What the documents look like
Cold-chain receiving at a store produces records that are organized around a delivery batch. Four document types appear in this category: cold-chain temperature records, arrival acceptance forms, rejection forms, and supplier delivery notes. Temperature records describe the transport temperature control for a batch. Arrival acceptance forms describe the condition of goods on arrival and the acceptance conclusion. Rejection forms record the reason a batch was refused. Supplier delivery notes record the delivery detail. These materials are produced per batch and per day.
For copy drafting, the temperature record and the arrival acceptance form for the same batch need to be retrieved together, so that a draft sentence can be checked against the complete basis. Quality-inspection photographs may also be stored with a batch. Whether photographs participate in retrieval depends on whether an image index was built at the time the material was stored and on the capability of the current model. This page addresses only the receiving stage and the drafting of product and marketing copy. It does not cover origin price inquiry, which belongs to fruit procurement.
Constraints at the retrieval step
Draft copy for this category needs to be checked against fields such as arrival condition, temperature range, and rejection reason. The knowledge-base search node can associate one or more knowledge bases that use the same vector model. If materials are spread across knowledge bases whose vector model identifiers differ, the node cannot associate them together. The identifiers must be checked first, and knowledge bases that do not match must be excluded. Retrieval is not authorization, and a similarity score does not separate businesses, batches, or suppliers.
Semantic retrieval works from vector distance. Full-text retrieval suits exact fields such as batch numbers and temperature values. Hybrid retrieval combines semantic and full-text recall and merges the two result sets through RRF. The minimum relevance value applies only under semantic retrieval or when result re-ranking is used; it is a filter threshold, not a final ordering rule. Question optimization performs coreference removal and question expansion and adds one model call before retrieval. The reference limit controls the length of quoted content in tokens. Quoted output is an array and its length can be zero; the downstream path still runs when nothing is retrieved.
A hypothetical paper exercise sample is given here for checking. The arrival acceptance form for a batch reads: "Batch A-2401, arrival temperature 2.8°C, acceptance conclusion: pass, approved for storage." The cold-chain temperature record for the same day reads: "Batch A-2401, temperature throughout 1.5°C to 3.2°C, no break in the chain." A draft sentence under review reads: "Temperature control was stable throughout this batch, and acceptance passed." The check compares the same batch and the same field. The acceptance conclusion field reads "pass," so "acceptance passed" matches the form. The temperature record does not contain a "stable" evaluation field, so "temperature control was stable" is not supported by the material.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason for the choice and comparison method |
|---|---|---|
| Associated knowledge bases | Select the cold-chain temperature record base and the arrival acceptance form base whose vector model identifiers match | Check the identifiers first, exclude bases whose models differ, and do not associate across models |
| Search mode | Hybrid retrieval | Batch numbers and temperature values need full-text hits, and natural-language questions need semantic recall; compare the two result paths |
| Reference limit | Hypothetical starting point of 2000 tokens | Test with acceptance forms and temperature records of different lengths to see whether quoted content stays complete, then adjust |
| Minimum relevance | Hypothetical starting point of 0.5 | Label relevant and irrelevant samples first, then observe how the quoted content changes after filtering |
| Question optimization | Enabled | In multi-turn follow-ups, compare the original question with the optimized question and check whether the batch subject was altered |
| Result re-ranking | Enabled according to the setting | When there are many candidates, compare the ordering by re-ranking score with the ordering by vector score |
Three common errors
- Merging the "pass" conclusion on the acceptance form with the "no break in the chain" description in the temperature record to infer "stable temperature control." The input is the two source texts. The comparison is whether the fields share the same subject, the same period, and the same field. The observable result is whether the draft sentence contains an evaluation word that the source texts do not state. In the sample, "temperature control was stable" is not supported.
- Treating the minimum relevance value as a permission or entity filter. The input is the similarity threshold setting. The comparison is whether returned quotations are separated by batch or supplier. The observable result is whether content from an unrelated batch appears. A similarity score does not perform an authorization filter.
- Associating knowledge bases that use different vector models. The input is the vector model identifier of each base. The comparison is whether the identifiers match. The observable result is whether the node permits the association. Bases with mismatched identifiers should be excluded, and no runtime result of such an association should be inferred.
How to verify the setup
- Input the two source texts and the draft sentence from the hypothetical sample. Compare "acceptance passed" against the acceptance conclusion field, and "temperature control was stable" against the temperature record fields. Observe whether the draft sentence keeps only wording the material supports. The condition is met when "acceptance passed" can be quoted and "temperature control was stable" is marked as unsupported.
- Input the batch number "A-2401" as the search term. Compare the quoted content returned by full-text retrieval with that returned by semantic retrieval. Observe whether the temperature record and the acceptance form for that batch are recalled. The condition is met when both paths include material for that batch. A first-position hit is not guaranteed.
- Input a clearly labelled hypothetical empty-result case, in which retrieval returns a quoted-content array of length zero. Compare whether the downstream copy-drafting step still runs and indicates that there is no quoted basis. The condition is met when the downstream path does not stop because of the empty quotation. This does not assert that a search under any invented rule will necessarily return an empty array.