What data looks like for this category
This category’s daily documents include process sheets, CAD patterns, layout plans, cutting orders, first article sample seals, process quality sheets, and packaging and size matching sheets. Data sources cover orders and size charts, CAD design files, surface and auxiliary material inspection records, hanging system operation data, needle detector test results, and customer sample seal materials. Updates to this data are scheduled per order, per style, and per batch. There are many styles, with strong seasonality. Cut pieces are irreversible, and manual processes are intensive. These traits create clear differences from fabric-only processing categories.
What constraints do these characteristics impose on model access and configuration?
Multi-batch, high-frequency updated data requires model access to adapt to dynamic document structures. Fixed chunking logic can cause loss of key process information, so this must be avoided. The irreversible nature of cut pieces means knowledge base recall for equipment inspection and maintenance Q&A must accurately match garment versions and process details. Strict limits must be placed on redundancy in context and recalled content to ensure this accuracy. Intensive manual processes mean large volumes of process quality-related text and numerical data must be processed. Model configuration must balance long text parsing and field extraction capabilities. Seasonal style changes also require configuration to support quick switching of knowledge base data sources, to adapt to process requirements for different batches.
How to set configurations
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of multi-process, long documents for this category, avoiding truncation of key process information |
maxTokens | 4000 token | Controls the length of single-round responses, adapting to parsing and output of documents such as process quality sheets and layout plans |
quoteMaxToken | 2000 token | Limits the total character count of knowledge base recall, avoiding redundant content interfering with accurate judgment of equipment inspection and maintenance |
temperature | 0.3–0.5 | Reduces random output probability, ensuring consistency of content such as process parameters and size requirements |
| Workflow/API tool selection | auto | Automatically selects whether to call tools, adapting to scenarios where historical maintenance records need to be retrieved during equipment inspection |
AIPROXY_API_TOKEN | Fill in the corresponding key according to the deployment environment | Ensures valid identity verification for model calls, avoiding permission exceptions |
AIPROXY_API_ENDPOINT | Fill in the corresponding interface address according to the deployment environment | Adapts to model call paths for local or cloud deployments, ensuring interface connectivity |
Three common configuration errors
- Incorrectly associating the
AIPROXY_API_TOKENconfiguration with environment variables, causing model calls to return a 403 status code (no body). The root cause is failure to load the environment variable for the corresponding key in the deployment configuration, or incorrect parameter entry. - Dialogue prompts in workflows fail to correctly recognize spaces, causing size combinations in process sheets such as "S/M" to be incorrectly split. The root cause is the prompt failing to explicitly specify retaining space formatting in text, or chunking configuration failing to retain original text spacing.
- Attempting to switch the model associated with the knowledge base causes recall anomalies. The root cause is failure to synchronously update call parameters for the corresponding model, leading to matching logic incompatible with the current model.
How to confirm successful configuration
- Initiate a test query containing key parameters from the process sheet, and verify that the model’s returned content accurately matches the process requirements in the document.
- View model call logs, and confirm that the configurations for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave successfully established a connection with no error messages. - Adjust the
temperatureparameter, then run the same query multiple times, and verify that the consistency of returned results meets expectations. - Import a batch of test cutting orders and process quality sheets, and verify that the content recalled by the knowledge base matches the input documents.
The values provided here are common starting points, and should be evaluated against samples specific to the deployment.