What data for this category includes
Data for this category primarily comes from design history files, device and material batch ledgers, MES systems, cleanroom environment monitoring equipment, sterilization equipment, and inspection and calibration systems. Daily documents include production process specifications, batch production records, cleanroom zone operation records, sterilization batch records, inspection reports, software version records, adverse event and traceability materials. Data is updated once per product batch or per device. Cleanroom environment data is updated on a scheduled monitoring plan. Data fields include batch unique identifiers, cleanroom environment parameters, sterilization process parameters, software version numbers, inspection results, and additional relevant fields.
Constraints imposed on tool calling and plugin workflows
Data for this category is scattered across multiple types of systems and equipment. Tool calling must adapt to the access formats and permission rules of different data sources. Data is updated per batch or on a scheduled basis, so tool calling must support configuration logic triggered by production batches to avoid mixing data across batches. Document types include controlled production records and traceability materials, so tool calling must restrict access to only compliant, controlled data sources to prevent unauthorized data from being called. A single call must handle a large number of fields, so the context window must be allocated reasonably to prevent call timeouts or content truncation caused by excessive data volume.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | required | Mandatory calling of specified tools to complete the quality audit process, complies with regulatory requirements for medical device production, and adapts to the tool calling logic of FastGPT v4.17.0 |
| Workflow/API function-call configuration | Triggered by production batch | This category’s data is updated per batch, aligns with production rhythms, and avoids calling unrelated data from historical batches |
| Single tool call timeout | 600 seconds | Covers the actual time required to pull data from multiple sources and perform data validation, prevents call interruptions due to timeout |
| Number of matching tool call results | Top 3 | Focuses on core quality records for the current batch, avoids excessive redundant data interfering with audit logic |
| Context window size | 800–1200 characters | Accommodates core fields of multiple types of production documents, prevents call failures due to content overflow |
Three common configuration mistakes
- Symptom: The tool call returns an empty batch unique identifier field with a 400 status code. Cause: The filter rule for pulling only controlled production records is not configured, and uncontrolled data not associated with the current batch is called.
- Symptom: A single tool call takes longer than 600 seconds, leading to conversation interruption. Cause: The timeout period is not adjusted based on the actual time required to pull data from multiple sources, and an overly short default configuration is used.
- Symptom: The sterilization parameters returned by the tool call do not match the current batch. Cause: The tool call is not triggered by production batch, and monitoring data from historical batches is called, which may cause quality audit deviations.
How to verify correct configuration
- Manually trigger a tool call, check that the returned data sources only include compliant data from design history files, MES systems, and cleanroom environment monitoring equipment.
- Review tool call logs to confirm that the trigger timing of each call aligns with the production node of the current batch.
- Simulate a multi-data-source pull scenario to confirm that the tool call does not experience timeout interruptions and that the time complies with actual business requirements.
- Cross-check the fields returned by the tool call to confirm that core quality audit fields are included, such as batch unique identifiers, sterilization parameters, and cleanroom environment data.
The values provided are common starting points and should be measured against the reader's own samples.