What the documents look like
Retail checkout and loyalty materials centre on price, membership benefits and settlement. The main sources are the POS system, the membership CRM, the payment platform and the price-tag system. Everyday documents include promotional price tags, membership points rules, daily checkout reports and exception refund slips. In a cross-border setting, one promotion can appear as Chinese, English and other language versions of the tag and the rule text, so retrieval needs to match semantically similar content across languages.
This category differs from convenience-store fresh-food ordering: checkout promotion work concerns price and membership entitlement, and does not handle short-shelf-life inventory. The brief gives an update frequency of daily or per shift, which means the searchable library content can change with the shift, and index maintenance needs to track that document update rhythm.
The four document types do not share one shape. A promotional price tag is short and bounded by an activity period, a member scope and a discount rule. A points rule describes calculation over a longer life. A daily checkout report is a period record. An exception refund slip is an event record with an amount and a reason. Index design has to respect these differences rather than treat all four as one text type.
Constraints at the vector indexes step
Vector search measures similarity through vector distance. Cross-language understanding is a documented strength of semantic retrieval, and precision depends on vector model quality, data quality and query quality. Knowledge bases linked for retrieval must use the same vector model. The first check is the actual model and input type; libraries carrying different vector models cannot be linked together. Record the identifiers, exclude the ones that do not match, and note what was actually observed.
One source data block can be associated with several indexes, and the indexes are checked separately from the source content. Build indexes from complete and focused information; retrieval coverage and index length are weighed together against the sample. Image retrieval also depends on whether an image description or image vector index was generated at ingestion time, and cannot be judged from the query side alone. Retrieval is a recall process and does not carry authorization or entity-separation duties. Semantic recall and relevance scores do not enforce permissions or isolate business entities.
Hypothetical paper exercise sample: a store price-tag system exports a promotion description whose source text reads, "In this store, from 1 March 2025 to 31 March 2025, members who buy Brand A cooking oil for 100 yuan or more receive 20 yuan off; points are calculated on the amount actually paid." A separate handover request draft reads, "Please confirm the above promotion is valid for all members for the whole of March and synchronise it to price tags in every language." The check compares the applicable period, the member scope and the points calculation basis in the draft against the source text. Synchronising to price tags in every language is a business action that requires a separate system implementation; retrieval or a form does not constitute authorization to execute it.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | How it is checked and why |
|---|---|---|
| Linked knowledge bases | Select a promotional price-tag library and a membership points rules library that share one vector model | Check the actual model identifier and input type first; exclude libraries with a different vector model |
| Search mode | Hybrid retrieval | Semantic recall and full-text recall are merged through RRF, which suits a mix of identifiers and natural-language queries |
| Minimum relevance | Set 0.5 as the hypothetical starting point for this exercise | This value takes effect only in semantic retrieval or when result reranking is used; adjust after comparing cited source text under the same question set |
| Citation limit | Set 2000 tokens as the hypothetical starting point for this exercise | Citation length is controlled in tokens, which avoids instability caused by large chunk-length differences |
| Question optimisation | Enable for multi-turn follow-up scenarios | Coreference resolution and question expansion add one model call; for short questions or speed-sensitive cases, decide from observed results |
| Index content | Build separate indexes for activity period, member scope and points basis on the promotional price tag | One data block can map to several vectors; indexes and source content are checked separately |
The table lists documented settings and implementation checks tied to this page. It does not describe database internals as mandatory items, and the two numeric values are labelled as hypothetical starting points for this exercise, not official defaults.
Three common errors
- Treating a retrieval result as an authorization basis. Input is the handover request draft above; the comparison object is the condition "members who buy Brand A cooking oil for 100 yuan or more receive 20 yuan off" in the source text. If the draft widens the scope to all members for the whole month while the source text does not state that scope, the observed result is that the conclusion is unsupported, and the evidence gap is recorded.
- Linking knowledge bases that carry different vector models. Input is two library identifiers labelled with different vector models; the comparison object is whether the model identifiers agree. If they do not agree, exclude that library. Do not run a cross-model retrieval to compare missed recalls.
- Treating image retrieval as a default path. Input is a plain-text query with no vision model configured; the comparison object is whether an image description or image vector index was generated at ingestion. If it was not generated, image content cannot be hit. The observed result is that the text side retrieves normally while the image side is essentially unavailable.
How to verify the setup
- Use the hypothetical sample source text and the draft sentence above. Input is the draft sentence; the comparison object is the applicable period, member scope and points basis in the source text. If the draft widens a scope the source text does not state, the judgement is unsupported, and the evidence gap is retained.
- Use two library identifiers labelled with different vector models. Input is the linking action; the comparison object is the model identifier field. If the identifiers differ, exclude that library and record the actual observation. Do not infer a runtime result.
- Use one plain-text query. Input is semantic retrieval; the comparison object is the returned cited content against the source text. If the cited content is semantically close to the question and comes from the target library, the condition is met. If the citation length is 0, the downstream design can be checked against an explicit paper input of []. Do not claim that a fictional rule search necessarily returns [].
This page addresses only retail checkout and loyalty / cross-border materials and multilingual retrieval / vector models and indexes. During operational handover, the authorization status of materials, actions and requests is a separate question; retrieval or a form is not authorization to execute a business action.