Data Characteristics
Regulatory submission documents for cardiovascular intervention medical devices primarily include product technical requirements, test reports, clinical evaluation reports, risk management reports, instructions for use, and label samples. Data sources are mainly internal R&D documents, clinical trial data, and regulations and guidelines published by the National Medical Products Administration (NMPA). The update frequency is relatively low, typically occurring during new product registration, product changes, or regulatory updates. Document structures are complex, often containing numerous charts, medical terminology, units of measurement (e.g., mm, mg, kPa), and specific formats for experimental data. Fields cover device material composition, dimensions, performance indicators, scope of application, and contraindications. These fields often have strict definitions and numerical ranges.
Constraints on Vector Models and Indexing
The complex document structure and specialized terminology in cardiovascular intervention data require vector models to effectively capture semantic information and differentiate between field meanings. The presence of many charts and specific data formats means that pure text vectorization may lose critical information, necessitating consideration of multimodal or preprocessing strategies. A low update frequency implies that index reconstruction does not need to be frequent, but each update must ensure accuracy and completeness. The precision of units of measurement and numerical ranges demands high recall and ranking accuracy for retrieval results. Models must understand and match numerical information to avoid misjudgments due to subtle differences. Given the need for regulatory compliance, the traceability and interpretability of retrieval results are crucial, requiring index design that supports precise localization to original document paragraphs.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic completeness and vector model processing efficiency, preventing information loss or insufficient context from segments that are too long or too short. |
Chunk overlap (Segment Overlap) | 100–200 characters (characters) | Ensures contextual continuity between paragraphs, reducing semantic fragmentation caused by segment boundaries. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Addresses the precision requirements for medical device documentation, avoiding the retrieval of irrelevant or ambiguous document segments. |
Recall count (Number of Retrieved Items) | Top 10–15 entries (top 10–15 items) | Provides sufficient candidate results for subsequent re-ranking or manual filtering, while managing computational resources. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Prevents parsing timeouts during the processing of large clinical reports or technical documents, which could lead to indexing failures. |
Model Version | text-embedding-ada-002 or higher | Handles specialized terminology and complex sentence structures, ensuring semantic accuracy of vector representations. |
Common Pitfalls
- An index remaining stuck at a certain stage, with the interface showing "Indexing..." but no progress, often indicates a file parsing timeout or out-of-memory issues when processing a single large document.
- After text submission, a collection is created successfully but retrieval results are empty. This may be due to an improper segmentation strategy, causing critical information to be fragmented or filtered out.
- The initial response time for vector retrieval is excessively long, for example, over
8 seconds(seconds). This could be caused by improper vector database deployment configuration, such as insufficient storage I/O performance or inadequate index optimization.
Verification of Configuration
- Upload various typical document types in a test environment to observe if indexing completes normally and if any error messages appear.
- Conduct multiple rounds of retrieval tests for different types of questions, such as regulatory clauses, product parameters, and clinical data. Evaluate the relevance and accuracy of retrieval results, and adjust the
Similarity threshold(Similarity Threshold) based on feedback from subject matter experts. - Monitor system resource usage during the indexing process to ensure CPU, memory, and disk I/O remain within acceptable ranges when processing large documents. This helps determine if parameters like
PARSE_FILE_TIMEOUT_SECONDSare appropriate.
The values provided are common starting points and should be measured against specific 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.