What the documents look like
Trade and FX administration material is organised around the authenticity of foreign exchange funds and the underlying trade background. This differs from letter of credit examination, which reviews a full document set. The working materials here are foreign exchange receipt slips, trade contracts, invoices and foreign exchange business registration records. Sources include State Administration of Foreign Exchange policy, bank online banking, enterprise financial ledgers and customs export data.
These materials frequently exist in both Chinese and English versions. A single transaction may involve contract clauses and registration fields in different languages, so retrieval has to handle cross-language semantic correspondence. Documents update on two rhythms: per-transaction items are produced as business occurs, while policy and directory content changes monthly. Verifying the receiving entity, processing settlement and explaining the trade background are the more time-consuming steps. Retrieval results need to point to a specific slip or registration entry so a person can compare entity names, amounts and periods.
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. The retrieval step only recalls references from content already ingested. It does not perform authorization decisions and does not constitute verification of the receiving entity, the trade background or directory status.
Semantic retrieval relies on vector distance and suits natural-language questions and cross-language matching. Full-text retrieval relies on keyword hits and suits numbers, models and proper nouns. Hybrid retrieval uses both recall paths, merges them through RRF, then applies ReRank for secondary ordering. The minimum relevance value takes effect only in semantic retrieval or when result reranking is used. It is a filtering threshold and does not exclude unrelated business or unauthorized records. The reference limit controls the amount of content cited per search, measured in tokens. Question optimization performs coreference resolution and question expansion and adds one model call. Image retrieval requires corresponding image input and model capability, and reranking requires a model enabled by configuration. These paths do not execute on every request.
Configuration and implementation checks
The table below lists officially documented settings or implementation check items relevant to this feature, built around this page's sample. The value column gives a hypothetical starting point for this trial, used for comparison with another set of values. It is not an official default.
| Configuration item | Hypothetical starting point for this trial | Check method and notes |
|---|---|---|
| Associated knowledge bases | Select one knowledge base with a consistent vector model identifier | Compare vector model identifiers across bases; exclude any base whose identifier differs |
| Search mode | Test semantic retrieval and full-text retrieval separately first | Use the same question to compare results from both paths and observe differences between number-type and natural-language queries |
| Reference limit | Start at a token amount covering one complete receipt slip and one contract clause | Compare with a smaller value and observe whether cited content is truncated |
| Minimum relevance | Start at a middle value after labelling relevant and irrelevant samples | Compare with higher and lower values and observe whether irrelevant samples are still returned |
| Question optimization | Enable for multi-turn follow-up questions containing references | Compare the original question with the optimized question and check whether the entity was altered |
| Result reranking | Enable when candidates are numerous | Compare ordering before and after enabling and check position changes of relevant entries |
Three common errors
- Treating retrieval recall as an authorization or verification result. Hypothetical drill sample: a foreign exchange receipt slip shows the payer as "HYPOTHETICAL OVERSEAS TRADING CO.", amount USD 50,000, value date 12 March 2024; a trade contract shows the seller as "Hypothetical Sample Import and Export Co., Ltd.", the buyer name spelled "HYPOTHETICAL OVERSEA TRADING CO.", amount USD 50,000, signing date 20 February 2024. The proposed sentence reads: "This receipt of foreign exchange has been verified as payment under the corresponding contract." The error is concluding that a business relationship exists once two materials are retrieved. Check: input the two source passages and the proposed sentence, compare the payer name with the buyer name character by character, and observe whether a spelling difference exists; when names differ, the sentence is unsupported.
- Using minimum relevance as a substitute for entity or permission filtering. Hypothetical drill sample: the question is "Who is the receiving entity for March 2024 receipts?", and the knowledge base also contains an unrelated record involving "HYPOTHETICAL LOGISTICS CO." The error is assuming that raising minimum relevance necessarily excludes the unrelated record. Check: input the question and both records, compare whether the unrelated entity still appears in returned content, and observe whether it disappears after the threshold change; if it does not disappear, the threshold does not perform entity filtering.
- Forcing association of knowledge bases with different vector models. Hypothetical drill sample: the vector model identifier of base A differs from base B, and the question points to a foreign exchange business registration record in base B. The error is assuming that associating both bases gives broader retrieval coverage. Check: input both base identifiers and the question, compare whether the identifiers match, and observe whether the association operation is permitted; when identifiers differ, exclude that base and record the actual observation rather than inferring runtime results.
How to verify the setup
- Input the receipt slip passage, the contract passage and the proposed sentence above. Compare the character sequence of the payer name with the buyer name and observe whether the two are fully identical. When a spelling difference exists, the proposed sentence "This receipt of foreign exchange has been verified as payment under the corresponding contract" is unsupported, and the evidence gap must be recorded.
- Input the question "Who is the receiving entity for March 2024 receipts?" together with the two records containing the unrelated entity. Compare the entity names appearing in returned content and observe whether the unrelated entity is still returned. If it is still returned, minimum relevance does not perform entity filtering, and a separate verification step is needed.
- Input the vector model identifiers of base A and base B, compare whether they match, and observe whether the association operation is permitted. When identifiers differ, exclude that base, record the actual observation, and do not run cross-model retrieval to compare missed recall. For the empty-result case, a clearly stated paper input of [] can be used to check downstream handling design, without claiming that a fictional rule's retrieval necessarily returns an empty array.