Tool Calling and Plugins for Pharmacovigilance in

Data Characteristics

Pharmacovigilance data for biopharmaceutical equipment primarily comes from performance parameters, maintenance records, and fault reports provided by equipment manufacturers, along with adverse event reports (MDRs) submitted by end-users. This data often exists as a mix of structured (e.g., database records, XML files) and unstructured (e.g., PDF documents, plain text reports) formats. Update frequency varies by data type: equipment parameters and maintenance manuals typically update when equipment models are revised or firmware is upgraded, while adverse event reports generate in real-time. Document structure for MDRs often follows templates from regulatory bodies (e.g., FDA's MedWatch form), including fields for event description, device information, and patient information. Fields include device serial number, batch number, fault code, adverse event type (e.g., malfunction, failure), and severity (e.g., serious injury, death). Units involve equipment operating time (hours), temperature (Celsius), and pressure (Pascals).

Constraints on Tool Calling and Plugins from Data Characteristics

The heterogeneous nature of biopharmaceutical equipment data, particularly the mix of structured and unstructured data, poses challenges for tool calling and plugins. Real-time adverse event reports require tools to process new data quickly and trigger corresponding analysis workflows. Document structures that follow regulatory templates mean information extraction must precisely match specific fields, such as identifying and extracting device_identifier or adverse_event_type from free text. The specialized nature of equipment operating parameters and fault codes requires plugins with targeted parsing capabilities to correctly interpret data meaning. Additionally, since data may contain sensitive information (e.g., patient privacy), tool calling and plugins must strictly adhere to data security and compliance requirements during processing, such as anonymizing specific fields to avoid direct exposure of raw data.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`2000 characters`	Captures critical information from common adverse event reports while controlling token consumption.
`Recall count`	`Top 10 entries`	Balances retrieval efficiency and relevance, covering potential equipment models, fault modes, or related cases.
`Similarity threshold`	`0.75–0.85`	Filters for highly relevant equipment faults and adverse event descriptions, reducing false positives.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Addresses parsing needs for large equipment maintenance manuals or complex MDR reports, preventing timeouts due to large files.
`Chunk size`	`500–700 characters`	Accommodates longer event descriptions in adverse event reports, ensuring contextual completeness and preventing truncation of key information.
`variable_update_mode`	`Update After Each Call`	Reflects real-time adverse event report processing volume or frequency of specific fault types, aiding monitoring.

Common Pitfalls

Symptom: API call returns 400 Bad Request with an error message Missing required field: device_id. Reason: Tool calling failed to correctly extract or map the device_id field from unstructured reports, resulting in a missing request parameter.
Symptom: Knowledge base retrieval results contain numerous irrelevant device models or symptom descriptions. Reason: The Similarity threshold (similarity threshold) is set too low, failing to effectively filter out equipment historical data or adverse event reports unrelated to the current query.
Symptom: The variable update tool fails to automatically update the call count for a specified classification problem. Reason: The variable_update_mode is set to manual update or trigger conditions are not configured correctly, preventing the counting logic from executing.

Verification Steps

Submit a simulated adverse event report containing typical equipment fault descriptions via the API. Verify that the tool call successfully extracts and correctly maps all key fields, and that the returned results include relevant equipment knowledge.
Perform multiple queries for specific equipment models or fault codes. Observe the retrieval results under the influence of Recall count (number of recalled items) and Similarity threshold (similarity threshold) to ensure high relevance of returned literature or cases.
After simulating multiple turns of interaction, check the variables configured by variable_update_mode to confirm their values increment or update according to the expected logic.

Note: The values provided are common starting points. Measure them against your own samples to determine optimal settings.

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.