What the documents look like
Douyin storefront operations center on product cards and the store experience score. The working materials are product qualification pages, experience score breakdowns, shipping violation appeals, and store announcements. In cross-border and multilingual retrieval, one qualification page or announcement may exist as a Chinese original and a translation, so retrieval has to cover both language expressions. Materials update daily and per order; the retrieval step concerns whether currently indexed content matches the wording of a question.
The four material types differ in what they record. A product qualification page states a subject, an object, a status, and a validity range. An experience score breakdown lists score components. A shipping violation appeal records an appeal stage. A store announcement states operating rules. None of these documents is an authorization record by itself, and retrieval of a document does not confer any 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 with different vector models cannot be associated together; the vector model identifier has to be checked first and non-matching bases excluded before retrieval runs. Cited content is output as an array, and the length can be 0. When nothing is found, the output path still completes.
Search modes include semantic search, full-text search, and hybrid search. Semantic search relies on vector distance and suits natural-language questions and cross-language matching. Full-text search relies on keyword hits and suits numbers, model names, and proper nouns. Hybrid search merges the two paths through RRF, which is a result-fusion process. Result reranking uses a ReRank model to reorder candidate text and produces a 0-1 score. Minimum relevance takes effect only under semantic search or when result reranking is used, and it is a filter threshold. The citation limit is measured in tokens and caps how much content each search may cite. Question optimization performs coreference resolution and question expansion before retrieval.
A hypothetical paper case: a Douyin store merchant maintains a cross-border product qualification page whose original text reads "Subject: A Trading Company; Object: a model of children's helmet; Status: test report submitted, awaiting platform review; Validity range: January 2025 to December 2025." A store announcement reads "This store's shipping timeliness follows platform rules." The draft sentence to be judged is "The test report for this model of children's helmet has passed review." The retrieval request is "find the qualification review status for this helmet model."
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point | Reason and comparison target |
|---|---|---|
| Associated knowledge bases | Associate only the base holding this qualification page and announcement | Check the vector model identifier first; exclude bases with a different vector model |
| Search mode | Semantic search and full-text search in parallel | Compare against semantic search alone, observing whether the model name is hit |
| Citation limit | Set to a hypothetical token value | Compare against the full paragraph length of the qualification page, observing truncation |
| Minimum relevance | Set to a hypothetical 0-1 value | Compare against labelled relevant and irrelevant samples |
| Question optimization | Enabled for this hypothetical trial | Compare against disabled, checking whether the subject is altered |
| Result reranking | Enabled for this hypothetical trial | Compare candidate ordering against no reranking, checking whether the model name ranks higher |
These values are hypothetical trial starting points and are not official defaults.
Three common errors
- Treating a retrieval result as an authorization status. Retrieving "test report submitted, awaiting platform review" does not support the conclusion that the report passed review. Awaiting review and passing review are different facts, and the draft sentence lacks evidence of passing review.
- Associating knowledge bases that use different vector models. The documents state that associated knowledge bases must use the same vector model. When models differ, the identifier should be checked and the non-matching base excluded; cross-model retrieval is not run for comparison.
- Treating minimum relevance as a permission or entity filter. This value is a similarity filter threshold and does not exclude unrelated businesses or unauthorized records. Retrieval is not authorization.
How to verify the setup
- Input the qualification page original text and the draft sentence from the hypothetical case, and compare "has passed review" in the draft against "awaiting platform review" in the original. The original states only a pending status, so the draft sentence is unsupported and should be recorded as pending verification, not as passed.
- Input the retrieval request "find the qualification review status for this helmet model" and compare whether the returned cited content includes the qualification page original text. If the returned content contains the wording "awaiting platform review," the check condition is met. If the cited content is an empty array, record that nothing was recalled and do not infer that the material does not exist.
- Input the store announcement original text from the hypothetical case and compare whether the announcement states any review-passed information. The announcement addresses shipping timeliness only and is unrelated to review status, so it cannot support the draft sentence. If the review outcome needs confirmation, record the evidence gap and wait for the platform review conclusion.