What the documents look like
Cross-border return refurbishment and resale handles reverse logistics and item condition. This differs from first-leg replenishment, which moves new stock toward a forward warehouse. The main data sources here are overseas warehouse WMS records, TikTok return center records, Amazon return reports, and product inspection photos. The everyday documents are return inspection forms, refurbishment records, scrap forms, and resale listing lists. These materials feed three decisions: the return grade, the refurbishment and packaging arrangement, and whether an item is scrapped or resold. When a retrieval result does not match the document state, the practical consequences include higher refund cost, repeat complaints, and a mismatch between inventory records and physical stock.
The knowledge base search node can be associated with one or more knowledge bases that use the same vector model. The smallest search unit is the library; a collection only classifies data and does not affect search behavior. One group of data can map to multiple vectors, and a hit on any one of those vectors recalls the whole group, with the vector score taken as the highest score. Return inspection forms, refurbishment records, scrap forms, and resale listing lists can therefore sit inside one library, but the vector model identifier of each associated library must be checked for consistency before association.
Constraints at the retrieval step
Retrieval is not authorization. The minimum relevance value is a filter threshold and takes effect only under semantic retrieval or when result reranking is used. It does not isolate permissions or business entities. Recalled content still requires manual comparison against the business state, and values collected on a form do not constitute approval, refund, or an inventory write. Quoted content is output as an array whose length can be zero, so the downstream chain still runs when nothing is found.
Hypothetical paper exercise sample. The source text of one overseas warehouse inspection record reads: "Return number RT-2031, item is a folding chair, inspection date 12 March 2025, visible scratches, function normal, grade B, can be resold after refurbishment and repacking." The refurbishment record of the same day reads: "RT-2031 folding chair, outer carton replaced, labeling completed, awaiting confirmation on the resale listing list." The draft sentence to be judged reads: "RT-2031 has been approved for scrap and the refund has been completed." That draft sentence asserts two business actions, scrap and refund. The materials do not contain a scrap form or a refund approval record, and the retrieval step cannot substitute for that authorization.
Semantic retrieval suits natural-language questions. Full-text retrieval suits numbers, models, and proper nouns. Hybrid retrieval merges multiple result paths through RRF. RRF is a fusion process, not a switch that is turned on. Result reranking requires a model and must be enabled in the settings; the rerank score runs from 0 to 1 and can be used for filtering. Question optimization performs coreference resolution and question expansion and adds one model call, so whether to enable it depends on observed effect. Image retrieval requires image input and matching model capability, and a text-only query does not trigger the image path.
Configuration and implementation checks
The configuration below stays with this page's sample and lists only documented settings or implementation checks tied to this feature and the procurement task. The values are hypothetical starting points for this exercise, used for comparison against a control value, and are not official defaults.
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge base | Associate one library containing return inspection forms and refurbishment records | Read the vector model identifier of that library and compare it one by one with the identifier of the library to be associated; do not associate together when the identifiers differ |
| Search mode | Try full-text retrieval first for number-type questions such as RT-2031 | Compare against semantic retrieval results and observe whether the number is matched exactly |
| Quote limit | Start at a length that covers one complete inspection record | Test with inspection records of different lengths to see whether content is truncated, then adjust |
| Minimum relevance | Start at 0.5 | Label relevant and irrelevant samples first, then observe whether the RT-2031 record survives filtering |
| Question optimization | Try enabling it first for multi-turn follow-up scenarios | Compare the original question with the optimized question and check whether the subject was altered incorrectly |
| Result reranking | Try enabling it first when candidates are numerous | Observe whether the rerank score places the RT-2031 record ahead of irrelevant records |
Three common errors
- Treating a retrieval hit as business authorization. The RT-2031 inspection record states grade B and resale after refurbishment; retrieving that record does not mean scrap or refund has been approved. Compare the actions asserted in the draft sentence against the scrap form and refund approval record, and record the result as pending verification when those documents are absent.
- Treating minimum relevance as a permission or entity filter. That value only filters low-relevance results and does not exclude unrelated business or unauthorized records. Compare the recall set before and after filtering and confirm whether unauthorized records are still returned.
- Associating libraries that use different vector models. The documentation requires associating knowledge bases that use the same vector model. Read the vector model identifier of each library first, exclude those whose identifiers differ, and record the actual observation without asserting runtime behavior for the excluded combination.
How to verify the setup
- Verification one: Input the inspection record source text and the draft sentence "RT-2031 has been approved for scrap and the refund has been completed" from the hypothetical sample, then compare the scrap and refund actions asserted in the draft sentence against the scrap form and refund approval record. When the inspection record appears in the retrieval result and no scrap form or refund approval record exists, the draft sentence is unsupported and is recorded as pending verification. No test is claimed to have been run.
- Verification two: Input the RT-2031 number question and run it under full-text retrieval and semantic retrieval separately, then compare how the number record is recalled under each mode. If full-text retrieval matches the number and semantic retrieval does not, record this as a mode difference and do not conclude that semantic retrieval has failed.
- Verification three: Input a question unrelated to return refurbishment and observe the returned quote array. If the array is non-empty, compare the returned content against the question topic and record it for manual review. If the array is empty, check against the documentation whether the downstream chain still runs. Do not claim that an unrelated query must return an empty array.
The retrieval library's same-vector-model requirement does not extend to business registration sites or other external systems. No system error or automatic business action is asserted beyond what the source text states. The optional paths in the overall retrieval flow do not all execute on every request: image retrieval requires matching image input and model capability, and reranking requires a model enabled in the settings.