What the documents look like
Category qualification access happens before listing or store onboarding. The object being checked is the entity's qualifications and the chain of authorization, and the material set named in the brief is a business license, a trademark registration certificate, a brand authorization chain, and an industry license. In the store-service and internal-training setting, frontline staff confirm with a brand channel manager or an onboarding specialist whether a specific document can support a target category. Sources named in the brief are platform recruitment pages, the national enterprise credit information publicity system, trademark office data, and license systems. Updates are irregular, and each onboarding may bring a new version.
At the retrieval step, the fields that matter are entity name, certificate number, authorization tier, licensed scope, and validity period. Material length varies widely, and numbers and proper nouns are common. The knowledge base search node can associate one or more knowledge bases that use the same vector model, and it outputs referenced content as an array whose length can be 0, so the output path runs even when nothing is found. The brief marks its confidence as broadly so, which means the surrounding background stays conditional.
Constraints at the retrieval step
Qualification material carries many exact numbers and licensed item names. Full-text retrieval suits numbers, models, proper nouns, and error codes; semantic retrieval depends on vector similarity and suits natural-language descriptions. Hybrid retrieval uses both recall paths and merges them through RRF, after which ReRank can perform a second ordering. The minimum relevance value is a 0 to 1 filter threshold that takes effect only in semantic retrieval or when result reranking is used; it filters low-relevance results and does not act as a permission or entity isolation mechanism. The same-vector-model requirement constrains knowledge base association only, and it does not extend to the enterprise credit site or any other external system.
Question optimization performs coreference resolution and question expansion and adds one model call before retrieval. It can improve retrieval in continuous follow-up turns, and when the current question is already clear, enabling it is a practical decision. The reference cap is measured in tokens and controls how much content each search may reference at most. Retrieval is not authorization, and a form collecting values is not business verification, approval, a database write, or external synchronization; those need their stated downstream implementation.
Hypothetical paper exercise. The source text to be checked reads: "Authorizer A, unified social credit code sample number, authorizes Authorized Party B to sell Class 25 apparel goods in the East China region, authorization period from January 1, 2026 to December 31, 2026, this authorization is a general authorization and does not include sub-authorization." A draft sentence awaiting review reads: "B may sell Class 25 apparel goods nationwide, authorized through the end of 2027." The comparison input is the source text above and the draft sentence, and the compared objects are the three fields of region, period, and authorization tier.
Configuration and implementation checks
| Configuration item | Hypothetical starting value for this exercise | Reason and comparison method |
|---|---|---|
| Associated knowledge base | Associate one base containing qualification material | Check that its vector model identifier matches the identifier used for retrieval, and exclude bases whose identifiers do not match |
| Search mode | Run full-text retrieval once first | Use a test question containing a certificate number to compare recall across semantic, full-text, and hybrid retrieval |
| Reference cap | Set to a token amount covering one complete authorization chain description | Test with actual material of different lengths to see whether content is truncated, then adjust |
| Minimum relevance | Start at 0.3 for this exercise | Label relevant and irrelevant samples, then compare whether the filter retains the needed entries |
| Question optimization | Enable in multi-turn follow-up settings | Compare the original question with the optimized question and check whether entity and region were altered |
| Result reranking | Enable per settings when a model is available | Compare candidate order before and after reranking and check scores against manual labels |
Three common errors
- Treating minimum relevance as a permission or entity filter. Minimum relevance is a similarity threshold and does not exclude unrelated businesses or unauthorized records. Check method: input a question unrelated to qualification, observe whether returned results can still include other entities' material, and record the actual observation without presuming the result must be empty.
- Associating knowledge bases that use different vector models. The documentation requires associating knowledge bases with the same vector model. Check method: verify each base's vector model identifier, exclude those whose identifiers do not match, and record the actual observation rather than describing a cross-model retrieval outcome.
- Treating a pending handover request as executed authorization. In the exercise sample, the draft sentence says nationwide scope and authorization through the end of 2027, while the source text limits the region to East China and the period to December 31, 2026. Check method: compare region, period, and authorization tier field by field, judge a contradiction only when the same field holds mutually exclusive values, and record unstated fields as pending verification.
How to verify the setup
- Input the exercise source text and the draft sentence, and compare the region field: the source text states East China, and the draft sentence states nationwide. The condition is met when retrieval returns the authorization chain source text and the comparison record notes the region field mismatch.
- Input a test question containing a certificate number, and compare recalled entries between full-text retrieval and semantic retrieval. The condition is met when the material entry containing that number can be located in the results; when it is not located, record it as not recalled rather than as nonexistent.
- Input a paper question with no corresponding material, and check that when the output referenced content is an array of length 0, the downstream step follows the empty-result branch. The condition is met when the output path runs and the reference array is empty, without asserting that a fictional rule search must return an empty array.