Data Characteristics for this Category
Cardiovascular intervention product data primarily originates from medical device manufacturers. Sources include product manuals, technical white papers, clinical research reports, operation guides, and product registration certificates. These documents are typically in PDF format, with some structured Excel sheets. They contain product models, specifications, materials, indications, contraindications, operating procedures, and performance parameters (e.g., guidewire diameter 0.014 inches, balloon diameter 2.5–4.0 mm, stent length 8–38 mm). Data updates are infrequent, mainly occurring during new product releases, existing product upgrades, or regulatory changes. Document internal structures are standardized, often including tables of contents and chapter headings. However, terminology and descriptions vary across manufacturers.
Constraints Imposed by these Characteristics on Workflow Orchestration
The highly specialized nature of cardiovascular intervention product data and the diversity of document formats impose specific requirements on workflow orchestration. First, complex charts and tables within PDF documents require enhanced document parsing capabilities. This ensures accurate extraction of critical parameters like rated burst pressure or radial support force. Second, the low update frequency means that initial knowledge base construction primarily involves importing large volumes of historical data. Subsequent incremental updates are infrequent. Terminology differences across manufacturers necessitate incorporating a terminology standardization or synonym mapping step in the workflow to reduce model understanding errors. Additionally, unit consistency (e.g., millimeters, inches, bar) in product specifications requires unified processing to prevent consultation errors due to unit confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
Chunk Length | 500–800 characters | Ensures individual chunks contain sufficient product details while avoiding excessive length that leads to information redundancy and reduced recall efficiency. |
Chunk Overlap Length | 50 characters | Helps the model understand cross-paragraph contextual information, especially when describing product operating procedures. |
Recall Count | Top 5–8 items | Covers core product information and relevant clinical evidence, preventing the omission of critical details. |
Similarity Threshold | 0.75 | Filters out irrelevant recall results, improving the accuracy and focus of the answers. |
Rerank Return Count | Top 3 items | Further refines recall results, presenting the most relevant information to the model for final answer generation. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates the parsing time for large product manuals or clinical reports, preventing file processing failures due to timeouts. |
Three Common Mistakes
- The image recognition node in the workflow reports an error:
Cannot provide image content, as I am a text-based AI. This occurs when a multimodal model is configured, but the AI chat node is not correctly enabled or the model itself does not support image input. - After a tool call end node, the tool execution result is still displayed on the screen. This happens when the
Output to chatoption of the tool call node is not disabled, or subsequent AI chat nodes are not configured to filter tool output. - The document parsing node reports a 404 error in the deployment environment. This indicates that after frontend packaging, the file storage path or access permissions on the server differ from the local development environment, preventing the parser from accessing uploaded files.
How to Verify Correct Configuration
- Upload a typical cardiovascular intervention product manual (PDF format). Observe whether the document parsing node completes parsing correctly and if the extracted text content is complete and free of garbled characters.
- Ask questions about key information such as product specifications and indications. Check if the AI's answer accurately cites data from the knowledge base and verifies that numerical values and units are correct.
- Simulate user inquiries containing images (e.g., product structure diagrams). Verify that the multimodal model correctly identifies image content and combines it with text information to respond.
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.