Tool Calling and Plugins for Pharmacovigilance in Cleanroom Management

Data Characteristics

Cleanroom management data originates from environmental monitoring systems, equipment operation logs, personnel access records, material transfer vouchers, and daily cleaning/disinfection reports. This data is typically structured and stored in LIMS (Laboratory Information Management Systems), MES (Manufacturing Execution Systems), or SCADA (Supervisory Control and Data Acquisition) systems. Some reports may exist as PDFs or scanned images. Data updates frequently. Environmental parameters like temperature, humidity, and differential pressure update every minute or even second. Personnel and material records generate in real-time. Document structures are relatively fixed, including key fields like batch number, area ID, timestamp, monitored values, and operator ID. Units strictly follow industry standards; for example, differential pressure uses Pa, particulate concentration uses Units/m³, and microbial counts use CFU/m³ or CFU/Dish.

Constraints on Tool Calling and Plugins

Cleanroom management data's high real-time nature requires low-latency tool calls. This ensures the pharmacovigilance system responds to anomalies promptly. Structured data sources allow direct querying and extraction via API interfaces, reducing text parsing complexity. However, large data volumes and frequent updates demand high concurrency and efficient data processing from API calls. Unstructured documents, such as PDF reports, require additional OCR or document parsing plugins to extract key information. Standardized fields and units aid data validation and conversion after tool calls, preventing misjudgments from inconsistent units. Data often resides across multiple heterogeneous systems, necessitating integration of multiple tools or plugins for unified data acquisition and correlated analysis.

Configuration Settings

Common Pitfalls

• Calling the Tianyi Cloud model returns an empty response, with logs showing "message". This may indicate a mismatch between the model's return format and expectations, or an incorrect parsing path for return fields in the FastGPT workflow.
• Workflows cannot directly call custom environment variables deployed in Docker. The workflow execution environment is isolated from host environment variables. Pass these variables via API parameters or preset variables.
• API calls involving the QWQ_32B process fail, but application configuration tests pass. This may be due to inconsistent parameters passed during the API call versus those in the application configuration test, or missing API request headers or authentication information.

Validation Steps

• Simulate environmental monitoring data anomalies to trigger tool calls. Check if the pharmacovigilance system receives expected alerts and verify the correctness of key fields in the alert content.
• For unstructured data sources like PDF reports, upload test reports. Verify if OCR or document parsing plugins accurately extract key information such as batch numbers and microbial counts. Compare extracted data with original reports.
• During peak periods or times of highest data update frequency, continuously monitor tool call success rates and response times. Ensure these meet business SLA requirements.
• Check FastGPT workflow logs. Confirm all tool call steps return a 200 OK status code and show no parsing errors or timeout records.

The values provided are common starting points. Measure them against your own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.