This document was written for FastGPT v4.17.0.
What this category’s data looks like
Data comes from daily business records of dyeing, setting, laboratory and quality departments in dyeing and finishing factories. It covers documents such as dyeing formulas, chemical material weighing records, dye vat operation curves, sample confirmation sheets, color difference reports, setting process sheets, and sewage ledgers. Data sources include customer color samples, formula libraries, dye chemical batch ledgers, dye vat control systems, colorimeters, setting machine operation data, and environmental monitoring records. Data is updated synchronously with each vat production run, each batch of materials, and each shift operation. Each individual data entry includes clear fields and corresponding units, such as material batch, process parameters, and test results.
Constraints on Model Access and Configuration
High-frequency updated data per vat and batch requires model access to support real-time data synchronization and incremental updates. This prevents knowledge base data lag from reducing the accuracy of inspection and maintenance recommendations. Multiple types of structured and semi-structured documents, such as dye vat curves and color difference reports, require parsing rules adapted to different formats. This ensures accurate parameter extraction and retention of original units. Data fields include precise units such as batch, temperature, and time. Model calls must strictly match the parameter formats required by the business scenario, to avoid production deviations caused by unit confusion. High-frequency small-batch data updates also require reasonable configuration of context and recall thresholds. This prevents redundant data from occupying system resources. Calls involving environmental monitoring data must strictly align with production scenarios, to ensure output complies with business specifications.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of long documents such as dye vat curves and process sheets, to avoid truncation of critical production parameters |
temperature | 0.2–0.4 | Reduces random output deviation, ensures that inspection and maintenance recommendations match fixed process parameters, and improves recommendation accuracy |
AIPROXY_API_ENDPOINT | Official access address of the corresponding model service provider | Ensures normal model call links, and complies with access requirements of compliant channels |
AIPROXY_API_TOKEN | Fill in the exclusive key generated by the service provider | Completes identity verification, to avoid unauthorized access or call failures |
| Workflow/API tool selection | auto | Allows the model to automatically select calling tools or knowledge base content based on inspection and maintenance scenarios, adapting to complex problem handling |
Three Common Configuration Errors
- Phenomenon: Permission errors occur when calling a specified model, or the target model cannot be loaded in a non-matching environment. Cause: The model channel supported by the current FastGPT environment is not confirmed, and a model only adapted to international versions is incorrectly configured for domestic environments.
- Phenomenon: The system directly refuses to answer when no matching content exists in the knowledge base, and cannot perform general reasoning. Cause: The model’s recall and reasoning logic are not correctly configured, and the strict matching mode relying solely on the knowledge base is incorrectly enabled.
- Phenomenon: Parameter format errors are returned when calling MCP tools. Cause: Tool call parameters are not formatted according to the unit requirements for indicators such as batch and temperature in the dyeing and finishing scenario, causing the parameters generated by the model to be unrecognizable by the tool.
How to Confirm Successful Configuration
- Test a standard dyeing formula query scenario, and verify that the model’s returned content includes correct process parameters and corresponding units.
- Trigger a test question with no matching content in the knowledge base, and confirm that the model can perform reasonable reasoning based on general production knowledge.
- Verify the interface of the specified model call channel, and check that the returned result includes expected inspection and maintenance recommendations with no error messages.
- Check system logs to confirm that data synchronization and model call latency meet the real-time requirements of production scenarios.
The values provided are common starting points and should be measured against the reader's own samples.