What the documents look like
Cross-border product compliance and labelling work runs on a small set of recurring document types: product test reports, English-language labels, MSDS files, and platform prohibited-item lists. These come from platform policy centres, testing laboratory reports, destination-country regulation libraries, and supplier materials. Updates are irregular; each new product listing can bring new files. Compliance judgement happens before listing and before import, and the core question is product safety and label evidence. A failure here can lead to platform delisting, customs seizure, or consumer claims.
For retrieval purposes, a single file often mixes Chinese and English text, model numbers, and regulatory clauses. A query may need cross-language semantic understanding and precise location of a model code, a substance name, or a prohibited-item entry. The vector model and index layer decides whether these materials can be recalled consistently.
Two features of this document set matter for the vector step. First, the same object can appear in several languages and formats, so a model number in a test report and the same number on an English label are two separate pieces of evidence. Second, the evidence is often partial: a report may cover one test item for one model while the label draft makes a broader claim. Retrieval can surface both, and the compliance reviewer still has to compare them.
Constraints at the vector indexes step
Knowledge bases joined in one retrieval must use the same vector model. Check the actual model and its input type before joining. A single source data block can be associated with multiple indexes; indexes and source content are checked separately. Build indexes from complete and focused information, and weigh retrieval coverage against index length using sample material. Image retrieval also depends on whether image descriptions or image vectors were generated at ingestion time, so the query side alone does not decide the outcome.
Vector search measures similarity by distance, and precision is affected by vector model quality, data quality, and retriever precision. Semantic retrieval suits natural-language and cross-language questions; full-text retrieval suits codes, model numbers, and proper nouns. Hybrid retrieval merges multiple result paths through RRF, and reranking reorders candidate text and returns a 0-1 score. The minimum relevance value is a filter threshold and applies only in semantic retrieval or when result reranking is used. The reference limit is controlled by tokens rather than top k. When retrieval returns nothing, referenced content is output as an array whose length can be zero, and the downstream path still runs.
A hypothetical paper exercise sample: a batch of exported small appliances has supplier material in which the test report states "this report covers model AB-1200, test item temperature rise, conclusion meets the limit values provided by the commissioning party," and the English label draft states "Model AB-1200, rated voltage 220V." The proposed handover sentence is "this model has obtained all destination-country compliance certifications and can be listed directly." Judging that sentence, the material supports only a temperature-rise test conclusion for model AB-1200. It does not state a destination-country certification body, certificate number, or effective scope, so the sentence is unsupported.
Configuration and implementation checks
The table lists only documented settings or implementation checks relevant to this feature and task.
| Configuration item | Value or check on this page | Basis and boundary |
|---|---|---|
| Vector model | Knowledge bases joined in one retrieval must use the same vector model; check the actual model and input type first | Bases with different vector models cannot be joined; check the identifier and exclude non-matching bases |
| Index count | One source data block can be associated with multiple indexes | Check indexes and source content separately; do not merge the two judgements |
| Index content | Build indexes from complete and focused information | Weigh retrieval coverage against index length using sample material |
| Image index | Check whether image descriptions or image vectors were generated at ingestion | Image retrieval requires image input and model capability; the query side alone does not decide |
| Search mode | Choose semantic, full-text, or hybrid retrieval for the sample | Hybrid retrieval merges through RRF; reranking requires a model and must be enabled by configuration |
| Minimum relevance | Starting value for this hypothetical trial: 0.4 | This value applies only in semantic retrieval or when result reranking is used; compare against referenced source text for the same question set |
Three common errors
- Joining knowledge bases that use different vector models. Input: two knowledge base identifiers. Comparison: their vector model identifiers. Observable result: whether the identifiers match. When they do not match, exclude the base and do not run a cross-model retrieval to compare missed recalls.
- Treating a vector score as a permission or entity filter. Input: one retrieval request. Comparison: returned references against the accessible scope. Observable result: whether records outside the permitted scope appear. Similarity does not perform authorization.
- Treating a pending handover request as approved. Input: the proposed handover sentence "this model has obtained all destination-country compliance certifications." Comparison: whether the material states a certification body and certificate number. Observable result: whether those fields exist. When they are absent, record the sentence as unsupported.
How to verify the setup
- Input the test report text and the proposed sentence from the hypothetical sample. Compare whether the proposed sentence exceeds the subject, object, and status stated in the source text. Observable result: the sentence is marked unsupported. This is an expected outcome, not a claim that a test was run.
- Input two knowledge base identifiers. Compare their vector model identifiers. Observable result: whether they match. When they do not match, exclude the base and do not invent a runtime result for a joint association.
- Input a query with no corresponding material. Compare the referenced content output. Observable result: whether it is an array whose length can be zero. An empty-result handover can be checked against the paper input [] for downstream design; this does not claim that a fictional rule retrieval necessarily returns [].