What data for this category looks like
The core data for this category comes from garment OEM/ODM, brand supply chains, and cutting and sewing workshops. Daily documents include process sheets, pattern pieces, layout plans, cutting orders, first article sealed samples, process quality sheets, and packaging size matching documents. Data sources cover order and size charts, CAD patterns, fabric and accessory inspection records, hanging system data, needle detector records, and customer sealed samples. Data updates are conducted per order, per style, per batch. Document fields include pattern parameters, process requirements, fabric specifications, size ratios, and other details. Units are adjusted based on the specific requirements of the corresponding order.
What constraints these characteristics impose on model access and configuration
Data for this category is updated frequently per order, per style, per batch. Model access must support rapid synchronization of the latest process documents and parameters. Multiple types of documents exist in mixed formats. Content parsing must be adapted for different structures such as process sheets and layout plans. Data fields have no unified units. Models must recognize custom specification parameters at the order level. The irreversible nature of cut pieces requires that Q&A results strictly align with the process requirements of the current order to avoid output deviations. Processes are intensive and styles have strong seasonal characteristics. Model responses must balance accuracy and timeliness. Gateway access must adapt to the low-latency requirements of production systems.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long context requirements of multiple documents such as process sheets and layout plans, covering complete process data for a single order |
maxTokens | 2000–3000 token | Ensures that Q&A results can fully output detailed content such as process requirements and size ratios |
quoteMaxToken | 1000–1500 characters | Limits the length of referenced documents to avoid overly long content interfering with model judgment |
temperature | 0.1–0.3 | Reduces randomness to ensure accurate output of process parameters, size requirements, and other content |
AIPROXY_API_ENDPOINT | Exclusive gateway address for production environments | Adapts to the network environment of internal enterprise production systems to ensure stable calls |
AIPROXY_API_TOKEN | Access key bound to the production system | Restricts unauthorized calls to protect data and system security |
Three common mistakes
- A test call returns a 404 status code error. The cause is incorrect configuration of
AIPROXY_API_ENDPOINT, or the gateway address has not been granted access permissions for the production environment. - No visible effect after modifying model configurations. The cause is that FastGPT v4.17.0 and later versions have migrated model configurations to the web interface, and local config files no longer take effect.
- After uploading a process document, the model prompts "null not uploaded", but the document can be downloaded normally. The cause is that the document field structure for this category was not correctly matched during document parsing, or the metadata format during upload does not meet platform requirements.
How to confirm configurations are set correctly
- Call the model gateway interface, and receive a normal response with no error messages.
- Upload the process sheet and layout plan for a single garment style. The model can correctly extract process requirements and size ratio content.
- Adjust the
temperatureparameter, observe changes in the randomness of Q&A results to verify that the configuration takes effect. - Simulate high-frequency calls to confirm that response delays meet the actual usage requirements of production systems.
The values provided are common starting points and should be evaluated against the reader's own samples.