What the data for this category looks like
Daily documents for the spinning and weaving category include cotton blending or material blending lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection reports, and defect logs. Data sources include raw fiber inspection reports, ERP systems, real-time data from spinning and weaving machines, evenness testers, fabric inspection machine test results, and order specification documents. Data update frequency changes with production batches, shifts, and the number of grey fabric pieces. Corresponding records are generated for each batch of raw materials, each shift’s production, and each piece of fabric produced. Most document structures use structured parameters, including fields such as fiber grade, yarn twist, and grey fabric defect type. Some inspection records include semi-structured defect description content.
What constraints these characteristics impose on model access and configuration
Frequent data updates and increasing single-batch data volume with production steps require that the model context length adapts to the total token count of complete single-batch documents. This avoids truncation of critical production parameters caused by a too-small maxContext setting. Data sources include real-time data streams from industrial equipment and offline inspection documents. This requires model access to support parsing logic for multi-format inputs, while also ensuring interface call stability to match real-time production rhythms. Core audit workflows for this category rely on accurate matching of fiber blending and process parameters. Model output must have high stability, with no random deviations. The semi-structured nature of defect logs requires the model to have targeted classification and extraction capabilities. Model output focus can be adjusted through configuration settings.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total token count of single-batch cotton blending lists, process sheets, and inspection records, avoiding truncation of critical production parameters |
maxTokens | 4000–6000 token | Covers the output length of a single audit conclusion, blending plan, or defect analysis, meeting business requirements |
temperature | 0.1–0.3 | Ensures stability of parameter extraction and plan generation, reducing the impact of random deviations on audit results |
quoteMaxToken | 1000–2000 token | Limits the token length of a single referenced document, avoiding redundant content interfering with model audit logic |
AIPROXY_API_ENDPOINT | Fill in the locally deployed FastGPT proxy address | Adapts to local private deployment model call links, ensuring data security |
AIPROXY_API_TOKEN | Use the exclusive key generated by the platform | Verifies interface call permissions, preventing unauthorized access |
| Workflow/API tool selection | auto | Allows the model to independently choose whether to call built-in tools to process raw equipment data, improving parsing accuracy |
Three common misconfigurations
- Symptom: For the same model version and configuration, the FastGPT online environment successfully extracts cotton blending list data, but the local deployment environment fails. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, leading to blocked model call links or failed permission verification. - Symptom: Attempting to access the o1 model results in failed normal calls, with interface error returns. Cause: Exclusive access parameters for the o1 model are not configured according to FastGPT official documentation, or the matching interface address is not filled in correctly.
- Symptom: After workflow orchestration, intermediate results cannot be hidden step-by-step, only the final output is displayed. Additionally, after importing defect logs, rerank model adaptation fails. Cause: The result display switch for intermediate nodes is not turned off, the context delivery link is not configured correctly, and a rerank model compatible with FastGPT v4.17.0 is not used.
How to verify correct configuration
- Upload a single standard cotton blending list, check whether the fiber grade, blending ratio, and other fields extracted by the model match the original document content.
- Call the test interface to confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no connection timeout or permission error messages. - Run the test workflow to confirm that processing results of intermediate steps are not displayed externally, only the final audit conclusion is output.
- Import multiple batches of grey fabric defect logs, check that the defect classification results returned by the model conform to business logic, with no obvious deviations.
The values listed above are common starting points, and should be evaluated against samples relevant to the specific use case.