What the documents look like
Douyin storefront operations center on product cards and store experience scores, a different rhythm from single-session livestream operation. The working materials are product qualification pages, experience score breakdowns, shipping violation appeals, and store announcements. Sources include the Douyin store workbench, the Douyin e-commerce learning center, Selected Alliance data, and logistics interfaces. The stated update frequency is daily and per order, so qualification pages and experience score breakdowns change continuously through ordinary operations, and any retrieved answer must correspond to the current version of the source text. Product selection questions typically carry model numbers, categories, and qualification status, so answers must rest on verifiable source wording.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. The smallest search unit is the knowledge base; a collection only classifies data and does not affect search results. Vector retrieval uses the PostgresSQL PG Vector plugin with an HNSW index, while MongoDB stores original data in the dataset.datas table and records vector IDs in the indexes field, so one data group can map to multiple vectors. Retrieval first recalls vectors, then looks up original data in MongoDB by vector ID, merging entries that map to the same data group and keeping the highest score. Semantic search, full-text search, hybrid search, reranking, and question optimization are optional paths. Image retrieval requires image input and the corresponding model capability. Reranking requires a rerank model and must be enabled by configuration. The reference limit is measured in tokens. The minimum relevance threshold applies only to semantic search or when result reranking is used. Retrieval results reflect content relevance only; they do not constitute an authorization decision and do not isolate unrelated businesses or unauthorized records.
Hypothetical paper exercise sample: a merchant knowledge base contains a product qualification page whose source text reads, "The quality inspection report for product A in this store is valid until June 30, 2025, and its current status is uploaded." A separate store announcement reads, "Starting July 1, 2025, product A is suspended from listing." A draft sentence to be judged reads, "The quality inspection report for product A remains within its validity period in July 2025." The draft sentence and the qualification page take mutually exclusive values on the same validity field for the same subject, so they conflict.
Configuration and implementation checks
| Configuration item | Setting or check relevant to this page | Note |
|---|---|---|
| Associated knowledge bases | Associate only knowledge bases that share the same vector model | Cross-model bases cannot be associated together; verify the identifier first and exclude non-matching ones |
| Search mode | Compare semantic, full-text, and hybrid retrieval separately against the sample question | No single mode is presumed suitable for all materials |
| Reference limit | Measured in tokens; check completeness using real materials of different lengths | For this hypothetical trial the starting value is set at 2000 tokens, chosen because it can hold several qualification source passages; compare against a smaller value to see whether content is truncated |
| Minimum relevance | Applies only to semantic search or when result reranking is used | Label relevant and irrelevant samples before adjusting the threshold |
| Question optimization | Used for coreference resolution and question expansion | Compare the original question with the optimized question and check whether the subject was altered incorrectly |
| Result reranking | Requires a rerank model and must be enabled by configuration | RRF is a fusion process for hybrid results, not a toggle |
Three common errors
- Treating a retrieval hit as authorization or proof of validity. The input is source text stating a quality inspection report is uploaded, together with a request asking whether listing may continue. Compare the retrieval result with the authorization status as two distinct facts. The observable result is that retrieval returns qualification content while the source text does not state the authorization status, so the request remains unsupported.
- Using the minimum relevance threshold to exclude unrelated businesses or unauthorized records. The input is a similarity threshold and a set of candidates. Compare the scope of the threshold with the scope of permissions. The observable result is that the threshold affects relevance filtering only and does not change record accessibility.
- Treating a pending review or a quotation validity period as an approval or a supply term. The input is source text with a "pending review" status and a draft sentence stating "approved." Compare the values on the same field. The observable result is that the two are mutually exclusive and the draft sentence is unsupported by the source text.
How to verify the setup
- Input the qualification page source text and the draft sentence from the hypothetical sample, compare the values on the validity field, and observe whether the draft sentence conflicts with the source text; a conflict means the draft is unsupported.
- Input the store announcement source text and the same draft sentence, compare the announcement date with the report validity period, and observe whether the two cover different periods; different periods mean the announcement cannot be used to conclude the report has lapsed.
- Input a clearly paper-based empty result [] and compare whether the downstream chain is designed to continue when the reference length is 0; the observable result is that the output chain proceeds as documented, without claiming that a fabricated rule search necessarily returns [].
The public technical facts checked for this feature (used to identify relevant content in the documents; the main text is written independently): [["Associated knowledge bases", "The knowledge base search node can associate one or more knowledge bases that use the same vector model."], ["Search mode", "Compare semantic, full-text, and hybrid retrieval against test questions; no single mode is presumed suitable for all materials."], ["Reference limit", "The unit is tokens; test content completeness with real materials of different lengths before adjusting the limit."], ["Minimum relevance", "Applies only to semantic search or when result reranking is used; label relevant and irrelevant samples before adjusting the threshold."], ["Question optimization", "Used for coreference resolution and question expansion; compare the original and optimized question and check whether the subject was altered incorrectly."]]