Data Characteristics in This Category
Pharmacovigilance data originates from clinical trial reports, real-world studies, adverse event reporting systems (e.g., international drug regulatory databases), medical literature, and social media monitoring. This data is typically a mix of structured (e.g., patient information, drug batches, adverse reaction codes in database records) and unstructured (e.g., free-text descriptions of adverse events, doctor's clinical notes) formats. Update frequency is high, with adverse event reports often required within specified timeframes, leading to real-time data streams. Document structures are complex, containing medical terminology, dosage units, patient vital signs, and involve standard coding systems like ICD-10 and MedDRA. In addition to basic patient identifiers, medication information, and adverse reaction descriptions, fields include severity, outcome, causality assessment, and past medical history. Units involve various types such as milligrams, milliliters, counts, and durations.
Constraints Imposed by These Characteristics on "Tool Calling and Plugins"
The complexity and real-time nature of pharmacovigilance data impose specific constraints on tool calling and plugins. First, the diversity of data sources requires plugins to have multi-modal data processing capabilities, able to parse structured database fields and unstructured text content, such as extracting key information from medical literature PDFs. Second, the real-time nature of data updates demands that tool calling mechanisms support high concurrency and low latency to ensure timely identification and analysis of adverse events. Furthermore, the specialized nature of medical terminology and coding systems necessitates that plugins either incorporate or call external medical knowledge graphs to accurately understand and standardize data. For example, standardizing different descriptions of the same adverse reaction from various report sources. Simultaneously, sensitive information in the data (such as patient privacy) places high demands on the security and compliance of tool calling, requiring strict data anonymization and access control.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2048 | Accommodates the text length of most adverse event reports while balancing model processing efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large PDF documents or complex structured data parsing, preventing timeouts. |
Recall count | Top 10 entries | Ensures retrieval of sufficient relevant adverse reaction cases or drug information from the knowledge base. |
Similarity threshold | 0.75-0.85 based on measurement | Balances recall and precision, avoids irrelevant information interference, and does not miss potential correlations. |
Workflow Cache time | 300 seconds | Balances data update frequency and query efficiency, avoiding redundant calculations. |
Plugin Concurrency | 5-10 | Handles sudden high-concurrency queries, such as batch processing newly collected adverse event reports. |
Three Common Mistakes
HTTP 504 Gateway Timeouterror when calling tools, due to excessive execution time for a single plugin when processing large amounts of historical adverse event data, exceeding the gateway's default timeout limit.- Certain key fields (e.g.,
MedDRA_Code) are empty in query results because the correct medical terminology mapping plugin was not configured, leading to the original text not being converted to standard codes. - The system confuses dosage units from different sources, for example, treating "mg" and "g" as equivalent, because the unit conversion tool did not correctly recognize or was not configured with unit standardization rules.
How to Verify Correct Configuration
- Select a test report containing various data types (structured, unstructured) and common medical terminology. Observe whether key fields are accurately extracted and standardized after tool calling.
- Simulate high-concurrency scenarios, such as simultaneously submitting multiple adverse event reports for processing. Check system response times and error logs to confirm that plugin concurrency meets expectations.
- Select data known to have unit discrepancies or heterogeneous terminology. Verify that the tool calling correctly performs unit conversion and terminology normalization, and that its thresholds align with manual verification results.
The values provided are common starting points and should be measured against the reader's 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.