What this category's data looks like
This category's data primarily comes from daily business documents and equipment-collected content from cotton spinning, chemical fiber spinning, woven, and knitting enterprises. Daily documents include cotton blending or ingredient lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection reports, and fabric defect records. Data sources cover raw fiber inspection reports, ERP systems, real-time data from spinning and weaving machines, evenness tester test results, fabric inspection machine collected data, and order specification parameters. Data updates are synchronized with each batch of raw materials, each shift's operations, and each bolt of fabric produced.
What constraints these characteristics impose on workflow orchestration
Since data updates per batch of raw materials, each shift, and each bolt of fabric, workflows must support associating documents by batch to avoid mixing cross-batch data. Structured numerical data collected from equipment, such as evenness and defect data, requires field mapping rules configured in the workflow to match raw device data to standard fields for process Q&A. Cotton blending and ingredient lists, plus process setup sheets, have batch-specific dedicated parameters. Workflows must support variable binding to document fragments associated with the corresponding batch. Large volumes of data from multiple devices and batches require workflows to limit the range of documents recalled in a single request, preventing context overload that harms Q&A accuracy. Process parameter units vary across different production processes; for example, twist uses twists per meter, and weave structure parameters use warp density/weft density. Workflows must unify parameter unit conversion logic to ensure Q&A output parameters meet production requirements.
How to set up the configuration
This configuration item is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind subsets of cotton blending sheets and process setup sheets for the current job batch | Spinning and weaving data updates per batch, so variable matching scope must be limited to avoid cross-batch data interference |
| Question classification node model | Use the initialized general process classification model | The default model adapts to the classification logic for process questions in this category; recalibration is required if switching models |
| AI chat component reply trigger timing | Execute subsequent actions only after stream replies are fully completed | Process Q&A must return complete parameters before triggering annotation or storage logic |
| Knowledge base selection scope | Only select the spinning and weaving process library and equipment operation record library | Exclusive data for this category is only stored in these two document types, so calling the full knowledge base is unnecessary |
| Single search limit | Top 8 results | Document volume per batch for this category is moderate; 8 results cover core process parameters and defect records |
nodeId | Bind the unique identifier of the question classification node | Precise association with the input source of subsequent AI chat nodes is required to avoid node confusion |
Three common configuration mistakes
- Phenomenon: No optional variable values are available when configuring
variables, making it impossible to bind process documents for the corresponding batch. Cause: The document sharding node that outputs batch-associated variables was not created first, or the data source mapping for the variable source was not configured in the workflow. - Phenomenon: The model options for the question classification node are empty, making it impossible to select an available model. Cause: No available AI models were added in the platform's global configuration, or model permissions were not enabled for the current workflow.
- Phenomenon: An error occurs when running the question classification node using the initialized AI model, and normalcy is restored after switching models. Cause: The initialized model has not completed calibration of the default classification rules, and preset configuration for the classification threshold is missing during the first run.
How to confirm the configuration is correct
- Manually trigger the workflow, input questions related to spinning or weaving processes, and check if the returned results only include process document content for the current job batch.
- Run the question classification node, confirm that model options display normally, and that classification results align with the category divisions of process questions.
- Trigger the reply flow of the AI chat component, wait for the reply to complete, then check if the annotation logic triggers normally.
- Check the binding configuration for
variables, confirm that the optional variable list includes documents such as the current batch's cotton blending sheets and process setup sheets.
The values provided here are common starting points, and should be evaluated against specific samples and use cases relevant to the deployment environment.