What the documents look like
For product questions in Brand outlet operations, this page examines FastGPT model connections. Inspect what a clearance list establishes about style, season and stock, then obtain product attributes needed for selection. The examples are hypothetical.
Constraints at the model connections step
A proposed clearance attachment lists style identifiers, seasonal labels and store-stock descriptions to identify product candidates. Inspect delivery of all three with their style relationships. Stock text does not establish material, sizing suitability or quality. If the answer interprets past-season as defective, retain the list statement and added judgment to inspect support scope. Clearance purpose and product condition are different matters. A returned answer does not establish availability of the attributes needed for selection.
Configuration and implementation checks
| Connection check | Outlet candidate-product material | Verification clue |
|---|---|---|
| Candidate identity | Style, season and store-stock description | Actual delivery of relationships |
| Attribute boundary | Stock supplies no size, material or quality conclusion | Trace unsupported statements |
| Name mapping | Inspect collection-to-style correspondence separately | Absent source versus input omission |
| Scope carryover | No unsupported mixing of seasons or stores | Retain request and precise output |
| Added attributes | Recheck new size and material evidence | Inspect images and business approval separately |
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 style-to-stock relationships disappear in input, compare the list with the actual request to locate transfer loss. A repeated product word does not establish correct candidate identification.
- If collection and style identifiers are merged, or season and store scopes cross, retain both source identities and output. Obtain actual correspondence rather than deciding equivalence from similar names or parameters.
- If past-season clearance becomes a defect or a sizing-suitability claim, trace quality and attribute evidence. Inventory background supplies neither; preserve selection limits while product material remains absent.
How to verify the setup
Ask separately about candidate style, relevant store and absent attributes, recording the stock statement adopted and remaining source gaps. Once size and material evidence is supplied, repeat the question and settings to inspect its conditions without allowing the earlier list to overwrite it. Colors or details in images need their own input check; text inspection does not validate image reading. Approval, pricing and purchase conditions require applicable material, and a selection answer does not execute business approval.
| Material context | Scope |
|---|---|
| Document types | Discount Approval, Slow-Moving Inventory List, Price Tag and Gross Profit Calculation Sheet |
| Candidate sources | ERP Inventory Age, POS Sales, Outlet Mall Activities and Historical Discounts |
| Technical baseline | FastGPT v4.17.2 |