What the documents look like
Marketplace category qualification access happens before a listing goes live or a store is onboarded, so the material being checked is the operating entity's credentials and the chain of authorization behind them. The documents handled day to day are the business license, trademark registration certificate, brand authorization chain, and industry license. These come from platform recruitment pages, the national enterprise credit information publicity system, trademark office data, and license systems. Updates are irregular, and each onboarding round collects them again.
For order fulfillment and after-sales question answering, retrieval has to answer questions such as whether a given entity holds a given authorization for a given category, which goods the authorization covers, and which date the validity period ends. A qualification record is only meaningful for retrieval when the entity, the object, the status, and the valid scope appear together. When any of those fields is missing, the answer can only stop at pending verification. Because the material changes irregularly and is reassembled per onboarding, the retrieval step cannot assume a stable corpus, and it cannot treat a returned passage as a completed business decision.
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. When identifiers do not match, the vector model identifier is checked first, the nonconforming base is excluded, and cross-model retrieval is not performed to compare missed recalls.
Retrieval combines recall paths according to the selected mode and settings. Question optimization, image handling, and reranking each have their own input and model conditions, and they do not all execute on every request. Semantic retrieval suits natural-language phrasing, while full-text retrieval suits identifiers, certificate numbers, and proper nouns. Hybrid retrieval merges the two paths through RRF, and RRF is a fusion process rather than a switch that can be turned on. The minimum relevance setting takes effect only under semantic retrieval or when result reranking is used; it is a filtering threshold and does not perform entity isolation or permission filtering. The same-vector-model requirement for associated knowledge bases constrains knowledge base association only and does not extend to business registration sites or other external systems.
The citation limit is measured in tokens and controls how much content a single search may cite. The output citation content is an array whose length can be zero, so the output path still completes when no content is found. Image retrieval requires the corresponding image input and model capability, and reranking requires a rerank model enabled through settings. The optional paths shown in the overall flow diagram do not mean every request executes all of them.
Hypothetical rehearsal sample: the source material reads, "Authorizing party A, authorized party B, authorized category household appliances, authorization period from January 1, 2024 to December 31, 2025, authorization letter signing date December 20, 2023." The draft sentence to be assessed reads, "B's authorization in the household appliance category expired on December 31, 2025." The handover request reads, "Please confirm B's authorization status before releasing this category for listing." The parties, object, period, and fields in this sample come from the quoted text, and no real customer or legal identity number is used.
Configuration and implementation checks
| Configuration item | Hypothetical trial starting point for this page | Reason for the choice and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only the qualification material base, with matching vector model identifiers | Check the vector model identifier first, exclude bases that do not match, then confirm the association result |
| Search mode | Try semantic retrieval and full-text retrieval separately first | Compare the citation content returned by both modes for the same test question, without assuming one mode suits all material |
| Citation limit | Start at a token amount that can hold the full text of one authorization letter | Test with material of different lengths to see whether content is truncated, then adjust the limit |
| Minimum relevance | Start at a middle value, with relevant and irrelevant samples labeled first | Takes effect only under semantic retrieval or result reranking; observe filtering against the labeled samples before adjusting |
| Question optimization | Off first, retrieve with the original question | Compare the original question with the optimized question and check whether the entity or object was altered |
| Result reranking | Not enabled initially | Compare ranking changes only after a rerank model is available and enabled through settings |
Three common errors
- Treating minimum relevance as a permission or entity filter. Relevance is a similarity score and does not distinguish whether a party holds an authorization. The input to check is the retrieval question "B's authorization status," the comparison is whether the returned citations contain the fields A, B, household appliances, and January 1, 2024 to December 31, 2025, and the observable result is whether qualification entries unrelated to the party appear.
- Treating a retrieval result as a confirmed authorization. Retrieval returns citation content and does not perform approval or release. The input to check is the handover request above, the comparison is whether the draft sentence states that the authorization status comes from material verification, and the observable result is whether "an authorization letter was retrieved" is written as "the authorization is in effect."
- Associating knowledge bases with different vector models. The input to check is the vector model identifiers of the two bases, the comparison is whether the identifiers match, and the observable result is whether the nonconforming base is excluded and recorded, without performing cross-model retrieval to infer missed or redundant recall.
How to verify the setup
- Input the source material above and the retrieval question "Which date does B's authorization period in the household appliance category end?" Compare whether the returned citation contains both parties A and B, the object household appliances, and the period January 1, 2024 to December 31, 2025. The condition is met when the citation content presents these fields completely; when it does not, record the gap and do not supply missing clauses.
- Input the draft sentence and the authorization source text. First confirm the point in time used to judge "expired," then check for renewal or change evidence. The source text supports the stated period ending December 31, 2025. The sample provides no judgment date and no later record, so the current authorization status remains pending verification and cannot be derived from the date wording alone.
- Input an explicit paper empty result [] and compare whether the downstream design handles a citation length of zero. The condition is met when the downstream branch processes the empty result and records that no content was retrieved; do not claim that this retrieval necessarily returns []. For cross-model bases, check identifiers first and exclude nonconforming ones, then record what is actually observed.
This page addresses only marketplace category qualification access, order fulfillment and after-sales question answering, and knowledge base retrieval and recall. Retrieval and forms collect or return values; business verification, approval, database writes, and external synchronization require their stated downstream implementation. A pending request is not an approval, an offered option is not an execution, and an unsigned termination is not a completed termination.