What the documents look like
Category access review happens before listing or onboarding, and its focus is the applying entity's qualifications and the chain of authorization. The working materials are business licenses, trademark registration certificates, brand authorization chains, and industry permits. These documents come from different issuing bodies, so language, numbering systems, and validity-period formats differ between them. In cross-border work, one underlying document frequently exists in both a Chinese version and a foreign-language version at the same time.
The retrieval step connects the document text awaiting review with qualification descriptions and category requirements already stored in the knowledge base, so that a person can then judge the match. Material updates are irregular and tied to each onboarding event; this page does not derive any batch or automatic synchronization mechanism from that pattern. The difficult part of multilingual retrieval is that the same entity may be written differently across language versions. Semantic retrieval can match across languages, but a matching result is only candidate content and does not confirm that a qualification is valid.
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. When this situation is suspected, check the vector model identifier of each knowledge base, exclude those that do not match, and record what was observed. Do not run a cross-model retrieval to compare missed recalls.
The retrieval output is an array of referenced content, and its length can be 0. Even when no content is found, the output path still completes. The reference limit is measured in tokens. The minimum relevance value takes effect only under semantic retrieval or when result reranking is used. Hybrid retrieval merges multiple result paths through RRF; RRF is a fusion process inside hybrid retrieval, not a switch that can be turned on. Reranking requires a model and must be enabled according to configuration. Image retrieval requires corresponding image input and model capability. Retrieval and similarity do not perform permission or entity isolation, and a retrieval result does not constitute authorization.
Hypothetical drill sample: a knowledge base already contains a qualification description whose source text reads: "The brand authorization letter states that the authorizing party is A Brand Management Co., Ltd., the authorized party is B Trading Co., Ltd., the authorized category is household appliances, and the authorization period is January 1, 2025 to December 31, 2025." A second entry, a business license description, reads: "B Trading Co., Ltd., unified social credit code 00000000000000000X, address No. 1, Some Road, Some District, Some City." The draft sentence to be assessed is: "B Trading Co., Ltd. has obtained the 2026 brand authorization for the household appliance category." The authorization year in the draft does not match the period stated in the authorization letter, so the draft sentence is judged unsupported.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this trial | Reason and comparison method |
|---|---|---|
| Associated knowledge bases | Associate only the qualification description base that uses the same vector model | Check each base's vector model identifier, exclude those with a different identifier, and do not associate across models |
| Search mode | Test the same question with semantic retrieval and with full-text retrieval separately | Compare the content each mode returns for the test question; do not assume one mode suits all materials |
| Reference limit | Start at a token amount that can hold one complete authorization clause | This value is a hypothetical starting point; test with materials of different lengths to see whether content stays complete, then compare against a smaller value |
| Minimum relevance | Start at 0.5 | This value is a hypothetical starting point; label relevant and irrelevant samples first, then compare filtering against a higher or lower value |
| Question optimization | Enable for multi-turn follow-up questions that contain references | Compare the original question with the optimized question and check whether the subject was altered incorrectly |
| Result reranking | Test when a rerank model is available and enabled per configuration | Compare candidate ordering before and after enabling, and confirm the rerank score can be used for filtering |
Three common errors
- Treating a retrieval hit as a verified qualification. Input the draft sentence and the authorization letter source text, then compare the authorization year field. If the draft says 2026 and the source states 2025, the two values are mutually exclusive in the same field, and the draft is judged unsupported. Retrieving related content does not mean the authorization holds.
- Associating knowledge bases that use different vector models. Input the vector model identifiers of the two bases and compare whether they match. If the identifiers differ, exclude the non-matching base and record the observation. Do not infer missed recalls or redundancy from this.
- Treating a pending-review status as granted authorization. Input the authorization material marked "pending review" in the handover request and compare its status field. Pending review does not demonstrate a lack of operating permission, and it does not constitute completed authorization. The evidence gap remains.
How to verify the setup
- Input the draft sentence and the authorization letter source text from the hypothetical sample, then compare the authorization year field. If the source states 2025 and the draft states 2026, the draft is judged unsupported, and the retrieval result does not change that conclusion.
- Input the vector model identifiers of the two knowledge bases proposed for association and compare whether they match. If they do not match, exclude the non-matching base and record it. Do not run a cross-model retrieval to compare missed recalls.
- Input a paper query with clearly no corresponding content, and check whether the downstream design can accept an empty result array. Do not claim that a fictional rule retrieval necessarily returns an empty array.