Data Characteristics for This Category
Surgical robot policy and SOP (Standard Operating Procedure) documents typically come in PDF, DOCX, or HTML formats. They cover equipment operation manuals, maintenance procedures, emergency plans, clinical application guidelines, and ethical review standards. These documents have a low update frequency, usually changing with equipment model iterations or regulatory revisions, which can take several months to over a year. Document structures are rigorous, containing numerous numbered sections, charts, flowcharts, and specialized terminology. Fields include equipment model, serial number, operation step number, risk level, consumable batch, calibration parameters, and fault codes. Units are often physical quantities like millimeters, seconds, volts, and Celsius, alongside extensive medical terminology and abbreviations.
Constraints on Deployment and Upgrade
The low update frequency of surgical robot policy documents means the knowledge base requires a large-scale, one-time data import during initial setup. Subsequent incremental updates are less frequent, but version management must be strict. The many charts, flowcharts, and complex layouts in these documents demand high accuracy in text extraction and structured processing. This may require custom parsers to ensure information completeness. Specialized terminology and abbreviations require the model to have strong domain understanding, potentially needing specific glossaries for enhancement. The rigor of fields and precision of units mean that numerical information extraction and comparison in the Q&A system must be highly accurate. This avoids critical information deviations due to unit confusion or misinterpretation of values. For multilingual SOPs, multilingual processing capabilities are also necessary.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | A single SOP or policy document may contain many images and complex layouts, resulting in a large file size. Sufficient upload capacity is needed. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Policy documents have strong semantic coherence. Longer segments help preserve context and reduce fragmentation, while avoiding overly long segments that affect recall efficiency. |
Recall count (Recall Count) | Top 10 entries (top 10) | This ensures coverage of multiple relevant sections in policy documents. Given the strictness of policy Q&A, recalling more potentially relevant snippets improves accuracy. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Policy Q&A demands high accuracy. Increasing the threshold appropriately filters out irrelevant recall results, reduces noise, and ensures the authority of returned content. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Parsing complex PDF or DOCX documents can be time-consuming. Increasing the timeout prevents failures due to incomplete parsing. |
maxContext | 4000 tokens | Policy Q&A often requires the model to synthesize information from multiple segments to provide complete answers. Expanding the context window improves answer quality. |
Three Common Mistakes
- Model call shows
Function call error: This usually indicates that the deployed Large Language Model (LLM) is not correctly configured or does not supportfunction calling. Alternatively, OneAPI routing rules might be incorrect, preventing the request from being properly forwarded or parsed. - Content parsing fails or chart information is lost after document upload: The file parser has insufficient support for complex layouts and embedded objects (e.g., flowcharts, formulas). This leads to some critical information not being effectively extracted and vectorized.
- Numerical parameters in Q&A results are incorrect or units are confused: The knowledge base fails to structurally identify and standardize numerical values and their units in the document. This causes the model to inaccurately reference or convert them in its answers.
How to Verify Correct Configuration
- Upload an SOP document with complex charts and multiple numbered sections. Check if the parsed text fully retains key information and structure, especially captions next to charts.
- Ask questions about critical fields like operation steps for specific equipment models or risk levels. Verify if the model's answers accurately quote the original text from the document and if numerical values and units are consistent.
- Simulate an equipment failure scenario and ask about the emergency handling procedure. Confirm that the model provides the correct steps according to the document's sequence and conditional logic, and check for any missing or incorrect steps.
- After upgrading FastGPT, re-index a policy document. Ensure the new version does not introduce regression issues in file parsing and vectorization, and verify the accuracy of historical Q&A records.
The values provided are common starting points. Measure them 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.