Data Characteristics in This Category
IVD diagnostic reagent pharmacovigilance data originates from post-market surveillance reports, clinical trial data, literature, and regulatory alerts. Data update frequencies vary; post-market surveillance reports are typically submitted periodically, while alerts may be real-time. Document structures are diverse, including structured database records, semi-structured PDF reports (e.g., instructions for use, user manuals), and unstructured text files (e.g., clinical observation notes). Core fields include reagent name, lot number, manufacturer, adverse event description, event time, patient demographics, diagnostic results, and intervention measures. Beyond standard time and quantity units, specific measurement units for diagnostic indicators are involved, such as IU/mL, ng/dL, and copies/mL. Accurate identification of these units is critical for quantitative analysis of adverse events.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The diversity of IVD diagnostic reagent data sources requires tool calling to support multi-format file parsing, especially the extraction and comprehension of information from PDFs and unstructured text. Uncertain update frequencies demand flexible trigger mechanisms for tool calling, allowing for both scheduled pulls and real-time alert responses. Diverse document structures challenge information extraction plugins, requiring different extraction logic configurations for various templates to ensure accurate identification and standardization of key fields. The presence of specific units necessitates that tools correctly handle unit conversion or normalization during data cleaning and comparison to prevent misjudgments due to unit differences. Furthermore, accurate extraction of identifiers like lot numbers and serial numbers is crucial for traceability and risk assessment, requiring tools to precisely match and identify these specific string formats.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
tool_call_model | gpt-4o-mini | Balances accuracy and cost, suitable for information extraction from complex text structures. |
max_tokens | 4096 | Accommodates lengthy description texts in adverse event reports, ensuring completeness. |
temperature | 0.3 | Reduces randomness in generated answers, improving stability and accuracy of information extraction. |
tool_timeout_seconds | 600 seconds | Accounts for potential time consumption in file parsing and external API calls, preventing premature timeouts. |
max_retries | 3 | Addresses transient external service failures or network fluctuations, enhancing tool calling robustness. |
response_format | json_object | Ensures structured data return from tools, facilitating subsequent processing and display. |
Common Pitfalls
- Tool call returns line chart data that cannot be displayed on the chat page. This occurs because the returned data format does not meet front-end rendering requirements and needs conversion to a front-end recognizable JSON structure.
- Model error
Your model may not support tool_call SyntaxError. This usually indicates that the selected model version is too old or tool calling functionality is not enabled. - After exporting a workflow, importing it into a new environment results in a "plugin not found" error. This happens because the plugin was not exported with the workflow or the corresponding plugin is not installed in the new environment.
Verification Steps
- Simulate submitting IVD diagnostic reagent adverse event reports in various formats to verify if the tool accurately extracts all key fields.
- Check tool call logs to ensure all external API calls are successful and return a 200 status code.
- Trigger a tool call on the chat page and observe if the returned structured data contains expected lot numbers, units, and adverse event descriptions.
- Query for a specific lot number to confirm the tool correctly identifies and calls the database plugin to return relevant traceability information.
The values provided are common starting points and should be measured against specific 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.