Model Integration and Configuration for Rehabilitation

Data Characteristics for This Category

Rehabilitation device registration dossiers draw from diverse data sources. These primarily include product technical requirements, inspection reports, clinical evaluation data, risk management reports, instruction manuals, and labels. Documents typically exist in formats like PDF, Word, and Excel; some may be scanned images. Data updates are infrequent, occurring mainly with product model iterations, regulatory policy changes, or the release of clinical trial results. Document structures are highly standardized. For example, technical requirements usually contain fixed fields such as product name, model specifications, performance indicators, and inspection methods. Clinical evaluation data includes sections on preclinical studies, clinical trial data, and literature reviews. Regarding fields and units, rehabilitation device performance parameters (e.g., torque, angle, speed, pressure) typically use International System of Units (SI) or industry-standard units, with strict requirements for numerical precision.

Constraints Imposed by These Characteristics on Model Integration and Configuration

The standardized document structure of rehabilitation device dossiers requires models to accurately extract structured information during data parsing. An example is precisely identifying performance indicators and their units from product technical requirements. The presence of scanned documents demands high OCR quality to ensure complete and accurate text content. Infrequent data updates mean the model's knowledge base update cycle can be relatively relaxed. However, each update requires rigorous incremental data validation and full index reconstruction to reflect the latest regulations or product information. The strict requirements for numerical precision and units in performance parameters mean models must accurately cite original values and units when generating content, avoiding information distortion due to floating-point errors or unit confusion. Additionally, multilingual instruction manuals and labels necessitate multilingual processing capabilities to ensure information consistency.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Individual PDF or Word files in dossiers may contain numerous charts and attachments, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient parsing time for large PDF files or scanned documents requiring lengthy OCR processing.
`Chunk size`	`800–1200 characters`	Balances the integrity of paragraphs in rehabilitation device technical documents with the model's context length processing.
`Recall count`	`Top 10 entries`	Ensures enough relevant information is recalled to cover complex cross-references within the dossier.
`Similarity threshold`	`0.75`	Increases similarity requirements for the rigor of technical documents, reducing interference from irrelevant content.
`Rerank result count`	`Top 5 entries`	Further filters recall results to focus on the most relevant and semantically accurate content.

Three Common Pitfalls

Model output contains errors in performance parameter values or units. This occurs because the initial document parsing stage lacks strict type validation and normalization of values and units.
Knowledge base query results do not reflect the latest regulatory requirements. This happens when the knowledge base update mechanism is not synchronized with the regulatory release cycle, causing the model to respond based on outdated information.
Local deployment environment model startup fails and restarts continuously. This is due to OPENAI_BASE_URL or other environment variables pointing to an unreachable or incorrect address, or issues with Docker container network configuration.

How to Verify Configuration

Upload typical dossier files (e.g., PDFs containing scanned images). Check if they parse successfully and generate text chunks. Confirm chunk content is complete and free of garbled characters.
Query the model about numerical values and units for performance parameters found in product technical requirements. Verify model output consistency with the original document.
In the model's invocation logs, check if the PARSE_FILE_TIMEOUT_SECONDS parameter causes file parsing timeouts. Adjust as needed.

Note: 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.