Tool Calling and Plugins for Solid Tumor Pharmacovigilance

Data Characteristics

Solid tumor pharmacovigilance involves diverse data sources. These include clinical trial reports, real-world evidence (RWE) data, electronic health records (EHR), patient-reported adverse events (AEs), and drug safety updates from regulatory agencies. Data update frequencies vary. Clinical trial data typically becomes available after study completion. Regulatory drug safety alerts can update in real-time. Raw documents are complex, containing structured data (e.g., lab results, drug dosages) and unstructured text (e.g., medical free text, patient descriptions). Key fields include drug name, adverse event terms (often MedDRA coded), onset time, severity, outcome, causality assessment, and patient demographics. Units of measurement include drug dosage (mg, g), time (days, weeks, months), and biological indicators (e.g., ng/mL, U/L).

Constraints Imposed by Data Characteristics on Tool Calling and Plugins

Solid tumor pharmacovigilance data characteristics directly impact tool calling and plugin configuration. First, heterogeneous data sources require tools to handle multiple file formats and offer flexible data extraction capabilities. The high proportion of unstructured text makes natural language processing (NLP) plugins central for extracting adverse events, drug associations, and temporal information. Second, asynchronous data updates, especially the real-time nature of regulatory alerts, necessitate event-driven tool calling to trigger data ingestion and analysis promptly. The use of specialized terminology like MedDRA coding demands high accuracy in model term understanding and entity recognition, requiring customized dictionaries or fine-tuned models. Furthermore, assessing adverse event severity and causality involves complex logical judgments, requiring tool plugins to support multi-step reasoning and external knowledge base queries.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192 token`	A larger context window is needed to process text containing detailed medical histories and adverse event descriptions.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Parsing complex PDF reports and clinical trial documents can be time-consuming.
`Chunk size`	`500–800 characters`	Balances semantic completeness and retrieval efficiency, preventing the splitting of critical information.
`Recall count`	`Top 10 entries`	Ensures coverage of potentially relevant adverse events or drug association information.
`Similarity threshold`	`0.75`	Descriptions of solid tumor drug adverse reactions may have subtle differences, requiring a balance between recall and precision.
`MedDRA_VERSION`	`26.0`	Ensures consistency with the current version of the international medical terminology dictionary, improving coding accuracy.

Three Common Mistakes

Tool call results are directly output to the user, leading to raw data leakage or confusing professional terminology for the user. This occurs due to a lack of orchestration steps for post-processing or summarizing tool outputs.
Uploaded files fail to parse correctly or content extraction is incomplete. This manifests as empty key fields or missing reports. The cause is often unsupported file formats or a PARSE_FILE_TIMEOUT_SECONDS configuration that is too low, leading to parsing interruptions for large or complex files.
When calling external systems via API, HTTP 401 or HTTP 403 errors frequently occur. This happens because API authentication information (e.g., Authorization header) is incorrectly configured or expired, causing the external system to deny access.

How to Verify Configuration

Upload a PDF document containing descriptions of solid tumor adverse events. Check if the knowledge base successfully extracts key fields such as drug names, adverse event terms, and onset times.
Simulate a user query like "query common adverse reactions for a specific drug." Verify that the system uses tool calls to retrieve a list of relevant adverse events from the knowledge base or external databases. Check if the number of retrieved items matches expectations.
Test a scenario involving an external API call, such as querying the latest regulatory alerts for a specific drug. Confirm that the API call succeeds, returns the most recent alert information, and that the information content is correct.

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.