Tool Use and Plugins for Cardiovascular Intervention

Data Characteristics

Cardiovascular intervention regulations and SOP documents originate from regulatory bodies, hospital ethics committees, and medical device manufacturers. Updates are infrequent, typically occurring every few months to a year, driven by policy changes, new technical guidelines, or device iterations. Documents are usually multi-layered, clearly structured PDFs, often containing numerous charts, flowcharts, and specialized terminology. For instance, a cardiovascular stent implantation SOP details pre-operative assessment, device preparation, puncture site selection, contrast agent dosage, and complication management. Fields include device models, material composition, operational steps, timelines, drug concentrations, dosage units (e.g., milligrams, milliliters, international units), and specific medical indicators (e.g., FFR, OCT values). Accuracy and consistency requirements are extremely high.

Constraints on Tool Use and Plugins

The prevalence of PDF documents with highly structured content demands robust parsing capabilities. Parsing hundreds of pages can lead to timeouts or resource exhaustion. High density of specialized terminology and abbreviations requires precise vocabulary recognition and contextual understanding to prevent tool call failures due to semantic drift. Although infrequent, updates involve critical content. The knowledge base must synchronize with the latest versions and manage older versions effectively, ensuring tool calls rely on current and accurate regulations. Furthermore, procedural instructions and conditional logic within regulations (e.g., "if XX occurs, then perform YY") require tools to accurately identify and convert them into executable action sequences. For example, querying usage contraindications or operational specifications based on a specific device model. Precise extraction and conversion of numerical information like dosages and units are critical for correct plugin execution.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates large PDF files, such as multi-chapter SOP compilations.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accounts for the time required to parse large, complex PDF documents.
`Chunk size` (Segment Length)	`800–1200 characters`	Adapts to longer entries and descriptions in regulatory documents, maintaining semantic integrity.
`Recall count` (Recall Count)	`Top 8 entries` (Top 8)	Increases relevant context recall, improving accuracy in understanding complex regulations.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures the professionalism and relevance of recalled content, avoiding over-generalization.
`LLM_MODEL_NAME`	`gpt-4o`	Addresses specialized terminology and complex logic, enhancing understanding and reasoning capabilities.

Common Pitfalls

When parsing large PDF documents, the system returns "file parsing failed" or becomes unresponsive for an extended period. This occurs because the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low or UPLOAD_FILE_MAX_SIZE is too restrictive, preventing the file from being processed within the allotted time.
Tool call results do not match expectations, for example, incorrect device models or dosage units. This typically happens when data in the knowledge base is outdated, or the document segmentation strategy fails to maintain contextual relevance for specialized terms and numerical values.
Tool calls are logged as successful, but downstream services are not triggered. The call log shows a record, but no actual business progress occurs. This could be due to inaccurate parameter mapping in the plugin definition or expired API keys or authentication information.

Verification Steps

Upload and parse a cardiovascular intervention SOP document containing numerous charts and hundreds of pages. Check parsing logs to confirm no timeouts or failures, and verify that document content can be previewed normally.
Ask questions related to specific device models or operational steps within the document. Observe if the model accurately cites relevant regulatory clauses. Check the tool call logs to ensure that passed parameters, such as device models, operation types, and dosage units, are correct and complete.
Simulate a scenario requiring an external service trigger, such as querying batch information for a specific stent model. Check logs to confirm successful tool invocation. Verify that the external system received the correct request by comparing actual call parameters with those defined in the plugin.

These values are common starting points and should be measured against your 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.