What the documents look like
Cross-border return refurbishment and resale handles reverse logistics and item condition. This work sits apart from first-leg replenishment at an overseas warehouse. The main data sources are the overseas warehouse WMS, the TikTok return center, Amazon return reports, and item inspection photos. Day-to-day documents include return inspection forms, refurbishment records, scrap sheets, and relisting checklists. These materials update daily or per batch, and the inspection conclusion, refurbishment record, and relisting checklist for one return batch need to be checked within the same time range.
Product information and selection questions ask about the grade decision, refurbishment arrangement, and sellable status of a given return batch. Retrieval targets the original passages of those documents. A retrieval result only presents document content. It does not constitute a return grade approval, a scrap authorization, or a listing permission.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases using different vector models cannot be associated together. Check the vector model identifier of each base first, exclude those that do not match, and record the actual observation. Do not run cross-model retrieval to compare results. A retrieval hit only means the material was recalled. It does not mean the batch has been approved for refurbishment or listing.
Search mode is compared across semantic retrieval, full-text retrieval, and hybrid retrieval according to the test question. No single mode is assumed to suit all materials. Hybrid retrieval merges multiple result paths through RRF. RRF is a result fusion process, not a standalone switch. Result reranking requires a rerank model and must be enabled through its setting. Image retrieval requires the corresponding image input and model capability. The paths listed in the overall document flow diagram do not mean every request executes all of them. The reference limit uses tokens as its unit. Minimum relevance takes effect only under semantic retrieval or when result reranking is used. It is a filter threshold, not a permission or entity isolation measure. Question optimization performs coreference resolution and question expansion, and it adds one model call.
Hypothetical paper exercise sample: An overseas warehouse inspection team submitted a return batch on March 10, 2025. The source material reads: "Return number RT-2025-0310-A, item is a portable juicer, inspection conclusion is minor surface scratches, function normal, recommend refurbished packaging then relisting, this conclusion is filled in by the inspector, not yet confirmed by the after-sales supervisor." The draft sentence to be judged is: "This batch has completed refurbished packaging and has been approved for relisting." According to the source, refurbished packaging is at the recommendation stage, and after-sales supervisor confirmation is not stated. The draft sentence is not supported.
Configuration and implementation checks
| Configuration item | Starting point for this hypothetical trial | Basis and comparison method |
|---|---|---|
| Associated knowledge bases | Return inspection form base and relisting checklist base; check vector model identifiers first | The document requires the same vector model for joint association; exclude bases whose identifiers do not match |
| Search mode | Run semantic retrieval and full-text retrieval on the same question set | Compare hit differences against the document description of the three modes; do not preset a conclusion |
| Reference limit | Set the trial starting point to the token amount needed to cover one complete inspection conclusion | Test with actual materials of different lengths to see whether content stays complete, then adjust |
| Minimum relevance | Label relevant and irrelevant samples first, then take a trial value between 0 and 1 | This value takes effect only under semantic retrieval or result reranking; compare against the labeled samples |
| Question optimization | Enable for multi-turn follow-ups containing references; disable for complete questions | Compare the original question with the optimized question and check whether the subject was changed incorrectly |
| Result reranking | Enable when candidates are numerous and a rerank model is available | Rerank score is 0 to 1; compare the score against the labeled samples |
Three common errors
- Treating a retrieval hit as authorization. In the sample, the inspection conclusion is filled in only by the inspector, and after-sales supervisor confirmation is not stated. Judging from this that relisting has been approved treats recalled content as an approval result. Retrieval does not carry an authorization function.
- Treating minimum relevance as a permission or entity filter. This value takes effect only under semantic retrieval or result reranking, and it filters low-relevance results. It cannot exclude unrelated business lines or unauthorized records.
- Forcing joint association across vector models. The document requires associating knowledge bases with the same vector model. When a base has a mismatched identifier, check the identifier and exclude it, record the actual observation, and do not infer runtime behavior after association.
How to verify the setup
- Input the hypothetical sample source text and the draft sentence "This batch has completed refurbished packaging and has been approved for relisting." Compare the subject, object, and status fields of the draft sentence against the source. The source states that refurbished packaging is a recommendation and that supervisor confirmation is not stated, so the draft sentence does not match the source and is judged unsupported. If retrieval returns that inspection passage, recall is working, but the conclusion still requires manual verification.
- Input a query containing only an exact return number. Compare the hit content of semantic retrieval and full-text retrieval. If semantic retrieval misses while full-text retrieval hits the passage with that number, exact identifier queries suit full-text or hybrid retrieval. If both miss, record it as not recalled and do not infer a cause.
- Input a paper empty result
[]and check whether the downstream node proceeds along the path where reference content has length 0, as the document describes. If the downstream node does not stop on the empty array, this matches the document statement that output length can be 0. If it stops, record that observation and do not claim a fabricated rule necessarily returns an empty array.
These judgments are paper checks. No actual retrieval, approval, or listing action was performed.