What the documents look like
Douyin storefront shelf operations revolve around product cards and the store experience score, which separates this work from live-room operations that depend on a single broadcast's rhythm. The materials 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. Updates arrive daily and per order.
For store service and internal training, these materials are organized into a searchable knowledge base so that operations staff can check handover items. Frequent updates mean retrieval results shift with ingestion progress: retrieval returns only content that has been ingested and has completed training.
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 built on different vector models cannot be associated together. The vector model identifier is checked first, and non-matching bases are excluded; no cross-model retrieval comparison is performed.
Search mode is compared across semantic retrieval, full-text retrieval, and hybrid retrieval according to the test question. No single mode is assumed to suit all materials. The citation limit is measured in tokens. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used. Question optimization performs coreference resolution and question expansion; the original question and the optimized question are compared to check whether the subject was altered incorrectly. Retrieval is the process of fetching content and does not perform authorization judgment. Forms collect values; business verification, approval, database writes, and external synchronization each require their own downstream implementation.
Hypothetical drill sample: the training handover material reads, "This store's product qualification page was updated in March 2025, covering category A; shipping violation appeal number S-2025-0312 is pending review; store announcement No. 7 describes the out-of-stock and unavailable-goods handling process." The draft sentence to be judged reads, "The store's qualification has passed review, and the appeal has been fully processed." Comparing against the original text, the qualification page states only an update and a covered category, with no review conclusion recorded; the appeal number is pending review, and pending review does not constitute completed processing. Neither conclusion in the draft sentence is supported and both remain to be verified.
Configuration and implementation checks
The table below is a hypothetical starting point for this drill, used to show what values are compared. It does not represent official default values.
| Configuration item | Hypothetical starting point and reason | Comparison and checking method |
|---|---|---|
| Associated knowledge base | Associate one base with a single vector model; check the identifier first | When changing bases, re-check the vector model identifier and exclude non-matching ones |
| Search mode | Use full-text retrieval for qualification questions and semantic retrieval for process questions | Test the same question under each mode and observe differences in returned content |
| Citation limit | Start at a token amount that covers one complete announcement | Test with announcements of different lengths to see whether content stays complete, then adjust |
| Minimum relevance | Start at 0.5 with relevant and irrelevant samples labeled first | Takes effect only in semantic retrieval or result reranking; compare filtered samples |
| Question optimization | Enable for multi-turn follow-up questions | Compare the original and optimized questions and check whether the subject was altered |
| Result reranking | Enable when candidates are numerous | Requires a reranking model enabled per settings; compare ordering changes |
Three common errors
- Treating a pending-review appeal as fully processed. The input is appeal number S-2025-0312 with status "pending review"; the comparison is between "pending review" and "fully processed"; the observable result is that the draft sentence's conclusion is unsupported.
- Treating a qualification page update as a passed review. The input is "updated in March 2025, covering category A"; the comparison is between an update record and a review conclusion; the observable result is that the original text records no review conclusion, leaving an evidence gap.
- Associating knowledge bases built on different vector models. The input is the vector model identifiers of two bases; the comparison is whether the identifiers match; the observable result is that non-matching bases are excluded and no cross-model retrieval is performed.
How to verify the setup
- Use the qualification page text "This store's product qualification page was updated in March 2025, covering category A" as input and compare whether the retrieved content matches that sentence. The condition is met when the returned content includes the update date and the category.
- Use the draft sentence "The store's qualification has passed review" as input and compare whether the returned content contains a review conclusion field. When no review conclusion is returned, the conclusion remains to be verified and the evidence gap is preserved.
- Use an empty array as a paper input and check how the downstream design handles empty citation content. The condition is met when the output path runs through with a citation length of 0; no claim is made that a fictional rule retrieval necessarily returns an empty array.