Tool Calling and Plugins for Cardiovascular Intervention

Data Characteristics

Quality documentation for cardiovascular intervention medical devices includes design and development documents, production process records, inspection reports, risk management files, clinical evaluation reports, regulatory compliance declarations, and post-market surveillance data. These documents originate from various sources: internal R&D departments, production workshops, quality control laboratories, external clinical trial institutions, and regulatory bodies. Document update frequency is relatively stable, typically linked to product lifecycle stages, regulatory updates, or major quality events, such as design changes or adverse event reports. Documents are often hierarchical PDFs, Word files, or scanned images. They contain both structured data (e.g., inspection parameters, batch information, serial numbers) and unstructured descriptions (e.g., clinical observations, risk assessments). Fields and units are highly specialized. For example, "stent diameter" is typically in millimeters (mm), "expansion pressure" in atmospheres (atm) or megapascals (MPa), and "fatigue test cycles" is a pure number.

Constraints on Tool Calling and Plugins

The complex data characteristics of cardiovascular intervention quality documentation impose specific requirements on tool calling and plugin functionalities. First, diverse document sources and heterogeneous formats demand robust file parsing capabilities from plugins. Plugins must handle various formats like PDF and Word, accurately extracting structured and unstructured information. Scanned documents require OCR plugin support. Second, documents contain numerous specialized terms and units of measurement. Tool calls must correctly identify and understand this domain-specific knowledge. For instance, when retrieving "stent diameter," the system must differentiate it from "catheter diameter" to avoid confusion. Frequent document revisions and version management mean plugins need to support version tracking and allow specifying or defaulting to the latest version during calls. Due to data sensitivity, tool calling and plugins must meet strict security and compliance requirements during data processing. This ensures data integrity, prevents leaks, and allows traceability of operations.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Cardiovascular intervention documents may contain many charts and complex layouts, requiring longer parsing times.
`maxContext`	`3000 Tokens`	Ensures that longer passages, such as complete test reports or risk assessments, can be processed in one go.
`file_upload_limit`	`100 MB`	Accommodates quality documents containing high-resolution images or numerous attachments.
`similarity_threshold`	`0.8`	Ensures retrieved document chunks are highly relevant to the query, reducing misinterpretation of domain-specific terms.
`return_top_k`	`Top 5`	Balances retrieval efficiency with information completeness, ensuring no critical information is missed.
`document_chunk_size`	`800–1200 characters`	Adapts to long sentences and specialized descriptions in documents, ensuring semantic integrity of chunks.

Common Pitfalls

Plugins do not display input and output parameters in the workflow: This usually indicates incorrect formatting of the parameters or outputs fields in the plugin.json definition file, or a missing api definition in the manifest.json file.
Document parsing tool errors after file upload: This often results from unsupported file formats, an UPLOAD_FILE_MAX_SIZE configuration that is too small causing file upload failure, or an incorrect file_path.
Slow response from external platform chat interface calls: This is often due to insufficient server resources for FastGPT deployment, an excessively large MAX_TOKENS setting leading to prolonged model inference time, or inadequate database query optimization.

Verification Steps

Upload a PDF document containing images and tables. Check if the file parsing status on the Document page is "successful." Verify if the segmentation results meet expectations, especially for text near tables and images.
Add a custom plugin to the workflow. Check if the plugin's input params and output outputs fields are correctly displayed on the interface and if their types match the definition.
Upload a large 50 MB Word document via the API. Observe if the upload process is smooth and check logs for file upload success related records.
Execute a question-answering session that includes a tool call. Verify if the tool correctly identifies and invokes the plugin. Check if the plugin's return results meet expectations, especially for numerical values with specific units of measurement.

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.