What the data for this category looks like
Typical entities in this category include apparel OEM/ODM providers, brand supply chains, and cutting and sewing workshops. Daily documents include process sheets, CAD patterns, layout plans, cutting orders, first sample seals, process quality sheets, and packaging and size matching sheets. Data sources cover order and size charts, CAD design files, material and accessory inspection records, hanging system process data, needle detector test results, and customer sealed samples. Data updates are completed per order, per style, per batch. Single batch associated documents use multiple formats, including structured process parameter sheets and unstructured photos and drawing files. Fields include garment-specific content such as size codes, cutting piece matching requirements, and packaging ratios.
What constraints these characteristics impose on model access and configuration
Data for this category is updated frequently with orders and styles. Frequent switching of knowledge base content for different batches is required. This means model access must support dynamic data source configuration. Scenarios with mixed input of multiple document types, including CAD drawings, process tables, and quality inspection photos, require multi-modal recognition capabilities. The total length of documents associated with a single order is relatively long. Proper control of context truncation logic is needed to avoid loss of core information such as key processes and size requirements. Structured data collected from equipment and manually filled quality inspection forms must be parsed uniformly. This requires models to support mixed-format input processing. Concurrent request volumes during business peak periods must match the processing complexity of single-batch documents, to avoid exceeding model service capabilities. This page is written based on FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | This category requires review of visual documents such as first sample seal photos and cutting layout plans, so multi-modal recognition capabilities must be enabled |
maxTokens | 8000–12000 token | The total length of documents including process sheets and process tables associated with a single order is relatively long. This setting ensures key processes and size requirements are not truncated |
maxContext | Calibrated at 1.2 times the total size of documents for a single order | Prevents 400 errors caused by context overflow, and matches the document volume per batch for this category |
temperature | 0.2–0.4 | Quality audits require consistent and rigorous results. Reducing randomness ensures unified audit standards |
AIPROXY_API_ENDPOINT | Fill in the local model server address | Adapts to large models deployed on enterprise premises, and meets data localization requirements |
AIPROXY_API_TOKEN | Fill in the exclusive token generated by the platform | Ensures the security of interface call permissions, and prevents unauthorized access |
Three common mistakes
- Shared pages have no response after more than 60 concurrent requests, while local model server resources have not reached their bottleneck. Cause: The concurrency upper limit is not adjusted based on the processing complexity of single-batch documents for this category. The default concurrency configuration does not match the mixed document processing requirements of this category.
- A 400 error is returned when uploading a process sheet or layout plan that exceeds the context length. Cause: Reasonable
maxContextandmaxTokensparameters are not set, and no file content interception preprocessing logic is added to the workflow, leading to context overflow. - The model return result does not include structured data from the process quality sheet, while the corresponding content exists within the <FilesContent> tag. Cause: The model's context reference rules are not configured correctly, or the parsing switch for the corresponding document type is not enabled, leading to structured content not being correctly included in the model input.
How to confirm the configuration is correct
- Initiate a test request for the full set of documents for a single order, and check whether the model's returned audit results include key requirements from the process sheet and size chart.
- Adjust the number of concurrent requests, observe the model's response status, and confirm that there are no unresponsive situations during business peak concurrency.
- Upload a test file that exceeds the conventional document length, and check whether a 400 error is returned. If an error occurs, adjust the
maxContextandmaxTokensparameters. - Upload a first sample seal photo, and check whether the model can correctly identify sewing defects or size deviations in the photo.
The values provided are common starting points and should be measured against the reader's own samples.