Trade and FX administration: model connections for multilingual work

What the documents look like

Trade and FX administration handles receiving advice, trade contracts, invoices and foreign exchange registration materials. The work centres on the reality of the foreign currency funds and the trade background; it does not review a full set of letter of credit documents. Compared with letter of credit examination, the operational focus falls on checking the receiving party, handling settlement, and explaining the trade background. Errors can lead to bank rejection, an abnormal FX classification, or funds that cannot be booked.

Materials arrive per transaction and per month. Cross-border and multilingual retrieval places receiving advices, contracts and registration materials in different languages behind one search entry. The receiving advice carries the payer, the payee, the amount, the currency and a remittance remark. The contract carries the signing parties, the contract number, the goods and the agreed settlement method. The registration material carries the recorded status of the FX business. These three sources do not share one format, one language or one issuing authority.

Constraints at the model connections step

Model access runs through AI Proxy. App and Admin must both set AIPROXYAPIENDPOINT and a non-empty AIPROXYAPITOKEN; when either is missing, startup validation fails. From V4.17.0, AI Proxy is a required dependency, and the older OpenAI environment variables or a single-model custom request address cannot replace these two settings. The endpoint is the AI Proxy service root address, for example http://aiproxy:3000, and it does not take /v1.

The model ID is the value of the model field in the request body and is globally unique. A model channel determines the protocol type and the upstream address. Model mapping translates the model that FastGPT requests into the model the upstream actually provides; the documented example maps gpt-4o-test to gpt-4o. A proxy address is a BaseUrl, not a complete model request address, and whether /v1 is needed depends on the provider.

Semantic recall and relevance scores carry no permission or entity-isolation duty. A relevance score is not a category, entity or permission filter, and retrieval is not authorization. The same-vector-model association requirement applies only to associations inside one vector library; it does not extend to business-registration websites or other external systems. Libraries built on different vector models cannot be associated together. Only the identifier is checked, non-matching records are excluded, and the actual observation is recorded.

Configuration and implementation checks

The table below uses one hypothetical paper trial sample. A trade finance officer receives a March 2026 receiving advice. The payer is "HONG KONG SUNRISE TRADING LIMITED", the payee is a domestic export enterprise, the amount is USD 48,200.00, and the remark reads "PAYMENT FOR CONTRACT NO. SR-2026-0312". The same batch contains a trade contract whose signing parties match the payer and payee above, contract number SR-2026-0312, goods described as household electrical appliance parts, and settlement by telegraphic transfer. The draft sentence to be judged is: "This receipt corresponds to contract SR-2026-0312 and settlement may be processed." The materials needed for the judgment are the receiving advice text, the contract text and the FX business registration material. Whether the registration material records the correspondence for this receipt is an unprovided field and remains unknown.

Three common errors

1. Treating AIPROXYAPITOKEN as an application API key. The input is the token configuration in the hypothetical sample; the comparison object is the server-side administrator token field; the observable result is whether startup validation passes. A token that is empty or identical to the application key is a configuration error.
2. Treating a semantic relevance score as permission or entity filtering. The input is the payer name from the hypothetical sample receiving advice; the comparison object is the subject field of returned records; the observable result is whether returned records contain a non-authorized subject. A relevance score does not exclude unrelated businesses or unauthorized records.
3. Treating registration material under review as an effective authorization. The input is the FX business registration material in the hypothetical sample; the comparison object is the registration status field against the draft sentence "settlement may be processed"; the observable result is whether the registration material records the correspondence for this receipt. When it does not record that correspondence, the draft sentence is unsupported, and settlement cannot be judged as available.

How to verify the setup

1. Input the hypothetical sample receiving advice text and contract text; compare whether the payer, payee and contract number match across the two; the observable result is that all three fields agree on the same subject and the same period. The draft sentence "corresponds to contract SR-2026-0312" gains support, while "settlement may be processed" still requires the registration material to record the correspondence.
2. Input the missing field in the hypothetical sample registration material; the comparison object is the settlement authorization in the draft sentence; the observable result is that when the registration material does not record the correspondence, the evidence gap is preserved and recorded as pending verification. No authorization clause is supplied to fill the gap.
3. Input the payer name from the hypothetical sample for retrieval; the comparison object is the subject field of returned records and the vector library identifier; the observable result is that records from a different vector model library are excluded and only records from the same library and same model are kept. For the empty-result branch, use the explicit paper input [] to check the downstream design, without asserting that a fictional rule search necessarily returns [].

Related reading

• self-host/config/env
• self-host/config/model/intro
• FastGPT model connections