Cross-border resale returns: model connections for copy drafting

What the documents look like

In cross-border returns refurbishment and resale, the working materials come from overseas warehouse WMS, TikTok return centers, Amazon return reports, and product inspection photographs. The daily documents are return inspection sheets, refurbishment records, scrap forms, and relisting checklists. These materials record judgments about the physical state of goods: return grade, whether packaging was replaced, and whether the item can be relisted. Product and marketing copy drafting must cite those state conclusions, and each promotional sentence in the draft needs a corresponding field in the materials to support it.

This differs from overseas warehouse replenishment management, which handles inbound replenishment rather than reverse logistics and item condition. The update frequency is daily or per batch. This page does not derive batch synchronization, full synchronization, real-time triggers, or transaction-triggered mechanisms from that frequency.

Constraints at the model connections step

Model access depends on AI Proxy. The App and any Admin service that reuses App/Service configuration must set AIPROXY_API_ENDPOINT and a non-empty AIPROXY_API_TOKEN; missing either item causes startup validation to fail. AIPROXY_API_ENDPOINT takes the service root address, with no /v1 appended. Model channels are created on the administrator model provider page, where protocol type, model mapping, proxy address (a BaseUrl), and API key are entered separately. A channel test sends an actual request and outputs each model's test result and request duration. The system needs at least one language model and one index model to work normally.

Model capability is checked against the input the application requires; multimodal input, tool calling, and thinking configuration are verified separately. Application parameters are set in the application or node configuration, and the same-named fields of a compatible chat interface cannot be assumed to override the orchestration. Model probing applies only to currently enabled system models. After a first failure, the system retries at most 3 times, so a single round makes at most 4 attempts; only when all 4 attempts fail is the model finally judged abnormal.

Hypothetical drill sample: an overseas warehouse inspector submits a return inspection sheet. The material text reads: "Return number RT-2041, item is a folding camping table, inspection conclusion is slight surface scratches, accessories complete, outer packaging damaged, outer packaging replaced, eligible for relisting." The draft sentence to be judged is: "This item is brand new and unused, with original packaging intact." The objects of comparison are the inspection conclusion field and the packaging field, and the question is whether the material supports that draft sentence.

Configuration and implementation checks

Three common errors

1. Entering AIPROXY_API_ENDPOINT as a complete model request address with /v1 appended. Check: take that address as input, compare it with the service root address the document requires, and observe whether startup validation passes.
2. Using an application API key as AIPROXY_API_TOKEN. Check: take that key as input, compare it with the server-side administrator token, and observe whether validation fails because the token is inconsistent or empty.
3. Treating the draft sentence "This item is brand new and unused, with original packaging intact" as supported by the material. Check: take the inspection sheet text as input, compare it field by field with the draft sentence, and observe whether the inspection conclusion "slight surface scratches" is mutually exclusive with "brand new and unused," and whether the packaging field "outer packaging replaced" is mutually exclusive with "original packaging intact."

How to verify the setup

1. Take the inspection sheet text and the draft sentence from the hypothetical sample as input, and compare the inspection conclusion, accessories, and packaging fields. The supported condition is met only when every description in the draft sentence finds a corresponding field for the same subject and the same period in the material. A mutually exclusive value is recorded as unsupported; a field not stated is recorded as pending verification.
2. Take the hypothetical AIPROXY_API_ENDPOINT and AIPROXY_API_TOKEN as input, and compare the entered form with the document requirements. The startup validation condition is met when the endpoint contains no /v1 and the token is non-empty and consistent with the server side. Any mismatch is recorded as a configuration gap.
3. Take the hypothetical channel configuration as input and run the channel test, then compare the test output. The channel-available condition is met when every model outputs a test result and a request duration. When a failure result appears, record that model and the error information, and do not infer a specific cause of failure.

The model probing alert webhook test pushes one simulated error message and one simulated recovery message. This is a documented test behavior and does not mean a real model has failed or recovered. Retrieval and relevance scores do not carry permission or entity-isolation duties.

Related reading

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