Tool Calling and Plugins for Pharmaceutical Vigilance in

Data Characteristics

Pharmaceutical vigilance data in nursing management originates from patient electronic medical records, physician order systems, nursing records, adverse event reporting systems, and drug inserts. This data updates frequently. Real-time data, such as vital signs and medication records, can update every minute. Adverse event reports may update daily or weekly. Document structures typically include both structured fields and unstructured text. Structured fields cover patient demographics, diagnoses, medication information (drug name, dosage, frequency, route), allergy history, comorbidities, adverse reaction type, occurrence time, severity, and intervention measures. Unstructured text includes nursing observation notes and physician consultation opinions. Field units are standardized, for example, dosage in mg or g, frequency in times/day, and time in hours or days.

Constraints on Tool Calling and Plugins

High-frequency real-time data requires low-latency processing from tool calls to ensure timely pharmaceutical vigilance. The coexistence of structured and unstructured data necessitates tools that can handle both structured queries and text comprehension. An example is extracting adverse reaction entities from free-text nursing records. Strict unit specifications demand robust data cleaning and parameter validation to prevent misinterpretations from inconsistent units. Data sources are decentralized with strict access controls. Tool calls must integrate multiple system interfaces and adhere to strict access control policies. Assessing adverse event severity often requires combining multi-dimensional information, which means tool plugins must perform complex logical reasoning and multi-field correlation analysis.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`apiTimeout`	`60 seconds`	Most external system APIs respond within tens of seconds. This prevents task backlog due to excessive waiting.
`maxRetryAttempts`	`3 times`	Improves call success rates during network fluctuations or temporary unavailability of external services.
`extractEntities`	`['drug name', 'dosage', 'adverse reactions', 'Occurrence Time']`	Ensures accurate identification of key pharmaceutical vigilance elements from unstructured text.
`contextWindow`	`8192 token`	Balances long-text processing capabilities, such as nursing records, with cost-effectiveness.
`functionCallMode`	`auto`	Allows the model to intelligently decide whether to call tools based on conversation content, increasing flexibility.
`maxConcurrentCalls`	`5`	Balances system resource utilization with real-time processing capability, preventing external system overload from excessive concurrency.

Common Pitfalls

Tool call returns status_code: 500 or timeout: This usually indicates an external system API timeout or internal error. Check the health status and API stability of the external service.
Tool returns empty or incorrectly formatted data fields: This may occur if the plugin's internal parsing logic does not match the external API's data structure, or if the external system returns unexpected data.
chatId inconsistency across multi-turn conversations during application calls: This can lead to session state loss, affecting context understanding and tool call accuracy. Ensure chatId is correctly passed as a session identifier in each API call.

Validation Steps

Simulate real nursing scenarios with multi-turn conversations. Observe if tool calls accurately identify pharmaceutical vigilance intentions and trigger correct external API calls.
Inspect data returned by tool calls. Verify that key fields like drug name, adverse reaction, and dosage match expectations and that units comply with specifications.
During peak or concurrent usage, monitor the average response time and success rate of tool calls. Ensure real-time requirements are met and check if the apiTimeout configuration is appropriate.
Verify that adverse drug reaction information from unstructured text, such as nursing records, is correctly extracted by the plugin and passed as parameters to subsequent processing flows.

The values provided are common starting points. Measure them against your 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.