Data Characteristics in this Category
Cardiovascular intervention medical device registration and declaration materials come from diverse sources. These primarily include clinical trial reports, animal study reports, biocompatibility reports, manufacturing process documents, quality standards, risk management reports, and product manuals. Documents are typically in PDF format, with some data in Word or Excel for material composition, performance parameters, or statistical results. Data update frequency correlates with product development cycles and regulatory updates, usually ranging from several months to several years. Document structures are highly standardized, adhering to guidelines from the National Medical Products Administration (NMPA) or international medical device regulatory bodies (e.g., FDA, CE). Field content is highly specialized, often containing medical terminology, engineering parameters, and biological indicators, such as device_diameter (mm), catheter_length (cm), material_composition (%), and implantation_success_rate (%). Accuracy of units is critical.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The complexity and specialization of cardiovascular intervention registration and declaration materials impose strict requirements on document parsing and chunking. First, PDF documents with mixed text and graphics, especially clinical trial reports containing numerous tables and images, require robust layout recognition to prevent text extraction distortion or table structure damage. Second, documents contain extensive specialized terminology and acronyms; standard text chunking can lead to semantic fragmentation, affecting subsequent retrieval and understanding. For instance, splitting "Percutaneous Coronary Intervention (PCI)" would lose its complete meaning. Additionally, due to infrequent but impactful data updates, the parsing system must support version management and incremental updates to ensure the knowledge base's timeliness and accuracy. Accurate identification of units and numerical values requires chunks to retain the association between numbers and units. For example, a stent diameter of 1.5mm must not be confused with 2.0mm, which impacts the subsequent question-answering system's ability to provide accurate product specifications.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters | Ensures individual chunks contain sufficient contextual information, preventing truncation of specialized terms or critical descriptions. |
Overlap Size | 100–150 characters | Maintains semantic continuity between chunks, especially when processing long sentences and specialized descriptions spanning multiple paragraphs. |
Parsing Mode | Smart Parsing | Prioritizes identification of tables, lists, and heading structures in PDFs, reducing text extraction errors caused by mixed text and graphics. |
Max File Size | 200 MB | Accommodates large clinical trial reports or declaration documents containing numerous images. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accounts for the parsing time of large, complex documents, preventing processing failures due to timeouts. |
Character Encoding | UTF-8 | Ensures correct handling of special characters, mathematical symbols, and multilingual content that may appear in documents. |
Three Common Pitfalls
- Table data is lost or formatted incorrectly in the knowledge base after document upload. This occurs because the parser fails to correctly identify table boundaries and cell content in the PDF, leading to table data being parsed as unstructured text.
- Queries about specific device models result in vague answers or irrelevant information. This usually happens when document chunking granularity is too large, causing a single chunk to mix multiple topics or information from different models.
- After updating a critical report, the question-answering system still provides outdated information. This indicates that the knowledge base's incremental update mechanism was not effectively triggered, or old chunks were not correctly replaced or deleted.
How to Verify Correct Configuration
- Upload a typical clinical trial report containing complex tables and graphics. Check the text content of the corresponding chunks in the knowledge base to confirm that table structures and critical data are extracted completely and correctly.
- Perform question-answering tests for detailed specifications of a particular device model (e.g.,
stent diameter,coating material) within the document. Confirm that the system accurately returns information specific to that model and does not confuse it with other models. - Select a document already in the knowledge base, upload its updated version, and then perform question-answering tests. Confirm that the system prioritizes recalling information from the latest version of the document and verifies that old version information no longer appears.
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.