What the documents look like
Payment risk work for a cross-border independent store revolves around card network disputes and the receiving account. The materials handled here are chargeback notices, fraudulent order lists, invoices, and proof of delivery. These sit on two sides of the same order: a funds side and a fulfillment side, and the status fields on each side do not line up. A chargeback notice carries a dispute stage and a reply deadline. A fraudulent order list carries risk markers. A proof of delivery carries carrier tracking nodes. Retrieval that returns only funds-side material can leave an after-sales reply without fulfillment evidence; retrieval that returns only fulfillment-side material can miss the dispute deadline. The supplied brief gives an update frequency of daily and per order. This page does not derive batch synchronization or transaction-triggered mechanisms from that frequency.
For the retrieval step, the practical consequence is that a single question about one order can require evidence from two document families whose fields are not aligned, and the reply must not treat one side's status as the other side's status.
Constraints at the retrieval step
The knowledge base search node can associate one or more knowledge bases that use the same vector model. Knowledge bases with different vector models cannot be associated together. Before retrieval, check the vector model identifier of each base, exclude any that do not match, and record the actual observation. Do not run a cross-model retrieval to compare missed recalls.
Search modes are semantic retrieval, full-text retrieval, and hybrid retrieval. Hybrid retrieval merges multiple result paths through RRF. RRF is a fusion process, not a standalone switch. Minimum relevance takes effect only under semantic retrieval or when result re-ranking is used. It is a filter threshold and does not perform permission or entity isolation. A similarity score cannot be used to exclude unauthorized records or unrelated business entities. The citation limit is measured in tokens. When long and short materials are mixed, adjust it against actual content completeness. Question optimization handles coreference resolution and question expansion and adds one model call. The output citation content is an array and its length can be 0, meaning the downstream path still runs when nothing is found. Image retrieval requires image input and the corresponding model capability. Re-ranking requires a model and is enabled according to settings. Optional paths in the documented flow do not mean every request executes all of them.
Hypothetical paper exercise sample: a risk specialist at an independent store holds a chargeback notice whose source text reads "Dispute number A-001, card network acceptance date 3 March 2025, reply deadline 17 March 2025, current status pending evidence submission." The proof of delivery for the same order reads "Waybill number B-002, delivery date 26 February 2025, recipient signature field blank." A proposed reply sentence reads "This order was delivered on 26 February 2025, and the proof of delivery can be submitted before the reply deadline." The question to decide is whether the blank recipient signature field affects whether delivery is established, and whether the proposed sentence goes beyond what the materials state.
Configuration and implementation checks
| Configuration item | Hypothetical starting point for this exercise | Reason for the choice and comparison method |
|---|---|---|
| Associated knowledge bases | Payment risk base and fulfillment base | First check whether the two bases share the same vector model identifier, exclude any that do not match, and do not run a cross-model retrieval comparison |
| Search mode | Hybrid retrieval | Dispute numbers and waybill numbers are exact queries while natural-language questions are semantic queries, so compare per test question |
| Citation limit | Set to 2000 tokens as a hypothetical starting point for this exercise | Chargeback notices and proofs of delivery have long fields, so compare against shorter materials to see whether content is truncated |
| Minimum relevance | Set to 0.5 as a hypothetical starting point for this exercise | First label relevant and irrelevant samples, then compare filtered results against 0.3 and 0.7 |
| Question optimization | Enabled for multi-turn follow-up scenarios | Compare the original question with the optimized question and check whether the subject was altered incorrectly |
| Result re-ranking | Enabled when candidates are numerous | After enabling according to settings, compare the ordering difference between re-rank scores and semantic scores |
Three common errors
- Treating minimum relevance as a permission or entity isolation mechanism. The input is a risk marker from a fraudulent order list, the comparison is between the similarity score and the record ownership field, and the observable result is whether a low-scoring record is still recalled. A high or low score does not indicate whether the record falls within the current authorization scope.
- Associating knowledge bases with different vector models and then inferring a runtime result. The input is the vector model identifier of each base, the comparison is whether the identifiers match, and the observable result is whether the association is permitted. The documentation states only that bases with the same vector model can be associated; it does not state recall behavior when they differ, so that behavior remains unknown.
- Reading a blank signature field in a proof of delivery as delivery not being established. The input is the source text "recipient signature field blank," the comparison is between the delivery date field and the signature field in the same material, and the observable result is whether the two fields are mutually exclusive. A blank signature field is an unstated item and does not constitute a negation of the delivery fact.
How to verify the setup
- Input the chargeback notice source text and the proposed reply sentence from the hypothetical sample, compare the claimed "was delivered" against the proof of delivery's "signature field blank," and observe whether the reply presents an unstated item as a confirmed fact. The condition is met when the reply preserves the evidence gap and marks it as pending verification.
- Input a risk marker from the fraudulent order list together with an unauthorized query request, compare the records returned by retrieval against the authorization scope of the request, and observe whether a low-similarity record is output as an authorized record. The condition is met when the retrieval result does not change the authorization state and the authorization decision is left to downstream implementation.
- Input an explicit paper-based empty result [] and compare how the downstream node is designed to handle citation content of length 0, observing whether the path runs and no citation is fabricated. The condition is met when the downstream accepts the empty array and does not claim that a fictional rule retrieval always returns an empty array.
This page addresses only cross-border independent store payment risk / order fulfillment and after-sales question answering / knowledge base retrieval and recall. During operations handover, the authorization state of materials, operations, and requests must not be treated as granted by retrieval or by a form.