Deployment and Upgrade for Rehabilitation Device

Data Characteristics

Rehabilitation device pharmacovigilance data has distinct characteristics, especially for implantable or long-term wearable smart rehabilitation aids. Data sources primarily include physiological parameters from device sensors, user logs, operational status reports from remote monitoring platforms, and adverse event reports submitted via apps or customer service channels. The update frequency varies: physiological parameters and device logs generate continuously at second or minute intervals, while adverse event reports are irregular and event-driven. Document structures are diverse. Sensor data often stores in structured time-series databases. User logs may be semi-structured JSON or XML. Adverse event reports are often unstructured text descriptions, containing patient symptoms, device models, and medication information. Fields and units are specific. Examples include device_location, battery_health (in %), vibration_intensity (in g), and patient vital signs like heart_rate (in bpm) and spo2 (in %).

Constraints on Deployment and Upgrade

The characteristics of rehabilitation device pharmacovigilance data impose specific constraints on FastGPT's deployment and upgrade. High-frequency time-series data volumes are large, requiring efficient data ingestion and storage optimization to prevent bottlenecks. The variety of device models and batches necessitates flexible data source configuration and version management. Semantic extraction and correlation analysis of unstructured adverse event reports demand higher precision from RAG (Retrieval Augmented Generation) models, especially for understanding medical terminology and rehabilitation-specific expressions. In offline deployment scenarios, robust data synchronization and model update mechanisms are crucial to handle unstable or restricted network environments. Additionally, rehabilitation device data involves patient privacy, requiring strict data security and access control configurations during deployment to ensure compliance. During upgrades, changes in data schema and model iterations need smooth transitions to avoid service interruptions or data parsing errors.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates adverse event reports with large logs or multimedia attachments.
`maxContext`	`3000 Tokens`	Balances understanding of lengthy adverse event texts with model inference efficiency.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient time for the system to parse large or complex device log files.
`Chunk size`	`800–1200 characters`	Optimizes semantic integrity of unstructured adverse event text, preventing truncation of key information.
`Recall count`	`Top 8 entries`	Increases the recall probability of relevant documents, improving accuracy for complex queries.
`Similarity threshold`	`0.75`	Balances recall and precision, ensuring relevance of retrieval results to rehabilitation device adverse events.

Common Pitfalls

RAG retrieval results do not include all relevant adverse event reports. This occurs when the text segmentation strategy fails to effectively process lengthy diagnostic or descriptive texts specific to rehabilitation devices, leading to key information being split or ignored.
The oneapi service repeatedly restarts after offline deployment, with logs showing failed to connect to upstream. This typically indicates incorrect network or port mapping for the oneapi service in the Docker Compose configuration, failing to point to an available model service.
The FastGPT administration page login port and sharing service port are not separated, allowing accidental access to the administration interface via sharing links. This happens when FASTGPT_WEB_PORT and FASTGPT_SERVICE_PORT are configured identically or not clearly distinguished in the docker-compose.yml file.

Verification Steps

Upload a simulated log file containing multiple rehabilitation device anomaly records. Verify successful file parsing and accurate retrieval of key fault codes (error_code) and device IDs (device_id).
Create a knowledge base with various rehabilitation scenarios and adverse reaction descriptions. Then, test with typical query statements. Confirm that RAG recalls relevant documents and that the semantic match of the results to the query intent exceeds the configured Similarity threshold.
In an offline environment, use the docker ps command to check that all FastGPT core service containers (including fastgpt-web, fastgpt-api, fastgpt-mongo, etc.) are in an Up state. Confirm that the fastgpt-api container logs show no persistent error messages.

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.