What the documents look like
Payment risk work for cross-border standalone stores centres on card network disputes and receiving accounts, which differs from order fulfilment work that centres on parcel status. The main data sources are Stripe or PayPal back-office records, Shopify orders, carrier proof of delivery, and customer service email. The documents handled day to day are payment chargeback notices, fraudulent order lists, invoices, and logistics signature proof. This page addresses the inventory procurement and supplier records task: procurement invoices and supplier records must be stored separately from the risk documents above, and vector models and indexes then support retrieval across them. The brief states update frequency as daily and per order; this page does not derive batch synchronisation, full synchronisation, real-time, or transaction-triggered mechanisms from that.
The material identity matters because a supplier record and a chargeback notice can both mention a supplier name, an amount, or a date while carrying different status fields. Retrieval returns text; it does not decide whether a supplier is approved, whether an invoice is payable, or whether a payment may proceed. Those determinations need their own downstream implementation and their own authorisation boundary.
Constraints at the vector indexes step
Knowledge bases that are associated for retrieval must use the same vector model. The actual model and its input type are checked first; libraries on different models cannot be associated together. An original data block can be linked to multiple indexes, and the index content is checked separately from the original text. Index content is built from complete and focused information, and retrieval coverage against index length is weighed with samples in hand.
Image retrieval depends on whether an image description or image vector index was generated at ingestion time. Uploading an image at query time does not by itself make content retrievable. After a model or index change, the same set of questions is compared against the returned source text; a vector score alone does not support a claim about outcome.
Semantic recall and relevance scores do not perform permission or entity isolation. Retrieval is not authorisation. A relevance threshold is a filter value, not a category, entity, or permission filter, and it does not exclude unrelated businesses or unauthorised records.
Hypothetical paper exercise sample: a risk specialist at a standalone store retains one supplier record. The source text reads: "Supplier A, supply scope: outdoor power supplies, settlement currency: USD, record status is pending review. Procurement invoice number INV-2024-0311, issue date 11 March 2024, quantity 200 units." A draft sentence under review reads: "Supplier A has passed review and procurement payment may be executed." That sentence concerns review status and payment authorisation and must be compared against the source status "pending review."
Configuration and implementation checks
The table below covers this sample and lists only documented settings or implementation checks. The numeric values are starting points for this paper exercise, not official defaults.
| Configuration item | Hypothetical starting point for this exercise | Reason and comparison object |
|---|---|---|
| Vector model | One model associated across all target libraries | Compare against the actual model identifier of each library to be associated; exclude any that differ |
| Index content | Separate indexes for supplier records and invoices | Compare against source block length and semantic completeness; weigh coverage against precision |
| Search mode | Hybrid search | Compare against semantic-only recall; numbered queries depend on full-text matching |
| Reference cap | 2000 tokens as the starting point for this exercise | Compare against sample chunk length; an excessive cap can introduce unrelated fragments |
| Minimum relevance | 0.5 as the starting point for this exercise | Compare against semantic or rerank scores; observe whether target content survives filtering |
| Result rerank | Enabled | Compare against ordering without rerank; observe relevance changes when candidates are numerous |
Three common errors
- Treating "pending review" as passed review and drafting a conclusion that procurement payment may be executed. The input is the draft sentence and the source status field; the comparison is "pending review" against "passed review"; the observable result is that the two values differ, so the draft sentence is unsupported by the source. The evidence gap remains: review status is unverified.
- Associating libraries on different vector models and expecting one retrieval to cover all records. The input is the model identifiers of several libraries; the comparison is each library's actual model; the observable result is that libraries whose identifiers differ must be excluded first. Cross-model retrieval is not run to compare missed recall.
- Treating the reference cap or minimum relevance as a permission or entity filter, expecting a higher threshold to exclude unrelated supplier records. The input is the threshold setting and the recall results; the comparison is returned content before and after filtering; the observable result is that relevance affects ordering and filtering only and does not change access permission.
How to verify the setup
- Input the hypothetical source text and the draft sentence; compare the source field "record status is pending review" against the draft phrase "has passed review"; the observable result is that the two values differ, so the draft sentence is unsupported and the evidence gap is retained as review status pending verification.
- Input the model identifiers of the libraries to be associated; compare each library's actual vector model; the observable result is that libraries with matching identifiers may be associated together, while those that differ are excluded with the actual observation recorded. No claim is made about runtime behaviour after exclusion.
- Input the same set of questions against the supplier record and the procurement invoice separately; compare the returned source text against the question subject; the observable result is that returned content corresponds to the target document. If the return is empty, check the downstream design against a clearly stated paper input of an empty array; no claim is made that a fictional rule necessarily returns an empty result.
The document search node outputs references as an array whose length may be zero, so an empty result still completes the output path. Whether rerank or image retrieval runs in a given request depends on the model being available and the setting being enabled; the optional paths in the overall flow diagram do not all execute on every request.