What this category's data looks like
Data for this category primarily comes from customer color swatches, recipe libraries, dye chemical batch records, dye vat control data, colorimeter readings, setting machine parameters, and environmental protection monitoring logs. Data is updated per vat, per batch, or per shift. Single-batch documents include dyeing recipes, chemical weighing records, dye vat curves, proofing confirmation sheets, color difference reports, setting process sheets, and other content. Fields cover physical parameters such as temperature, feeding amount, color difference ΔE value, as well as qualitative descriptions such as color streaks, hand feel, shrinkage rate, and more.
What constraints do these characteristics impose in the model access and configuration workflow
High-frequency vat-level or batch-level data updates require the model context window to adapt to the total token count of single-batch documents, to avoid redundant information interfering with audit logic. Multimodal image data such as color swatches and color difference reports requires the model to support visual parsing capabilities. Documents that mix structured physical parameters and unstructured descriptions require the model to adapt to both text and numerical parsing. The rigor of quality audits requires consistent model output to reduce random deviations. In addition, audits of compliance data such as sewage logs require stable model output formats to facilitate subsequent verification.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total token count of documents related to a single vat or batch, avoiding context overload |
vision | Enabled | Required to process image audit data such as customer color swatches and color difference reports |
temperature | 0.2–0.4 | Ensures consistent output for quality audits, reducing subjective bias |
maxTokens | 1500–2000 token | Adapts to the standard output length of a single vat audit report |
AIPROXY_API_ENDPOINT | Fill in the actual access address of the proxy service, such as https://api.local:11434/v1 | Points to the correct service path for model deployment |
AIPROXY_API_TOKEN | Fill in the authentication key assigned by the proxy | Completes identity verification for model access |
Three common mistakes
- Phenomenon: The third-party vendor option for Volcano Ark cannot be found in the model channel configuration interface, and third-party vendor access cannot be completed. Cause: The third-party vendor access switch has not been enabled in the FastGPT model management module, or the proxy route for the corresponding vendor has not been configured.
- Phenomenon: After accessing the Qwen QWEN2.5-VL-7B-instruct multimodal model, no parsing results are returned when uploading color swatch or color difference report images. Cause: The
visionconfiguration item has not been enabled, or necessary parameters for image parsing were not included during model invocation. - Phenomenon: A 404 error is returned when adding a local Ollama model via AI proxy. Cause: The address entered for
AIPROXY_API_ENDPOINTdoes not point to the actual service port of Ollama, or the proxy service is not running properly.
How to confirm the configuration is complete
- Navigate to the model channel management page of FastGPT v4.17.0, check the configured model list, and confirm that the target model or vendor has been successfully added.
- Upload a single customer color swatch image, trigger a model analysis task, and check whether valid parsing results related to color and color difference are returned.
- Submit dye vat curve and recipe documents for a single vat, and check that the audit results output by the model conform to the preset structured format requirements.
- View the model invocation logs, and confirm that the configuration parameters such as
maxContextandtemperaturematch the preset values.
The values provided are common starting points and should be measured against the reader's own samples.