Data Characteristics in This Category
Data for intelligent triage in pharmacovigilance primarily comes from drug inserts, medical literature, adverse event reporting systems (e.g., national adverse drug reaction monitoring systems), clinical trial data, and medication information in electronic health records. Data update frequencies vary. Drug inserts and medical literature typically update annually or irregularly. Adverse event reporting system data accumulates continuously. Document structures are diverse, including structured data (e.g., adverse event codes, generic drug names, batch numbers, dosage and administration) and unstructured text (e.g., patient chief complaints, physician diagnoses, adverse event descriptions). Fields and units have specific characteristics, such as dosage units (mg, g, IU), administration frequency (once daily, hourly), adverse event severity grading (mild, moderate, severe), and time units (days, weeks, months). ATC classification codes for drugs and ICD-10 disease diagnosis codes are common data identifiers.
Constraints from Data Characteristics on Tool Calling and Plugins
Pharmacovigilance data characteristics impose specific requirements on tool calling and plugin configuration. Data source heterogeneity necessitates support for multi-source data connectors, such as SQL query tools for structured databases and NLP parsing plugins for unstructured text. Inconsistent update frequencies require tool calling to have scheduled tasks or event-triggered mechanisms to ensure data synchronization. Diverse document structures mean plugins need robust text parsing capabilities to extract key information from free text and standardize data across different formats. Specific fields and units require plugins to correctly identify and convert units during data processing. For example, they must unify dosage calculations across different formulations or administration routes and understand specialized medical terminology and abbreviations. Pharmacovigilance expertise also requires tool calling to integrate medical knowledge graphs or terminology databases to improve the accuracy of adverse event identification and correlation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4096 tokens | Balances understanding complex medical texts with computational resource consumption. |
Recall count | Top 10 entries | Ensures comprehensive recall of relevant medical literature or adverse event reports. |
Similarity threshold | 0.75 | Balances relevance and noise, preventing interference from irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accommodates parsing time for large medical literature or drug inserts. |
Chunk size | 500 characters | Adapts to the logical structure of medical texts, facilitating model comprehension. |
Rerank result count | Top 5 entries | Improves the precision and user experience of the final results. |
Three Common Pitfalls
- Receiving a
400 InternalError.Algo.InvalidParametererror when calling external APIs often indicates that the passed parameter format, type, or value range does not conform to the API interface definition. Examples include incorrect dosage units or adverse event codes. - The knowledge base fails to effectively utilize dynamic data returned by HTTP, preventing the AI from analyzing real-time adverse event data. This occurs due to a lack of automated processes to structure HTTP response content and import it into the knowledge base.
- Inability to call specific medical domain model APIs outside FastGPT's existing model list usually results from incorrect configuration of external model interface access credentials, endpoint URLs, or request body formats within the FastGPT platform.
How to Verify Correct Configuration
- Simulate user queries to check if intelligent triage accurately identifies drug names and adverse event symptoms, and provides reasonable medication advice or risk warnings.
- Observe log output to confirm that tool calling plugins process different data sources without parsing failures, data loss, or format conversion errors.
- Randomly sample adverse event reports to verify if the intelligent triage system can correctly extract key information (e.g., drugs, symptoms, time) from free text and correlate it with data in structured databases.
- Test queries under extreme or edge cases, such as rare adverse drug reactions or complex medication regimens, to evaluate the accuracy and stability of the system's response.
Note: The values provided are common starting points. Measure them against your own samples for optimal performance.
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.