Data Characteristics for This Category
Surgical robot product data typically originates from manufacturer technical manuals, product specifications, clinical reports, and maintenance guides. These documents update infrequently, primarily for new product releases, software upgrades, or significant clinical data additions. Document structures are rigorous, often in PDF format, containing detailed mechanical parameters, electrical parameters, software versions, operating procedures, indications, contraindications, and maintenance cycles. Fields and units are highly specialized. For example, "degrees of freedom" is typically expressed in degree, "repeatability" in mm or μm, and "workspace" in m³ or cm³. Compatibility information for specific modules and consumable model/batch information are also important components.
Constraints Imposed by These Characteristics on Forms and Interactions
The specialized nature of surgical robot data requires highly structured form designs. This ensures users can accurately input or select specific parameters. Low update frequency means that once data is imported, a robust version management mechanism is necessary to handle infrequent but critical data changes. Documents are typically in PDF format, which challenges information extraction and structuring. This requires more intelligent document parsing capabilities to recognize tables, diagrams, and specialized terminology. The strictness of fields and units demands form input fields with validation functions. For example, the repeatability field should restrict input to numerical values and allow selection of mm or μm units. Furthermore, module compatibility information for different surgical robot models is complex. Form interactions need to support multi-level linked selections to accurately match user query requirements.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 | Accommodates long text parsing from technical manuals, preventing information truncation. |
Chunk size (Chunk Length) | 500–700 characters (characters) | Balances the integrity of professional terminology with recall efficiency, avoiding semantic fragmentation. |
Recall count (Recall Count) | Top 8 entries (top 8) | Ensures coverage of sufficient relevant technical parameters and operating procedures. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements 0.75–0.85 | Requires a higher threshold for professional terminology and precise parameters to ensure matching accuracy. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5) | Focuses on core technical parameters and solutions most likely to be of interest to the user. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles the parsing time for large PDF technical documents, preventing timeout failures. |
Three Common Mistakes
- Dropdown lists in forms are empty, preventing AI model selection. This indicates incorrect model configuration or permissions, leading to empty data returns from the frontend API.
- Returned reference content does not meet expectations, even when the
Return Referencefunction is not explicitly enabled. This may be due to changes in default behavior or configuration logic after a system version upgrade. Check configurations related to version4.9.4. - The
Select Knowledge Baseglobal variable cannot be dynamically assigned. This indicates a missing logic node in the workflow design or improper variable scope configuration, preventing the system from getting or setting specific knowledge bases at runtime.
How to Verify Configuration
- Submit a query containing a specific surgical robot model and parameters. Observe if the returned results accurately match the corresponding data in the technical manual.
- Select different module combinations in the form. Check if linked options update correctly and guide to the right compatibility information.
- Upload a new surgical robot product technical manual. Check if the parsing process completes successfully and if key fields (e.g.,
repeatability,自由度) are extracted correctly. - Simulate a query about maintenance cycles. Verify if the system provides accurate maintenance recommendations based on product model and usage duration, including links to relevant operation documents.
The values provided are common starting points and should be measured against the reader's 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.