What the documents look like
Cross-border return refurbishment and resale handles reverse logistics and item condition. This differs 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 quality-inspection photos. Daily documents include return quality-inspection sheets, refurbishment records, scrap sheets, and relisting lists. The most time-consuming steps are judging the return grade, arranging refurbishment and packaging, and deciding between scrap and resale. These documents often arrive as single items or small batches. The same item can carry an inspection conclusion, a refurbishment process record, and a relisting status at the same time. Chunking must keep the subject, object, status, and effective scope together.
Constraints at the document chunking step
Parsing and chunking must follow the structure of each document type: return quality-inspection sheets, refurbishment records, scrap sheets, and relisting lists. In an inspection sheet, the grade decision conditions and the corresponding item status should sit in the same data chunk so that conditions and conclusions are not split apart. The process step and packaging status in a refurbishment record, and the sellable status in a relisting list, each need their own preserved index. In FastGPT, one set of data can map to multiple vector indexes. During retrieval, vectors are recalled first, then the vector ID is used to find the original data in the MongoDB dataset.datas table; entries that map to the same original data are merged and the highest score is taken. Hybrid retrieval fuses semantic and full-text results through RRF. Result reranking depends on a ReRank model. Both require the corresponding settings and model capability.
Hypothetical paper exercise sample: an overseas warehouse inspection record reads "Return number RT-2041, item is a folding table, surface scratches, function test normal, inspection grade B, can be arranged for relisting after refurbishment and packaging." The draft sentence to be judged reads "RT-2041 folding table has been approved for scrap." The draft sentence and the source differ in the value of the item status field. The source states relisting after refurbishment; the draft states approved scrap. The two are mutually exclusive values in the same field, so the draft is an unsupported statement.
Configuration and implementation checks
| Configuration item | Setting or check relevant to this page | Relation to the sample |
|---|---|---|
| Search mode | Semantic retrieval, full-text retrieval, or hybrid retrieval | Search with "folding table" and "RT-2041" separately and compare what is recalled |
| Result reranking | A ReRank model is required and must be enabled per settings | When candidates are numerous, compare rerank scores and filtering |
| Citation limit | Citation content is controlled by tokens | Check whether inspection conditions and conclusions return in the same chunk |
| Minimum relevance | A 0-1 value, effective only in semantic retrieval or with result reranking | Check whether the sample source text remains after filtering |
| Question optimization | Coreference resolution and question expansion, adding a model call before retrieval | Check the completed result during a follow-up such as "how is this batch handled" |
| Index status | Check the actual training and index status | After a document change, a successful upload cannot be treated as retrieval already updated |
For this paper exercise, a starting minimum relevance of 0.5 may be set. The reason is that this value sits in the middle of the 0-1 range, which makes differences before and after filtering easier to observe, and it can be compared against recall results with no threshold set. This value is only a starting point for this paper check, not an official default.
Three common errors
- Splitting the inspection grade and the relisting status into different data chunks, so retrieval recalls only the grade and loses the sellable status. Check: enter "RT-2041 folding table inspection grade" and compare whether the returned content contains both grade B and relisting after refurbishment. If only the grade returns, the chunking is incomplete.
- Treating the draft sentence "has been approved for scrap" as a source fact. Check: enter "RT-2041 folding table scrap" and compare whether the source contains a scrap-approval field. The source contains only the inspection grade and the refurbishment arrangement, so the draft sentence is unsupported.
- Treating a retrieval result as authorized execution. Check: enter "RT-2041 execute scrap" and compare whether the system returns any scrap execution record or approval status. Retrieval returns document content only and does not constitute authorization for a scrap operation.
How to verify the setup
- Enter "RT-2041 folding table inspection grade B" and compare whether the returned content includes the source wording on surface scratches, normal function, grade B, and relisting after refurbishment. If the returned content lacks the sellable status, the chunking needs adjustment.
- Enter "RT-2041 folding table approved for scrap" and compare whether a scrap-approval field appears in the returned content. The source does not state this field, so the expected result is unsupported, and the evidence gap should be recorded.
- Enter "folding table relisting" and compare whether the returned content matches the sellable status in the relisting list. If the result is empty, a paper input of
[]can be used to check the downstream design for handling empty citations. No claim can be made that a fictional rule makes retrieval always return an empty array.
Retrieval or a form does not constitute authorized execution, and a pending review does not demonstrate a lack of operating permission. Libraries using different vector models cannot be associated together. The identifiers must be checked first and non-matching libraries excluded. Cross-model retrieval must not be run to compare missed recalls.