What data for this category includes
Daily documents for the spinning and weaving category typically include cotton blending or batching lists, process setup sheets, equipment operation records, yarn inspection reports, fabric inspection reports, and defect records. Data sources cover raw fiber inspection, ERP systems, spinning and loom equipment data, evenness testers, fabric inspectors, and order specifications. Update frequency adjusts per raw material batch, per shift, and per fabric piece. Document fields include fiber metrics, spinning twist, loom speed, defect types, and inspection values. Common units are grams, meters, revolutions per minute, and pieces, among others.
Constraints on model access and configuration
High data update frequency and coverage of dynamic changes across multiple batches and shifts require model access configuration to support real-time loading of the latest process and inspection data. Multiple document types with significant field differences require configuring parsing logic adapted to multi-source heterogeneous documents, to ensure effective association of all types of business documents. A large number of equipment and the continuous propagation of defects require the model to associate inspection records across multiple devices, and output maintenance suggestions with global relevance. Errors in fiber batching, parameter setting, and other links directly affect finished product quality. The configuration must ensure that the model’s output results can be traced back to the original business documents.
Configuration Settings
This configuration guide applies to FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long context requirements of multi-source documents, covering the full-link business data from a single batch of raw materials to finished products |
temperature | 0.2–0.4 | Reduces random output deviations, ensures consistency between maintenance suggestions and process parameters, and meets the rigor requirements of spinning and weaving |
quoteMaxToken | 1500–2000 token | Retains sufficient original document fragments as evidence, supporting the traceability of inspection and maintenance suggestions |
| Workflow/API tool selection | auto | Automatically matches Q&A scenarios, adapting to different business needs such as equipment parameter query and defect analysis |
AIPROXY_API_ENDPOINT | Fill in the dedicated node according to the deployment environment | Adapts to enterprise intranet or public cloud deployment scenarios, ensuring data transmission security and stability |
AIPROXY_API_TOKEN | Dedicated key bound to the corresponding model | Ensures compliance with model call permissions, avoiding unauthorized access requests |
Three Common Configuration Errors
- Phenomenon: Some large models fail to load and call normally. The interface shows the model is not selectable, or a call error occurs. Cause: The dedicated access node and key for the corresponding model are not added in the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations, or the selected model is not within the platform’s supported range. - Phenomenon: The maintenance suggestions output by the model do not match the current process parameters, and the latest equipment operation records are not referenced. Cause: The
maxContextconfiguration value is too low, failing to load complete process documents and real-time data, resulting in missing context. - Phenomenon: A response timeout error occurs when importing multiple shifts of equipment inspection data in batches. Cause:
maxTokensis not adjusted to adapt to the length of batch data, exceeding the single-processing limit of the platform.
How to Verify Successful Configuration
- Upload a single cotton blending list and defect record document, trigger an equipment inspection and maintenance Q&A request, and check if the returned results are associated with specific fiber metrics and defect types in the document.
- View the platform’s model call logs, confirm that the configurations of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare properly connected, with no authentication or connection error returns. - Adjust the
temperatureparameter value, verify if the consistency of the output results meets expectations, and ensure the rigor of maintenance suggestions. - Import multiple batches of equipment operation data, check if the model can correctly associate parameter changes across different shifts and output consistent inspection suggestions.
The values provided are common starting points and should be measured against the reader's own samples.