What the data for this category looks like
This category of data comes from customer manuscripts, color management systems, printing press operation data, ink and substrate batch records, spectrometers and barcode detectors. Daily documents include pre-press files, imposition sheets, printing process sheets, ink color matching records, first article approval samples, barcode inspection reports, die-cutting and lamination ledgers. Data is updated per order, per production batch, or each time a printing plate is replaced. A single document includes fields such as printing size, color code, die-cutting parameters, barcode rules, and others. Units include millimeters, gram weight, color sequence number, and more.
What constraints these characteristics impose during the "model access and configuration" phase
Pre-press files and imposition sheets contain mixed image and text content. This requires the model to support visual input capabilities. Data updated frequently per order, batch, or plate change needs to adapt to dynamically adjusted context lengths. Structured fields such as color codes and die-cutting parameters on printing process sheets are mixed with unstructured text. This requires the model to handle both structured extraction and natural language understanding. The precise numerical requirements of barcode inspection and die-cutting records require limiting the error range of model-generated content. Format differences across multiple data sources require configuring unified standardized rules for data access to avoid confusion in field parsing.
How to set the configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | Required to process image-based documents such as pre-press files and first article approval samples |
maxContext | 8000–12000 token | Adapts to long context requirements for multi-batch production data |
temperature | 0.2–0.4 | Controls format consistency of quotations and documents, reduces numerical deviation |
| Workflow/API tool selection | auto | Calls document parsing tools as needed to handle structured fields |
quoteMaxToken | 2000 token | Limits the length of referenced production data to avoid context overflow |
AIPROXY_API_ENDPOINT | Fill in according to local deployment address | Adapts to local large model access requirements |
Three common configuration mistakes
- Request Timeout errors occur during conversations. The cause is failure to adjust
maxContextto fit long production order data, leading to context exceeding the model's processing limit. - Tool function calls cannot be triggered. The cause is incorrect configuration of
toolChoiceorAIPROXY_API_TOKEN, leading to the local large model being unable to call preset tools. - Extracted color codes and die-cutting parameters have deviations. The cause is an excessively high
temperaturevalue, leading to insufficient consistency in model-generated content that cannot match the precise requirements of printing processes.
How to confirm the configuration is correct
- Upload a single pre-press file and imposition sheet, check whether the model can correctly recognize text and size information in the image, and confirm that the visual configuration is active.
- Import a collection of multi-batch production data, check whether context loading works normally, and confirm that the context length configuration fits the current data scale.
- Submit a test request including structured field extraction, check whether tool calls are triggered normally, and confirm that the tool call configuration is correct.
- Submit a test request for quotation preparation, check the numerical consistency of the generated content, and confirm that the temperature parameter value meets business requirements.
The values provided are common starting points and should be evaluated against the reader's own samples.