Tool Calling and Plugins for Cardiovascular Intervention

Data Characteristics

Data for cardiovascular intervention products primarily comes from medical device manufacturers' product manuals, clinical study reports, registration and approval documents, and industry association standards and guidelines. These documents are typically in PDF format, containing numerous images, charts, and structured information. Data update frequency is relatively stable; updates are released when new products launch or existing products iterate, but not frequently. Document structures are often highly standardized, adhering to medical device technical requirements and industry norms. Common fields include product model, specifications, material, coating, dimensions (e.g., stent diameter mm, length mm, guidewire diameter inch), indications, contraindications, usage instructions, and adverse event rates. Units are precise and consistent.

Constraints on Tool Calling and Plugins

The characteristics of cardiovascular intervention product data impose specific requirements on tool calling and plugin configuration. First, the large number of PDF documents and images necessitates robust document parsing capabilities and multimodal content recognition and processing. Structured product parameters require precise field matching for tool calls. For example, when querying the diameter range of a specific stent, the system must recognize and extract numerical values with mm units. The relatively low data update frequency means moderate requirements for caching strategies and real-time data synchronization. The standardized document structure facilitates information extraction using regular expressions or specific tags, but tools must also handle complex tables and nested structures. Furthermore, product information often involves specialized terminology and clinical details, requiring plugins to understand medical terms to avoid misinterpretations or omissions of critical information.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	To process PDF documents containing many images and charts
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	To ensure complete parsing of large, complex PDF files
`Chunk size`	`800–1200 characters`	Balances context completeness and retrieval efficiency, avoids cutting off key descriptions
`Recall count`	`Top 8 entries`	Covers various product specifications or related clinical information, improves recall accuracy
`Similarity threshold`	`0.75`	Ensures high relevance of retrieval results, avoids interference from irrelevant product information
`http_timeout`	`30000 ms`	Addresses potential slow external API responses or large data volumes

Common Pitfalls

Receiving Invalid URL or code: 500 when calling an external API typically indicates incorrect parameter formatting passed to the API or an error in URL concatenation.
Tool calling nodes output redundant or unnecessary AI responses due to incorrect configuration of the tool's is_agent_response parameter or post-processing logic.
After uploading multimodal files, some image or chart content is not effectively recognized and extracted. This may be due to limitations indicated by Multimodal file size is or insufficient configuration of the image parsing service.

Verification Steps

Upload a typical product manual PDF containing tables and images. Check if the parsed text content is complete and structurally correct, especially for key fields like product specifications and usage instructions.
Initiate a query for a specific product model. Observe if the tool call accurately extracts corresponding parameters (e.g., stent diameter mm, length mm), and verify the consistency of the returned results with the original document.
Trigger a query that requires an external API call (e.g., checking the latest registration status of a product). Review the tool call logs to confirm that API request parameters, response status code 200, and returned data meet expectations.

The values provided 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.