Data Characteristics
High-value consumable registration documents draw data from product technical requirements, registration inspection reports, clinical evaluation reports, instructions for use, and various standard documents. These documents are typically published by national medical product administrations (NMPA), industry associations, or manufacturers. Regulatory documents and guidelines may update several times annually. Product-specific technical requirements and registration certificates update as needed throughout the product lifecycle. Documents are primarily PDF scans or electronic files. They contain numerous tables, diagrams, and specialized terminology. Fields and units are highly standardized. Dimensions often use millimeters (mm) and grams (g). Performance indicators may involve Pascals (Pa) and Newtons (N). Numerical precision is strictly required.
Constraints on Reference and Traceability
Data source authority and accuracy are critical for high-value consumable registration. Documents are often PDF scans or complex tables. This requires robust document parsing capabilities to accurately extract text, table data, and diagram descriptions. Frequent updates to regulatory documents and guidelines necessitate version management and incremental updates in the knowledge base. This ensures real-time validity of referenced content. Standardized terminology and units demand high precision from text embedding models to prevent citation errors from semantic misunderstandings. Furthermore, due to the rigorous nature of registration documents, every reference must trace directly to a specific page or paragraph in the original document, ensuring verifiability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500-800 characters | Balances semantic completeness and recall accuracy, suits regulatory document paragraph length |
Recall count (Recall Count) | 8-12 entries | Ensures coverage of multiple sources, avoids irrelevant interference |
Similarity threshold (Similarity Threshold) | 0.85-0.9 | Improves relevance of recall results, filters low-quality matches |
Rerank result count (Rerank Return Count) | 3-5 entries | Refines final references, focuses on core supporting information |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing of large PDF files, prevents timeouts |
maxContext | 4000 token | Maintains conversational context during follow-up questions |
Common Mistakes
- Knowledge base answers do not display references, or reference links are unclickable. This occurs due to document parsing failure or incorrect association of reference metadata.
- In continuous follow-up scenarios, model answers lose context from previous turns. This happens when the
maxContextparameter is set too low, leading to loss of contextual information. - References point to irrelevant documents or content. This indicates the similarity threshold is set too low, failing to effectively filter high-quality matching segments.
Verification Steps
- Upload multiple high-value consumable PDF documents. Verify the system correctly parses their text content and identifies table and diagram text.
- Ask specific questions. Check if answers include references and if clicking reference links accurately navigates to the corresponding location in the original document.
- Simulate regulatory updates. Upload new document versions. Observe if the knowledge base content updates promptly and prioritizes the latest version data in responses.
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.