Model Integration and Configuration for Rehabilitation

Data Characteristics for Rehabilitation Equipment

Rehabilitation equipment data typically originates from product manuals, technical specifications, clinical application guidelines, maintenance handbooks, and industry standard documents. This data updates infrequently, primarily during new product releases, existing product upgrades, or regulatory changes. Documents are mainly in PDF, Word, or structured database formats. Content covers device models, features, technical parameters, scope of application, contraindications, operating procedures, and maintenance recommendations. Common fields include device model, serial number, rated power, voltage range, frequency, treatment mode, treatment intensity, dimensions, and weight. Units are typically international standard units like V (Volts), Hz (Hertz), W (Watts), kg (kilograms), cm (centimeters), and may include specific medical measurement units.

Constraints from Data Characteristics on Model Integration and Configuration

Infrequent data updates for rehabilitation equipment mean that after initial model training and knowledge base construction, routine maintenance frequency can be lower. However, monitor new product launches and regulatory updates. Predominantly unstructured text documents require robust text extraction and information structuring capabilities, necessitating finer text segmentation and entity recognition. High precision is required for technical parameters and units. The model must avoid unit confusion or numerical errors when processing this information, as this directly impacts recall accuracy and question-answering quality. For example, an incorrect voltage range can lead to misleading product recommendations. Additionally, accurate identification of product models and serial numbers is crucial, as they often serve as unique identifiers, requiring the model to have high-precision matching capabilities for exact queries.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Rehabilitation equipment product manuals are often large; support for uploading complete documents is necessary.
`maxContext`	`3000 Tokens`	Ensures the model can process long text contexts containing detailed technical parameters and multi-step operating instructions.
`Chunk size` (Segment Length)	`800–1200 characters`	Balances semantic completeness and segment size, preventing critical information from being split or single segments from becoming too long, which increases processing burden.
`Recall count` (Recall Count)	`Top 8 entries` (Top 8)	Rehabilitation equipment inquiries often require multi-dimensional information; increasing recall appropriately improves coverage.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures recalled results have a high degree of relevance to the user's query regarding technical parameters and functional descriptions.
`Rerank result count` (Reranked Return Count)	`Top 3 entries` (Top 3)	After reranking, focus on the most relevant key information that directly answers the user's question.

Common Pitfalls

The model provides incorrect units or numerical deviations when answering device parameters. This typically results from a failure to effectively identify and standardize field units during the data preprocessing stage.
When a user queries information for a specific device model, the model fails to accurately recall the corresponding document. This occurs because entity recognition and indexing of product models in the knowledge base are not precise enough.
Uploading large product manuals results in file parsing failed or processing timeout errors. This may be related to a PARSE_FILE_TIMEOUT_SECONDS setting that is too short or an inappropriate file segmentation strategy.

Verification Steps

Select several representative rehabilitation devices. Upload their official product manuals. Check if the knowledge base correctly extracts and displays key technical parameters and functional descriptions.
For the uploaded devices, simulate user questions about voltage range, treatment mode, or target users. Verify if the model provides accurate answers, including units.
Test the model's precise matching capability for queries containing device model or serial number. Ensure it can directly locate information for specific products.
Randomly select documents from the knowledge base. Check if segmentation is reasonable. Ensure critical information paragraphs are not improperly split, which would affect semantic integrity.

Note: The values provided are common starting points. Measure performance 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.