What data looks like for this category
Data for this category mainly comes from design documents, standard instrument and environmental monitoring data, calibration software, laboratory information systems, and customer range requirements. Daily produced documents include assembly and commissioning sheets, calibration procedures, original calibration records, error curves, metrology certificates, and software version records. Data is updated per instrument per calibration. Fields include instrument unique ID, calibration date, standard instrument traceability number, measured error value, and corresponding unit of measurement. Some documents must be associated with customer-customized range parameters.
What constraints these characteristics impose on the "tool calling and plugins" workflow
Data sources for this category are scattered and update frequently. Tool calling must support real-time pulling of the latest calibration records and metrology certificates to avoid loading expired caches. Multiple types of documents, including error curve tables and certificates with traceability numbers, require plugins to adapt parsing logic for both structured and unstructured documents. This ensures accurate extraction of core fields such as instrument ID and standard instrument traceability information. Measurement deviations will be directly passed to customer production links. Tool calling must add field integrity checks to ensure mandatory content such as environmental conditions and traceability chains is covered during review.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to cross-review of multiple types of calibration documents, automatically match tool calling scenarios |
| Workflow/API function-call configuration | Triggered per review node | Call tools only when calibration records and certificates need to be verified, to avoid invalid requests |
| Single tool call timeout | 300 seconds | Adapt to time requirements for error curve calculation and multi-document traceability verification |
| Maximum returned document count | Top 8 entries | Cover core document set for a single instrument per calibration, avoid context overload |
| Context window threshold | 12000 token | Adapt to content length of multiple calibration documents and error curves, meet information requirements for cross-review |
| FastGPT Version | FastGPT v4.17.0 | Ensure compatibility of plugin calls and correct parameter parsing |
Three common mistakes
- Phenomenon: Plugin preview can retrieve document content, but final output is empty. Cause: Context parameters for tool calling are not correctly bound, causing the review logic to fail to associate with the current instrument's calibration records.
- Phenomenon: The number of documents returned by tool calling is insufficient to cover all calibration-related content. Cause: The maximum returned document count is set too low, and does not match the number of documents for a single instrument per calibration.
- Phenomenon: The interface displays a long wait after calling, and review cannot be completed. Cause: The single tool call timeout is set too short, and does not adapt to time requirements for error curve calculation and multi-document traceability verification.
How to confirm the configuration is complete
- Input the calibration ID of a single instrument to call the tool, check if core documents such as assembly and commissioning sheets, calibration procedures, and metrology certificates can be retrieved.
- View tool calling logs, confirm that the scenarios automatically matched by
toolChoicemeet expectations, and there are no invalid repeated calls. - Check if the returned results include mandatory verification fields such as instrument ID, standard instrument traceability number, measured error value, and corresponding unit of measurement.
- Simulate multiple rounds of calls, confirm that the timeout setting adapts to the time requirements of document parsing, and there are no frequent timeouts or no result returns.
The values given are common starting points and should be measured against the reader's own samples.