What the documents look like
Fresh-food concession counters in hypermarkets are often run by on-site supplier staff under a sales-sharing arrangement. Operating reports and exception reviews therefore draw on several document types at once: the concession contract, daily sales reports, loss sheets, and food purchase inspection records. These materials come from different holders. The hypermarket fresh-food department, the concession supplier, and the store manager each keep part of the evidence.
The category brief gives the data update frequency as daily and per batch. That detail matters here only where a retrieval question depends on which batch or which day a record covers. It does not by itself create any synchronization, batch import, or transaction-triggered mechanism.
This page covers one task: knowledge base retrieval and recall inside operating reports and exception reviews. Retrieved passages support cross-checking. They do not decide whether a contract is effective, whether a loss has been confirmed, or whether an inspection record is complete. Those questions go back to the source material or to the corresponding system.
Constraints at the retrieval step
A knowledge base consists of libraries, collections, and data. The smallest search unit is the library. A 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. Before association, check the vector model identifier, exclude those that do not match, and record the actual observation. Do not run a cross-model retrieval to compare missed recall.
Semantic retrieval relies on vector distance. Full-text retrieval suits numbers, models, proper nouns, and error codes. Hybrid retrieval merges multiple result paths through RRF. RRF is a fusion process, not a switch that can be turned on. The minimum relevance score takes effect only under semantic retrieval or when result re-ranking is used. It is a filter threshold, not a permission or entity isolation mechanism. The reference limit is measured in tokens. Reference content is output as an array and its length can be 0, so the output path completes even when nothing is found.
Image retrieval requires corresponding image input and model capability. Re-ranking requires a re-rank model and must be enabled in settings. Optional paths shown in the overall document flow diagram do not mean every request executes all of them.
Hypothetical paper exercise sample
The following is invented for this paper exercise. It points to no real customer or legal identity number.
Material text for cross-checking (invented):
"Appendix 3 of the concession contract states: fresh-food counter loss sharing is confirmed batch by batch according to store inventory results. When on-site supplier personnel dispute an inventory difference, they must raise it within two working days after the inventory sheet is delivered. The daily sales report shows that on March 12 the counter's sales were 4,820 yuan. Loss sheet number LS-0312 records a loss of 136 yuan with status 'pending supplier confirmation'. The food purchase inspection record shows arrival batch PD-0312 on March 12 with inspection status 'inspected'."
Draft sentence to be judged (invented):
"On March 12 the loss was confirmed by the supplier and can be deducted directly from that day's sales."
Configuration and implementation checks
The table below lists, around the sample above, settings already described in the official documents or implementation checks relevant to this feature and task. The values are hypothetical starting points for this exercise, used for comparison. They are not official defaults.
| Configuration item | Hypothetical starting point and reason | Comparison method |
|---|---|---|
| Associated knowledge bases | Associate only knowledge bases using the same vector model; check the model identifier first | Check model identifiers one by one, exclude non-matching ones, and record |
| Search mode | Test the same question separately with semantic, full-text, and hybrid retrieval | Compare whether returned reference content covers contract, report, loss sheet, and inspection record |
| Reference limit | Set a starting point in token units; test with actual materials of different lengths | Compare whether reference content truncates key clauses under different limits |
| Minimum relevance score | Takes effect only under semantic retrieval or re-ranking; label relevant and irrelevant samples before adjusting the threshold | Compare whether relevant samples are retained and irrelevant samples decrease after adjustment |
| Question optimization | Used for coreference resolution and question expansion; compare the original and optimized question | Check whether the subject of the optimized question has been changed incorrectly |
| Result re-ranking | Requires a re-rank model enabled in settings; the re-rank score is 0-1 | Compare candidate ordering and score filtering before and after re-ranking |
Three common errors
- Treating a retrieval hit as an authorization conclusion. The input is the sample text stating that loss sheet number LS-0312 has status "pending supplier confirmation". Compare it with the draft sentence "confirmed by the supplier". The observable result is that the material shows only a pending status, and the draft sentence exceeds what the source supports. Pending review does not show a lack of operating permission, and it does not equal confirmation.
- Forcing knowledge bases with different vector models into a shared association. The input is the vector model identifiers of two knowledge bases. Compare whether the two identifiers are the same. The observable result is that different identifiers mean they cannot be associated together; exclude them and record the observation. Do not continue by inventing any runtime result of a shared association.
- Treating the minimum relevance score as a permission or entity filter. The input is the reference content returned by one semantic retrieval. Compare whether the reference content includes supplier records the requester has no right to view. The observable result is that a relevance score reflects similarity only, retrieval does not perform authorization decisions, and unauthorized content must be blocked by a downstream implementation.
How to verify the setup
- Against the sample material and the draft sentence, input "loss sheet LS-0312 has status pending supplier confirmation" and the draft sentence "confirmed by the supplier". Compare the two values for the same field. The observable condition to be met is that the draft sentence is judged unsupported, and the evidence gap is preserved rather than written as a negation or a conflict.
- For empty-result handling, input the explicit paper input
[]. Compare whether the downstream design continues along the output path according to a reference length of 0. The observable condition to be met is that the downstream can handle an empty array. Do not claim that any invented rule will necessarily return[]on retrieval. - For cross-model association, input the vector model identifiers of two knowledge bases. Compare whether the identifiers match. The observable condition to be met is that when identifiers differ, the association is excluded and the actual observation is recorded. Do not run a cross-model retrieval to compare missed recall.
The above are expected judgments in a paper exercise. They were not actually executed, and they do not constitute any conclusion that an approval, refund, or other business action has been completed.