What the documents look like
Platform multi-store matrix management handles several document families at the same time: store authorization lists, price control tables, cross-territory complaint forms, and monthly sales reports. These materials originate from platform merchant back ends, ERP channel pricing, brand anti-counterfeiting systems, and distributor reports. They share a focus on cross-store authorization and price order, and the subjects, stores, periods, and price fields inside them are often spread across separate files. The cross-border materials and multilingual retrieval task therefore meets the same source written in more than one language, the same store carrying more than one authorization stage, and the same price field appearing in different reports.
Document parsing and chunking must preserve a document identity and version that can be traced back to the source. Segmentation completeness requires that a condition, its explanation, and the content it constrains are not split apart. Original data and retrieval indexes are distinct, and one set of original data can correspond to multiple indexes. Retrieval results reflect similarity and recall paths only, and do not carry authorization decisions.
Constraints at the document chunking step
Store authorization lists may be written in Chinese, English, or a local language. Semantic retrieval offers cross-language understanding, while its accuracy depends on model training quality and sentence completeness. Numbers, models, and proper nouns suit full-text retrieval or hybrid retrieval; natural-language questions suit semantic retrieval or hybrid retrieval. Hybrid retrieval merges multiple result paths through RRF, and reranking produces a score between 0 and 1. The minimum relevance value takes effect only under semantic retrieval or when reranking is used.
The reference limit is controlled by tokens per search, and top k is not used. Question optimization adds one model call before retrieval, which helps continuous conversations and short questions and also increases elapsed time. Image retrieval requires that a corresponding index was built at ingestion time and depends on a vision model or a vector model that supports images. Libraries using different vector models cannot be associated together; the identifiers should be checked first and non-matching ones excluded. Retrieval itself does not constitute authorization and does not perform entity isolation.
Hypothetical paper exercise sample: one store authorization list states "Authorized store: Store A; authorization period: January 1, 2025 to June 30, 2025; authorization scope: online sales in mainland China." A price control table states "Store A, March 2025, suggested retail price 199 yuan." A draft sentence awaiting review reads "Store A may still sell online in mainland China in August 2025." The review compares the subject Store A, the period August 2025 against the authorization end date June 30, 2025, and the authorization scope field. The draft sentence is not supported, because the authorization period does not cover August 2025 and no renewal or extension clause is available for comparison.
Configuration and implementation checks
| Check item | Document basis or implementation check | Use in this page's sample |
|---|---|---|
| Search mode | Semantic, full-text, and hybrid retrieval are available | Use full-text retrieval to check the date field of the authorization period |
| Reference limit | Each search references at most n tokens | A hypothetical starting value of 2000 tokens, chosen because the short sample sentences must keep their conditions intact |
| Minimum relevance | A 0-1 value, effective only under semantic retrieval or reranking | A hypothetical starting value of 0.5, compared against the rerank score |
| Question optimization | Coreference removal and question expansion before retrieval | The sample question contains "still may," so whether it refers to the original authorization period must be confirmed |
| Rerank | ReRank performs a second ordering, score 0-1 | Enabled when candidates are numerous, filtering by score |
| Index status | Original data and indexes are distinct; one set of original data may have multiple indexes | After the authorization list is updated, check training and index status |
The values in the table are hypothetical starting points for this exercise and do not represent official defaults.
Three common errors
- Treating a successful upload as an updated retrieval index. Input is the updated authorization list file; compare the upload-complete state against training and index status, and observe whether retrieval still returns the old period. If the old period is returned, the index has not been updated.
- Treating semantic similarity as an authorization decision. Input is the draft sentence "Store A may still sell in August 2025"; compare the authorization period field returned by retrieval against the period in the draft sentence, and observe whether similar content is mistaken for a valid authorization. Similar content does not change the period field.
- Associating libraries that use different vector models. Input is the vector model identifier of two libraries; compare whether the identifiers match, and observe whether libraries with different models are merged into one retrieval. When identifiers do not match, exclude them and do not continue comparing recall results.
How to verify the setup
- Use the sample source text "authorization period: January 1, 2025 to June 30, 2025" as input, compare August 2025 in the draft sentence against that period, and observe whether retrieval returns that source text. The check is satisfied when the source text is returned and the draft sentence is not marked as authorized.
- Run full-text retrieval on a number or date field, input "June 30, 2025," compare whether the returned passage contains the complete authorization period and authorization scope, and observe whether the condition has been split apart. The check is satisfied when the passage keeps the period and scope intact.
- Check empty-result handling. Input the explicit paper input [], compare whether the downstream design distinguishes an empty result from unsupported content, and observe whether a fabricated authorization appears. The check is satisfied when the downstream records the gap and produces no authorization conclusion.