Data Characteristics
Site Management Organizations (SMO) in pharmacovigilance handle data primarily from Clinical Research Forms (CRFs), subject diary cards, lab results, imaging reports, and investigator-reported Adverse Events (AEs) and Serious Adverse Events (SAEs). This data combines structured formats (e.g., database records, XML files) and unstructured formats (e.g., PDFs, scanned handwritten notes). Data updates frequently, especially during clinical trials, with AE/SAE reports potentially updating daily. Document structures vary, including standard ICH-GCP formats and custom templates from research centers. Fields include patient identifiers, drug information, event descriptions, and start/end dates. Unique fields often include trial drug batch numbers, subject enrollment IDs, research center codes, and reporter qualifications. Units involve dosage (mg, g), frequency (times/day), and duration (days, hours).
Constraints on Tool Calling and Plugins
The diverse nature of SMO data requires tool-calling plugins with robust parsing capabilities to handle various input formats. High-frequency data updates necessitate real-time or near real-time plugin calls to capture the latest adverse event reports promptly. Mixed structured and unstructured data challenges plugin preprocessing capabilities, such as extracting key information from PDF reports and linking it with structured database data. Unique fields like trial drug batch numbers and research center codes must pass as critical parameters during tool calls to ensure accurate data retrieval and analysis. Due to the strictness of pharmacovigilance, plugins must ensure data integrity and traceability. Any data loss or conversion error can lead to severe compliance issues. Therefore, tool calling requires robust error handling and logging mechanisms.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000-8000 tokens | Pharmacovigilance reports often contain detailed descriptions, requiring a sufficient context window. Larger values may increase latency. |
toolCallTimeout | 600 seconds | External database queries or complex document parsing can be time-consuming; this prevents timeouts. |
maxToolRetries | 3 times | Retries improve success rates for occasional external service failures and reduce manual intervention. |
extractSchema | JSON Schema file | Ensures extracted AE/SAE information from unstructured reports conforms to predefined formats for subsequent processing. |
logLevel | INFO or DEBUG | Detailed logging of tool call inputs, outputs, and errors facilitates troubleshooting and compliance audits. |
Common Pitfalls
- Symptom: Model response time is excessively long, reaching hundreds of seconds. Cause: External database queries or document processing within tool calls are complex, and query scope or document size limits are not applied.
- Symptom: Database connection errors or permission denials occur after configuring a database connection plugin. Cause: Incorrect credentials in the database connection string or the database firewall does not allow access from the FastGPT service IP.
- Symptom: Batch execution nodes fail in API call mode but work correctly during online debugging. Cause: API calls may not pass necessary session states or context information, leading to missing or inconsistent variables required by tool calls in batch execution.
Verification Steps
- Perform simulated calls for typical adverse event reports. Verify that the structured data output by the tool is complete and matches expected field definitions.
- Continuously monitor the average response time of tool calls. Ensure it remains within acceptable business processing windows and calibrate timeout thresholds based on actual load.
- Review tool call failure logs. Confirm error messages are clear and traceable. Verify the retry mechanism functions as expected.
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.