Data Characteristics for This Category
Registration and declaration materials for high-value consumables primarily originate from regulatory documents and technical review guidelines published by the National Medical Products Administration (NMPA). They also include internal corporate documents such as product technical specifications, clinical trial reports, and risk management reports.
Data update frequency is relatively stable. Regulatory documents are typically revised or new versions released annually. Product technical documents are updated with product iterations.
Document structure: Regulatory documents are often in PDF format, containing numerous hierarchical headings and standardized statements. Internal corporate documents may include various formats like Word and Excel. Content covers product parameters, material composition, performance indicators, testing methods, and clinical data.
Fields and units: Data involves chemical names of materials, purity percentages, physical properties (e.g., tensile strength in MPa, elastic modulus in GPa), and biological evaluation indicators (e.g., cytotoxicity grade, hemolysis rate in %). Precision and standardization requirements are extremely high.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The complex hierarchical structure of regulatory documents and technical guidelines requires the knowledge base to identify and maintain parent-child relationships within documents. This ensures contextual completeness of retrieval results.
Precise parameters and units in product technical documents challenge chunking strategies. The system must avoid breaking numerical values and units apart, which would affect recall accuracy.
Due to the rigorous nature of declaration materials, the completeness and relevance of recall results are critical. Simple keyword matching is insufficient. Semantic understanding and multi-dimensional filtering are necessary.
Format differences across various source documents demand robust file parsing capabilities from the knowledge base. This ensures all valid information is correctly extracted and indexed.
Identifying specialized terminology, such as biological evaluation indicators, requires the knowledge base to possess domain-specific vocabulary understanding to improve recall precision.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Accommodates longer paragraphs in regulatory documents and technical specifications, maintaining contextual coherence. |
Chunk Overlap Length (Overlap Length) | 100–200 characters | Ensures continuity of information across chunks, reducing semantic loss due to chunking. |
Recall count (Recall Count) | Top 8–12 entries | High-value consumable declaration materials involve multi-dimensional information; increasing recall count improves coverage. |
Similarity threshold (Similarity Threshold) | Calibrate by actual measurement | Ensures recall results are relevant but not overly generalized; requires adjustment based on specific datasets. |
Rerank result count (Rerank Return Count) | Top 5 entries | Selects the most relevant items from a large number of recall results, reducing model processing load and improving accuracy. |
Max Upload File Size | 200 MB | Meets the upload requirements for large clinical trial reports or multimedia materials. |
Three Common Mistakes
- The knowledge base query succeeds, but no content is output to the large language model. Logs show successful knowledge base query but an empty return result. This happens when paragraph IDs returned by the knowledge base are filtered before being passed to the large language model, resulting in no effective information being provided.
- Retrieval results contain a large amount of irrelevant or fragmented information, preventing the formation of complete context. This occurs when the chunking strategy is too aggressive, breaking up complete descriptions of regulatory clauses or product parameters.
- After document upload, some table data or special format content is not correctly indexed, making it impossible to retrieve. This happens when the file parser fails to recognize or process specific versions of Word/Excel file structures.
How to Verify Configuration
- Upload typical regulatory documents and product technical documents. Retrieve key concepts or parameters from them. Check if the recall results include complete relevant paragraphs.
- For documents containing precise numerical values and units, perform retrieval. Confirm that numerical values and units remain intact in the recalled items and are not incorrectly truncated.
- Simulate common questions during the declaration process. Evaluate the contextual coherence and information completeness of the recall results. Confirm that they can support subsequent question answering or material generation.
Note: The values provided are common starting points. They should be measured against your own samples to determine the optimal configuration for your specific use case.
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.