What the documents look like
TikTok Shop US store operations sit under local return windows, return grading, and overseas logistics constraints. Daily materials include product listings, return and refund records, return grading evidence, and settlement statements. Mistakes in this environment can lead to platform automatic refunds, higher return fees, or failed appeals.
For cross-border materials and multilingual retrieval, these materials must be cited with traceable sources. A citation must correspond to the specific original material. A retrieval result does not replace manual confirmation of the valid version of a material.
The data update frequency given for this category is daily and per order. Where update frequency does not affect the citation tracing step, it is omitted here.
Constraints at the citation tracing step
The knowledge base search node outputs a citation array, and the length may be zero. An empty array does not mean the workflow has errored or terminated. Linked knowledge bases require the same vector model before they can be linked together; knowledge bases with different vector models cannot be linked together.
The minimum relevance value takes effect only during semantic retrieval or when result reranking is used. This value is a filtering threshold. It does not perform permission isolation or entity isolation. Hybrid retrieval combines multiple result paths through RRF. A rerank score can be used for filtering, but the final ordering is not necessarily strictly by a single vector similarity. Image retrieval requires image input and model capability. Reranking requires a model and must be enabled according to settings. These are optional paths, and not every request executes all of them.
Hypothetical drill sample: A merchant has one return record. The original text reads: "Order number US-2024-001, buyer applied for return on March 10, 2024, return reason marked as size mismatch, item status marked as signed for, return window closes March 25, 2024." A separate draft sentence is pending judgment: "This order has completed a refund." The check must confirm whether the refund record contains a corresponding refund completion field, and whether that field shares the same subject, the same period, and the same field as the return record. The current material does not state a refund completion status. The draft sentence is unsupported.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point for this page | Verification method |
|---|---|---|
| Linked knowledge bases | Select knowledge bases with the same vector model as the current one | Check the knowledge base identifier and vector model identifier, and exclude libraries with different vector models |
| Search mode | Hybrid of semantic retrieval and full-text retrieval | Compare numbered and model-type content against natural-language content in the material, and observe recall differences |
| Minimum relevance | Hypothetical trial starting point 0.5 for this page | This value takes effect only during semantic retrieval or result reranking; compare filtering effects against 0.3 and 0.7 |
| Citation limit | Hypothetical trial starting point 2000 tokens for this page | Control citation content length by tokens; compare citation completeness against 1000 and 4000 |
| Question optimization | Enabled for multi-turn follow-up scenarios | Observe changes in retrieval terms after coreference resolution and question expansion; compare against the disabled state |
| Result reranking | Enabled when candidate results are numerous | Rerank scores range from 0 to 1 and are used for filtering; compare ordering changes against the disabled state |
Three common errors
- Treating a citation array length of zero from retrieval output as a workflow error termination. Check the input: the citation array output by the search node. Compare: whether the array length is zero. Observe the result: when the array length is zero, the downstream chain still runs through.
- Treating the minimum relevance value as a permission or entity isolation setting. Check the input: the minimum relevance value. Compare: whether this value is used to filter low-relevance results. Observe the result: this value does not exclude unrelated businesses or unauthorized records.
- Treating a pending refund application as a completed refund. Check the input: the refund completion field in the return record. Compare: whether that field shares the same subject, the same period, and the same field as the return record. Observe the result: when the field is not stated, the claim is unsupported and cannot be judged as a completed refund.
How to verify the setup
- Input the hypothetical sample return record text. Compare the draft sentence "This order has completed a refund" against the refund completion field in the original text. Observe whether the original text states that field. When it is not stated, record the result as unsupported. Do not judge it as a contradiction or a negation.
- Input the knowledge base identifier and the vector model identifier. Compare whether the two are consistent. Observe whether libraries with different vector models are excluded. Do not run cross-model retrieval to compare missed recalls.
- Input the citation limit hypothetical trial starting point of 2000 tokens and the minimum relevance hypothetical trial starting point of 0.5. Compare the citation content length and the number of results after filtering. Observe whether the citation array length can be zero and whether the downstream chain still runs through. Do not guarantee a first-hit match or full recall.