What the documents look like
Douyin storefront operations center on product cards and the store experience score. The working materials for this category include product qualification pages, experience score breakdowns, shipping violation appeals, and store announcements. These materials update daily and per order, so the source version changes with ongoing business actions. A product information and selection question must anchor its answer to a specific material version, because the same question can map to different qualification states on different dates. The stated consequences of error in this category are traffic restriction, violation point deductions, and consumer compensation. Citation tracing is the step that reduces these risks. This page covers only citation tracing for product information and selection questions within this category.
The materials differ in what they can support. A qualification page carries a subject, an object, a validity range, and a document number. A shipping violation appeal carries a status field and a target order. A store announcement carries a version date and a sale statement. None of these materials, on its own, proves that a selection recommendation is safe. The tracing step compares what a drafted answer claims against the exact field values in the cited source.
Constraints at the citation tracing step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. The citation output is an array, and its length can be zero. When no content is found, the output path still completes. The search result must be passed to the answering step, and the answer must then be checked line by line against the cited source text. Material timeliness checks use the current version and the historical version of a document; whether a citation is adopted still requires review against the effective version.
Semantic retrieval, full-text retrieval, hybrid retrieval, RRF fusion, ReRank, and image retrieval are optional paths. Image retrieval requires the corresponding image input and model capability. ReRank requires a model and must be enabled by configuration. The minimum relevance score takes effect only under semantic retrieval or when result reordering is used, and it acts as a filter threshold. It does not perform permission or entity isolation. Retrieval is not authorization. A form collecting a value does not mean business verification, approval, a database write, or external synchronization has occurred.
Hypothetical paper exercise sample. Source text one: "The product qualification page for a certain insulated cup states: food-contact material test report number HYP-2024-0317, valid until December 31, 2024, applicable subject is a certain daily goods firm." Source text two: "The shipping violation appeal submitted by this firm on November 20, 2024 has status pending review, and the appeal target is the order of November 18." Source text three: "The store announcement dated November 25, 2024 states: this insulated cup is on normal sale." Draft sentence to be judged: "This insulated cup has complete qualifications and is safe for selection." The comparison items required for judgment are: whether the qualification page subject and the announcement subject match, whether the test report validity period covers the draft date, and whether the appeal status supports a conclusion of complete qualifications.
Configuration and implementation checks
| Configuration or check item | Hypothetical starting point for this exercise | Reason for selection and comparison method |
|---|---|---|
| Associated knowledge bases | Select two knowledge bases with the same vector model: product qualification pages and store announcements | First verify the vector model identifier matches, then exclude any base with a different model; do not run cross-model retrieval |
| Search mode | Use semantic retrieval together with full-text retrieval | Full-text retrieval suits exact queries such as numbers and models; semantic retrieval suits natural-language phrasing; results are fused via RRF |
| Citation limit | Hypothetical starting point for this exercise set to 3000 tokens | The citation limit is controlled by tokens; compare against the total length of the sample materials before adjusting |
| Minimum relevance score | Hypothetical starting point for this exercise set to 0.4 | This value takes effect only under semantic retrieval or result reordering; compare against the relevance distribution of returned results |
| Question optimization | Not enabled for this exercise | The sample question has a clear subject; enabling it adds one model call and latency, so decide by actual effect |
| Reply format | Keep default | The downstream flow does not require a fixed structure, so no change to json_schema is needed |
Three common errors
- Treating a retrieval hit as authorization. The input is the draft sentence above and a retrieved qualification page fragment. The wrong approach is to declare "complete qualifications" as soon as the qualification page is recalled. The correct comparison is whether the qualification page validity period covers the draft date and whether the subject matches the announcement. The observable result is that when the validity period does not cover the date or the subject does not match, the conclusion is not supported.
- Treating a pending appeal as a completed resolution. The input is the appeal record with status "pending review." The wrong approach is to conclude from this that the violation has been cleared. The correct comparison is the appeal status field against the value "completed." The observable result is that when the status remains pending review, the record can only be noted as pending verification.
- Forcing association of knowledge bases with different vector models. The input is two knowledge bases whose vector model identifiers differ. The wrong approach is to select both for association and run retrieval. The correct comparison is whether the vector model identifiers match. The observable result is that when the identifiers differ, exclude that base and record the actual observation, without inferring a runtime result.
How to verify the setup
- Use the hypothetical sample qualification page text and the draft sentence as input. Compare the draft conclusion against the qualification page validity period and subject fields. The observable result is that the conclusion is supported only when the validity period covers the draft date and the subject matches; otherwise record it as unsupported.
- Use the hypothetical sample appeal record text as input. Compare the appeal status field against the value "completed." The observable result is that when the status is pending review, record it as pending verification, and do not judge it as resolved.
- Use an empty citation array as a paper input. Compare whether the downstream answering step is designed to accept a citation of length zero. The observable result is that the link design passes when the downstream step accepts the empty array without error. Do not claim that a fictional rule retrieval will necessarily return an empty array.