What the documents look like
Fresh-food weighing and sorting for inventory procurement and supplier records centres on four document types: sorting task sheets, weighing labels, short-weight compensation forms and quality inspection records. Weighing and sorting happen inside the warehouse fulfilment stage, so the core data is weight and specification. The stated data sources are WMS, electronic scales, the order system and inspection photographs, with updates described as per order and daily.
Each document type carries different fields, subjects and states. A sorting task sheet carries specification and batch requirements. A weighing label carries the actual measured weight. A short-weight compensation form carries the difference handling. A quality inspection record carries the supplier's arrival quality. These differences matter at the vector index step, because the index content and the original data are reviewed separately for each document type. Field lengths, subjects and status values differ across the four types, so their fields are not merged into one index.
Constraints at the vector indexes step
Knowledge bases selected for a single retrieval must use the same vector model. The actual model and its input type are checked first. One original data block can be associated with multiple indexes, and the index and the original content are reviewed separately. Indexes are built from complete and focused information, and retrieval coverage against index length is weighed against the available samples. Image retrieval also depends on whether an image description or image vector was generated at ingestion time; uploading an image on the query side is not sufficient on its own. After a model or index change, the same set of questions is used to compare the cited original text. Vector scores alone do not support a performance commitment.
Hypothetical paper exercise: a front warehouse holds a sorting task sheet dated 10 June 2025 with the original text "Task sheet FR-0610-07, item is leafy greens 500g specification, sorter required to label according to the electronic scale reading, inspection record must attach arrival photographs." The same day's short-weight compensation form reads "Compensation form BC-0610-03, order tail 4821, weighing label shows 470g, customer reports 30g short, status pending quality inspection supervisor review." A draft sentence reads "All arrivals from this supplier on that day passed quality inspection, and the compensation form has been fully processed." The draft touches two fields: supplier arrival inspection status and compensation form processing status. The sample states only that photographs must be attached; the compensation form status is pending review. Neither field records completion, so the draft sentence is unsupported.
Configuration and implementation checks
| Configuration item | Hypothetical starting value for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Only the inventory procurement and supplier records base using the same vector model | Check each base's model identifier first, exclude bases whose model differs, and do not run a cross-model retrieval comparison |
| Index content | Sorting task sheets indexed by task sheet number, item, specification and batch | Build indexes separately for weighing labels and compensation forms, then compare whether the same question returns complete original text |
| Image index | At quality inspection record ingestion, confirm whether an image description or image vector was generated | Check this separately from whether an image was uploaded on the query side; an upload alone does not mean the index exists |
| Search mode | Hypothetical starting point is hybrid retrieval | Compare full-text hits for identifier-type questions and semantic hits for natural-language questions |
| Citation cap | Hypothetical starting point is 2000 tokens | Chunk lengths differ widely, so compare the cited original text for the same question at different caps |
| Minimum relevance | Hypothetical starting point is 0.3 | Applies only in semantic retrieval or when result re-ranking is used; compare cited entries before and after filtering |
Three common errors
- Treating a query-side image upload as proof that an image index exists. Input: the quality inspection record was ingested without an image vector index. Compare the ingestion index field against the query-side image input. Observable result: a pure image query does not hit that record. Image retrieval requires the corresponding image input and model capability.
- Treating pending review as completed processing. Input: the compensation form status is "pending quality inspection supervisor review". Compare the draft sentence "has been fully processed" against the original status field. Observable result: the two do not agree, so the draft sentence is unsupported.
- Associating knowledge bases that use different vector models. Input: two knowledge bases with different model identifiers. Compare the model identifiers. Observable result: the identifiers differ, so the non-matching base is excluded and the actual observation is recorded. No outcome of a combined association is invented.
How to verify the setup
- Input: sorting task sheet original text "Task sheet FR-0610-07, item is leafy greens 500g specification". Compare whether the draft sentence cites that task sheet number and specification. Observable result: the cited original text contains that task sheet number and the specification agrees, so the condition is met.
- Input: short-weight compensation form original text "status pending quality inspection supervisor review". Compare the draft sentence "the compensation form has been fully processed". Observable result: the status field is pending review, so the draft sentence is unsupported and the evidence gap is recorded.
- Input: quality inspection record original text "must attach arrival photographs". Compare the draft sentence "passed quality inspection". Observable result: the original text states only that photographs must be attached and records no pass conclusion, so the draft sentence is unsupported. For the empty-result downstream path, a paper input of
[]can be used to check the downstream design; this does not assert that a hypothetical rule retrieval always returns[].