Tool Calling and Plugins for Smart Triage Registration and

Data Characteristics in This Category

Data for smart triage registration and declaration preparation primarily comes from clinical guidelines, drug inserts, medical device registration certificates, regulatory approval documents, and various medical literature. Update frequencies vary: regulatory documents typically revise annually or with policy changes, clinical guidelines update every 1-3 years, and medical literature publishes continuously. Document structures are mostly unstructured text, such as PDF regulatory files, Word clinical trial reports, and JSON or XML drug databases. Fields and units often include drug dosage (e.g., mg/kg), treatment duration (e.g., days, weeks), indication descriptions, adverse event rates (percentages), and various medical terminology codes (e.g., ICD-10).

Constraints Imposed by These Characteristics on Tool Calling and Plugins

Data characteristics for smart triage documents impose specific requirements on tool calling and plugins. First, the update frequency of regulations and guidelines mandates that the knowledge base synchronization mechanism supports regular automatic or manual updates to ensure compliance of declaration documents. Second, the large volume of unstructured documents, such as PDFs and Word files, requires plugins to have robust document parsing capabilities to accurately extract key information like drug names, indications, and dosages. The presence of medical terminology codes requires tool calls to interact with external terminology services or local mapping tables for standardized concept conversion. Finally, numerical information like dosages and durations in the data requires plugins to recognize units during processing and perform necessary calculations or comparisons to avoid misinterpretations due to inconsistent units.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`chunkSize`	`500-800 characters`	Medical documents have strong contextual relevance; a moderate chunk size helps maintain semantic integrity.
`overlapSize`	`100 characters`	Ensures sufficient overlap between adjacent chunks to prevent critical information from being cut off.
`maxContext`	`4096 tokens`	Complex medical questions require the model to have a longer context understanding capability.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Processing large PDF or DOCX files requires ample parsing time.
`similarityThreshold`	`0.75`	Ensures recalled regulation or guideline snippets are highly relevant to the user query, improving accuracy.
`rerankTopN`	`top 5`	After reranking, selecting a small number of the most relevant results for fine-grained processing reduces interference from irrelevant information.

Common Pitfalls

When calling external APIs, document paths in the response are inaccessible, manifesting as HTTP 403 or 404 errors. This usually indicates API authentication failure or an incorrect resource path.
Plugin input parameters have no effect after configuration, resulting in unexpected model output or parameters not being passed correctly. This often occurs because input field names do not match the plugin definition, or data types are incompatible.
Large model services return empty responses, possibly showing Connection Timeout or Empty Response in logs. This might be due to unstable network connectivity with the model service or high model load causing response delays.

Verification Steps

Upload a PDF document containing complex tables and medical terminology. Check if the knowledge base can accurately extract key data after chunking, and verify the effect of chunkSize and overlapSize.
Formulate a question that requires calling an external drug database API. Observe if the model correctly triggers the tool call and check the request and response payloads in the logs.
Simulate a triage scenario involving dosage calculation. Check if the plugin correctly identifies numerical values and units, and returns calculation results consistent with medical logic.

The values provided are common starting points and should be measured against specific 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.