What data for this category looks like
Data for this category comes from design documents, standard instrument and environmental monitoring data, calibration software, laboratory information systems, and customer range requirements. Daily documents include assembly and commissioning sheets, calibration procedures, original calibration records, error curves, metrology certificates, and software version records. These documents are updated after each calibration of every device. Data fields include traceability number, error value, calibration environment temperature and humidity, measuring instrument number, and calibration date. Most units use international standard units such as pascals, degrees Celsius, and milliamps.
Constraints During Model Access and Configuration
Calibration records and metrology certificates for this category are core compliant deliverables. Data is updated for each device after every calibration. Model access must accurately bind device traceability numbers and calibration batch information. Multi-field metrology attributes have strict unit requirements. Configuration must preserve field units and traceability chain details to avoid losing compliant content. Original calibration records and error curves have structured features. The model must distinguish between original collected data and calculation results. Configuration must support targeted calls for different document types. Calibration data has legal attributes. Configuration must ensure the model output does not alter key fields such as traceability number and calibration date.
How to Set Configuration Parameters
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate the context of a single device’s calibration records, error curves, and tender documents, to avoid truncating key compliant fields |
maxTokens | 2000–3000 token | Used for generating quotation descriptions and calibration compliance descriptions in tender documents, to ensure full coverage of device parameters in the output |
quoteMaxToken | 1500 token | Limits the character length of referenced calibration records and metrology certificates, to avoid context overflow caused by overly long referenced content |
temperature | 0.1–0.3 | Tender documents and quotations must remain rigorous and compliant, reduce random output deviation, and ensure accurate reference of calibration data |
AIPROXY_API_ENDPOINT | Fill in the official access address provided by the selected model service provider | Matches the API call path of the model service provider, to ensure normal delivery of requests for FastGPT v4.17.0 |
| Workflow/API tool selection | auto | Automatically select between calling the knowledge base or generation tools based on document type, to adapt to mixed processing needs for calibration records and tender documents |
Three Common Mistakes to Avoid
- Phenomenon: API request returns 404 status code or cannot connect. Cause:
AIPROXY_API_ENDPOINTis not filled in with the official access address provided by the selected model service provider, resulting in an incorrect request path. - Phenomenon: Large output deviation between the called API model and locally deployed model, with inaccurate association between quotations and calibration data. Cause: The context window or temperature parameter configuration of the local model does not match the default parameters of the API model, and no adaptation is made for the structured features of calibration documents.
- Phenomenon: Traceability number or calibration date fields are missing from the generated tender document. Cause:
quoteMaxTokenis not configured to limit reference length, resulting in truncation of key compliant fields, or context association configuration is not enabled, leading to failed field matching.
How to Confirm Proper Configuration
- Submit a test request, check the status code returned by the API, confirm that it returns the success status code defined by the model service provider.
- Import the calibration records and tender template of a single device, trigger the generation action, check whether the core fields of calibration data are included in the generated content.
- Adjust the
temperatureparameter, compare the deviation between two generation results, confirm that the output meets rigor requirements. - Import calibration data from multiple devices, verify context loading status, confirm that no key information is truncated or lost.
The values provided are common starting points and should be evaluated against the reader’s own samples.