What data for this category looks like
Data for this category comes from metrology management systems, external calibration institution certificates, reference standards, LIMS, and environmental monitoring equipment. Daily processed documents include gauge ledgers, calibration plans, calibration certificates, interim verification records, original test records, reference material ledgers, and deviation handling documents. Data updates are triggered by calibration cycles, single test results, or abnormal events. Core fields include gauge number, calibration date, measured value, uncertainty, traceability number, and usage status.
What constraints these characteristics impose on model access and configuration
Multi-source heterogeneous data sources require model access to support cross-system data pulling and file import format adaptation. Complex document structures and multiple fields require models to have accurate field extraction and association capabilities. Non-fixed update rhythms require configurations to support dynamic loading of the latest data, without relying on fixed scheduled synchronization mechanisms. The rigor of traceability chains requires model context to retain complete metrology data associations, avoiding truncation of key information. Configurations must also adapt to the high-precision requirements of metrology data, preventing generated results from deviating from actual measured data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of long texts such as calibration certificates and original test records, avoiding truncation of traceability chain associated information |
maxTokens | 4000 token | Covers the output length requirements for single calibration certificates or batch metrology data audits |
temperature | 0.1–0.3 | Adapts to the rigor requirements of metrology audits, reduces random generation deviations, and ensures results match actual measured data |
| Workflow/API tool selection | auto | Automatically triggers tool call actions such as data extraction and field verification, adapting to multi-document audit workflows |
AIPROXY_API_ENDPOINT | Fill in according to local deployment nodes | Connects to locally deployed large models or third-party model services, adapting to internal enterprise deployment environments |
vision | true | Supports image recognition of calibration certificates and test records, adapting to processing requirements for scanned paper certificates |
Three common mistakes
- Phenomenon: A
context length exceedederror is returned when calling the model. Cause: ThemaxContextparameter was not adjusted for metrology long text data, resulting in exceeding the context threshold supported by the model. - Phenomenon: The specified large model cannot be loaded during the model call phase. Cause: The
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters were not configured correctly, resulting in failed model connection. - Phenomenon: Automatic extraction of the uncertainty field from calibration certificates fails during tool calls. Cause: The
toolChoiceparameter was not set to auto, or the corresponding data extraction rule was not configured.
How to confirm the configuration is complete
- Upload a scanned copy of a paper calibration certificate, initiate an audit request in FastGPT v4.17.0, and confirm that the model can correctly extract the gauge number, calibration date, and measured uncertainty fields.
- Upload a text file containing batch metrology data, test that the context of the model's returned results is not truncated, and no
context length exceedederror occurs. - View the tool call log, confirm that the
toolChoiceparameter triggered the automatic data extraction action, and the returned fields match the actual document content. - Verify the model connection status, confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters are configured correctly, and no connection timeout or authentication failure prompts are displayed.
The values provided are common starting points and should be measured against the reader's own samples.