What the documents look like
Convenience fresh-food ordering runs on short shelf life and high frequency. A store places orders daily or by shift, and the same cycle produces order forms, damage reports, near-expiry handling sheets and cold-chain temperature records. These four document types feed next-day sales forecasting, shelf-life checks and damage registration. The retrieval task for procurement and supplier material has to work across this set so that store managers, fresh-food buyers and regional supervisors can check the same body of evidence.
Sources include POS sales, the head-office ordering system, store inventory and cold-chain temperature logs. Within a single day, documents sit at different stages. An order form may already be submitted, a damage report may await review, and a near-expiry handling sheet may record an intention only. Retrieval returns reference content. It does not move any document into or out of a review, approval or execution state.
Two features of this category shape the retrieval step directly. First, because shelf life is short, the same item appears across several document types on the same date, and a question about one field can easily be answered from the wrong document. Second, because ordering repeats every day or shift, near-duplicate documents accumulate, and a retrieval result can match a different date or a different store while looking superficially relevant. Both conditions make the comparison of subject, date and field the core of verification rather than an afterthought.
Constraints at the retrieval step
The knowledge base search node can associate one or more libraries that use the same vector model. Libraries built on different vector models cannot be associated together. The vector model identifier should be checked first, non-matching libraries excluded, and the actual observation recorded. Search operates on the whole library; a collection is a classification device and does not affect search results.
Search mode is compared per test question across semantic, full-text and hybrid retrieval. No single mode is assumed to suit all material. The reference limit is measured in tokens. The minimum relevance value takes effect only under semantic retrieval or when result re-ranking is used. Question optimization performs coreference resolution and question expansion and adds one model call before retrieval. Image retrieval requires a corresponding image input and model capability. Re-ranking requires a model and must be enabled by configuration. Reference content is output as an array whose length may be zero, and the downstream link still runs when nothing is retrieved.
Hypothetical paper test case: the order form text for a store is "Fresh-food order form: Store A, order date 10 March 2025, item rice ball, order quantity 120 units, status submitted, supplier A." The temperature record text for the same day is "Temperature record: Store A, 10 March 2025, refrigerated cabinet temperature 4 degrees Celsius, recorder B." The draft sentence to be judged is "Store A completed the rice ball damage report on 10 March 2025." That draft sentence concerns a damage fact. Neither source states a damage quantity, a damage report number or a damage review status, so the sentence is unsupported.
Configuration and implementation checks
Configuration below centers on the sample above. The values are starting points for this hypothetical trial, used for comparison against other values, and are not official defaults.
| Configuration item | Hypothetical trial starting point | Reason and comparison method |
|---|---|---|
| Associated libraries | Only fresh-food libraries using the same vector model | Check the vector model identifier first, exclude non-matching libraries, record the actual observation |
| Search mode | Hybrid retrieval | Compare reference content for the same test question against semantic-only and full-text-only runs |
| Reference limit | 800 tokens | Compare against 400 and 1200, observe whether long and short material is retrieved completely |
| Minimum relevance | 0.3 | Takes effect only under semantic retrieval or re-ranking; compare against 0.2 and 0.4 on relevant and irrelevant samples |
| Question optimization | Enabled | Compare the original question with the optimized question, check whether the subject was altered |
| Result re-ranking | Enabled per configuration | Where a re-ranking model is available, compare ranking scores before and after enabling |
Three common errors
- Treating a retrieval hit as authorization to execute. Input the question "Store A damage report 10 March" and retrieve the order form and the temperature record. Compare the draft sentence with the two source texts. The sources state no damage quantity and no review status, so the observable result is that the draft sentence is unsupported and the damage report cannot be treated as completed on this evidence.
- Treating minimum relevance as a permission or entity filter. Input the same test question and compare reference content at different thresholds. The observable result is that the value only reflects similarity filtering and does not exclude unrelated business lines or unauthorized records.
- Forcing libraries with different vector models into one association. Input two libraries with different vector model identifiers and compare the identifiers. The observable result is that non-matching identifiers must be excluded; no cross-model retrieval is executed to compare missed recalls.
How to verify the setup
- Input the hypothetical sample order form text and temperature record text, together with the draft sentence "Store A completed the rice ball damage report on 10 March 2025." Compare subject, date and fields between the draft sentence and the two source texts. Observable result: neither source states a damage quantity or review status, so the judgment is unsupported.
- Input the same test question and run semantic retrieval, full-text retrieval and hybrid retrieval separately. Compare whether the reference content contains the order form and the temperature record. Observable result: retrieved content differs across modes; record the actual differences without assuming one mode is necessarily better.
- Input an explicit paper input of an empty array and check how the downstream design handles an empty reference array. Compare whether the output link runs. Observable result: the link still runs when the reference length is zero; no claim is made that a fictional rule returns an empty array.
This page addresses only convenience fresh-food ordering, procurement and supplier material, and knowledge base retrieval and recall. When operations are handed over, the authorization state of each document, action and request must be checked; retrieval and forms are not authorization to execute.