What the documents look like
Retail checkout and loyalty materials centre on price and member entitlement. Promotional price tags, member points rules, checkout daily reports and abnormal refund slips form the main document surface. This category differs from convenience-store fresh-food ordering: checkout promotion work concerns prices and member benefits and does not handle short-shelf-life inventory. That difference shapes the retrieval questions, which tend to point at a specific price, a points ratio, or a refund status. Data sources include the POS system, member CRM, payment platforms and the price-tag system, with updates at daily and per-shift frequency.
For retrieval this matters because the daily report and the refund slip for the same store and the same shift may sit in different documents. A question about one shift can therefore match material whose subject and period must be aligned before any comparison is meaningful. The retrieval step returns candidate content; it does not decide whether that content applies to a given store, shift or member.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases using different vector models cannot be associated together; the model identifier is checked and non-matching bases are excluded. Search modes are compared against the test question rather than assumed: semantic retrieval, full-text retrieval and hybrid retrieval each behave differently, and no single mode is presumed suitable for all material. The citation limit is expressed in tokens, so material of different lengths is used to test whether content stays complete. The minimum relevance value is a filter threshold and takes effect only under semantic retrieval or when result reranking is used. It is not a final ordering rule, and after question optimisation, hybrid retrieval, image retrieval or reranking, results may be merged from several recall paths rather than ordered by a single vector similarity. Question optimisation performs coreference removal and question expansion; it is compared against the original question to check whether the subject was altered. Retrieval and recall do not carry permission or entity isolation, and the same-vector-model requirement for associated knowledge bases does not extend to any external system.
A hypothetical paper exercise sample is set out here for later checks. The source material reads: "The 12 March 2025 morning-shift checkout daily report for Store A records member points accrued at 1 point per 1 yuan, and no abnormal refund slip on that day." The draft sentence to be assessed reads: "During that shift the store applied member points at 2 points per 1 yuan and there was one abnormal refund." The source carries the subject Store A, the object member points, the status of no abnormal refund, and the effective scope of the shift. The draft sentence differs from the source in two places: the points ratio and the refund status. The source does not state a refund amount field, so that field remains unsupported or pending verification rather than negated or contradicted. A contradiction requires the same subject, the same period and the same field with explicitly mutually exclusive values.
Configuration and implementation checks
The table below lists, for this sample, documented settings or implementation checks relevant to this feature and the named task. The values are hypothetical starting points for this exercise; each carries a reason and a comparison method, and none is an official default.
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only the promotion rules base and the daily report base that use the same vector model | Check the vector model identifier for consistency, then associate; exclude bases with a different model |
| Search mode | Run semantic retrieval and full-text retrieval separately | Use the same test question and compare the recalled content under each mode |
| Citation limit | Set a token amount that covers the complete daily report fields | Test with daily reports of different lengths and observe whether content is truncated |
| Minimum relevance | Label relevant and irrelevant samples first, then adjust the threshold | Takes effect only under semantic retrieval or result reranking; compare before and after filtering |
| Question optimisation | Enable for follow-up questions containing coreference and check the subject | Compare the original question with the optimised question and confirm the subject was not altered |
| Result reranking | Enable when candidates are numerous and check the score | The reranked score is 0-1; compare the ordering change by score |
Three common errors
- Treating a retrieval hit as authorised execution. In the sample, retrieving the points rule does not mean member points may be changed or a refund executed on that basis. Retrieval returns content; business verification, approval, database writes and external synchronisation require separately connected implementation.
- Associating knowledge bases that use different vector models. If the daily report base and the rules base carry different vector model identifiers in the sample, the identifiers are checked and non-matching bases excluded. Cross-model retrieval is not performed in order to compare missed recalls.
- Reading an unstated field as a negation or a conflict. The sample source does not state a refund amount field; that field is unsupported or pending verification and is not a negation or a contradiction. A determination requires the same subject, the same period and the same field, with explicitly mutually exclusive values.
How to verify the setup
- Input the sample source text and the draft sentence, then compare the points ratio and the refund status fields. The source gives 1 point per 1 yuan and no abnormal refund; the draft gives 2 points per 1 yuan and one abnormal refund. The two differences identify the draft error. The expectation is a field-by-field comparison that marks the differences; no actual test is claimed.
- Input the sample source text and compare whether the citation content returned by retrieval contains the subject Store A, the shift period and the no-abnormal-refund status. If any element is missing from the citation content, record the gap. An empty result can be checked against a downstream design using the explicit paper input [], and no claim is made that a fictional rules query necessarily returns [].
- Input a follow-up question containing coreference and compare whether the subject is the same before and after question optimisation. If the optimised subject is changed to another store or another period, record it as question optimisation altering the subject. This check is a manual comparison and does not depend on an automatic judgement switch.
All three checks address the primary task named on this page: retrieval and recall within operating reports and exception review. Retrieval checks state observations and failure branches only; they do not guarantee a first hit, full recall, or that an unrelated query returns nothing. A pending review status does not demonstrate a lack of operating permission, and a quotation validity period does not indicate a supply period. Missing evidence remains a gap.