What the documents look like
Channel diversion review addresses regional and price order, and it works from anti-counterfeit code records, e-commerce platform prices, distributor shipment records, and market inspection material. Within inventory procurement and supplier material, the documents in scope are diversion complaint forms, anti-counterfeit code records, penalty notices, and investigation photographs. These materials carry identifiers, batch numbers, region names, and product models. Many passages read as semantically similar while pointing to different parties, so retrieval needs to cover natural-language descriptions and to match exact identifiers.
The update rhythm is weekly and irregular. This rhythm does not by itself change how the vector index is built or searched, so it is not treated as a configuration input on this page.
When investigation photographs are present, whether they can be retrieved depends on whether an image description or image vector was generated at ingestion. Uploading an image at query time cannot compensate for an index that was never created at ingestion.
Two category facts shape this step. First, the material mixes free-text complaint narratives with exact codes, so a single retrieval mode rarely serves every question. Second, the parties named in the material are the subject of the review, and retrieval returns passages that mention them; retrieval does not decide who is authorized to do what.
Constraints at the vector indexes step
Knowledge bases joined in one retrieval must use the same vector model. Confirm the actual model identifier and the input type each knowledge base accepts before joining them. A shared vector model is a retrieval precondition and does not extend to any external system.
One original data block can be associated with several indexes. The index content and the original text are checked separately, because the indexes field holds an array of vector IDs and one data block can map to multiple vectors.
Index content is built from complete and focused information. Shorter, more precise index content tends to raise precision while changing retrieval coverage, so coverage and index length are weighed against samples from the actual material.
Image retrieval depends on whether an image description or image vector was generated at ingestion. A visual model, or a vector model that accepts image input, is a separate condition from the query itself.
After a model or index change, compare cited original text using the same set of questions. A vector score alone does not support a claim about outcome quality.
Retrieval carries no permission or entity isolation. A similarity score is not an authorization judgment about a distributor or a region, and it is not a filter that excludes unrelated businesses or unauthorized records.
Hypothetical paper exercise sample. A brand channel-control staff member receives a diversion complaint form. The original text reads: "Complaint subject: East China regional distributor A; product model involved: X-200; discovery date: 10 March 2025; complaint basis: goods listed on an e-commerce platform below the suggested retail price." A separate draft handover request is pending judgment: "Please retrieve distributor A's authorized supply scope and confirm whether distributor A is permitted to sell X-200 on e-commerce platforms." The authorized supply scope and the e-commerce sales permission do not appear in the complaint form. Both are unsupported items.
Configuration and implementation checks
| Configuration or check item | Value or practice on this page | Basis and explanation |
|---|---|---|
| Vector model consistency | Confirm each knowledge base's actual model identifier and input type; do not join knowledge bases whose identifiers differ | Knowledge bases joined in one retrieval must use the same vector model |
| Index and original data | One original data block may map to multiple indexes; check index content and original text separately | The indexes field records corresponding vector IDs as an array |
| Index content trade-off | Build indexes from complete and focused information; compare coverage and length against samples | Shorter, more precise index content can raise precision, and retrieval coverage changes accordingly |
| Image index | Confirm whether an image description or image vector was generated at ingestion | Whether an image can be retrieved depends on the index created at ingestion |
| Reference upper limit | For this paper exercise, start from a hypothetical token count and compare against the cited original text length for the same question set | The reference upper limit is measured in tokens, and top k is not used |
| Minimum relevance | For this paper exercise, start from a hypothetical value between 0 and 1 and compare against semantic retrieval or rerank scores | This value takes effect only in semantic retrieval or when result reranking is used |
The two hypothetical starting values above are paper exercise inputs chosen to make the comparison concrete. They are not official defaults and were not tested. The comparison method matters more than the number: the reference upper limit is compared against the length of cited original text for the same question set, and the minimum relevance is compared against semantic retrieval or rerank scores.
Three common errors
- Treating a retrieval result as an authorization conclusion. The input is the draft sentence "Please retrieve distributor A's authorized supply scope and confirm whether distributor A is permitted to sell X-200 on e-commerce platforms." The comparison is whether the retrieved original text states an authorized scope and an e-commerce sales permission. When the returned text contains only the complaint subject, model, date, and complaint basis, the request is recorded as unsupported, and no permitted or not-permitted judgment is produced.
- Joining knowledge bases that use different vector models. The input is the model identifiers of two knowledge bases. The comparison is whether the identifiers match. When they differ, the knowledge bases are excluded from joint association, and the observed identifier values are recorded. A cross-model retrieval is not run to compare missed recalls.
- Treating a similarity score as an entity filter. The input is a diversion complaint that is semantically close to the sample but concerns a different party. The comparison is whether the subject field in the returned content points to distributor A. When the subject differs, the result is recorded as not relevant, and a high score does not bring it into scope.
How to verify the setup
- Enter the complaint form original text and the draft sentence from the hypothetical paper exercise. Compare whether the cited original text returned by retrieval contains an authorized supply scope field and an e-commerce sales permission field. When the returned content contains only the complaint subject, model, date, and complaint basis, record the request as unsupported. A first-hit guarantee is not expected, and full recall is not expected.
- Enter the model identifiers of two knowledge bases. Compare whether the identifiers are the same. Only matching identifiers may be used for joint association. When they differ, exclude them and record the identifier values actually observed.
- Enter a query with no corresponding material. Compare whether the returned result is a reference array of length zero. The dataset search node outputs cited content as an array whose length may be zero, so the downstream branch can continue on an empty array. Record that observation. Do not infer that any rule-based retrieval necessarily returns an empty result.
Across all three checks, the material needed for the check must sit inside a target retrieval scope to which the operator has access, or inside an explicit manual input. When it is missing, record the gap and leave it open. An unsigned termination is not a completed termination, a pending request is not an approval, and an offered option is not execution.