What the documents look like
Payment risk documents revolve around card network disputes and receiving accounts, and they sit in a different evidence chain from parcel fulfilment status. Chargeback notices, fraud order lists, invoices and proof of delivery are produced by different systems in different languages. For the same dispute, the correspondence among subject, order number and date range has to be checked document by document. The brief gives an update frequency of daily and per order, so the retrieval side sees heterogeneous text accumulating order by order.
The practical constraint for multilingual retrieval here is that a Chinese question has to match evidence text written in English or a local language. Semantic recall depends on how well the vector model covers cross-language meaning, while precise fields such as identifiers and chargeback reason codes depend more on full-text hits. That difference shapes how the vector model and index are chosen and checked.
Constraints at the vector indexes step
Knowledge bases joined in one association must use the same vector model. Check the actual model and its input type first, and exclude bases whose identifiers do not match from a shared association. Cross-model retrieval is not performed in order to compare recall differences.
One source data block can be associated with several indexes. Index and source content are checked separately, and index content is built from complete, focused information. Retrieval coverage and index length are weighed together against samples. Image retrieval also depends on whether image description or image vector indexes were generated at ingestion time; uploading an image on the query side is not sufficient on its own.
Semantic recall and relevance scores do not carry permission or entity isolation. The same-vector-model requirement for associated knowledge bases does not extend to external systems. Retrieval is a fetch action; forms collect values; business verification, approval, database writes and external synchronization each need their own downstream implementation.
Hypothetical paper exercise sample: a cross-border store risk role provides three source excerpts. First, a chargeback notice excerpt: "Card network notice, order A-1042, 12 March 2026, dispute reason code 4855, amount USD 210, status pending evidence." Second, a proof-of-delivery excerpt: "Carrier record, order A-1042, signed for by the recipient on 8 March 2026, delivery location matches the shipping address on this order." Third, a fraud order list excerpt: "Order A-1042, marked high risk on 12 March 2026, handling status pending review." The draft sentence to be judged is: "The chargeback evidence for order A-1042 has been completed and the account freeze has been lifted." This draft sentence concerns retrieval and handover, and does not concern the completion of any actual business action.
Configuration and implementation checks
The table lists only settings documented in the supplied material or implementation check items relevant to this page. The values are starting points for this hypothetical exercise.
| Configuration or check item | Starting point for this exercise and reason | Comparison and observation method |
|---|---|---|
| Vector model of associated knowledge bases | Start with the same vector model, because a shared association of several bases requires matching model identifiers | Check each base's model identifier and input type; exclude those that do not match |
| Search mode | Start with hybrid search, because the material contains precise fields such as reason codes alongside multilingual natural-language description | Run the same question set and observe semantic recall versus full-text hits separately |
| Minimum relevance | Start at 0.3, because this value takes effect only in semantic search or with result reranking and filters low-relevance results | Compare against the source text and observe whether target evidence survives filtering |
| Reference cap | Start at 2000 tokens, because the cap is counted in tokens and relates to chunk length variation | Observe whether reference content is trimmed and whether target fields are still covered |
| Question optimisation | Start with it enabled, because follow-up and short questions in multilingual retrieval often lack a subject | Inspect the completed question in the conversation detail and compare retrieval hits |
| Result reranking | Start with it enabled, because hybrid search has a wide scope and cannot be filtered by similarity directly | Observe rerank scores against source text and filter by score |
Three common errors
- Extending the same-vector-model requirement to external systems. Input: the model identifier of the retrieval base and the identifier of an external system. Compare whether the two fall within the same requirement scope. Observable result: treating the external system identifier as a precondition for association is an error.
- Treating an image uploaded on the query side as proof that the image is retrievable. Input: whether image description or image vector indexes were generated at ingestion. Compare the query side and the ingestion side. Observable result: judging retrievability from the query-side image alone is an error.
- Treating a retrieval hit or a form submission as authorisation or business completion. Input: a pending-review status field and a retrieval result. Compare the status field against the authorisation fact. Observable result: reading pending review as approved, or reading retrieval as executed, is an error.
How to verify the setup
- Check one: input the three source excerpts and the draft sentence from the hypothetical sample. Compare the draft sentence's claims that "evidence has been completed" and "the freeze has been lifted" against the source fields "pending evidence" and "pending review". Observable result: the source does not state completion or lifting, so the draft sentence is unsupported and the evidence gap must be recorded.
- Check two: input reason code 4855 for order A-1042 as a precise query. Compare what the full-text path and the semantic path each return. Observable result: the full-text path hits the reason code field, the semantic path hits the delivery description, and after fusion the reference needs to cover both. A first hit or full recall is not guaranteed.
- Check three: input a query with no corresponding order. Compare the returned references against the empty-result condition. Observable result: reference length can be 0, and a downstream design can be checked with the explicit paper input []. No claim is made that a fictional rule-based search always returns an empty array.
All of the above are paper expectations and were not actually executed. After any model or index change, compare the same question set against the source text; do not promise outcomes from vector scores alone.