What the documents look like
Fruit-store procurement document comparison works across purchase orders, acceptance sheets, pesticide-residue test reports and loss records. Compared with convenience-store prepared-food ordering, fruit purchasing relies more on wholesale-market prices and grade-based acceptance than on head-office standardized meals. That difference shapes how context is organized for comparison: within a single day, different batches of fruit may carry different grades, origins and unit prices, so materials must be aligned by batch rather than aggregated by store or supplier.
The materials relevant here are purchase orders, acceptance sheets, pesticide-residue test reports and loss records. A comparison must hold the same batch, the same period and the same field in scope. When a purchase order, an acceptance sheet, a test report and a loss record are created at different points in time, treating values from different batches as a single comparison can misread ordinary batch variation as a contradiction.
Constraints at the context limits step
Contract and transaction-material comparison requires placing the relevant fragments of several documents into the model context. The maximum context length of a model determines how much content a single request can reference. The reply limit only constrains output length; the two values serve different roles and cannot substitute for each other. The knowledge-base reference limit is expressed in tokens, so a long single document may be trimmed, and the actual included content must be checked against a question sample. A larger memory-turn setting brings more earlier conversation into the request, which may increase consumption and affect response speed.
The following is a hypothetical paper exercise, used only to illustrate the checking method.
Hypothetical paper exercise: On 10 March 2025, a store fresh-produce supervisor submits a purchase order stating "Batch A, apples, grade 1, quantity 200 boxes, unit price 80 yuan." On the same day, a supplier quotation states "Batch A, apples, grade 1, quantity 200 boxes, unit price 80 yuan, quotation valid until 12 March 2025." The draft sentence to be assessed is: "The Batch A apple purchase unit price matches the supplier quotation, and the supply period has been confirmed." The check compares the subject, batch, grade, quantity and unit price fields in the two documents. The quotation validity date only marks the period during which the quotation is available; it does not state a supply period.
Configuration and implementation checks
| Configuration item | Relation to the materials on this page | Hypothetical starting point for this exercise and reason |
|---|---|---|
| AI model | Determines referenceable content length and multimodal capability | Choose a model whose context length covers the total tokens of the materials; compare against the material total |
| Memory turns | Affects how much earlier conversation is carried in | Start with a small number of retained turns; compare against the context needed for a single comparison |
| Reply limit | Limits output length | Start with a value sufficient to express comparison basis and differences; compare against the length the draft sentence needs |
| Reference limit | Controls included reference content by token | Start from the length of a single document; compare against the content actually included for the question sample |
| Reply format | Used when fixed fields must be carried downstream | If downstream reads fields, select json_schema and supply the format requirement |
| Minimum relevance | Filters low-relevance results | Applies only under semantic retrieval or result reranking; compare against candidate content |
These values are hypothetical starting points for this exercise. They are not official default values and must be adjusted after comparing against actual material length and the question sample.
Three common errors
- Treating the quotation validity date as a supply period. Input: the supplier quotation text above and the draft sentence. Compared fields: "quotation valid until 12 March 2025" and "supply period." Observable result: the draft sentence states the supply period has been confirmed, while the material does not state a supply period, so that conclusion is not supported.
- Mixing materials from different batches into one comparison. Input: the Batch A purchase order and a material from another batch. Compared field: batch identifier. Observable result: the two materials carry different batch identifiers, so a unit-price difference cannot be judged as a contradiction.
- Treating the reply limit as the full context window. Input: the total tokens of the materials and the reply-limit setting. Compared objects: maximum context and reply limit. Observable result: the reply limit only constrains output; whether material is trimmed depends on the reference limit and the model maximum context.
How to verify the setup
- Using the hypothetical paper exercise above, input the purchase order text, the quotation text and the draft sentence. Compare the unit price and supply period in the draft sentence against the fields in the two materials. The condition is met when the unit price fields match, and the supply period is recorded as not stated in the materials and pending verification.
- Using the explicit paper input
[]to check empty-result handling, compare the downstream node output when no reference content is present. The condition is met when the downstream design handles empty references without asserting that a fictional rule search always returns[]. - Using the same question under empty history and under history-bearing input, compare the actual input content of the two requests. The condition is met when the difference caused by carried history can be excluded; record the actual observation without guaranteeing a first-hit or full recall.
Libraries across different vector models cannot be jointly associated; verify identifiers first and exclude non-matching ones, then record the actual observation. Retrieval or a form does not constitute authorization. Business verification, approval, database writes and external synchronization each require their own downstream implementation.