What this category of data looks like
Data related to equipment inspection and maintenance for pharmaceutical dosage production comes from approved prescription processes, material batches, MES/electronic batch records, equipment and environmental monitoring systems, LIMS, and quality management systems. Data updates are performed per production batch, key process steps, and monitoring plans. Covered content includes master production documents, batch production records, batch packaging records, cleaning records, inspection records, deviation/CAPA/change documents, and batch release review forms. Data fields include equipment runtime, vibration values, temperature, feeding amount, production batch number, and more. Corresponding units are hours, mm/s, ℃, kg, and serial number format.
What constraints do these characteristics impose on the tool calling and plugins workflow
Data sources for this category are scattered and must strictly meet GMP compliance requirements. Tool calling must support integration with heterogeneous interfaces from multiple systems to ensure data traceability. Data is updated per production batch, so tool triggers must bind the current production batch parameter to avoid cross-batch data confusion. Document structures are complex and field requirements are precise, so tool calling must support specified field extraction to avoid redundant data interfering with Q&A results. Abnormal handling for equipment inspection and maintenance is directly linked to batch release, so tool call failures must trigger clear alerts to prevent production delays or compliance risks.
How to configure settings
This configuration is based on FastGPT v4.17.0. Tool calling logic can be configured using the toolChoice and functionCall parameters.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Automatically matches tool calling needs for equipment inspection and maintenance scenarios, no manual function specification required |
| Workflow/API function-call configuration | Enabled | Supports calling external equipment interfaces to obtain inspection data, meets data traceability requirements under GMP |
| DingTalk Webhook push configuration | Trigger within 10 seconds after an exception is generated | Timely pushes inspection exceptions to operation and maintenance personnel to ensure production response timeliness |
| Multimodal visual analysis parameters | Calibrated based on actual measurements | Adapts to the 1024×768 pixel specification for equipment inspection photos, ensuring parsing accuracy of Qwen2.5-vl |
| API call retry count | 3 times | Addresses call failures caused by network fluctuations, ensuring stability of equipment data acquisition |
| Context window size | 8000 token | Accommodates complete batch records and equipment historical data, supporting precise inspection and maintenance Q&A |
Three common configuration mistakes
- Symptom: The multimodal visual model returns "Failed to fetch" or an image parsing failure error. Cause: Download permissions adapted to the internal network device image storage path are not configured, preventing retrieval of inspection photos.
- Symptom: DingTalk Webhook push has no response or the message content does not match the current production batch. Cause: The push trigger is not bound to the production batch parameter, leading to incorrect data association.
- Symptom: Tool calls return empty fields or non-compliant formats. Cause: Precise data source field extraction rules are not specified, leading to equipment data pulled from MES or LIMS that does not meet the needs of inspection and maintenance Q&A.
How to confirm the configuration is correct
- Initiate a simulated equipment inspection Q&A to verify whether the tool automatically calls the corresponding data source interface to obtain equipment operating data.
- Trigger an equipment exception alert to check whether the DingTalk Webhook pushes the correct exception information within the specified time.
- Upload an equipment inspection photo that meets the 1024×768 pixel specification to verify whether the multimodal model can correctly parse the photo and return maintenance suggestions.
- View the tool call log to confirm that each call’s parameters and return results comply with preset configuration rules.
The values provided are common starting points and should be measured against the reader's own samples.