What the documents look like
Douyin storefront operations revolve around product cards and the store experience score. The typical operators are Douyin store merchants, content commerce operators, and product card operators. The documents handled day to day include product qualification pages, experience score breakdowns, shipping violation appeals, and store announcements. Data sources include the Douyin store workbench, the Douyin e-commerce learning center, Selected Alliance data, and logistics interfaces.
For order fulfillment and after-sales question answering, retrieval therefore centers on qualification status, the composition of the experience score, shipping violation appeal materials, and the stated positions in store announcements. The data update cadence is daily and per order, which means the material a retrieval step cites has version ordering: the same field can hold different values on different dates. The retrieval step itself does not decide whether a qualification is valid, whether an appeal succeeds, or whether compensation has been executed. It returns reference content for downstream steps to use.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Semantic retrieval computes similarity from vector distance. Full-text retrieval depends on keyword hits. Hybrid retrieval merges the two result paths through RRF. A rerank model produces a 0-1 score used for filtering. The minimum relevance value takes effect only under semantic retrieval or when result reranking is used. The reference cap is measured in tokens. Question optimization performs coreference resolution and question expansion, and it adds one model call before retrieval. Reference content is output as an array whose length can be 0; even when nothing is found, the output path still completes. These conditions show that retrieval results are candidate references, not authorization, approval, or business execution.
Hypothetical exercise sample: a Douyin store operator is handling an order after-sales handover. The available material states: "Store announcement (March 2024 version) states: this store's shipping time limit follows the product card page label; for out-of-stock orders, the merchant initiates a refund after confirming no stock is available. Experience score breakdown (1 March 2024 to 31 March 2024) shows the logistics experience sub-score was reduced because of one delayed shipment. Shipping violation appeal record shows the appeal status for that order is 'pending review'." The draft sentence to be judged is: "The store's out-of-stock order refund has been completed by the merchant, the delayed shipment appeal has passed, and the related restriction can be lifted." This sentence involves three facts: refund execution, appeal outcome, and restriction removal. The material supports only the announcement position, the deduction record, and the pending review status.
Configuration and implementation checks
| Configuration or check item | Hypothetical starting point for this exercise and reason | Comparison method |
|---|---|---|
| Associated knowledge bases | Associate only the four material bases for product qualification pages, experience score breakdowns, shipping violation appeals, and store announcements that use the same vector model | Check whether the vector model identifier of each base matches; exclude any that do not match from the association scope |
| Search mode | Use full-text retrieval for questions about delayed shipment deductions, and semantic retrieval for questions about out-of-stock refund flow | Run the same test question under both modes and compare whether the reference content covers the subject and the status |
| Reference cap | Start at the token amount needed to cover one complete announcement paragraph, because such text often spans several sentences | Test with announcement and experience score paragraphs of different lengths and observe where truncation occurs |
| Minimum relevance | Set only when semantic retrieval or reranking is enabled; the starting point is based on labeled relevant and irrelevant samples | Compare filtered results using relevant and irrelevant questions from the samples, then adjust gradually |
| Question optimization | Enable for short questions in multi-turn follow-ups; disable for single-turn questions that already contain a complete subject | Compare the original question with the optimized question and check whether the subject and period were rewritten |
| Result reranking | Enable when a rerank model is available and candidates are numerous | Compare the reference order and scores for the same question before and after reranking |
Three common errors
- Treating a pending-review appeal as passed. In the hypothetical sample the appeal status is "pending review," and the draft sentence says "the appeal has passed." The input is the appeal record status field; the comparison is between the two mutually exclusive values "pending review" and "passed"; the observable result is that the draft sentence does not match the material.
- Treating an announcement position as an executed refund. The hypothetical sample states only that the merchant initiates a refund for out-of-stock orders, and does not state that the refund for that order is complete. The input is the announcement text and the order status field; the comparison is between "initiates a refund" and "refund completed"; the observable result is that the draft sentence lacks execution evidence.
- Treating a retrieval hit as authorization to lift a restriction. The hypothetical sample contains no authorization record for lifting a restriction. The input is the reference content returned by retrieval; the comparison is between the reference content and the authorization field; the observable result is that the reference content contains no authorization information, and the draft sentence's removal conclusion is unsupported.
How to verify the setup
- Input the three original passages from the hypothetical sample: the store announcement, the experience score breakdown, and the appeal record, together with the draft sentence. Compare the three items in the draft sentence, "refund has been completed by the merchant," "appeal has passed," and "related restriction can be lifted," against the corresponding fields in the original text. The draft sentence is supported only when the original text explicitly states refund completion, appeal passage, and authorization removal. In the sample, all three items are unsupported or pending verification.
- Input the appeal status field value "pending review" from the hypothetical sample, and compare whether "pending review" and "passed" are mutually exclusive values of the same field. The observable result is that the two are mutually exclusive, and the draft sentence's "has passed" contradicts the material.
- Input an explicit paper input
[]as reference content and check whether the downstream design still completes the output path when the reference length is 0. The observable result is whether downstream handles it as empty reference. Do not infer that a fictional rule retrieval necessarily returns an empty array.
Knowledge bases with different vector models cannot be associated together. Check the identifiers, exclude those that do not match, and record the actual observation. Do not run cross-model retrieval to compare missed recalls. Image retrieval requires an image input and the corresponding model capability. Reranking requires a model and must be enabled according to settings. Retrieval and similarity scores do not carry permission or entity isolation duties. Business verification, approval, database writes, and external synchronization each need their own downstream implementation.