What this category's data looks like
Data for this category comes from daily business documents of enterprise metrology rooms and CNAS system laboratories. These documents include gauge ledgers, calibration plans, calibration certificates, periodic inspection records, original test records, reference material ledgers, and deviation handling documents. Data sources cover metrology management systems, LIMS platforms, external calibration institution certificates, standard instruments, and environmental monitoring data. Updates are triggered by calibration cycles, completion of single tests, or abnormal situations. Fields include instrument unique ID, calibration validity period, measurement uncertainty, traceability chain information, environmental parameters, and more.
What constraints do these characteristics impose on the workflow orchestration link
Data for this category comes from scattered sources with large format differences. Workflow orchestration must adapt to multi-data-source access logic to avoid missing data during crawling. Updates are triggered by calibration cycles, tests, or abnormal events. Trigger-based update nodes must be configured instead of fixed-cycle synchronization. Fields include strongly correlated information such as traceability chains and measurement uncertainty. Workflows must retain the association relationships between fields to avoid losing context after splitting. Error consequences in abnormal scenarios affect quality judgments. A pre-check link must be added to the workflow to ensure input data compliance.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind three types of variables: instrument unique ID, calibration validity period, and traceability chain ID | Data for this category requires association with strongly identifying fields to ensure Q&A matching accurately corresponds to the target measuring instrument |
nodeId | Assign a fixed nodeId to the calibration certificate parsing node | Fixed node identifiers are required to associate historical records and verification logic, avoiding data loss caused by node changes |
| Question classification trigger rules | Trigger classification by document type | This category includes multiple types of documents such as calibration certificates and test records, so corresponding classification logic must be matched by document type |
| Text extraction timeout | 600 seconds | Calibration certificates and original test records may contain multi-page content, so sufficient time must be reserved for content parsing |
| Single text extraction limit | First 10 documents | A single query for this category involves up to 10 associated calibration files at most, avoiding context overload |
| Context window size | 8000 token | Metrology documents for this category contain multiple long text segments, so sufficient context space must be reserved to retain traceability chain information |
Three Common Errors
- When creating a new question classification node, using the initialized AI model without modifications will trigger a parameter verification failure prompt during operation, saving, or publishing, and the node status will show abnormal. The cause is that the default prompt of the initialized AI model does not adapt to metrology professional terminology and document structure, causing the classification logic to fail to match the business rules of this category.
- The output JSON of the workflow text content extraction node is empty, and the log shows no valid matching content. The cause is that corresponding parsing rules are not configured for structured documents such as calibration certificates and original test records, resulting in failure to extract valid fields.
- The historical record panel of workflow global variables does not display the output content of the specified reply component, only retaining user questions. The cause is that the reply content binding logic for global variables is not enabled in the workflow, causing AI replies to not be written to historical storage.
How to Confirm Configuration is Complete
This verification process applies to FastGPT v4.17.0.
- Run the workflow, enter a known measuring instrument ID, and check whether the returned result includes the validity period, measurement uncertainty, and traceability chain information of the corresponding calibration certificate.
- View the historical record panel of global variables, and confirm that it contains the complete content of user questions and AI replies.
- Switch back to the initialized AI model, run the question classification node, and confirm that there are no errors and the classification results conform to the document classification rules of this category.
- Upload a PDF file of a calibration certificate, trigger the text extraction node, and check that the output JSON includes core fields such as calibration date and instrument ID.
The values provided are common starting points and should be measured against the reader's own samples.