Data Characteristics for Imaging Equipment
Imaging equipment, such as medical CT, MRI, and ultrasound diagnostic devices, requires registration documents. These documents include technical requirements, test reports, clinical evaluation reports, instruction manuals, labels, manufacturing process flows, and quality management system files. They typically exist in multiple formats like PDF, Word, and Excel. The content is highly structured, involving extensive specialized terminology, units of measurement (e.g., mGy·cm, dB, MHz), and charts. Data update frequency is relatively low, occurring mainly during product iterations, standard updates, or regulatory policy adjustments. Field names and units in documents must strictly adhere to National Medical Products Administration (NMPA) and international standards (e.g., IEC 60601 series).
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The complex structure and specialized nature of imaging equipment documentation impose specific requirements on tool calling and plugins. First, multi-format documents necessitate support for various file parsers to ensure complete content extraction. Second, the abundance of specialized terminology and standard units demands precise entity recognition and semantic understanding from the model to avoid information deviations due to misinterpretation. For example, accurately recognizing and validating parameters like CTDIvol or SAR is crucial. Extracting and correlating chart data presents another challenge, as many key performance indicators appear in graphical form. Finally, while document update frequency is low, updates often involve multiple related documents, requiring tools to handle batch document synchronization and version management.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Individual imaging equipment registration files can be large, especially PDFs containing high-resolution images. |
maxContext | 8192 token | Ensures full context for lengthy technical descriptions and clinical reports in registration documents. |
Chunk size | 800 characters | Balances semantic completeness and retrieval efficiency, preventing excessive truncation of critical information. |
Similarity threshold | 0.78–0.85 | Guarantees the accuracy of recall results, filtering out irrelevant technical document snippets. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient time to process large PDFs or complex Word documents, preventing timeouts. |
Rerank result count | Top 5 entries | Further optimizes relevance after initial recall, focusing on the most critical pieces of information. |
Common Pitfalls
- Tool calls return empty or incomplete content. This happens because the document parser has insufficient support for specific formats of imaging equipment registration documents, failing to extract all content correctly.
- Knowledge base search response times are too long, leading to call timeouts. This occurs due to suboptimal chunking strategies for large documents during index building, or underlying storage I/O performance bottlenecks.
- The model generates factual errors in the declaration content, such as confusing performance parameters of different device models. This is caused by data ambiguity in the knowledge base, or the model's failure to correctly identify and utilize tools for precise fact-checking.
Verification Steps
- Upload and parse typical imaging equipment registration documents (e.g., a medical CT technical requirements PDF). Check if the knowledge base completely displays the document content, especially tables and figure captions.
- Perform knowledge base searches for specialized terminology unique to imaging equipment (e.g.,
detector quantum efficiency,spatial resolution). Verify the relevance and accuracy of the recall results. - Simulate common questions from declaration documents. Use tool calls to query specific performance parameters or standard clauses. Verify that the returned results are consistent with the original document content and error-free.
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.