Tool Calling and Plugins for Phase II-III Clinical Products

Data Characteristics for This Category

Phase II-III clinical trial data primarily originates from clinical study reports, case report forms (CRFs), statistical analysis plans (SAPs), and regulatory submission documents. The update frequency for this data is relatively low, typically released as interim or final study reports, with cycles ranging from months to years. Document structures are highly standardized, adhering to international guidelines like ICH GCP, for example, CDISC SDTM and ADaM dataset standards. Field naming is strict, including identifiers such as USUBJID (unique subject identifier), TRT01A (actual treatment group), and ADVERSE_EVENT_TERM (adverse event term). Units strictly follow the International System of Units (SI), such as mg or μg for dosage and days or weeks for time. The data often contains a large volume of medical terminology and abbreviations.

Constraints Imposed by These Characteristics on "Tool Calling and Plugins"

The standardized structure and strict field naming of Phase II-III clinical data require precise matching during data parsing for tool calls, avoiding ambiguous identification. The low update frequency means frequent data synchronization tools are not necessary, but once a new report is released, it requires complete and accurate import. The specialized medical terminology and abbreviations within documents demand that tools possess strong domain vocabulary understanding when processing natural language queries, potentially requiring pre-loaded specialized dictionaries. Strict unit specifications require tools to correctly identify and convert units during numerical comparisons or calculations to prevent errors due to unit inconsistencies. Furthermore, due to data sensitivity, permission control and data security during tool calling are especially critical, ensuring only authorized users and models can access specific data sources.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for This Value
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical report files are often large, containing charts and detailed data.
`maxContext`	`32000`	Ensures the model can process the context of clinical documents containing multiple detailed pages.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large files and OCR recognition can be time-consuming; this avoids timeout interruptions.
`Chunk size` (Segment Length)	`800–1200 characters`	Balances semantic integrity of paragraphs with model processing efficiency, reducing cross-segment context loss.
`Similarity threshold` (Similarity Threshold)	`0.75`	Clinical data requires high recall accuracy; increasing the threshold reduces irrelevant results.
`Rerank result count` (Reranked Return Count)	`Top 5`	Ensures the most relevant key information is presented first, reducing noise for the model.

Three Common Mistakes

An HTTP 500 error occurs when calling an external interface. This is typically due to incorrect API keys or authentication parameters configured in custom plugins, leading to the backend service rejecting the request.
The model fails to automatically decide to read uploaded clinical trial report files, evidenced by answers not referencing file content. This happens when the model is not sufficiently prompted about the relevance of file content to the user's query, or when the trigger conditions for file content parsing in the tool calling logic are too strict.
Failure to extract fields from data returned by an external tool, for example, the adverse_events field is empty. This is because the regular expression or JSON Path expression in the custom tool's code execution module does not match the actual returned data structure, failing to correctly parse the target field.

How to Verify Configuration

Upload a typical Phase II-III clinical trial report PDF file. Observe if the file processing status shows "success" and check if corresponding document segments are generated in the knowledge base.
Ask questions about specific adverse events or efficacy data within the report. Check if the model can accurately cite the original report content through tool calling and confirm if the returned data units (e.g., mg) are correct.
Simulate a scenario requiring a call to an external database to query drug interactions. Verify if the model can correctly trigger the custom tool and extract key information from the returned results, such as the value of the interaction_severity field.

Note: 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.