Data Characteristics
Data related to surgical robot regulations originates from NMPA regulatory documents, internal surgical procedures, equipment operation manuals, clinical application guidelines, and quality management system documents. These documents are primarily in PDF, DOCX, or scanned image formats. Update frequency is generally low; regulatory documents might update 1-2 times annually, while internal SOPs are revised based on technological iterations or clinical needs. Document structure is rigorous, typically including chapters, clauses, and attachments. Fields cover equipment models, operating steps, risk assessments, emergency plans, personnel qualifications, and maintenance cycles. Units are often standard measurements (e.g., millimeters, volts, hours) or specific medical terminology.
Constraints on Knowledge Base Retrieval and Recall
The rigorous structure and low update frequency of surgical robot regulation documents require the knowledge base to effectively parse hierarchical relationships during data import and ensure content stability. The specialized and diverse nature of the fields means the knowledge base needs to accurately identify and associate specific terminology to avoid recall deviations due to semantic ambiguity. For example, queries involving specific equipment models like "da Vinci Surgical System" or "orthopedic robot" should precisely recall corresponding operating procedures. Furthermore, the authoritative nature of regulatory documents demands highly accurate recall results, preventing misinterpretation. Non-textual information, such as images and charts, in documents requires robust rich-text processing capabilities from the knowledge base to ensure information completeness.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk Length | 500–800 characters | Ensures each knowledge chunk contains sufficient context while avoiding information overload. |
Chunk Overlap Rate | 0.1 | Maintains contextual continuity and reduces the risk of critical information being truncated. |
Recall Count | Top 5 | Balances recall precision with computational resource consumption, covering core relevant information. |
Similarity Threshold | Calibrate empirically 0.75–0.85 | Ensures recall results are highly relevant to the query intent, reducing noise. |
Rerank Model | BGE-large-zh-v1.5 | Improves recall ranking effectiveness, prioritizing the most relevant regulatory clauses. |
Extractor | Table Extractor | Effectively processes tabular data in regulatory documents, such as equipment maintenance schedules. |
Common Pitfalls
- Irrelevant surgical instrument information appears in query results because the knowledge base chunking strategy is too broad, failing to differentiate between surgical robots and general instruments.
- When users ask about specific emergency plans, the system returns general operating guidelines. This occurs because the knowledge base fails to effectively identify and index specific sections or attachments within documents.
- Regulatory clause numbers or version information are missing from recall results. This happens when metadata fields are not correctly extracted during document parsing.
Verification
- Select at least 10 typical queries covering different equipment models, operating procedures, and emergency plans. Check if recall results are precisely matched.
- Randomly select 5 regulatory documents containing tables. Verify that the
Table Extractorcorrectly parses and indexes table content, checking for field completeness. - Simulate queries about the latest regulatory updates. Check if the knowledge base can recall the latest relevant documents published by NMPA and verify document version numbers.
The values provided are common starting points and should be measured against specific 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.