What the documents look like
Fruit-store procurement work runs on purchase orders, acceptance sheets, pesticide-residue test reports and loss records. These materials are produced daily or per batch. A purchase order states the item name, quantity, unit price and supplier. An acceptance sheet states the arriving batch, the grade decision and the quantity actually received. A pesticide-residue test report states the sample, the test items and the conclusion. A loss record states the written-off quantity and the reason.
Contract-to-transaction comparison requires placing the contract text against these transaction materials field by field. Because materials update daily or per batch, different batches on the same day can carry different acceptance conclusions. Comparison therefore has to lock onto the same batch, the same date and the same item name. Without that lock, values for the same field cannot be placed side by side.
This category differs from convenience-store prepared-food ordering. Fruit procurement leans more on wholesale-market price conditions and grade-based acceptance than on a headquarters standard meal. The people involved include fresh-produce buyers, store fresh-produce supervisors and wholesale-market stalls. Errors here carry consequences such as spoilage losses, food-safety complaints and store stockouts.
Constraints at the retrieval step
The knowledge-base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases built on different vector models cannot be associated together. At the check stage, confirm the vector model identifier first, exclude those that do not match, and record what was actually observed. Do not run a cross-model retrieval to compare missed recalls.
Search modes are semantic retrieval, full-text retrieval and hybrid retrieval. Semantic retrieval relies on vector distance and suits natural-language questions. Full-text retrieval relies on keyword hits and suits numbers, model designations and proper nouns. Hybrid retrieval uses both and merges the results through RRF. RRF is a merge process for hybrid results, not a switch that can be turned on. The reference ceiling is measured in tokens. The minimum relevance value takes effect only under semantic retrieval or when result reranking is used. When question optimization is enabled by configuration, it performs coreference resolution and question expansion and adds one model call.
Retrieval output is an array, and its length can be 0. Even when nothing is found, the output path still runs through. Retrieval and similarity scores do not carry permission or entity-isolation functions. The minimum relevance value cannot be treated as a way to exclude unrelated businesses or unauthorized records.
Hypothetical paper exercise
The following are paper materials used for this check. They involve no real customer and no real entity.
Purchase order text: "Purchase order number CG-2024-0312, store is Chengdong Fresh Store, supplier is Lvyuan Fruit and Vegetable Stall, item is Red Fuji apple, quantity 200 kg, unit price 6.5 yuan per kg, arrival date 12 March 2024."
Acceptance sheet text: "Acceptance sheet number YS-2024-0312, corresponding purchase order CG-2024-0312, actually received 195 kg, grade decision is Grade 1, acceptance date 12 March 2024, acceptor signature field blank."
Proposed judgment sentence: "This batch of Red Fuji apples has completed acceptance at 200 kg."
Handover request awaiting decision: "Please confirm the accepted quantity for CG-2024-0312 based on the existing materials and approve settlement."
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason for the choice and comparison object |
|---|---|---|
| Associated knowledge bases | Associate only one procurement-materials knowledge base using the same vector model | Check the vector model identifier first, compare it against the identifier of the base to be associated, and exclude any with a mismatched identifier |
| Search mode | Test number-type questions with full-text retrieval first, then test natural-language questions with semantic retrieval | A purchase order number is an exact query; place it against semantic retrieval results and observe the difference in hits between the two modes |
| Reference ceiling | Starting point of 2000 tokens | This value is the hypothetical starting point for this exercise; compare acceptance sheets of different lengths to see whether content is truncated |
| Minimum relevance | Starting point of 0.5 | This value is the hypothetical starting point for this exercise; label relevant and irrelevant samples first, then compare against the labels before adjusting |
| Question optimization | Off first, enabled only for short multi-turn questions | Compare against results with it enabled and check whether the subject is altered incorrectly |
| Result reranking | Used only when a reranking model is available and enabled by configuration | Compare against candidate results without reranking and observe the change in ordering |
Three common errors
- Treating a retrieval score as a permission decision. Minimum relevance is a filter threshold, not an authorization basis. If a similarity score is used to exclude unauthorized records, the input is one low-scoring acceptance sheet that is nonetheless related to the current store, the comparison object is that record's actual ownership, and the observable result is that the record is wrongly excluded while the permission state remains unchanged.
- Inferring acceptance completion from a blank signature field. The source text carries no signature evidence, and no completion record or applicable process is provided. Check the signature and the completion claim separately. Missing evidence stays pending. A blank field cannot be read directly as either not accepted or completed.
- Associating knowledge bases across vector models. The input is two knowledge bases with different vector model identifiers, the comparison object is the two model identifiers, and the observable result is that the identifiers do not match. Exclude the non-matching one first and associate only the compatible base.
How to verify the setup
- For the proposed judgment sentence "This batch of Red Fuji apples has completed acceptance at 200 kg," the input is the purchase order text and the acceptance sheet text, and the comparison objects are the actually-received quantity field and the acceptance status field. The purchase quantity of 200 kg and the actually-received 195 kg are different fields, and the quantity difference should be recorded. The draft sentence writes the accepted quantity as 200 kg, which conflicts with the same-batch actual receipt of 195 kg. The blank signature field only shows that signature evidence is missing; the acceptance completion status needs separate checking, and no conclusion about non-acceptance is drawn from the blank itself.
- For the handover request awaiting decision, "Confirm the accepted quantity for CG-2024-0312 based on the existing materials and approve settlement," the input is that request plus the two materials, and the comparison object is whether the materials state a settlement approval. The materials state no approval field. When that condition holds, record it as an evidence gap and do not output a conclusion that approval was granted.
- For empty-result continuation, the input is the paper input
[], and the comparison object is the downstream node design. Check whether the downstream path runs through with a reference length of 0. This check does not claim that any invented rule retrieval necessarily returns an empty array.
This page addresses only fruit-store procurement, contract-to-transaction comparison, and knowledge-base retrieval and recall. Which materials, operations and requests sit at which authorization state during an operational handover is a separate matter; retrieval and forms are not authorization execution.