Data Characteristics for This Category
Surgical robot registration and declaration documents draw from diverse data sources. These include domestic and international regulations and standards, technical review guidelines, clinical trial reports, product manuals, user manuals, patent literature, and early-stage R&D documents. Data update frequencies vary; regulations and guidelines typically update annually or every few years, while clinical trial data and product iteration information may update more frequently. Document structures are complex, containing numerous charts, images, and specialized terminology. For example, technical requirements often list performance indicators and test methods in tabular form, and clinical reports include detailed case data and statistical analyses. Fields and units are highly specialized. For instance, precision metrics involve micrometer or sub-millimeter units, and force feedback parameters may use Newton-meters (N·m) as units, often accompanied by specific abbreviations and symbols.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The complex data structure of surgical robot declaration documents means traditional text segmentation methods can sever critical information links, affecting recall quality. For example, if tabular data from technical requirements is split, retrieval may fail to provide a complete performance indicator and its corresponding test method. The abundance of specialized terminology and abbreviations requires the knowledge base to understand this domain-specific language, improving semantic matching accuracy. Otherwise, "synonymous but different words" or "homonymous but different meanings" recall biases may occur. Inconsistent update frequencies mean the knowledge base needs to support incremental updates and version management to ensure retrieval results are timely and accurate. Additionally, cross-references and logical connections may exist between different document types. Single-document retrieval is insufficient to support complete declaration document preparation; the knowledge base needs cross-document associative retrieval capabilities. Information within images and charts, if not effectively structured and extracted, becomes a retrieval blind spot.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters (characters) | Balances semantic completeness and segment recall efficiency, preventing overly long segments from introducing noise and overly short segments from losing context. |
Chunk overlap (Segment Overlap) | 100–150 characters (characters) | Ensures contextual continuity at segment boundaries, reducing semantic discontinuity caused by splitting. |
embedding_model | text-embedding-3-large | Higher model dimensionality and semantic understanding capabilities are suitable for processing highly specialized medical device texts. |
Recall count (Recall Count) | 10–15 entries (items) | Ensures richness of recall results, providing sufficient information for subsequent re-ranking and generation. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Based on actual test data, balances recall precision and recall rate, avoiding low-relevance results. |
Rerank result count (Re-ranked Return Count) | 3–5 entries (items) | Focuses on the most relevant knowledge snippets, reduces the input length for large model processing, and improves generation efficiency and quality. |
Three Common Mistakes
- After multi-turn conversations, question-answering performance significantly degrades. Starting a new conversation restores accuracy. This typically occurs because the context window accumulates too much irrelevant information, diluting the semantic weight of key queries.
- After changing the
embedding_model, the existing knowledge base is not re-indexed. This leads to a mismatch between new queries and the vector space of the old index, causing severe deviation in recall results. - Critical technical parameters or clinical data are missing from retrieval results. The reason is that tabular or image content within documents was not effectively parsed and structured into the knowledge base.
How to Confirm Proper Configuration
- Construct queries containing specialized terminology and abbreviations. Check if recall results include corresponding definitions, explanations, or relevant technical parameters, and confirm the contextual completeness of the recalled snippets.
- For frequently updated regulatory documents or guidelines, upload new versions and then perform retrieval. Verify if the knowledge base prioritizes recalling the latest version's content and excludes interference from older versions.
- Select typical questions from declaration documents, such as the test method for a specific performance indicator. Observe if the recall results accurately present the complete table or paragraph containing that indicator and test method, and check the reasonableness of the
Similarity threshold(similarity threshold). - Query text content that includes chart or image descriptions. Verify if the knowledge base can recall textual descriptions or related explanations of these visual elements.
The values provided are common starting points and should be measured 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.