What the documents look like
Channel diversion review handles regional and price order. It does not handle rebate amounts. The working materials are diversion complaint forms, anti-counterfeit code records, penalty notices and investigation photographs. Sources include anti-counterfeit code systems, e-commerce platform prices, distributor shipment records and market inspections.
In cross-border work, one shipment can correspond to complaint forms and photo captions written in several languages. Retrieval therefore has to match across languages, not only within one language. Materials arrive weekly or irregularly: complaint forms and inspection photographs appear as events occur. The retrieval side faces a document set that is loaded in batches with uneven timestamps, so a returned citation reflects content already stored in the knowledge base. It does not represent live channel status. Anti-counterfeit code records and penalty notices belong to different stages. A retrieval hit shows that a document exists.
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases using different vector models cannot be associated together. Verify the model identifier first and exclude any base that does not match. Do not run a cross-model search to compare missed recalls.
Constraints at the retrieval step
Semantic retrieval depends on vector distance and supports cross-language understanding, which suits natural-language complaint descriptions. Full-text retrieval depends on keyword hits, which suits exact queries such as anti-counterfeit codes, model numbers and identifiers. Hybrid retrieval uses both paths and merges them through RRF. RRF is a fusion process for mixed results, not a switch to be turned on. Reranking requires an available model and must be enabled according to settings. The optional paths listed in the overall flow diagram do not mean every request executes all of them.
The citation limit is measured in tokens and controls how much content one search may cite. Minimum relevance is a 0-1 filter threshold that takes effect only in semantic retrieval or when result reranking is used. It is a filter condition and does not perform permission or entity isolation. The same-vector-model requirement for associated knowledge bases does not extend to business registration sites or other external systems.
Hypothetical paper exercise sample: a brand channel control team receives a complaint form whose original text reads: "Complaint number TS-2024-0417, subject is East China distributor A, product model A-100, complaint states that a listing below the suggested retail price was found on an e-commerce platform, two inspection photographs attached, complaint date 17 April 2024." A penalty notice loaded in the same batch reads: "Notice number CF-2024-0031, subject is East China distributor A, cause is cross-region sales, status is pending review, issue date blank." The draft sentence to be judged is: "Distributor A has already been penalized and the cross-region sales fact is established." This draft sentence disagrees with the penalty notice on the status field: the original status is pending review, while the draft states that a penalty has been imposed.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison object |
|---|---|---|
| Associated knowledge bases | Complaint form base and penalty notice base, with vector model identifiers verified as identical | Compare against base identifiers; exclude bases with a different vector model |
| Search mode | Semantic retrieval first, then hybrid retrieval as a comparison | Compare hit content for the same complaint form under both modes |
| Citation limit | Starting point set to cover the token volume of one complete complaint form | Compare against actual documents of different lengths and observe whether content is truncated |
| Minimum relevance | Starting value 0.5, with relevant and irrelevant samples labelled first | Compare against labelled samples and observe whether irrelevant fragments are filtered |
| Question optimization | Enabled for short multilingual questions, disabled for questions containing identifiers | Compare the original question with the optimized question and check whether the subject was altered |
| Result reranking | Enabled when a reranking model is available, observed by rerank score | Compare against candidate order without reranking and observe ordering changes |
These values are hypothetical starting points for this exercise. They are not official defaults. Image retrieval requires a corresponding image input and model capability. Whether inspection photographs are hit depends on whether a matching index was created at ingestion time.
Three common errors
- Treating a retrieval hit as authorization or as a factual finding. The input is a complaint form retrieval result, the comparison object is the status field in the complaint form original, and the observable result is that the complaint form exists. Whether a penalty holds requires separate approval and execution stages. Retrieval does not complete that action.
- Reading a pending-review status as an imposed penalty. The input is penalty notice CF-2024-0031, the comparison object is the status field "pending review", and the observable result is that this field disagrees with the draft sentence "has already been penalized". The draft sentence is unsupported.
- Forcing knowledge bases with different vector models into one association. The input is the vector model identifiers of two bases, the comparison object is whether the identifiers match, and the observable result is that a mismatched base is excluded. Do not infer missed recalls or redundancy as a runtime outcome.
How to verify the setup
- Input the complaint form original from the hypothetical sample together with the draft sentence "Distributor A has already been penalized and the cross-region sales fact is established". Compare against the status field of penalty notice CF-2024-0031. The observable result is that the status is pending review while the draft sentence states an imposed penalty. Judge the draft as unsupported and preserve the evidence gap.
- Input the same complaint form original and compare the cited content returned by semantic retrieval and by hybrid retrieval. Observe whether both modes hit the subject and model fields of that complaint form. If one mode does not hit, record that mode and the query terms. Do not judge the result as missing data.
- Input an explicit paper input
[]and compare how the downstream node handles an empty citation array. Observe whether the output path completes. This check addresses empty-result handling and does not claim that a fictional rule search necessarily returns an empty array.
Operational handover must distinguish which materials, operations and requests are at which authorization stage. Retrieval and forms are not authorization execution. A pending request is not approval, an offered option is not execution, and an unsigned termination is not a completed termination.