Deployment and Upgrades for Surgical Robotics

Data Characteristics

Surgical robotics pharmacovigilance data originates from clinical trial reports, real-world usage data, post-market surveillance reports, and medical device reporting (MDR) databases. This data updates frequently. New adverse event reports can emerge at any time, especially during post-market surveillance. Document structures typically include structured data (e.g., patient information, surgical parameters, adverse event codes, diagnostic results, treatment measures) and extensive unstructured text (e.g., doctor's notes, patient interviews, surgical video annotations). In addition to general pharmacovigilance fields, specific robot operation parameters are present. These include robotic arm trajectory, force feedback data, surgical duration, and instrument wear. Units cover millimeters, Newtons, seconds, and percentages. This data usually includes timestamps.

Constraints Imposed by These Characteristics on Deployment and Upgrades

The high update frequency and complex structure of surgical robotics pharmacovigilance data challenge FastGPT's real-time deployment and data processing capabilities. The mix of unstructured text and structured data requires knowledge base segmentation strategies to effectively integrate different information types and maintain contextual coherence. The introduction of robot-specific parameters necessitates more refined feature extraction during data preprocessing. This ensures models accurately understand and recall these critical details. Due to the sensitivity of medical data, deployment environments must meet strict security compliance requirements. Upgrade processes must ensure data integrity and service continuity, preventing potential data leaks or interruptions. Accurate parsing of complex fields and units directly impacts the model's accuracy in identifying adverse events and the effectiveness of alerts.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	3000–4000 token	Balances the detail level of robot surgical records with model processing capacity, preventing context loss.
`Chunk size` (Segment Length)	800–1200 characters	Accommodates the mix of descriptive text and structured data in clinical reports, maintaining information completeness.
`Recall count` (Recall Count)	8–12 items	Ensures coverage of sufficient relevant adverse event reports or operational records, improving recall rate.
`Similarity threshold` (Similarity Threshold)	Calibrated by actual measurement	Balances recall accuracy and relevance for different query types and data distributions. An initial value can be 0.75.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Handles complex surgical reports containing numerous images or video links, preventing parsing failures due to timeouts.
`UPLOAD_FILE_MAX_SIZE`	500 MB	Addresses large adverse event report files containing multimedia attachments, such as surgical video clips.

Common Pitfalls

Knowledge base click errors or empty model stream responses: Model services (e.g., xinference) are likely not correctly configured for FastGPT calls after deployment, or the glm4-chat model failed to load.
SQL error permission issues: This typically occurs after a Docker restart when the mounted volume permissions for the fastgpt_db SQL database change, preventing FastGPT from writing or reading data.
Model thinking settings not taking effect: The ollama deployed deepseek model API address or port is usually misconfigured, or the API_KEY in FastGPT does not match the model service.

Verification Steps

Upload a simulated adverse event report containing surgical robot-specific parameters (e.g., robotic arm angle, force feedback). Verify that the knowledge base segmentation fully retains these critical fields.
Conduct multiple Q&A tests on a report known to contain a specific adverse event (e.g., instrument wear). Observe whether the model accurately identifies and cites relevant evidence.
Monitor large file uploads and parsing processes via the FastGPT backend's log system under PARSE_FILE_TIMEOUT_SECONDS and UPLOAD_FILE_MAX_SIZE parameters. Confirm no timeouts or file-too-large errors occur.
On the FastGPT interface, verify the model's output context length and the number of cited knowledge items against the maxContext and Recall count (Recall Count) settings. Ensure they are within the expected range.

The values provided are common starting points. Measure 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.