What the documents look like
For product questions in Store cold-chain receiving, this page examines FastGPT model connections. Compare the material received with the sources used in the answer. The examples are hypothetical.
Constraints at the model connections step
The salmon-cut name in a delivery note is a point for checking text input, not proof of the batch’s quality, temperature condition or origin. Inspect delivery of the name in the actual request, then check for its expansion into an acceptance conclusion. Cold-chain sources may separately include temperature records and inspection photos; this test covers only the supplied text. A successful call does not establish delivery of those other sources. Retain the service response separately from source-delivery evidence.
Configuration and implementation checks
| Connection or implementation check | Object in the selection text | Inspection result |
|---|---|---|
| Product name | Salmon-cut wording in the delivery note | Actual inclusion in the model request |
| Acceptance scope | The name supplies no temperature or quality proof | No expansion into a receipt-acceptance conclusion |
| Specification comparison | Weights and units in both notes | Inspect input omission and generation overwrite |
| Origin gap | No origin description in the delivery note | Trace attribution after adding the source |
| Other input paths | Inspect photos and temperature records separately | Do not infer validation from a text call |
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 a delivery note alone yields a claimed two-source specification comparison, retain source list and request. Check availability and delivery of the acceptance note before diagnosing comparison capability.
- If both weights arrive but output overwrites one, compare fields, units, batch and response to locate generation loss. Actual weight and correction responsibility still require receiving evidence.
- If the salmon name or earlier history produces origin, quality or temperature conclusions, trace their sources. A delivery name supplies no acceptance proof; add evidence and repeat the source check.
How to verify the setup
The delivery note supplies no origin information. Model settings cannot provide that source. Inspect whether output guesses origin from the name, language or earlier conversational content, marking unsupported statements. After obtaining origin documentation, hold the question and application settings constant while checking delivery and attribution. If the actual source is a photo, test that input path separately; the result of this text test does not validate photo reading.
| Material context | Scope |
|---|---|
| Document types | Cold Chain Temperature Records, Arrival Acceptance Slip, Rejection Slip and Supplier Delivery Note |
| Candidate sources | Cold Chain Vehicle Temperature Control, WMS, Store Receiving System and Quality Inspection Photos |
| Technical baseline | FastGPT v4.17.2 |