What data for this category looks like
The data for this category typically comes from garment OEM/ODM workshops and brand supply chain links. Daily documents include process sheets, CAD patterns, layout plans, cutting orders, first article sealed samples, process quality sheets, and packaging size matching sheets. Data sources cover order and size charts, CAD design files, material inspection records, hanging production system data, needle detector test results, and customer sealed sample materials. Data update frequency follows per order, per style, per batch. Different orders have significant differences in pattern styles, process requirements, and packaging ratios. Each individual document’s fields include size parameters, fabric specifications, process nodes, inspection standards, and more. There is no unified fixed field format, so adaptation to multiple types of unstructured and semi-structured data is required.
What constraints these characteristics impose on model access and configuration
The multi-batch, multi-style nature of this category requires model configurations to support dynamic adaptation to the field rules of different orders. Unstructured data such as process sheets and pattern images requires models to have visual understanding capabilities. Frequent order-level updates require that configured context windows support flexible adjustment, to avoid being unable to hold all process and inspection data for a single order due to a fixed context size. Multi-source semi-structured data requires configuring parameters to support mixed input. It must also be compatible with standardized outputs from needle detectors and hanging production systems, as well as non-standard formats of manually filled process sheets, to prevent the model from failing to correctly parse field differences. In addition, the irreversible business nature of cut pieces means model outputs in the audit link must have high accuracy, and the deviation range of generated results must be strictly controlled.
Configuration Settings
This page is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | This category has a large number of image-based business documents such as CAD patterns and first article sealed samples, so visual models are needed to parse image content |
maxContext | 8000–12000 token | The total length of documents such as process sheets, layout plans, and process quality sheets for a single order is relatively long, so full business context must be accommodated |
temperature | 0.1–0.3 | The inspection and audit link requires rigorous, unbiased judgment results, so the probability of random generation is reduced |
AIPROXY_API_ENDPOINT | Fill in according to the privately deployed proxy address | Adapt to local or privately deployed large model services, avoiding delays and security risks of public network requests |
| Workflow/API tool selection | auto | Automatically adapt to mixed text and image input scenarios, flexibly call corresponding processing tools |
Three Common Misconfigurations
- Symptom: An error
rerank error fastgptis prompted when accessing the reranker model offline. Cause: The dependent packages of the reranker model are not correctly installed to the FastGPT operating environment, or the configured model path does not match the actual deployment path. - Symptom: Interface buttons cannot be triggered after uploading garment pattern images. The log shows that the upload is successful but there is no response. Cause: The
visionconfiguration is not correctly associated with the visual model interface, or the interface request timeout is not set with a reasonable duration. - Symptom: When using
bge-m3-type vector models, the semantic retrieval value is abnormally large, or an error is returned when calling the deployed vector model via curl. Cause: The correct vector normalization parameters are not configured, or the field matching logic between the input text and the retrieval library does not comply with the multi-size and multi-process field rules of this category, or the request header and port mapping are not correctly configured.
How to Verify Successful Configuration
- Upload a single garment process sheet image, trigger visual parsing, and check whether key information such as pattern and process requirements can be correctly extracted.
- Input the size chart and packaging size matching sheet text of a specific order, call the model for audit, and check whether the output audit result complies with the field rules of the order.
- Test the reranker model access, use more than 20 business document fragments as input, and check whether rearranged results can be returned normally without errors.
- Test the vector model access, use curl to request the deployed vector service, and check whether vector results can be returned normally without permission or connection errors.
The values provided are common starting points and should be measured against the reader's own samples.