What the documents look like
Payment risk control for cross-border independent stores centers on card network disputes and receiving accounts. The material set is chargeback notices, fraudulent order lists, invoices, and carrier proof of delivery. These arrive from Stripe or PayPal backends, Shopify orders, carrier signature records, and customer service email. The brief describes this category as roughly accurate in confidence.
Multilingual conditions shape retrieval directly. One dispute may carry an English chargeback notice, a Chinese internal fraud list, and a delivery proof in the carrier's local language. Semantic retrieval supports cross-language understanding, so a Chinese question can match an English knowledge point. That capability is the working basis for this task.
Material identity, object, stage, and validity scope must enter the knowledge base together with the material. Retrieval returns content only. It does not change any authorization state.
Update frequency does not change the retrieval function described on this page and is omitted.
Constraints at the retrieval step
The knowledge base is organized as library, collection, and data. The smallest search unit is the library. Collections classify data for management and do not affect search results. The knowledge base search node can associate one or more libraries that use the same vector model. Libraries with different vector models cannot be associated together. Check the vector model identifier and exclude non-matching libraries. Do not run cross-model retrieval to compare missed recall.
The retriever is the PostgresSQL PG Vector plugin with an HNSW index. MongoDB stores original data and the indexes field in the dataset.datas table. Hybrid retrieval merges semantic and full-text results through RRF. RRF is a fusion process, not an on-off switch. Minimum relevance takes effect only in semantic retrieval or when result reranking is used. It is a filter threshold and does not perform permission or entity isolation. The reference limit uses tokens as its unit. Question optimization performs coreference resolution and question expansion and adds one model call. Image retrieval requires image input and corresponding model capability. Reranking requires a model and must be enabled by configuration. Optional paths in the overall flow diagram do not mean every request executes all of them.
Hypothetical paper exercise sample. Material text: "This chargeback notice number CB-771, card network is Example Card Network, transaction date 2024-03-12, dispute amount 120 USD, status is awaiting merchant evidence." "Fraudulent order list row 14, order number A-2201, marked high risk, review status is pending review." "Carrier proof of delivery shows signed by recipient on 2024-03-15, signature language is English." Proposed draft sentence to be judged: "This order has completed termination." Judgment: unsupported. The material shows pending review and a signature only. No termination field appears. Pending review does not demonstrate absence of operating authority. Signing, reaching agreement, and contract effectiveness are three distinct facts.
Configuration and implementation checks
The settings below are official documented settings or implementation checks relevant to this page. Numeric values are hypothetical starting points for this exercise, not official defaults.
| Configuration item | Hypothetical starting point and reason | Comparison and check method |
|---|---|---|
| Associated knowledge base | Associate one library using the same vector model, avoiding cross-model association | Check each library's vector model identifier, exclude non-matching ones, and record |
| Search mode | Run semantic retrieval first, then run full-text retrieval on the same question | Compare returned content from both paths and observe whether identifier-type questions are matched |
| Reference limit | Start at a token amount covering one full chargeback notice | Compare with actual materials of different lengths to see whether content is truncated |
| Minimum relevance | Label relevant and irrelevant samples first, then choose a 0-1 starting point | Compare whether irrelevant fragments decrease before and after threshold adjustment |
| Question optimization | Enable only for multi-turn follow-ups or short questions | Compare original and optimized questions to check whether the subject was altered incorrectly |
| Result reranking | Enable when candidates are numerous, and filter by its score | Compare ordering before and after reranking, confirming the question's subject and predicate are complete |
Three common errors
- Treating retrieval results as authorization evidence. Input "can order A-2201 be terminated," retrieval returns fraudulent list row 14 "pending review." Treating that as completed termination converts pending review into approval. Compare whether a termination field and an effective statement exist in the material. If missing, record as unsupported.
- Forcing joint association across vector models. A question points to two libraries. If their vector model identifiers differ and they are still merged for retrieval, then explained as missed recall or redundancy, that fabricates a run result. Check identifiers only, exclude non-matching ones, and record the actual observation.
- Treating a similarity threshold as permission or entity filtering. Input "list all high-risk orders." Assuming that raising minimum relevance excludes unrelated business or unauthorized records confuses relevance with authorization. State that retrieval does not perform permission isolation, and that authorization requires a separate system implementation.
How to verify the setup
- Input the hypothetical material text "This chargeback notice number CB-771 ... status is awaiting merchant evidence." Compare the proposed sentence "This order has completed termination" against the original for the same subject, same period, and same field. The original has no termination field, so the expected judgment is unsupported. If a "completed termination" conclusion appears, the check fails.
- Input "order A-2201 review status." Compare whether returned content contains "pending review." Expect the status to be returned without inferring approval or rejection. If "terminated" or "no permission" is returned, the check fails, and the evidence gap is recorded.
- Input an explicit paper empty result []. Check whether the downstream design accepts a reference chain of length 0. Expect downstream to continue through the empty reference. If the downstream design depends on a reference always being present, the check fails. This check does not claim that a fictional rule retrieval necessarily returns [].
This page addresses only cross-border independent store payment risk control / cross-border material and multilingual retrieval / knowledge base retrieval and recall. At operational handover, which materials, operations, and requests sit at which authorization state cannot be treated as authorized execution through retrieval or forms.