What the documents look like
A fresh-food concession counter in a hypermarket is often run by a supplier who stations staff on site and is paid through a sales-share arrangement. Documents therefore involve three parties at once: the hypermarket fresh-food department, the concession supplier, and the store manager. The everyday document set is the concession contract, the daily sales report, the loss report, and the food purchase inspection record. The inspection record is updated per batch; the daily sales report and loss report are updated daily.
Cross-border material and multilingual retrieval runs into a specific difficulty here. The inspection documents for one delivery may be issued by an overseas supplier, with product name, place of origin, and batch written in different languages, while the concession contract and loss allocation still have to be reconciled against the store's own figures. The vector model and index step has to make these differently worded, differently sourced documents findable through one retrieval entry point, and it has to keep a checkable original-text reference for each result.
Constraints at the vector indexes step
Knowledge bases that are associated for retrieval must use the same vector model. The actual model and the supported input types are checked first. One original data chunk can be linked to several indexes, and the index content and the original text content are checked separately. Index content is built from complete and focused information, and retrieval coverage against index length is weighed against the sample at hand. Image retrieval additionally depends on whether an image description or an image vector was generated at ingestion time, so the query side alone does not settle it. After a model or index change, the same set of questions is used to compare the returned original text, and a vector score on its own does not support a claim about outcome.
Semantic retrieval computes similarity through vector distance and can work across languages, so a Chinese question can match an English knowledge point. Full-text retrieval depends on keyword hits and suits numbers, models, and proper nouns. Hybrid retrieval uses both recall paths and merges them through RRF. The minimum relevance value is a filter threshold and takes effect only in semantic retrieval or when result reranking is used; it is not a permission or entity isolation mechanism. The same-vector-model requirement for associated retrieval bases does not extend to external systems.
Configuration and implementation checks
Hypothetical paper exercise sample: a store's fresh-food concession counter receives a delivery from an overseas supplier. The material text reads: "Supplier: A-Fresh Co.; Product: frozen shrimp; Batch: AF-2024-0312; Origin: Ecuador; inspection status is awaiting store receipt confirmation." A drafted sentence reads: "This shrimp batch has completed purchase inspection and is cleared for shelf placement." The request to be judged reads: "Please retrieve the inspection record for this batch and confirm the shelf-placement status." In this sample the subject is A-Fresh Co., the object is frozen shrimp batch AF-2024-0312, the status is awaiting store receipt confirmation, and the valid scope is that batch only.
| Configuration item | Hypothetical trial starting point for this exercise | Reason for the choice and the comparison object |
|---|---|---|
| Vector model | Associated knowledge bases use one and the same vector model | Compared against the model identifier of another knowledge base; a different identifier excludes association |
| Index content | Each batch inspection record is indexed by batch number, product name, and origin | Compared against the full original text length, weighing coverage against index length |
| Search mode | Hybrid retrieval | Compared against semantic retrieval alone, since number-type queries need keyword hits |
| Reference cap | Hypothetical trial starting point of 2000 tokens | Compared against the sample original text length, so the whole batch record is covered |
| Minimum relevance | Hypothetical trial starting point of 0.3 | Compared against 0, to observe whether the request to be judged still recalls that batch |
| Image index | At ingestion, whether an image description or image vector was generated is confirmed | Checked separately from whether an image was uploaded on the query side |
Three common errors
- Treating retrieval output as an authorization basis. Input the request "Please retrieve the inspection record for this batch and confirm the shelf-placement status," compare the retrieved original text against the sample status "awaiting store receipt confirmation." If the original text still shows a pending status, the drafted sentence "has completed purchase inspection and is cleared for shelf placement" is unsupported. Retrieval by itself does not create authorization.
- Associating knowledge bases across different vector models. Input the model identifiers of the two knowledge bases and compare whether they match. If the identifiers differ, exclude the association and record it; do not run a cross-model search in order to compare missed recalls.
- Treating minimum relevance as an entity or permission filter. Input records for the same batch under a different supplier and compare whether the minimum relevance setting changes the recall scope. A relevance threshold filters low-relevance results only; it does not exclude unrelated businesses or unauthorized records.
How to verify the setup
- Input the sample original text and the drafted sentence, then compare the drafted claim "has completed inspection and is cleared for shelf placement" against the original wording "awaiting store receipt confirmation." If the original text does not state that inspection was completed, the claim is unsupported and the evidence gap is recorded.
- Input the request to be judged together with batch number AF-2024-0312, and compare whether the recalled reference contains that batch's original text. If the referenced content matches the original and the status reads pending, the retrieval path is usable. If the return is empty, use an explicit paper input of [] to check the downstream design; do not claim that a rule-based search necessarily returns an empty array.
- Input the model identifiers of the two knowledge bases and compare whether they are the same. If they match, joint association is allowed; if they differ, exclude the non-matching one and record the actual observation. Do not invent an operating result for a joint association that was never run.
This page addresses only the fresh-food concession counter, cross-border material and multilingual retrieval, and the vector model and index step. In operational handover, which materials, actions, and requests sit at which authorization state is a separate question, and neither retrieval nor a form counts as authorization execution.