What this type of data looks like
This type of data comes from design documents, standard instrument monitoring data, calibration software outputs, laboratory information systems, and customer range requirements. A single device will generate an assembly and debugging sheet, calibration procedure, original calibration record, error curve, metrology certificate, and software version record each time it is calibrated. Fields include calibrated device number, calibration date, ambient temperature and humidity, standard instrument parameters, measurement point error value, qualified judgment result, and more. Units include metrology-specific units such as pascals, degrees Celsius, milliliters per minute, etc. Data update frequency is tied to the calibration cycle, and a corresponding dataset is generated each time calibration is completed.
What constraints these characteristics impose on the tool calling and plugins workflow
Datasets for this type are generated once per single device per calibration, with a fixed structure but high field precision requirements. Tool calling requires pre-configured mapping rules for metrology-specific fields to ensure parameter unit matching. Data sources are scattered, covering calibration software, laboratory information systems, and standard instrument monitoring data, so multi-source data pulling plugins must be configured for integration. Calibration data must be fully traceable; tool calling must carry traceability parameters such as device number and calibration date to avoid generating invalid content. Data update frequency is tied to the calibration cycle, so real-time trigger logic must be set up to avoid calling expired datasets.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to tool calling requirements for different metrology documents, automatically matches tools for calibration data pulling, error calculation, and related tasks |
| Workflow/API function-call configuration | Enabled | Allows calling external calibration data interfaces to transfer metrology-specific parameters such as device number and calibration date |
| Single tool call timeout | 300–600 seconds | Allows sufficient execution time for multi-point data collection and error calculation during calibration data pulling |
| Maximum tool call retries | 2 times | Prevents call failures caused by temporary network fluctuations |
| Number of calibration records pulled | Top 5 | Meets the metrology data requirements of most bid documents |
| Tool call context window | 8000 token | Adapts to the long-text generation requirements of bid documents |
This document was written based on FastGPT v4.17.0.
Three common errors
- Symptom: Calibration data interface calls return the
Failed to fetcherror. Cause: Correct laboratory information system interface address is not configured, or network policies restrict outbound access for tool calls. - Symptom: Metrology parameter units are missing or incorrect in generated bid documents. Cause:
functionCallis not enabled, or field verification is not activated, and mapping verification for specialized units is not performed. - Symptom: Calibration data pulled by tool calls is an expired version. Cause: The trigger rule is set to fixed-time pulling, and real-time data update logic is not bound.
How to verify correct configuration
- Manually trigger a tool call, check if the returned calibration data includes required fields such as device number, calibration date, and error value.
- View tool call logs to confirm that the trigger condition is bound to the calibration completion event, and no timeout or connection failure errors appear.
- Test passing parameters that do not meet metrology unit requirements, check if field verification interception is triggered.
- Compare the pulled calibration data with original records in the laboratory information system to confirm data consistency.
The values listed are common starting points and should be evaluated against the reader's own samples.