What the documents look like
A brand dealer ordering event serves offline dealer pre-booking rather than platform storefront management. The document set for this task is the ordering handbook, the ordering contract, the sample list, and the credit approval record. Product planning writes the handbook, the channel department collects order quantities, and credit approval defines the payment boundary. The material refreshes on a quarterly and annual cycle. Selection questions from dealers concern models, policy terms, samples, and credit lines, and every retrieved answer needs to be checked back against the source text.
For the vector model and index step, three of these traits shape the work. The knowledge base selected for retrieval must use the same vector model, so the actual model and its input type are checked before anything else. One source data block can be linked to several indexes, and the index content is verified separately from the original text. Image retrieval depends on whether an image description index or an image vector index was created when the material was stored, so the query side alone does not determine whether an image can be found.
Constraints at the vector indexes step
Retrieval association requires the same vector model across the linked knowledge bases. When the models differ, the bases cannot be linked together; the check is to confirm the identifiers, exclude the non-matching ones, and record what was observed. Semantic recall and relevance scores do not perform permission or entity isolation, and a relevance threshold is a filter value rather than a category, entity, or authorization filter. Retrieval is not authorization.
Index content is built from complete and focused information. Retrieval coverage and index length are weighed against the sample at hand, since a shorter index raises precision while reducing coverage. Image retrieval also depends on the index generated at storage time, not only on what the query provides. After a model or index change, the same group of questions is used to compare the cited source text; a vector score alone does not support a claim about outcome. The documented retrieval path combines problem optimization, semantic recall, full-text recall, image description or image vector recall, RRF fusion, and ReRank, and the optional paths in that flow do not all execute on every request. Image retrieval requires an image input and matching model capability, and ReRank requires an available model that is enabled in the settings.
Configuration and implementation checks
Hypothetical drill sample: a brand channel department prepares page 3 of the 2026 Spring/Summer Ordering Handbook. The source text reads: "Type A samples accept pre-orders, with a first-batch cap of 200 units per store, and the credit line is subject to the finance approval form." A draft sentence reads: "Type A samples have received credit approval and can be ordered without limit." A pending request reads: "Please confirm that Type A sample credit is approved and the order cap is released."
| Configuration item | Assumed starting value for this drill | Object and comparison method |
|---|---|---|
| Linked knowledge base | Only ordering handbook bases using the same vector model | Check each base identifier and exclude those with a different vector model |
| Index content | Index built from the handbook source paragraph | Compare index text against the source subject, quantity, and condition |
| Image index | Description generated when the sample list image was stored | Check the storage record for an image description index |
| Search mode | Hybrid retrieval with RRF fusion | Observe whether semantic and full-text paths are both invoked |
| Minimum relevance | Assumed drill starting value 0.5 | Adjust after comparing cited source text across the same question group |
| Citation limit | Assumed drill starting value 2000 tokens | Compare against the length of the returned cited source text |
The starting values are assumed trial values for this drill, not official defaults.
Three common errors
- Treating the draft sentence "has received credit approval" as a source fact. The source text states only that the credit line is subject to the finance approval form, and it does not record that the form was signed. The claim is unsupported.
- Expanding "a first-batch cap of 200 units per store" into "ordered without limit." The source text carries an explicit quantity cap, and the draft sentence contradicts that field.
- Using a vector similarity score in place of a business authorization decision. A retrieval score does not constitute credit approval or a release of the order cap, and authorization requires a separate downstream implementation.
How to verify the setup
- Input the draft sentence "Type A samples have received credit approval and can be ordered without limit." Compare it against the source text "the credit line is subject to the finance approval form" and "a first-batch cap of 200 units per store." The condition is met when the draft is judged unsupported and contradictory with the quantity field.
- Input the pending request "Please confirm that Type A sample credit is approved and the order cap is released." Compare the request against the authorization boundary in the source text. The condition is met when the evidence gap is recorded and the request stays in a pending-verification state.
- Input the paper empty result []. Compare the downstream design against a citation output of length zero. The condition is met when the path completes without asserting that a fabricated rule must return [].
A pending request is not approval, an offered option is not execution, and an unsigned termination is not a completed termination. Where the source text does not state a field, that field remains unknown.