What data for this category looks like
Data for this category originates from color printing, label, carton, flexible packaging, and pharmaceutical packaging processing enterprises. Data sources include customer manuscripts, color management systems, printing press operation data, ink and substrate batch records, spectrometer test results, and barcode detector reports. Daily documents cover pre-press files, imposition sheets, printing process sheets, first-piece proofing records, die-cutting and lamination logs, and more. Fields include batch numbers, printing parameters, color deviation values, test qualification status, and additional relevant metrics. Data updates occur per order, per production batch, and each time a printing plate is replaced.
What constraints this category’s characteristics impose on model access and configuration
This category has high data update frequency, and includes both text and image documents. Model access must support multi-source data synchronization and cross-type content processing. Documents such as pre-press files and first-piece proofing records have version differences, so models must support content version identification and comparison. The data volume of a single production batch varies with order scale, so configuration must balance context processing capabilities for both small-batch and large-batch scenarios. Field formats for test data such as barcode and die-cutting results are inconsistent, so models must support structured extraction and field alignment to avoid analysis failure caused by data format deviations.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Meets the context length requirement for multiple documents per single order in this category, preventing loss of critical batch and test data due to context overflow |
vision | Enabled | Required to process image documents such as pre-press files and first-piece proofing records, and identify text, color deviation, and plate version information |
| Workflow/API tool selection | auto | Supports automatic tool calls to perform operations such as long text splitting and structured extraction, adapting to multi-source data processing needs |
maxTokens | 2000–3000 token per request | Matches the length of single documents such as printing process sheets and test reports, preventing single requests from exceeding model limits |
AIPROXY_API_ENDPOINT | Fill in the deployed domestic model proxy address | Adapts to the requirement of using domestic models, replacing the native OpenAI interface |
temperature | 0.2–0.4 | Ensures consistency of quality traceability and defect analysis results, avoiding analysis deviations caused by randomness |
Three common errors
- Symptom: When creating a knowledge base, it is not possible to select a locally deployed domestic model, and the interface prompts an interface connection failure. Cause: The
AIPROXY_API_ENDPOINTparameter is not configured correctly, only the interface token is filled in. - Symptom: The workflow runs with the error "request text too long, exceeds model limit", returning status code 400. Cause: Long text returned by the HTTP API is not segmented before being passed directly to the model, causing it to exceed the
maxTokenslimit. - Symptom: The content extraction node returns empty results, and the tool call log shows that functionCall is not triggered. Cause: The
toolChoiceparameter is configured incorrectly, not set to auto or specifying the corresponding tool, preventing the model from automatically calling the extraction tool.
How to confirm the configuration is correct
- On the model configuration page of FastGPT v4.17.0, check the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters, initiate a test request to verify the model connection status, and confirm that a normal response is returned. - Upload a single printing process sheet and first-piece proofing image, test whether the model can correctly identify text content and color deviation, verifying that the
visionandmaxContextconfigurations take effect. - Trigger the workflow to process a long text test report, confirm that the returned results are correctly segmented, and no text overflow error occurs.
- View the tool call log, confirm that the
toolChoiceparameter triggers automatic tool calls, and the content extraction node normally returns structured data.
The values provided are common starting points and should be evaluated against local test data.