Tool Calling and Plugins for Pharmacovigilance Quality

Data Characteristics

Pharmacovigilance data centers on adverse drug reaction (ADR) reports, risk management plans (RMPs), product information, and related regulatory documents. Data sources include clinical trial data, post-marketing surveillance data, global ADR databases, and medical literature. Update frequency is driven by regulatory requirements and events, such as new ADRs, safety signals, or regulatory revisions. Document structures are highly standardized, such as ICH E2B report formats and EU PSUR templates. These documents contain structured fields like patient information, drug information, ADR descriptions, and outcomes, along with medical terminology and units (e.g., mg, IU, times/day).

Constraints on Tool Calling and Plugins

Pharmacovigilance data's standardized and highly sensitive nature requires strict adherence to data privacy and security regulations during tool calls, especially when handling patient-related information. Its highly structured nature demands precise field matching during external tool calls for Extract, Transform, Load (ETL) operations to prevent information loss or misalignment. For example, accurately identifying terminology is critical when extracting MedDRA or WHO-DD codes from unstructured reports. The event-driven update frequency means tools need to support real-time or near real-time triggering mechanisms to initiate analysis processes immediately when new data emerges. Furthermore, multi-language and cross-regional regulatory differences require tools to flexibly adapt to specific formats and terminology when processing documents from different countries or regions.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	Pharmacovigilance reports often contain detailed information, leading to potentially large single files. This balances upload efficiency and storage costs.
`maxContext`	`3000 Tokens`	Ensures coverage of a typical adverse reaction report's core content, maintaining contextual completeness.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient time for parsing complex PDFs or scanned documents, which can be time-consuming, preventing timeouts.
`Segment Length`	`500 characters`	Ensures each segment contains a complete medical concept or event description, improving recall accuracy.
`Recall Count`	`Top 8`	Pharmacovigilance demands high relevance. Increasing the recall count appropriately covers potentially related information.
`Similarity Threshold`	`0.75`	Strictly controls similarity to ensure recalled results highly match medical terminology and event descriptions.

Common Pitfalls

Receiving common:code_error.e or unAuthApiKey when calling a large model: This typically indicates an incorrect API Key configuration or insufficient authorization scope, preventing access to the model service.
The model configuration does not enable stream mode, causing errors when calling a large model that only supports stream: The model service requires a specific interaction mode; a mismatch leads to connection failure.
The PgVector plugin version is too low, preventing support for new vectorization models or indexing features: This is a compatibility issue between the plugin and the core system, leading to abnormal data storage or retrieval functions.

Verification Steps

Upload a typical adverse reaction report and observe the file parsing logs to confirm correct content identification and segmentation.
Perform searches for key medical terms within the report and check the relevance of the recall results to determine if Recall Count and Similarity Threshold are appropriate.
Call a plugin to execute a specific data extraction task, such as extracting MedDRA codes, and verify that the tool's output fields match the expected format.
Simulate high-concurrency requests and monitor system resource utilization and response times to ensure the configuration supports daily usage requirements.

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.