Data Characteristics
Cardiovascular intervention R&D documents originate from various sources. These include clinical trial reports, device design specifications, biocompatibility assessments, regulatory registration files, academic papers, and patent applications. Document update frequency is relatively low. Updates typically align with R&D project phases or regulatory changes.
Document structure is complex. They often contain numerous charts, formulas, and specialized terminology. Most documents are in PDF or DOCX formats. Fields include device models, material compositions, test parameters, clinical indicators, and statistical results. Unit systems are strict, such as millimeters (mm), milligrams (mg), Pascals (Pa), and Hertz (Hz).
Constraints on Knowledge Base Retrieval and Recall
The characteristics of cardiovascular intervention R&D documents impose specific requirements on knowledge base retrieval and recall. Complex document structures and the prevalence of charts and formulas challenge automatic text extraction. This can lead to critical information loss.
Specialized terminology and strict unit systems require retrieval models to accurately understand context. This prevents mis-recall due to semantic ambiguity or unit confusion. Low update frequency means knowledge base content is relatively stable. However, efficient and accurate incremental updates are necessary when processing new or revised documents.
Different document types may have extensive cross-references. The RAG mechanism must effectively handle inter-document relationships to improve recall comprehensiveness.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk Size | 500–800 characters | Ensures each knowledge block contains sufficient context while avoiding information overload. |
Chunk Overlap | 50–100 characters | Maintains contextual coherence and handles key information spanning paragraphs. |
Recall Count | Top 5–8 items | Balances recall breadth with subsequent processing burden, covering multiple potentially relevant knowledge points. |
Similarity Threshold | Calibrate by measurement | Adjust through test sets based on actual document content and query types, ensuring high recall and accuracy. |
Rerank Count | Top 3 items | Focuses on the most relevant knowledge points, reducing the model's burden of processing irrelevant information. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Accommodates the upload requirements for large clinical trial reports or design specification files. |
Common Pitfalls
- Image content in some documents does not display in previews or Q&A after knowledge base upload. This occurs because the document parser fails to identify or extract image objects.
- Retrieval results contain many similar but not perfectly matching answers, or critical information is missing. This likely results from an unreasonable chunking strategy, leading to incorrect semantic unit segmentation.
- The system responds too slowly or times out when processing specific queries. This can be due to an excessive number of documents recalled in a single query or insufficient vector database query efficiency.
Validation
- Select typical queries. Verify that recall results include all relevant facts from the documents. Check that key fields and units are accurate.
- Upload documents containing complex charts and formulas. Confirm that the parsed text content is completely extracted, with no important information missing.
- Simulate high-concurrency query scenarios. Monitor system response times to ensure they remain within acceptable limits.
- Perform regular incremental updates to the knowledge base. Verify that retrieval and recall functions work correctly for both new and old documents.
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.