What the documents look like
Procurement and supplier files for cross-border product compliance carry product test reports, English labels, MSDS documents and platform prohibited-item lists. These materials arrive from scattered sources, change without a fixed schedule, and need re-checking at every new product launch. A buyer obtains them from the supplier, then decides whether destination-country requirements are already covered.
Vector models and indexes make these materials retrievable. The chunking applied at ingestion, the length of index content, and whether an image index was generated all decide whether the corresponding evidence can later be recalled. A supplier scan that contains only images and had no image description index or image vector index generated at ingestion cannot be matched by uploading an image on the query side.
Constraints at the vector indexes step
Knowledge bases joined for one retrieval must use the same vector model. Before selecting a library, verify the actual model and its input type; exclude libraries that do not match. Do not run cross-model retrieval to compare missed recalls. One source data block can map to several indexes, and the indexes and the original content are checked separately. Index content should be complete and focused, and retrieval coverage against index length needs to be weighed against samples. Image retrieval also depends on whether an image description or image vector index was generated at ingestion; uploading an image at query time is only one condition.
Semantic recall and relevance scores do not handle permission or entity separation. The minimum relevance value is a filter threshold, not a final ranking rule. After question optimization, hybrid retrieval, image retrieval or result reranking is enabled, the final result may be fused from multiple recall paths and does not necessarily follow a single vector similarity order. RRF is a fusion process for mixed results and is not an enable switch. The citation limit controls the maximum content cited per search in tokens.
Hypothetical paper exercise sample: a supplier submits an English label draft in March 2026. The body contains "Product name: Type A cleaner", "Batch code: 20260310", "Country of destination: Germany". In the same batch there is a pending handover request: "Please enter the above label draft into the knowledge base so procurement can retrieve it when checking destination-country label requirements." The request is pending, not approved, and not executed. The parties, object, status and scope above are used only for paper checking on this page.
Configuration and implementation checks
| Configuration item | Relevant value or check on this page | Basis and boundary |
|---|---|---|
| Vector model of associated knowledge bases | Check the actual vector model identifier of each library; associate only libraries with matching identifiers | Official description requires the same vector model |
| Index to source data mapping | Check the indexes array of one source data block against its original content | Indexes and original content are checked separately |
| Index content length | For this exercise, the starting point is a short index focused on label fields, compared with a long index containing a full-page description for recall coverage | Starting point is this exercise's assumption, not an official default |
| Image index | At ingestion, confirm whether an image description index or image vector index was generated | Uploading an image at query time is not sufficient for a match |
| Search mode | Use full-text or hybrid retrieval for code and model number queries | Official tuning guidance |
| Citation limit and minimum relevance | Set the citation limit in tokens; minimum relevance takes effect only in semantic retrieval or reranking | Official parameter description |
Three common errors
- Treating a pending request as already ingested. The input is the handover request above; compare its status field "pending" with "executed"; observing that the status is still pending means it was not executed, and the material cannot be treated as retrievable in the library.
- Forcing an association across vector models. The input is two knowledge bases with different vector model identifiers; compare whether the identifiers match; observing a mismatch means exclusion, and no prediction of recall behaviour after a forced association is made.
- Using a query-side image upload in place of an ingestion index. The input is an image-only scan and one image query; compare whether the ingestion record contains an image description index or image vector index; observing the absence records a gap, and the query-side action is not treated as an established index.
How to verify the setup
- Take the label draft text from the hypothetical sample, enter the query "Batch code 20260310", and compare whether the recalled content contains that batch number text. The condition is met when the cited content contains the field and its source points to that source data block. This is the expectation; it was not tested.
- Take the handover request from the hypothetical sample, enter its status field, and compare the status value with "executed". Observing that it is still pending confirms ingestion did not occur; this is unsupported, and is not judged as a denial or a conflict.
- Take two libraries with different vector model identifiers, enter the identifier fields, and compare whether they match. Observing a mismatch means exclusion from association. If downstream handling of an empty result needs checking, a paper input of
[]can be used to check the downstream design; no claim is made that a fictional-rule retrieval always returns[].