What the documents look like
Channel diversion reviews address regional and price-order questions. The objects of work are the direction of goods movement and abnormal selling prices, which sit in a different business line from rebate amount calculation. The main material sources are the anti-counterfeit code system, e-commerce platform prices, distributor shipment records, and market inspections. Daily documents include diversion complaint forms, anti-counterfeit code records, penalty notices, and investigation photographs. Material updates arrive weekly and irregularly; this page does not derive batch synchronization or real-time mechanisms from that cadence.
The shape of the material directly affects retrieval. Anti-counterfeit code records carry codes and batch numbers, which are precise-query objects. Complaint forms and investigation photographs are mostly natural-language descriptions or image content. When both kinds of content sit in one knowledge base, the recall paths need separate checks: code-type questions depend on full-text retrieval, while narrative questions depend on semantic retrieval.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that share the same vector model. Libraries built on different vector models cannot be associated together. Operationally, check the vector model identifier first, exclude those that do not match, and record the actual observation; do not run a cross-model search to compare missed recalls.
Cited content is output as an array, and its length can be 0. Even when nothing is retrieved, the output link still runs through. The citation limit is measured in tokens. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used. Hybrid retrieval fuses multiple result paths through RRF; RRF is a fusion process and is not described as a standalone switch. Result reranking requires an available model and must be enabled according to settings. Image retrieval requires image input and either a vision model or a vector model that supports images. The paths listed in the overall document flow diagram do not mean every request executes all of them.
The retrieval step does not perform authorization judgment. A similarity score is a relevance measure and does not serve as a permission or entity-isolation mechanism. Records returned by retrieval still require downstream business review.
Hypothetical paper exercise sample: a brand channel-control role compiles East China diversion complaints in March 2025. The paper material reads: "Complaint form CX-2025-0312 states that distributor A shipped 40 cases to an out-of-region store on 5 March 2025, with anti-counterfeit code range A1001 to A1040, and two investigation photographs." A separate draft sentence reads: "Distributor A has confirmed the diversion and accepted the penalty." The items to check are whether the draft sentence is supported by the material, and whether the penalty notice is in an effective state. The material does not state the signing or effective information of the penalty notice; that part is an evidence gap.
Configuration and implementation checks
| Configuration or check item | Value or action on this page | Basis and explanation |
|---|---|---|
| Associated knowledge bases | Associate only diversion-review libraries with matching vector model identifiers | Sharing the same vector model is the joint-association condition; exclude mismatches first |
| Search mode | Use full-text retrieval for code-type questions, semantic or hybrid retrieval for complaint narratives | Compare the three modes against test questions; do not presume one mode suits all material |
| Citation limit | Set a hypothetical trial starting point of 3000 tokens and compare completeness with a 1500-token result | This value is only a trial starting point, not an official default; observe whether codes and photo descriptions are truncated |
| Minimum relevance | Label relevant and irrelevant samples first, then adjust the threshold | This value takes effect only in semantic retrieval or result reranking; it is not used for permission filtering |
| Question optimization | Compare whether the subject stays consistent between the original and optimized question in multi-turn follow-ups | Used for coreference resolution and question expansion; adds one model call |
| Result reranking | With a model available and enabled per settings, filter using the 0-1 score | The rerank score can be used for filtering; enabled status must be checked in practice |
Three common errors
- Treating a similarity score as a permission boundary. The input is a retrieval request and returned citations; the comparison object is the cited records against the current operator's accessible scope; the observable result is whether records the operator may not view appear in the citations. Score level does not change access permission, and unauthorized-access judgment requires downstream business implementation.
- Associating knowledge bases built on different vector models directly. The input is the vector model identifiers of multiple libraries; the comparison object is whether the identifiers match; the observable result is whether the association action is permitted. When identifiers differ, exclude that library and record it; do not infer runtime behavior after association.
- Writing a pending-review state as an effective penalty. The input is the draft sentence and the penalty notice material; the comparison object is the state value for the same subject, same period, and same field; the observable result is whether the material states signing and effectiveness. When not stated, record it as pending verification; a pending review does not demonstrate a lack of operating permission.
How to verify the setup
- Code query against the sample text. The input is "anti-counterfeit code range A1001 to A1040"; the comparison object is whether the returned citations include the record containing that code range; the observable result is that the corresponding code range content appears in the citations. If it does not appear, record the failure branch and check whether the record has completed training and which retrieval mode was selected.
- Support check against the draft sentence. The input is the draft sentence "Distributor A has confirmed the diversion and accepted the penalty"; the comparison object is the same-subject, same-period, same-field statements in the complaint form text and the penalty notice material; the observable result is whether the material states confirmation and effectiveness. When the material does not state them, judge it as unsupported and preserve the evidence gap.
- Empty-result handoff check. The input is a paper input of an empty citation array; the comparison object is the downstream node's design for handling a citation array of length 0; the observable result is whether the output link runs through and whether the downstream gives a clear branch. This check does not claim that a fictional rule retrieval necessarily returns an empty array.
All three checks use the hypothetical exercise sample as their object. Expected results require observation after actual configuration; this page does not claim that testing was performed or approval was obtained.