Data Characteristics
Imaging equipment (CT, MRI, ultrasound) registration data comes from several sources. These include National Medical Products Administration (NMPA) regulations, technical review guidelines, standard catalogs, and internal company documents. Internal documents include product technical requirements, test reports, clinical evaluation reports, and draft instructions. These documents are typically PDFs, Word files, or Excel spreadsheets.
Regulatory documents update annually. Technical guidelines and standards update irregularly based on technology and regulatory needs. Internal documents update with product development cycles. Documents have a strict structure. They contain specialized terms, charts, test data, equipment parameters (e.g., resolution, scan time, dose), and specific terminology (e.g., "spatial resolution," "signal-to-noise ratio," "artifacts"). Units of measurement vary, covering physical quantities and medical imaging metrics, such as millimeters (mm), Sieverts (Sv), Gauss (G), and frames per second (fps).
Constraints on Knowledge Base Retrieval and Recall
The specialized and structured nature of imaging equipment registration data imposes specific requirements on knowledge base retrieval and recall.
Regulatory and technical guidelines update infrequently but have a significant impact. The knowledge base must quickly identify and update relevant clauses to ensure recalled content is current. Documents contain many charts and specialized terms, especially in performance metrics and clinical application descriptions. Traditional text chunking methods can fragment critical information, affecting recall completeness. For example, a "spatial resolution" paragraph might include text, diagrams, and test data. Simple text splitting cannot effectively link these. Diverse units of measurement for equipment parameters require precise matching during retrieval to avoid misinterpretations due to unit differences. Accurate recall needs both semantic text understanding and structured data extraction to handle complex, nested information across different document types.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | Accommodates long sentences and chart descriptions in imaging equipment data, preventing critical information from being cut off. |
Overlap Length | 100–150 characters | Ensures contextual continuity, especially when specialized terms and parameter descriptions span multiple chunks. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Improves the precision of recall results, filtering out irrelevant regulations or technical clauses. |
Recall count (Number of Retrieved Chunks) | 8–12 chunks | Balances coverage while avoiding excessive noise that could interfere with judgment. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large PDF files, particularly those with many embedded images and tables. |
maxContext | 3000 Tokens | Ensures sufficient technical details and regulatory references are included when generating responses. |
Common Pitfalls
- The knowledge base cannot recognize image content in Q&A, leading to ineffective answers for graphical descriptions of equipment performance. This occurs when image OCR (Optical Character Recognition) is not enabled or configured correctly during document parsing, preventing image content from being extracted as searchable text.
- Retrieval results contain many generic regulatory clauses that do not match specific equipment models. This happens when the knowledge base lacks effective metadata management during construction, failing to link regulatory clauses to specific product series or equipment types.
- When querying specific technical parameters, recall results lack relevant chart data, leading to incomplete information. This occurs when the document chunking strategy relies too heavily on plain text, failing to treat charts and their corresponding text descriptions as a single unit for processing and indexing.
How to Verify Configuration
- Upload imaging equipment technical documents containing complex charts and specialized terminology. Check document parsing logs to confirm if image OCR successfully extracted text from charts.
- For a specific imaging equipment model, ask questions about its core performance parameters (e.g., "low-contrast resolution of CT equipment"). Verify that recall results include technical requirements, test reports, or relevant regulatory clauses for that model, and confirm the accuracy of parameter values and units.
- Conduct a simulated regulation update test. Replace an old version of a regulatory document in the knowledge base. Then query related questions and check if recall results prioritize the new regulation to ensure timeliness.
- Query technical guidelines spanning multiple chapters and pages. Confirm that the recalled text segments provide complete context to avoid misinterpretation.
The values provided are common starting points. Measure them 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.