What the documents look like
Foreign-exchange receipt, settlement, and trade directory materials centre on the authenticity of FX funds and the underlying trade background. This differs from letter-of-credit document examination, which reviews a full document set. The working materials here are receipt advices, trade contracts, invoices, and FX business registration records. Sources include State Administration of Foreign Exchange policy, bank online portals, corporate finance ledgers, and customs export data. In store service and internal training, retrieval over these materials answers questions about the receipt counterparty, settlement handling, and explanation of trade background. Retrieval output does not constitute authorization or business execution. Materials update per transaction and monthly, and the retrieval library is expected to stay aligned with the business materials.
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 using different vector models cannot be associated together. Before retrieval, check the library identifier and the vector model identifier, exclude those that do not match, and record what is actually observed. Do not run cross-model retrieval to compare missed recalls.
Search mode can be semantic retrieval, full-text retrieval, or hybrid retrieval. Compare modes against the test question rather than assuming one mode suits all materials. The reference limit is measured in tokens. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used. Question optimization performs coreference resolution and question expansion; compare the original question with the optimized question and check whether the subject was altered. Hybrid retrieval merges multiple result paths through RRF. RRF is a fusion process, not a toggle. Image retrieval requires a corresponding image input and model capability. Reranking requires a model and must be enabled according to settings. Retrieval and relevance scores do not enforce permission or entity isolation, and a retrieval hit does not demonstrate operating authority.
Configuration and implementation checks
The following is built around a hypothetical drill sample. The values are starting points for this hypothetical trial and are not official defaults.
Hypothetical drill sample: A foreign-trade enterprise finance staff member submits a receipt advice stating the payer as "Company A", the payee as "Company B", the amount as "USD 50,000", and the date as "10 May 2024". The trade contract states the seller as "Company B", the buyer as "Company C", and the amount as "USD 50,000". The proposed sentence is: "This receipt corresponds to the trade contract between Company B and Company C, the receipt counterparty is consistent, and settlement can be processed." The task is to determine whether the proposed sentence is supported by the materials.
| Configuration item | Hypothetical setting for this trial | Verification method |
|---|---|---|
| Associated knowledge base | Associate one receipt-and-settlement library using the same vector model | Check the library identifier and vector model identifier; exclude libraries with a different model |
| Search mode | Hybrid retrieval as the starting point for this hypothetical trial | Test semantic, full-text, and hybrid results on the sample question and compare hit differences |
| Reference limit | 2000 tokens as the starting point for this hypothetical trial | Test content completeness with receipt and contract materials of different lengths, comparing against 1000 and 3000 |
| Minimum relevance | 0.5 as the starting point for this hypothetical trial | Label relevant and irrelevant samples first, then compare filtering at 0.3, 0.5, and 0.7 |
| Question optimization | Enabled as the starting point for this hypothetical trial | Compare the original and optimized questions and check whether the subject was altered |
| Result reranking | Enabled as the starting point for this hypothetical trial | Compare ordering before and after reranking and check the relation between score and question completeness |
Three common errors
- Treating retrieval output as authorization to execute. The input is the receipt advice and contract above. Compare the retrieved content with the business authorization state. A retrieval hit does not mean settlement has been approved, and a pending review does not demonstrate a lack of operating authority.
- Associating knowledge bases across different vector models. The input is two knowledge base identifiers with different vector models. Compare whether the model identifiers match. When they do not match, exclude them, do not run cross-model retrieval, and do not infer missed recalls or redundant results.
- Judging the counterparty correspondence in the proposed sentence as directly established. The input is the receipt advice payer "Company A" and the contract buyer "Company C". Compare the values of the same field. The payer and the buyer differ, so the statement in the proposed sentence about a consistent receipt counterparty is not supported.
How to verify the setup
- Input the receipt advice and contract text above. Compare whether the retrieved content includes the payer "Company A", the payee "Company B", the contract buyer "Company C", and the amount field. Observe whether the returned content matches the source text. Do not guarantee a first-position hit or full recall.
- Input the proposed sentence "This receipt corresponds to the trade contract between Company B and Company C, the receipt counterparty is consistent, and settlement can be processed". Compare the receipt advice payer with the contract buyer. The values of the same field differ, so the proposed sentence is not supported. Record the evidence gap.
- Input an empty reference array []. Check whether the downstream design accepts an empty result. Do not claim that retrieval under a fictional rule necessarily returns []. At the same time, check that the associated library vector model identifiers match, exclude libraries with different models, and record what is actually observed.