no termination condition or completion record, so the termination state remains unverified.
- Associating knowledge bases that use different vector models. Input is the model identifiers of two knowledge bases; the comparison object is whether they match. Exclude any that does not match, and do not run cross-model retrieval to compare missed recalls.
How to verify the setup
- Input the hypothetical source text and the draft sentence "Member-day points at both Store A and Store B accrue at 2x in March." Compare the store scope field. The source text states only Store A; the Store B portion is unsupported, and the evidence gap must be recorded.
- Input the source for refund slip R-0312. List review status, signature on the termination confirmation, and termination completion evidence separately. The source records pending review and an unsigned confirmation. It provides no termination condition or completion record, so completion remains unverified; neither completion nor incompletion is established.
- Input the vector model identifiers of two knowledge bases. Compare whether they match. Only a match permits shared association; when they do not match, exclude that knowledge base and record the actual observation. For the empty-result path, use the paper input [] to check downstream design; do not claim that retrieval necessarily returns an empty array.