What the data for this category looks like
Daily documents for this category include cotton blending lists, process setup sheets, equipment operation records, yarn and grey fabric inspection reports, and defect ledgers. Data sources cover raw fiber inspection data, ERP systems, real-time collected data from spinning and weaving looms, evenness testers, fabric inspection machine test results, and order specification requirements. Data updates trigger synchronously with each batch of raw materials, each shift’s production, and each roll of fabric produced. Document fields include fiber fineness, spinning twist, weaving structure parameters, defect type and location, and some fields use units specific to the textile industry.
What constraints these characteristics impose on model access and configuration
This category has high-frequency data updates and scattered data sources. The model access link must support real-time or batch synchronization of multi-source data, and adapt to the field structures of multiple document types. A single document contains many professional parameters. The configured model must accurately recognize specialized fields and units to avoid parameter parsing errors. Batch data generated by continuous production creates context load. The maximum text length processed by the model in a single session must be limited to prevent timeouts or content truncation. Most defect records are unstructured text. Appropriate model capabilities must be configured to complete anomaly classification and standardized report generation.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of process and inspection documents generated per shift, avoiding context overflow |
maxTokens | 2000–3000 token | Meets the output length requirements for process parameter calculation and exception reports, while balancing generation efficiency |
temperature | 0.3–0.5 | Maintains accuracy of professional parameter parsing and anomaly classification, reducing generation randomness |
AIPROXY_API_ENDPOINT | Local model deployment address, such as http://localhost:11434/v1 | Adapts to local model deployment needs in spinning and weaving scenarios, reducing network latency |
AIPROXY_API_TOKEN | Access key generated by the local model | Ensures security of model access, preventing unauthorized access |
| Multi-source data synchronization frequency | Every 4 hours | Matches the update rhythm of shift production, ensuring timeliness of knowledge base data |
Three common mistakes
- Issue: After adding a locally deployed Ollama large language model and embedding model, no corresponding option appears in the text understanding model dropdown when creating a knowledge base. Cause: The embedding model was not added to FastGPT’s model channel association list, only the large language model channel was configured separately.
- Issue: After connecting a local DeepSeek model, the debug preview automatically switches to other public models, making conversation impossible. Cause: The API key and endpoint for the custom model were not correctly bound, and the system called the preset external model by default.
- Issue: After accessing a model deployed via Xinference, frequent content truncation errors occur when generating batch process reports. Cause: The
maxContextparameter was not adjusted to fit the total length of batch documents, exceeding the model’s preset context limit.
How to confirm the configuration is correct
- Navigate to the model channel management page of FastGPT v4.17.0, confirm that all added large language models and embedding models show a normal online status.
- Create a test knowledge base, upload a single cotton blending list and defect record, trigger the text understanding process, and confirm that the model can normally parse professional fields such as fiber fineness and twist.
- Submit a test request for process parameter calculation and exception report generation, check whether the generated result contains parameters and classification content that comply with textile industry specifications.
- Check the multi-source data synchronization logs, confirm that data from ERP systems and equipment collection can be normally synchronized to the corresponding knowledge base.
The values provided here are common starting points, and should be evaluated against the user’s own samples.