Data Characteristics
Cardiovascular intervention quality documents include medical device registration certificates, product technical requirements, clinical evaluation reports, production process specifications, inspection operating procedures, adverse event reports, risk management reports, and post-market surveillance files. These documents typically exist as PDFs, Word files, or scanned images. They have complex structures and contain extensive specialized terminology, charts, and data. Data update frequency is relatively stable; batch updates occur with new product registrations or regulatory changes, while adverse event reports may generate in real-time. Document fields include product model, serial number, production batch, sterilization batch, production date, expiration date, testing parameters, inspection results, judgment criteria, units of measurement (e.g., millimeters, milligrams, unit doses), and various medical codes.
Constraints on Deployment and Upgrades
The complex structure and specialized nature of cardiovascular intervention quality documents impose specific requirements on FastGPT's deployment and upgrades. Documents containing charts and scanned images necessitate efficient OCR processing for accurate text extraction. Frequent batch updates and real-time adverse event reports require the knowledge base to support incremental updates and rapid indexing to ensure information timeliness. Extensive specialized terminology and units of measurement, such as mm, mg, and IU, require the tokenizer to correctly identify and index them, preventing semantic loss. Additionally, these documents often involve strict compliance requirements, demanding high standards for data isolation and access control. Therefore, deployment must prioritize multi-tenant isolation and permission management configurations to ensure data separation between different product lines or departments.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large clinical reports or PDFs with multiple images |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Ensures sufficient processing time for complex documents (e.g., OCR for scanned images) |
Chunk Length | 800–1200 characters | Balances specialized terminology context and retrieval efficiency |
Recall Count | Top 8 | Increases coverage for complex queries in cardiovascular intervention |
Similarity Threshold | 0.75 or based on actual measurement | Filters irrelevant results, improving answer accuracy |
Rerank Return Count | Top 3 | Selects the most relevant passages, reducing model processing burden |
Common Pitfalls
- Query results contain a large amount of irrelevant information: This occurs when the tokenizer configuration is not optimized for cardiovascular intervention's specialized terminology, leading to general tokenization strategies failing to accurately identify and index terms.
- Large PDF documents remain in "processing" status for an extended period or directly report errors after upload: This happens when
PARSE_FILE_TIMEOUT_SECONDSis set too low, not allowing enough time for OCR and chunking of complex documents. - After a knowledge base update, the model still responds with outdated information: This indicates that incremental indexing of the knowledge base was not correctly triggered, or the cache was not refreshed promptly, causing the model to rely on old data.
Verification Steps
- Upload a registration certificate document containing charts and scanned pages. Verify that the content is fully extracted and searchable.
- Query using specialized cardiovascular intervention terms (e.g., "stent diameter," "balloon inflation pressure"). Verify that answers are accurate and include specific numerical values from the document.
- Simulate an update with a new batch of documents. Check the time taken for knowledge base index updates and confirm that the updated document content is promptly recalled.
- Use the API or interface to verify that access for different permissioned users to specific quality documents complies with expected data isolation rules.
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.