Data Characteristics
Surgical robot R&D documentation primarily includes design specifications, test reports, clinical trial data, regulatory approval materials, and maintenance manuals. These documents have a high update frequency, especially during product iteration and clinical validation phases. Document structure is complex, containing numerous charts, CAD model links, embedded code snippets, and specialized terminology. Fields and units are highly specialized. Examples include "degrees of freedom (DOF)" and "repeatability" measured in micrometers (µm), force feedback in Newtons (N) or milliNewtons (mN), and response time in milliseconds (ms). Documents often involve medical imaging standards (DICOM) and communication protocols (e.g., EtherCAT). The primary language is technical English, frequently mixed with Chinese annotations and regulatory requirements.
Constraints Imposed by Data Characteristics on Vector Models and Indexing
The complex structure and multimodal content of surgical robot documentation require vector models to have strong multilingual understanding and sensitivity to technical contexts. High-precision numerical fields and units challenge models to extract key information while maintaining semantic integrity. Traditional text segmentation might disrupt the association between numerical values and their units. Frequent updates and version management needs make incremental indexing mechanisms and conflict resolution critical. Embedded CAD model links and code snippets in documents require indexing strategies to identify and effectively associate these non-textual information types, or provide placeholders for subsequent retrieval. Regulatory compliance demands extremely high standards for document content accuracy; any semantic deviation could lead to severe consequences. Therefore, the precision of vector recall is paramount.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
embedding_model | bge-large-zh-1.5 | Performs well with mixed Chinese and technical English documents; higher dimensionality helps capture subtle semantic differences. |
Chunk size (Chunk Length) | 800–1200 characters (characters) | Balances semantic completeness with indexing efficiency, preventing critical information from being split. |
Chunk Overlap Length (Chunk Overlap Length) | 100–150 characters (characters) | Ensures contextual continuity across chunks, reducing information loss. |
Recall count (Recall Count) | 5–8 entries (items) | Balances recall breadth with computational efficiency, ensuring highly relevant segments are retrieved. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Dynamically adjust based on the specialized nature of surgical robot documentation and recall accuracy requirements, typically 0.75–0.85. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Accommodates parsing time for large design specifications or clinical reports, preventing timeouts. |
Common Pitfalls
- Symptom: Indexing files remain in an "indexing" state for a long time, eventually reporting an error or becoming unresponsive. Reason: File content is too large or contains complex structures, leading to parsing timeouts. Embedded objects or unusual characters are not handled correctly.
- Symptom: Retrieval results show low-relevance document segments unrelated to the query intent, or critical numerical values and units are incorrectly separated. Reason: The vector model does not fully understand the specialized terminology and numerical associations in the surgical robot domain. The text segmentation strategy is too generic.
- Symptom: When relevant knowledge exists in knowledge base A, the system still retrieves irrelevant content from knowledge base B. Reason: Knowledge base priority settings are unclear or ineffective, or the query routing logic is not optimized according to business requirements.
How to Verify Configuration
- Select a batch of typical queries. Compare the relevance of recalled results with expected document segments to evaluate recall precision and completeness.
- Upload test documents containing complex charts, code blocks, and DICOM links. Observe if the indexing process completes smoothly and attempt to retrieve these specific elements.
- Simulate incremental indexing of documents at different update frequencies. Check the timeliness and stability of index updates.
- Perform retrievals for key specialized terms and numerical units. Verify if the model's ability to identify and associate this information meets expectations.
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.