Data Profile for This Category
Daily documents for this category include process sheets, patterns, layout plans, cutting orders, first sample approvals, process quality sheets, and packaging size code sheets. Data comes from order and size charts, CAD patterns, fabric and accessory inspection records, hanging system data, needle detector results, and customer sample approvals. Data updates trigger with each order, each garment style, and each production batch. Different documents have dedicated unique fields. For example, process sheets include process requirements. Packaging size code sheets note size ratios and packaging specifications. All data is generated around specific requirements for garment silhouettes, production processes, and packaging.
Constraints for Model Access and Configuration
High-frequency data updates tied to each order, style, and batch, plus the requirement that a single bid links to multiple dedicated documents, create multiple constraints for model access and configuration. First, bid scenarios with linked multiple documents require adaptation to dynamically changing context lengths. This avoids truncating critical silhouette and process information. Second, cutting pieces are irreversible, and process breakdowns demand precision. The model’s call validation logic must match category-specific fields such as fabric batch numbers and size ratios. Third, the wide range of styles and strong seasonal nature means that when processing bid documents in bulk, call parameters must be set appropriately. This prevents timeouts or call failures. Fourth, document structures vary widely across different orders. The configuration must support flexible adaptation to category-specific document fields.
Configuration Settings
This configuration guide is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length of multiple process sheets and layout plans linked to a single order, avoiding truncation of critical silhouette and process information |
maxTokens | 2000–3000 token | Meets output length requirements for process breakdown and fabric consumption calculation in bid quotations |
temperature | 0.2–0.4 | Reduces random output to ensure precise matching of category-specific rules such as cutting piece ratios and size requirements |
| Single request timeout | 600 seconds | Adapts to processing time for scenarios with linked multiple documents per order, preventing premature model processing interruptions |
AIPROXY_API_ENDPOINT | Fill in according to deployment environment | Connects to dedicated model services, adapting to internal data isolation requirements of enterprises |
AIPROXY_API_TOKEN | Fill in according to authorization credentials | Ensures compliance of model call permissions, preventing unauthorized access |
Three Common Configuration Mistakes
- Model call duration exceeds the set timeout threshold. This manifests as the interface returning a 408 request timeout status code or tasks marked as failed. The cause is failure to adjust the
maxContextparameter for scenarios with linked multiple documents per category, leading to excessively long model processing times that exceed the preset timeout. - Bulk execution nodes complete during online debugging but API calls do not finish. This manifests as the API returning partial batch bid results without a final completion status. The cause is failure to configure the concurrency limit for bulk calls. In category bulk bid scenarios with multiple orders, concurrent requests exceed service limits, causing nodes to fail to complete all executions.
- No requestId for large model calls can be located. This manifests as no corresponding call records in the FastGPT backend logs. The cause is failure to enable FastGPT’s request log recording configuration, failing to correctly link the call chain, making it impossible to track specific model requests.
How to Verify Correct Configuration
- Submit a single order bid document for testing. Check that the model’s output process breakdown matches the content of the process sheet, and confirm that the
maxContextandmaxTokensparameters adapt to document lengths. - Initiate a bulk call test. Check that the API returns complete results without timeouts or interruptions, and confirm that the single request timeout and concurrency parameters adapt to the category’s bulk bid scenario.
- View the FastGPT backend logs. Confirm that requestIds for corresponding calls exist, and confirm that the log configuration is correct.
- Adjust the
temperatureparameter to approximately 0.3. Run multiple tests on the same bid document, check that the output results have no obvious random deviations, and confirm that the parameter configuration is reasonable.
The values provided are common starting points and should be evaluated against the reader’s own samples.