What the documents look like
Returns refurbishment records follow the physical state of goods moving backward through the reverse-logistics chain. The main sources are the overseas warehouse WMS, the TikTok returns center, Amazon return reports, and product quality-inspection photos. Day-to-day documents are the return inspection form, the refurbishment record, the scrap form, and the relisting checklist. These records update daily or per batch, and they track one item across return, inspection, refurbishment, scrap, or resale.
Three facts shape retrieval for this category. First, the subject matter is split across systems, and the same return number can carry different field conventions in the WMS and in the return report, so the scope of a citation must be confirmed before retrieval. Second, inspection photos and text records coexist; whether an image can be recalled depends on whether an image-description index or an image-vector index was created at ingestion and on the current model capability. Third, multilingual demand comes from cross-border teams: semantic retrieval supports cross-language understanding, while exact queries for part numbers and model codes fit full-text or hybrid retrieval.
A hypothetical paper exercise sample is used throughout this page. On 10 March 2025 an overseas-warehouse inspection group submits a batch of return forms. The source text reads: "Return RT-2025-0310-07, item portable juicer, visible scratches, functional test passed, graded B for refurbishment; refurbishment record FR-0310-07 states the outer shell was replaced and the item relisted." A draft sentence to be assessed reads: "This return has completed refund and terminated processing." The source text does not state refund or termination, so the draft sentence is unsupported. Refurbishment and relisting are recorded and do not form a mutually exclusive value with termination in the same field, so no contradiction can be derived from them.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Bases using different vector models cannot be associated together. Retrieval searches the whole base; a collection only classifies data and does not affect retrieval. Cited content is returned as an array whose length can be 0, and the output path still runs even when nothing is found. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used; it is a filtering threshold and does not perform permission or entity isolation. Retrieval itself is not authorization, and whether a given return record is visible depends on whether its base is in the associated scope and on access rights.
The documented retrieval chain combines several paths. Problem optimization performs coreference resolution and question expansion before retrieval. Semantic retrieval, full-text retrieval, or hybrid retrieval recalls candidates. When the input contains an image, image-description retrieval or image-vector retrieval is added according to model capability. RRF merges multiple channels, Rerank reorders text results, and similarity filtering plus a citation limit produce the final cited content. These optional paths do not all run on every request: image retrieval requires an image input and the relevant model capability, and reranking requires a rerank model and must be enabled in the settings.
Configuration and implementation checks
| Configuration item | Hypothetical starting value for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge base | Associate only the return inspection base that uses the same vector model | Check the vector model identifier first, exclude bases with a different model, and do not run cross-model retrieval |
| Search mode | Test semantic retrieval and full-text retrieval in separate rounds | Use the same batch of inspection-form questions and compare the recalled content of the two modes |
| Citation limit | Start from a token amount that can cover one complete inspection form | Test with inspection forms of different lengths, observe whether content is truncated, then adjust |
| Minimum relevance | Set a starting value after labeling relevant and irrelevant samples | This value takes effect only in semantic retrieval or reranking; compare results before and after filtering |
| Problem optimization | Enable for multi-turn follow-ups and short questions | Compare the original question with the optimized question and check whether the return number subject was altered |
| Result reranking | Enable when candidates are numerous and filter by score | Reranking requires a model and must be enabled in the settings; compare ordering before and after |
Three common errors
- Treating a retrieval hit as an approval. Returned cited content does not mean a refund or scrap decision has been approved; refund, scrap, and relisting are business actions that need separate downstream system implementation. Check: input an inspection record that exists only in the base, compare the retrieval output with the approval fields, and observe whether an approval status is replaced by retrieval results.
- Treating minimum relevance as a permission or entity filter. The value is a relevance threshold and does not exclude unrelated businesses or unauthorized records. Check: ask about a semantically similar return with a different subject, compare results before and after filtering, and observe whether the unrelated subject is still recalled.
- Treating RRF as a toggle. RRF is the fusion process for hybrid retrieval results, not an independent configuration that can be switched on. Check: inspect the configuration interface for a control named RRF, and if none exists, record that the documentation provides no such control.
How to verify the setup
- Verification one: input the hypothetical source text "Return RT-2025-0310-07, visible scratches, functional test passed, graded B for refurbishment," compare whether the retrieved content contains that return number and the grade-B field, and observe whether the returned citation matches the source text. If the returned content contains refund or termination wording, the recalled content does not match the source and the data source must be checked.
- Verification two: input the draft sentence "This return has completed refund and terminated processing," compare whether the source text contains refund and termination fields, and observe whether the only available conclusion is that the claim is unsupported. The assessment must use the same subject, the same period, and the same field; when the source text does not state a field, the evidence gap remains.
- Verification three: use a knowledge base identifier from a different vector model and run an association check, compare whether the two vector model identifiers match, and observe whether the mismatched base is excluded from the associated scope. Do not run cross-model retrieval to compare missed recalls; only check the identifier and record the actual observation. For empty-result handling, a clearly labeled paper input of
[]can be used to check the downstream design, without claiming that a fictional rule retrieval necessarily returns[].