What the documents look like
Fresh-food concession counters in hypermarkets differ structurally from convenience-store ready-to-eat counters. Under the concession model, a supplier operates on site and shares revenue by sales. Procurement and supplier records must therefore support both food-safety inspection and gross-margin reconciliation at the same time. The main data sources are the POS system, supplier delivery notes, food-safety ledgers, and store inventory counts. Day-to-day documents concentrate in four types: concession contracts, daily sales reports, shrinkage sheets, and food incoming inspection records. These documents update daily or per batch, so parsing results need to point back to a specific batch and date.
The object of document parsing and chunking in this task is those four document types. A concession contract fixes the parties, the revenue-share terms, and the authorization boundary. Delivery notes and incoming inspection records fix batch, product name, and quantity. Daily sales reports and shrinkage sheets fix the basis for allocating sales and shrinkage. Chunking must preserve the constraint relationships among these fields, so that splitting a contract clause away from a specific batch record does not destroy the ability to reconcile them.
Constraints at the document chunking step
Segmentation completeness requires cutting by the structure of the specific material. The revenue-share clause, validity period, and authorization boundary in a concession contract should stay within the same data group. Batch, product name, quantity, and inspection conclusion in a food incoming inspection record should remain complete, so that a condition is not separated from what it constrains. One group of source data can map to multiple indexes, which provides multi-vector mapping for longer text, so that the data can still be recalled when any one of its vectors is retrieved.
Data and index need to be distinguished. The dataset.datas table in MongoDB stores the vector source-data information, and the indexes field records the corresponding vector IDs. The vector field in PostgresSQL stores the vector. During retrieval, vectors are recalled first, then the source data is located in MongoDB by vector ID and merged. After a material change, the actual training and index state needs to be checked; a successful upload cannot be treated directly as a completed retrieval update. Semantic recall and relevance only affect content matching and do not carry permission or entity isolation. Retrieval itself does not constitute authorization.
Configuration and implementation checks
The following configuration is built around a hypothetical drill sample on this page. The sample material text is: "Party A is the fresh-food department of a certain hypermarket; Party B is a certain vegetable concession supplier. The two parties agree that leaf-vegetable shrinkage is borne by Party B at eight percent, and any amount above that is allocated by the revenue-share ratio. From March 1, 2025 to March 31, 2025, Party B operates the leaf-vegetable counter on site, and incoming inspection records accompany each batch delivery note." The draft sentence to be assessed is: "All leaf-vegetable shrinkage of Party B is borne by Party A, and Party B is authorized to decide incoming batches on its own." This draft sentence is inconsistent with the sample text on the allocation party and the authorization boundary.
| Configuration item | Hypothetical starting point for this drill | Reason for the choice and comparison method |
|---|---|---|
| Search mode | Hybrid retrieval | The sample contains a revenue-share ratio and dates, so both semantic and keyword coverage are needed; compare recall completeness against semantic retrieval alone |
| Reference limit | Hypothetical starting point of 3000 tokens | The sample passage contains contract clauses and batch records; compare against 1500 tokens to see whether constraint conditions are truncated |
| Minimum relevance | Hypothetical starting point of 0.4 | The draft sentence and the source text overlap on some surface words; compare against 0.6 for filtering effect |
| Question optimization | Hypothetical on | The draft sentence contains a reference; compare against the off state for coreference resolution |
| Result rerank | Hypothetical enabled | Candidates contain multiple passages from contract and reports; compare against no rerank for ordering position |
| Full-text retrieval | Hypothetical in use | The sample contains dates and percentages; compare against semantic retrieval for exact hits |
Three common errors
- Treating "all borne by Party A" in the draft sentence directly as a source-text fact. The input is the draft sentence and the sample source text; the comparison object is the shrinkage allocation party. The source text says Party B bears eight percent; the draft sentence says Party A bears all. The two take mutually exclusive values on the same field, so the draft sentence is not supported by the sample source text. A pending-review state does not demonstrate a lack of operating permission, and the evidence gap must be preserved.
- Treating a successful upload as a completed retrieval update. The input is the changed concession contract; the comparison object is the actual training and index state. Upload and index update are two stages, and the
indexesfield indataset.datasmust be checked against the vector records to see whether they correspond to the new version. Before that check, retrieval results may still point to the old version. - Treating a semantic recall score as permission or entity filtering. The input is a retrieval request; the comparison object is the recalled content and the authorization scope. A relevance value reflects only content similarity and does not exclude unauthorized records. Returned retrieval results need a separate check against the subject and authorization boundary; a high or low score cannot be used to infer access permission.
How to verify the setup
- Verification one: input the sample source text and the draft sentence, compare the shrinkage allocation party field. The source text says Party B bears eight percent, the draft sentence says Party A bears all, and the same field takes mutually exclusive values, so the draft sentence is judged unsupported. The expected observation is that the comparison result is marked inconsistent, and the source text is retained as the basis for reconciliation.
- Verification two: input the changed contract version, compare the upload record with the vector IDs corresponding to the
indexesfield, and check whether the new version generated a corresponding index. The expected observation is that when the index points to the new version, the update is judged complete; when the index still points to the old version, a pending-review note is recorded. - Verification three: input a retrieval request and the returned content, compare the returned records' subject and authorization scope, and check whether unauthorized supplier records are included. The expected observation is that when returned content exceeds the authorization scope, the gap is recorded, and the relevance score is not used as a basis for authorization. For the empty-result case, a clearly stated paper input of an empty array can be used to check the downstream design, without claiming that a rule-based retrieval will necessarily return an empty array.