Data Characteristics for This Category
IVD diagnostic reagent registration data originates from diverse sources. These sources primarily include product manuals, registration certificates, clinical trial reports, quality management system documents, production process flows, and raw material supplier qualifications. Documents are often unstructured or semi-structured text in formats such as PDF, Word, and Excel. They contain extensive specialized terminology, charts, and tables. Data update frequency is relatively low, typically occurring during product registration, changes, or regulatory updates. Document structure is rigorous, with fixed chapter divisions and content requirements. Fields and units involve chemical measurements, biological activity units, and clinical indicators (e.g., IU/mL, ng/dL, optical density OD). Strict requirements apply to numerical precision and unit standardization. Some data may exist as scanned images, requiring OCR recognition.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The data characteristics of IVD diagnostic reagent registration documents impose multiple constraints on deployment and upgrade processes. First, a large volume of unstructured and semi-structured documents, especially PDFs containing complex tables and charts, demands high-fidelity text extraction and parsing capabilities. This can lead to content omissions or parsing errors. Second, accurate recognition of specialized terminology and measurement units requires models with strong domain knowledge; otherwise, ambiguity will be introduced during knowledge base construction. The low update frequency means each update may involve replacing or revising many documents. This requires the knowledge base to have an efficient incremental update mechanism and accurately identify document versions. Additionally, the presence of scanned documents increases reliance on OCR recognition, and its accuracy directly affects subsequent text processing. Deployment must consider file storage capacity. Upgrades must ensure compatibility between new and old data formats to prevent data loss or knowledge base reconstruction.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | IVD data includes large clinical reports and images, requiring support for large file uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex PDFs and OCR recognition of scanned documents is time-consuming, preventing parsing timeouts. |
maxContext | 4096 | Ensures the model can cover long passages in clinical reports, providing sufficient context. |
Chunk size | 800–1200 characters | Balances semantic completeness with model processing efficiency, avoiding truncation of critical information. |
Similarity threshold | 0.8 | IVD terminology is precise; a high threshold ensures accuracy and relevance of recall results. |
Rerank result count | Top 5 entries | Registration data query results require precision, reducing interference from irrelevant information and focusing on core content. |
Three Common Pitfalls
- Knowledge base query results are empty or irrelevant: This often occurs when complex tabular data is not correctly extracted, or specialized domain terminology is not adequately understood by the model.
- Incomplete content extraction after document upload: This usually happens due to failed OCR recognition of embedded image text or scanned documents within PDFs, leading to the loss of critical information.
- Old version information persists after a knowledge base update: This indicates that the incremental update mechanism is not correctly configured, failing to effectively cover or delete outdated document fragments.
How to Confirm Proper Configuration
- Upload representative IVD registration document PDFs. Check if the knowledge base can fully extract all text content, especially tables and image captions.
- Use query terms containing specific IVD terminology and measurement units. Verify that recall results are accurate and relevant, and confirm that fields and units are correct.
- Simulate a data update process by replacing some old documents. Then, query related content to confirm the knowledge base correctly reflects the latest version information.
Note: The values provided are common starting points. Measure 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.