What the data for this category looks like
Typical entities in this category include flow, pressure, temperature, and analytical instrument manufacturers, as well as metrology laboratories. Daily generated documents include assembly and commissioning sheets, calibration procedures, original calibration records, error curves, metrology certificates, and software version records. Data sources cover design documents, standard instrument and environmental monitoring data, calibration software, laboratory information systems, and customer range requirements. The update frequency is once per calibration per device. Each calibration-related document includes fields such as tested part number, calibration point parameters, environmental condition records, multi-point measurement values, error calculation results, and traceability chain information. Units correspond to the instrument measurement type: for example, ℃ for temperature calibration records, kPa for pressure calibration records.
Constraints imposed on model access and configuration by these characteristics
First, data sources are scattered across multiple systems and tools. This requires model access configurations to support multi-source data import and unified parsing, to avoid errors caused by manual organization. Second, documents generated for a single device during each calibration have mixed structural features: both structured calibration parameter fields and unstructured error curve descriptions and certificate text. This requires configurations to adapt to mixed-format document parsing. Third, traceability chain information and error calculation results in calibration records are core audit content. Configurations must be ensured not to truncate key content, while filtering irrelevant historical records. Fourth, the update frequency is once per device per calibration, which requires configurations to support single-batch processing of calibration documents for a single device, adapting to small-batch, high-frequency business scenarios. Finally, the rigor of metrology audits requires the audit results output by the model to be consistent, so output randomness must be limited.
How to configure the settings
In FastGPT v4.17.0, the following configuration items can be set directly on the model access page:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The total token count of complete calibration records, error curves, metrology certificates and other documents for a single device typically falls within this range, preventing truncation of key traceability chain and error calculation information |
maxTokens | 2000–3000 token | The output length used to generate audit conclusions or extract structured fields, covering complete error analysis and audit opinions |
quoteMaxToken | 1500–2000 token | Limits the length of referenced original record fragments, ensuring only calibration data and environmental conditions relevant to audits are extracted, avoiding inclusion of irrelevant content |
| Workflow/API tool selection | auto | Automatically selects appropriate tools to extract structured fields, adapting to mixed structured parameters and unstructured text in calibration records |
temperature | 0.1–0.3 | Reduces output randomness, ensuring consistency of audit results and meeting the rigor requirements of metrology audits |
AIPROXY_API_ENDPOINT | Fill in the actual deployed proxy address | Adapts to domestic or overseas model call links, avoiding cross-region access restrictions |
Three common configuration errors
- Symptom: When calling the model access configuration workflow via API, no prompt information selected or input by the user is received. Cause: The output parameters of the interaction node are not correctly configured in the workflow, or the correct session context parameters are not specified during API calls.
- Symptom: An incorrect port number is entered when configuring
AIPROXY_API_ENDPOINT, but the model service can still be accessed normally. Cause: The proxy service does not verify endpoint legitimacy, or there is a redundant default fallback link, causing the incorrect configuration to not trigger an interception. - Symptom: The number of relevant results returned by semantic retrieval is too large or the scores are too high, making it impossible to filter valid calibration records. Cause: No reasonable context filtering rules are configured, causing retrieval to not limit document fragments relevant to calibration audits, resulting in inclusion of irrelevant historical records.
How to confirm the configuration is correct
- Upload the complete calibration record document for a single device, trigger the test process for the model access configuration, and check whether the output audit result includes all key fields. Adjust configuration items to match the expected extraction range.
- Call the API interface to send a test request, check whether the returned result includes the expected interaction prompts or structured extraction content, and verify the validity of the parameter configuration.
- Modify
AIPROXY_API_ENDPOINTto an invalid address, trigger a call, and check whether the corresponding error prompt is returned, to verify the validity of the security configuration. - Adjust the value of the
temperatureparameter, observe the randomness changes of the output results, confirm that the parameter takes effect, then restore it to the reasonable range.
The values provided here are common starting points, and should be evaluated against the relevant samples.