What the documents look like
Returned-goods inspection at a reverse-logistics warehouse revolves around four document types: the return inbound note, inspection photographs, the grade determination sheet, and the resale listing sheet. The return inbound note records the origin of the returned parcel and its condition on arrival. Inspection photographs show the actual appearance of the goods. The grade determination sheet carries the conclusion for each item, whether resale, downgrade, or disposal. The resale listing sheet takes forward the portion that can be sold again. These records update per item and are summarized daily. In cross-border work, one batch of returned goods may carry platform after-sales records and carrier return receipts in different languages, and inspection staff need to locate the corresponding entries across those languages.
The retrieval step handles the documents themselves. It does not handle refund approval or inventory write-back. Whether the data has completed training, whether the index covers images, and whether the vector model supports image input all affect whether the description or vector associated with an inspection photograph can be recalled. Retrieval results are candidate references only and do not constitute authorization for how a returned item is handled.
Constraints at the vector indexes step
Knowledge base search uses the library as its smallest search unit; collections serve only to classify and manage data and do not affect search results. One original data block can be associated with multiple indexes, and the index content is checked separately from the original text. When index content is complete and focused, retrieval precision improves; an overly long index sacrifices retrieval range. Whether an image can be retrieved depends on whether an image description index or image vector index was generated at ingestion time. Uploading an image on the query side does not fill that gap.
Knowledge bases associated for retrieval must use the same vector model. Knowledge bases with different vector models cannot be associated together; the model identifier must be checked first and non-conforming ones excluded. Semantic retrieval relies on vector distance, and cross-language understanding depends on model capability. Full-text retrieval suits precise queries such as numbers and model designations. Hybrid retrieval merges semantic and full-text results through RRF. Result reranking uses a ReRank model to reorder candidates a second time. The minimum relevance value is a filter threshold that takes effect only under semantic retrieval or when result reranking is used, and it does not perform permission or entity isolation.
Hypothetical paper exercise: A certain after-sales warehouse receives a batch of returned items. The return inbound note states "Return number RT-0417, item is a portable juicer, arrival date April 17, outer packaging damaged." The inspection photograph shows a crack in the cup body, and the grade determination sheet gives a preliminary judgment of "pending review." A draft handover request reads "RT-0417 has been determined as disposal, please delist directly." That draft sentence conflicts with the "pending review" status on the grade determination sheet, and the disposal conclusion is not supported.
Configuration and implementation checks
| Configuration item | Relevant value or check for this page | Relation to the exercise |
|---|---|---|
| Associated knowledge bases | Select one or more knowledge bases with the same vector model | The return inbound note library and the inspection photograph library require model identifier checks |
| Search mode | Semantic retrieval, full-text retrieval, or hybrid retrieval | Return number RT-0417 suits full-text or hybrid retrieval |
| Index content | One original data block can associate multiple indexes, checked separately | Inspection photographs require confirmation that an image index was built at ingestion |
| Result reranking | A ReRank model is available and enabled per configuration | Used for second-pass ordering when candidates are numerous, not mandatory |
| Minimum relevance | A 0-1 value, effective only under semantic retrieval or result reranking | The hypothetical trial starting value here is 0.4, chosen because the small sample needs recall preserved, compared against 0.6 to observe filtering differences |
| Reference limit | Maximum tokens cited per search | Controls the length of reference content returned to the model |
Three common errors
- Treating a retrieval hit as handling authorization. The draft sentence "RT-0417 has been determined as disposal, please delist directly" takes retrieved return information as a disposal conclusion, while the grade determination sheet actually reads "pending review." Retrieval and forms collect values; business approval and inventory write-back require downstream implementation.
- Associating knowledge bases across vector models. If the return inbound note library and the inspection photograph library use different vector models, they cannot be associated together. The model identifier should be checked and non-conforming ones excluded, without running cross-model retrieval to compare missed recalls.
- Treating minimum relevance as permission or entity filtering. That value is a filter threshold and does not exclude unrelated business or unauthorized records. Retrieval is not authorization, and a similarity setting does not perform record isolation.
How to verify the setup
- Input the return inbound note text from the hypothetical exercise, "Return number RT-0417, item is a portable juicer, arrival date April 17, outer packaging damaged," and query "RT-0417" using full-text or hybrid retrieval. Compare whether the returned references contain that return number text. If the return number appears in the references with matching original text, the condition is met; if the return is empty or cites another number, record the gap.
- Input the draft sentence "RT-0417 has been determined as disposal, please delist directly" and compare it field by field against the grade determination sheet text "pending review." When the same subject, same period, and same field carry mutually exclusive values, that is a contradiction. The disposal conclusion in the draft sentence is not supported, and the pending review status is retained.
- Input an inspection photograph query and compare whether the returned references come from original data with an image index already built. If no image description or image vector index was generated at ingestion, an empty image query return is expected; record the index gap at that point without concluding that model capability is insufficient.
An empty-result handoff can be checked against the explicit paper input [] to verify downstream design; it does not mean that a search under fictional rules is guaranteed to return an empty array. After adjusting the model or index, compare reference original text using the same set of questions, and do not promise results based on vector scores alone.