Data Characteristics
Quality documentation for medical imaging equipment (e.g., CT, MRI, X-ray machines) originates from manufacturers (technical specifications, operation manuals, maintenance guides, calibration reports) and regulatory bodies (compliance documents). These documents are primarily PDF files. Older models may have scanned or image-based documents. Document updates are infrequent, typically occurring with new equipment models, software upgrades, or regulatory changes.
Document structure is highly standardized, including tables of contents, chapter headings, numbered lists, figures, and appendices. Common fields include technical parameters (e.g., voltage kV, current mA, exposure time s), performance indicators (e.g., spatial resolution lp/mm, contrast cd/m²), calibration data, error codes, and serial numbers SN for specific components. Units adhere to the International System of Units SI and industry standards.
Constraints from Document Characteristics on Parsing and Chunking
The standardized structure of medical imaging equipment quality documentation demands precise parsing. Accurate identification of tables of contents, chapter headings, and figure captions is crucial for logical chunking. The presence of scanned and image-based documents increases OCR difficulty, potentially leading to inaccurate or missing text extraction.
Documents contain numerous technical parameters and performance indicators. Chunking must preserve the association between numbers and units to prevent information distortion. For example, separating 120 kV from its descriptive text reduces question-answering accuracy. Low update frequency means a stable knowledge base after initial parsing, but the initial build must cover all historical versions. The specialized nature of fields and standardized units requires the parser to correctly identify and tag these entities during lexical analysis, providing high-quality indexing for subsequent retrieval and question answering.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters (characters) | Ensures completeness of technical parameters, performance indicators, and their contextual descriptions, preventing truncation of critical information. |
Chunk overlap (Chunk Overlap) | 100–200 characters (characters) | Maintains semantic continuity between chunks, especially in cross-paragraph technical discussions, enhancing recall ability. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Provides sufficient parsing time for large technical manuals and documents with numerous figures. |
maxContext | 3500–4000 token | Ensures enough context to accommodate multiple relevant technical detail chunks, supporting complex questions. |
pdf_ocr_enabled | true | Addresses older or scanned medical imaging equipment documents, ensuring text extractability. |
Knowledge Base Type | Advanced Q&A (Advanced Q&A) | Medical imaging equipment quality documentation requires combining information from multiple chunks to answer complex technical questions; Advanced Q&A is a better fit. |
Common Pitfalls
- Uploading large PDF files results in no knowledge base updates for extended periods and no obvious backend errors. This is caused by
PARSE_FILE_TIMEOUT_SECONDSbeing set too low, leading to premature termination of the parsing process. - Knowledge base retrieval results show mismatches between technical parameter values and units, or missing critical numerical information. This occurs when
Chunk size(Chunk Length) is set too small, separating technical parameters from their descriptive text during chunking. - After API file upload, knowledge base chunk content does not match the original document, especially for figures or scanned areas. This happens when
pdf_ocr_enabledis not enabled, preventing image content from being recognized.
Verification Steps
- Select 5-10 medical imaging equipment documents of varying types and lengths. Upload them to the knowledge base and verify their parsing status.
- For typical passages containing technical parameters, figure captions, and error codes, use the knowledge base Q&A function. Verify that answers accurately and completely cite this information, and check the integrity of the cited chunks.
- Examine the average length and overlap of chunks in the knowledge base. Ensure they align with the expected configuration, e.g.,
Chunk size(Chunk Length) is close to800–1200 characters(characters). - For documents known to contain scanned content, verify OCR results to ensure no significant text errors or omissions.
The values provided are common starting points and should be measured against the reader's 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.