What the documents look like
Fresh-food weighing and sorting happens inside the warehouse fulfillment step. The core facts are weight and specification. The main data sources are WMS, electronic scales, the order system, and quality-inspection photos. The daily documents are sorting task sheets, weighing labels, short-weight compensation forms, and quality-inspection records. These materials update per order and per day.
Product-information and selection questions rely on these materials. A sorting task sheet carries the specification a picker must meet. A weighing label records the weight and specification of one item. A short-weight compensation form records how a weight difference was handled. A quality-inspection record captures a sampling result. Together these documents form the factual basis for selection questions asked by pickers and quality-inspection supervisors.
A sorting task sheet and a weighing label can disagree about the same item on the same date. A compensation form can show a customer-reported weight below the label value while the inspection record shows a passing scale reading. The index must let a question reach the right document, and the answer must keep the two records separate.
Constraints at the vector indexes step
Knowledge bases joined to one search must use the same vector model. Check the actual model and the input type before joining any knowledge base. A single source data block can be linked to multiple indexes. Check the index content and the original text separately. Build indexes from complete, focused information. Retrieval coverage and index length need to be weighed against sample material. Image retrieval also depends on whether an image description or image vector was generated at ingestion time. Checking the query side alone is not enough. After a model or index change, compare the cited original text against the same set of questions. A vector score alone does not support a promise about results.
Hypothetical paper exercise sample. One quality-inspection record reads: "On 10 March 2025, a picker weighed spinach against a sorting task sheet. The single-item specification was marked 250 g ±10 g. The electronic scale reading was 248 g. The item was judged acceptable." A separate short-weight compensation form reads: "On 10 March 2025, a customer reported that the actual weight of the spinach was 230 g, below the labeled 250 g, and applied for compensation." The proposed sentence under review is: "All spinach in this batch passed weighing and no compensation is needed." The comparison uses the single-item scale reading in the inspection record and the customer report in the compensation form as inputs, then compares how the two materials record the weight conclusion for the same product on the same date.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Vector model | The model identifier used by the joined knowledge base | Check the actual model and input type first; knowledge bases on different models cannot be joined |
| Index content | Complete specification fields from the sorting task sheet and weighing label | Compare field by field against the original text to confirm the index covers specification and weight |
| Source data and index links | One source data block linked to multiple indexes | Check whether each index and its original text correspond |
| Image index | Description or vector generated when the quality-inspection photo was ingested | Confirm whether the index was built at ingestion; check the query side separately |
| Citation limit | A token amount covering one task sheet, set as the starting point for this exercise | Compare against the cited original-text length for the same question set |
| Minimum relevance | A middle value, set as the starting point for this exercise | Compare against semantic-search or rerank scores and observe the filtering effect |
The citation limit is measured in tokens, not in a top-k item count. Minimum relevance applies only under semantic search or when result reranking is used. RRF is a fusion step for combining result paths, not a switch. Image retrieval requires an actual image input and a capable model. Reranking requires a model and must be enabled by configuration. A pending compensation form is not an approved or executed compensation.
Three common errors
- Treating a vector similarity score as a permission or entity filter. The same-model requirement for joined knowledge bases does not extend to external systems, and a similarity setting does not exclude unrelated business lines or unauthorized records. Retrieval is not authorization.
- Treating a compensation form under review as already approved and executed. A pending status does not show that operating authority is absent. Check the submission status and the approval status of the compensation form separately.
- Treating image retrieval as running on every request. Image retrieval needs a corresponding image input and model capability. If no image index was generated at ingestion, the query side cannot recall it.
How to verify the setup
- Input the quality-inspection record text and the short-weight compensation form text from the hypothetical sample. Compare how the two materials record the spinach weight for 10 March 2025. Observe whether the proposed sentence "All spinach in this batch passed weighing and no compensation is needed" agrees with the customer report in the compensation form. If the two materials give different conclusions, the proposed sentence is not supported.
- Input the same question set against the sorting task sheet and the weighing label. Compare whether the returned cited original text contains the specification and weight fields. Observe whether the cited content covers the field range marked when the index was built. If the cited original text lacks the specification field, check the correspondence between index content and original text.
- Input a text question with no image. Compare whether returned results come only from the text index. Observe whether the image-description or image-vector path was triggered. If no image was uploaded and the model does not support image input, the image retrieval path does not run; record the recall source actually observed. A paper input of an empty array can be used to check the downstream design for an empty citation output, which the search node documents as a possible length.