Data Characteristics
Pharmacovigilance data for attenuated inactivated vaccines originates from sources such as the National Medical Products Administration (NMPA) Adverse Reaction Monitoring System, spontaneous reports from enterprises, and the Vaccine Adverse Event Reporting System (VAERS). This data typically exists in both structured reports and unstructured text. Structured reports include fields like vaccine name, batch number, vaccination date, adverse reaction description, severity, and prognosis. Some fields use enumerated values, while others are free text. Unstructured data includes case records, follow-up notes, and medical imaging reports. This data is detailed but lacks a uniform format. Data update frequency depends on reporting mechanisms and approval processes, usually daily or weekly, but historical data volumes are substantial. Adverse reaction descriptions often contain medical terminology, abbreviations, and colloquialisms, posing challenges for natural language processing.
Constraints on Tool Calling and Plugins
The characteristics of attenuated inactivated vaccine pharmacovigilance data impose specific requirements on tool calling and plugins. First, diverse and frequently updated data sources demand efficient data capture and real-time synchronization capabilities from tools. Second, the coexistence of structured and unstructured data requires plugins to handle both tabular data parsing and natural language text understanding. This includes identifying adverse events and drug associations. Medical terminology and abbreviations in adverse reaction descriptions necessitate integrating professional dictionaries or medical knowledge graphs during tool calls to improve the accuracy of entity recognition and event extraction. Due to the seriousness of vaccine adverse reactions, recall accuracy and comprehensiveness are critical. Plugins must balance precise matching with semantic similarity retrieval during information retrieval to avoid missing key information. Furthermore, large volumes of historical data challenge tool query performance and concurrency.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 token | Accommodates long adverse reaction reports and multi-turn conversational context requirements. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Allows sufficient time to parse large unstructured reports, such as case records. |
Chunk size | 500-750 characters | Ensures the completeness of vaccine adverse reaction descriptions and reduces semantic fragmentation. |
Recall count | Top 10-15 entries | Maximizes coverage of potentially relevant adverse reaction events, improving recall rate. |
Similarity threshold | 0.75-0.85 | Balances precise matching with semantic relevance, filtering low-quality results. |
Rerank result count | Top 5 entries | Focuses on the most relevant adverse reaction information, improving user reading efficiency. |
Common Pitfalls
- Slow model response after tool calls, or context loss in multi-turn conversations. This often results from a
maxContextparameter set too low, causing the model to frequently truncate historical conversations or fail to load complete tool results. - Key fields are empty or garbled in the results returned after calling an adverse reaction monitoring tool. This can occur if tool plugin parameter mapping is incorrect, failing to pass recognized entities to the tool, or if the tool's data encoding is incompatible with the platform.
- When calling a multi-parameter tool within a workflow, the model fails to automatically complete all necessary parameters. This typically happens due to unclear
parametersdefinitions for the tool, or if the model is not effectively guided to ask for multiple parameters in multi-parameter prompts.
Verification
- Simulate multiple adverse reaction reports containing complex medical terminology and abbreviations. Observe if the tool accurately identifies and extracts key information, and verify extracted entities against expectations.
- Test adverse reaction descriptions of varying lengths. Check the completeness and response time of results after tool calls. Ensure no significant performance degradation or truncation occurs when processing long texts.
- In multi-turn conversational scenarios, verify the model's ability to maintain context coherence after tool calls. Confirm it can perform logical reasoning and respond based on tool results, especially by proactively asking for additional parameters when needed.
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.