What this category of data looks like
Data for this category comes from daily business outputs of dyeing and finishing factories’ dyeing, setting, laboratory, and quality departments. It includes documents such as dyeing formulas, chemical dosing records, dye vat operation curves, sample confirmation sheets, color difference reports, setting process sheets, and sewage ledgers. Data sources cover customer color samples, formula libraries, dye chemical batch records, dye vat control systems, colorimeters, setting machine parameters, and environmental monitoring data. Records are generated per vat, per batch, and per shift. Each individual record contains fields like process parameters, batch information, and test results. Some data includes physical units such as temperature, concentration, and batch numbers.
What constraints these characteristics impose on model access and configuration
This category has high-frequency, small-batch business data. Model access configuration must adapt to real-time processing requirements for short-cycle data, to avoid task interruptions caused by context overflow. Mixed input of multiple types of business documents and directly connected device data requires configuration to support multi-modal data parsing and association. Each individual record contains professional process parameters and physical units. The model must correctly recognize field meanings and unit specifications. Frequent batch updates require configuration items to support rapid iterative adjustments, while ensuring consistency of audit results. This avoids serious consequences such as full-vat rework or scrap caused by parameter fluctuations. In FastGPT v4.17.0, relevant configuration logic has been optimized for the business characteristics of this category.
How to set the configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The total length of single-vat process records and associated documents for this category usually falls within this range, to avoid audit interruptions caused by context overflow |
maxTokens | 2000–3000 token | Inspection and audit results need to cover core content such as process compliance and color difference judgment. This range ensures complete and non-redundant output |
temperature | 0.1–0.3 | Audits for this category need to maintain consistency in professional judgment, to avoid result fluctuations caused by overly high parameters |
vision | Enabled | This category includes visual documents such as color difference reports and sample photos, so image parsing support is required to assist audits |
AIPROXY_API_ENDPOINT | Exclusive API endpoint address where FastGPT is deployed | Data for this category must be processed in a secure and controllable environment, to avoid external transmission risks |
quoteMaxToken | 1500–2000 token | The length of referenced original documents must be limited to ensure audit logic focuses on core process parameters |
Three common configuration mistakes
- The symptom is no available model channel options in the FastGPT backend model management page, with a configuration error prompt returned when testing the model. The cause is incorrect filling of the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters, preventing the platform from establishing a connection with the model service. - The symptom is model audit tasks timing out or returning truncated results, with logs showing context length exceeds the limit. The cause is the
maxContextvalue being lower than the total content length of single-vat process records and associated documents, so the model cannot load all data to be audited. - The symptom is garbled characters or poorly formatted reference fragments in audit results, or failure to parse visual documents. The cause is the
visionparameter not being enabled, or an unreasonablequoteMaxTokenvalue, preventing the model from correctly processing visual input or formatted reference content.
How to confirm the configuration is correct
- Log in to the FastGPT backend, enter the model access configuration page, and confirm the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters are filled correctly. - Initiate an audit test for a single-vat process record, check that the task starts normally and returns complete results with no context overrun errors.
- Upload a color difference report image and a dye vat curve document, check that the model correctly parses the image content and associates text parameters.
- View the reference fragments in the audit result, confirm the format is clear and free of garbled characters, and that the reference length meets business requirements.
The values provided are common starting points and should be evaluated against the reader's own samples.