What the documents look like
For product questions in Certificates of origin, this page examines FastGPT model connections. Test source delivery with invoice and packing-list text, without inferring origin eligibility from name and quantity. The examples are hypothetical.
Constraints at the model connections step
This proposed invoice retains the selected coffee-bean name and quantity as two points for a source-delivery test. Inspect whether the actual request contains both, then check whether the answer still concerns this batch of material. An origin-certificate application needs further evidence, and this example supplies no basis for establishing origin eligibility. Successful model connectivity cannot replace those review conditions. Record the call response separately from the fields actually read, without presenting a connection test as a completed certificate decision.
Configuration and implementation checks
| Connection or implementation check | Material in the reading example | Inspection object |
|---|---|---|
| Original input | Coffee-bean name and quantity | Whether the request actually includes both statements |
| Task scope | Origin-eligibility evidence is not supplied | Inspect connectivity separately from certificate review |
| Net-weight difference | Keep invoice and packing-list entries independent | Delivery of both originals, units and subjects |
| Missing conditions | Shelf-life and storage sources are absent | Trace related answer statements individually |
| Supplementary-source retest | Hold the question and application settings constant | Inspect delivery and actual use of the new source |
Documented conditions
| Documented item / implementation check | Condition |
|---|---|
AI Proxy dependency | Inspect services, channels and application for the deployed version. |
AIPROXY_API_ENDPOINT | Use the AI Proxy service root address. |
AIPROXY_API_TOKEN | Use a nonempty administrator token matching the service. |
Model capabilities | Test capabilities required by the actual input path. |
Application parameters | Same-named interface fields need not override application settings. |
Three common errors
- If names, quantities or packing-list weight are omitted but comparison is claimed, locate each item in actual input. Retain gaps and output while investigating delivery of the second source.
- If both net weights arrive but the answer selects one, retain units, subject and versions. Obtain measurement or revision evidence; changing models cannot determine which operational record applies.
- If product name and quantity produce origin-eligibility, shelf-life or storage conclusions, trace the necessary sources. Readable fields do not substantiate those judgments; keep unprovided conditions unresolved.
How to verify the setup
The invoice supplies no shelf-life or storage-condition evidence; a product name and quantity do not provide it. The model may still generate related wording, so trace each statement to a source instead of treating a fluent answer as an obtained product description. After adding the applicable product or storage document, hold the question and application settings constant. Inspect delivery of the added content and the originals used in the answer. This text-input test also does not validate an image path it never exercises.
| Material context | Scope |
|---|---|
| Document types | Certificate of Origin Application, Commercial Invoice, Packing List and Production Cost Data |
| Candidate sources | China International Trade Single Window, CCPIT System, Enterprise Production Records and Customer Customs Clearance Requirements |
| Technical baseline | FastGPT v4.17.2 |