Data Characteristics for This Category
Imaging equipment registration and declaration documents draw from diverse sources. These include product technical requirements, registration inspection reports, clinical evaluation reports, risk management reports, instruction manuals, and user manuals. Documents are typically in PDF, Word, or Excel formats and contain extensive images, charts, and tabular data. Data update frequency is relatively low, occurring mainly during product iterations, regulatory updates, or re-registration before certificate expiration. Document structures generally follow the NMPA's medical device registration and declaration requirements, exhibiting high standardization and regulation. Fields and units involve medical imaging parameters (e.g., resolution, signal-to-noise ratio), electrical safety indicators (e.g., voltage, current), mechanical performance parameters (e.g., range of motion, load-bearing capacity), and biocompatibility data. Units strictly adhere to the International System of Units (SI) and industry standards.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The low update frequency of imaging equipment declaration documents means incremental knowledge base updates are less urgent than in high-frequency domains. Prioritize efficient large-scale one-time data imports during deployment. The presence of numerous images and tables in documents demands higher file parsing capabilities from FastGPT. Ensure the stability and accuracy of OCR and table parsing modules during deployment. The strict document structure and standardized field requirements necessitate refined text segmentation strategies during knowledge base construction. This avoids semantic fragmentation and ensures accurate identification and extraction of key parameters and indicators. Furthermore, given the medical terminology and regulatory clauses, thorough domain knowledge fine-tuning of the model is required post-deployment to ensure accuracy in specialized Q&A. During system upgrades, focus on improvements in multi-modal parsing capabilities and domain model compatibility in new versions. This ensures a smooth transition without impacting the quality of existing knowledge base Q&A.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 100 MB | Single imaging equipment declaration files can be large; large file uploads must be supported. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances document structural integrity with retrieval efficiency, avoiding context loss from over-segmentation. |
Recall count (Recall Count) | Top 5 entries (top 5) | Ensures relevant information coverage while avoiding the introduction of too much irrelevant content. |
Similarity threshold (Similarity Threshold) | 0.75 | Improves the relevance of recall results and filters out low-quality matches. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles complex PDF files containing many images and tables, preventing parsing timeouts. |
maxContext | 2000 characters (characters) | Ensures the Q&A model can process longer specialized terms and regulatory clauses. |
Three Common Mistakes
- Key technical parameters are missing from knowledge base Q&A results. This occurs when the file parsing fails to correctly identify and extract data from tables or images.
- After a system upgrade, some historical Q&A records fail to load correctly. This is due to incomplete compatibility between new and old database structures or data serialization methods.
- Voice input functionality errors in a private deployment environment. This happens when essential audio processing libraries are missing or permissions are not correctly configured within the Docker container environment.
How to Verify Correct Configuration
- Upload a registration inspection report containing complex tables and images. Verify that key data points are accurately indexed by the knowledge base.
- Perform a minor version upgrade. Verify that the existing knowledge base and historical conversation data migrate smoothly and are accessible.
- Ask questions about specific regulatory clauses in imaging equipment registration and declaration documents. Check the accuracy and completeness of the model's answers against professional standards.
- Attempt to use the voice input function during Q&A. Confirm that the speech recognition and semantic understanding processes are correct.
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.