What the documents look like
Settlement and foreign-exchange question answering for cross-border platforms concerns the collection of funds from completed orders. The working materials come from platform settlement centers, bank statements, payment institution back ends, and ERP orders. The documents handled day to day are platform settlement statements, exchange rate tables, service fee invoices, and receipt reconciliation tables. These materials update weekly or monthly. This category differs from cross-border payment risk control in that it does not handle fraud chargebacks; it addresses settlement and exchange information after a transaction has completed.
In product information and selection question answering, user questions typically carry qualifiers such as platform name, settlement period, currency, and fee type. A question may ask which settlement statement for a given month and platform a particular service fee belongs to. These retrieval terms often lack a complete subject and depend on a combination of semantic recall and full-text recall. Identifiers, currency codes, and settlement batch numbers in the materials are precise query objects suited to full-text retrieval; fee attribution and exchange rate difference causes are natural language phrasings suited to semantic retrieval.
Constraints at the vector indexes step
Knowledge bases joined in one retrieval must use the same vector model. Check the actual model and input type first. One source data block can be associated with multiple indexes; verify indexes and source content separately. Index content should be complete and focused, and retrieval coverage against index length needs to be weighed against samples. Image retrieval also depends on whether image descriptions or image vectors were generated at ingestion time; the query side alone does not determine this. After a model or index adjustment, compare cited source text using the same set of questions. Do not promise results based on vector scores alone.
Hybrid retrieval merges multiple result paths through RRF; this is a result fusion process, not a switch. Reranking requires a model and must be enabled per configuration. The minimum relevance score is a filter threshold that takes effect only in semantic retrieval or when result reranking is used; it does not perform permission or entity isolation. The same-vector-model requirement for retrieval libraries does not extend to external systems requiring the same model. Semantic recall and relevance scores do not determine whether a record falls within the scope a current user is authorized to view; authorization judgment requires a separate system implementation.
Hypothetical paper exercise sample
The following is paper exercise material and does not refer to any real customer or platform.
Source text one (platform settlement statement excerpt): "Settlement batch S-2406-A, platform P, settlement period June 1, 2024 to June 30, 2024, currency USD, service fee amount 120.00, deduction status: deducted."
Source text two (receipt reconciliation table excerpt): "Reconciliation batch S-2406-A, receiving account ending 8821, credited amount 4,880.00 USD, credit date July 3, 2024, reconciliation status is pending verification."
Draft sentence to be judged: "The service fee for batch S-2406-A has been deducted from the settlement amount, and the reconciliation status is verified."
Judgment point: Source text one states the service fee deduction status as deducted. Source text two states the reconciliation status as pending verification. The second half of the draft sentence does not match the value in the same field in source text two and is unsupported.
Configuration and implementation checks
| Configuration or check item | Relevant value or action for this page | Basis and explanation |
|---|---|---|
| Vector model of joined knowledge bases | Check whether the libraries being joined use the same vector model identifier | Same vector model is the precondition for joint association; exclude non-matching ones when identifiers differ |
| Index to source data correspondence | Check whether the vector IDs recorded in the indexes field of dataset.datas point to the same source data block | One data group can correspond to multiple vectors; verify indexes and source text separately |
| Search mode | Select semantic retrieval, full-text retrieval, or hybrid retrieval according to question type | Identifier-type questions favor full-text retrieval; natural language questions favor semantic retrieval |
| Reference limit | For this exercise, the hypothetical starting value is 2000 tokens, used for comparison against the displayed value | This value is an exercise starting point, not an official default; the comparison object is the actual configured value |
| Minimum relevance score | Check the value only when semantic retrieval or result reranking is in effect | This value is a filter threshold and does not perform permission filtering |
| Rerank enablement status | Check whether a ReRank model is configured and enabled per settings | When no model is configured, the reranking path does not execute |
Three common errors
- Treating a retrieval score as a permission judgment. The input is a semantic retrieval request, the comparison object is the user's accessible scope against the recalled records, and the observable result is that score level has no correspondence to record ownership. Relevance scores contain no entity or permission filtering; authorization requires a downstream system implementation.
- Writing a pending verification status as verified. The input is the reconciliation status field in source text two, the comparison object is the status description in the draft sentence, and the observable result is that the two values differ. Only mutually exclusive values in the same field constitute a contradiction; a value not stated or supported only by stage evidence is unsupported.
- Treating RRF as a switch that can be turned on. The input is the hybrid retrieval configuration item, the comparison object is the role of RRF in the documentation, and the observable result is that RRF is a multi-path result fusion process with no corresponding switch in the configuration items. After question optimization or image retrieval is enabled, final results are not necessarily ordered by a single vector similarity.
How to verify the setup
- Verification one: For the draft sentence in the hypothetical sample, input the source text of materials one and two, compare the second half of the draft sentence against the reconciliation status field in source text two, and observe that "pending verification" and "verified" do not match, so the sentence is unsupported. The expectation is to identify this mismatch, without claiming it was actually tested.
- Verification two: For the joined knowledge bases, input the vector model identifier of each library, compare whether the identifiers match, and observe that only matching identifiers permit joint association. When identifiers differ, exclude the non-matching ones and record this; do not run cross-model retrieval to compare missed recalls.
- Verification three: For empty-result handling, input a paper retrieval result of array [], compare the downstream node's handling design for a reference array of length 0, and observe that the output path completes. Do not claim that a fictional rule retrieval necessarily returns [].
After these checks are complete, model or index adjustments require comparing cited source text using the same set of questions. Do not promise results based on vector scores alone.