Data Characteristics in this Category
Medical affairs pharmacovigilance data originates from post-market drug surveillance, clinical trial reports, real-world evidence, medical literature, and patient reports. This data updates frequently. Global adverse drug reaction databases, such as WHO VigiBase, receive daily additions. Document formats vary. Examples include structured Case Report Forms (CRFs), semi-structured medical journal articles, and unstructured patient feedback text. Fields include drug generic name, batch number, indication, adverse event (AE), severity, occurrence date, reporter type, and causality assessment. Adverse event descriptions often contain medical terminology and free text. Units include milligrams (mg), grams (g), and milliliters (ml) for dosage. Frequency uses days (d), weeks (w), and months (m). Timestamps or dates specify time points.
Constraints from these Characteristics on "Tool Calling and Plugins"
High-frequency data updates require real-time or near real-time processing capabilities for tool calling to avoid information lag. Diverse document structures, especially large volumes of unstructured text, challenge plugin text parsing and information extraction. Accurate identification and standardization of medical entities are necessary. The specificity of fields and complexity of medical terminology demand effective use of medical dictionaries or ontologies for entity recognition and relationship extraction, improving accuracy. For example, similar adverse reaction descriptions for different drugs require contextual differentiation. Multilingual reports also necessitate multilingual processing capabilities for plugins. For processing time, large datasets and complex logic require optimized model inference and tool execution to avoid impacting pharmacovigilance response efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 | Insufficient context length can lead to loss of critical information when processing medical reports, especially for lengthy clinical summaries or medical records. |
Chunk size (Segment Length) | 500–800 characters (characters) | Medical text is dense. Shorter segment lengths improve retrieval accuracy and prevent single segments from containing too much irrelevant information. |
Recall count (Recall Count) | Top 10 entries (Top 10) | Pharmacovigilance analysis requires comprehensive information support. Increasing recall count helps cover more potentially relevant document snippets. |
Similarity threshold (Similarity Threshold) | 0.75 | This ensures recalled documents are highly relevant to the user query, reducing false positives, especially when distinguishing similar adverse reactions. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | File parsing can be time-consuming when processing large PDFs or scanned reports. Sufficient timeout is necessary. |
max_tokens | 2048 | Generating adverse reaction summaries or analysis reports requires sufficiently long output sequences to include detailed medical descriptions. |
Three Common Mistakes
- Tool calling fails with a log message "Workflow validation failed. Please check for missing parameters, missing values, or incorrect connections." This typically indicates unassigned mandatory parameters in the plugin configuration or data type mismatches, such as expecting a numerical value but receiving a string.
- Model output for adverse event descriptions is inaccurate or misses critical information. This can result from insufficient recognition of medical terminology by text parsing plugins or from knowledge base snippets failing to cover all necessary information.
- Tool calling response time is too long, leading to a poor overall application experience. This can relate to external API response speed, excessive model inference computation, or concurrency exceeding system capacity.
How to Confirm Correct Configuration
- Test the configured tool calling process with typical adverse reaction report texts. Check if key medical entities (e.g., drug names, adverse events, dosages) are accurately extracted.
- Simulate high concurrency scenarios. Observe tool calling response times and success rates to ensure stable operation during peak business periods.
- Review reports generated by the model based on tool calling results. Compare them against original data to assess information completeness and accuracy, especially for critical fields like causality assessment and severity evaluation.
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.