Data Characteristics
Rehabilitation device R&D documents come from various sources. These include design specifications, test reports, clinical trial data, user manual drafts, and regulatory compliance files. Document update frequencies vary. Design iterations might lead to weekly revisions, while regulatory compliance files update annually or when regulations change.
Document structures often contain numerous tables, charts, CAD drawing references, and specialized terminology. Examples include biomechanical parameters, kinematic data, and material properties. Fields frequently involve dimensions like force units (Newtons, Pascals), angles (degrees), time (seconds), and device-specific parameters (e.g., stroke, damping coefficient). Standardized naming conventions and unit systems differ across documents, posing challenges for automated parsing.
Constraints on Model Integration and Configuration
The complexity of rehabilitation device R&D documents imposes specific requirements on model integration and configuration.
First, diverse data sources necessitate configuring multiple file parsers. These parsers must effectively handle formats such as PDF, DOCX, and XLSX, and extract structured information.
Second, extensive specialized terminology and abbreviations in documents require the model to possess strong domain knowledge understanding. This necessitates introducing or fine-tuning embedding models for the biomedical field.
Third, rapid design iterations and regulatory updates demand frequent incremental updates to the knowledge base. This requires configuring efficient indexing update strategies and handling differences between document versions.
Finally, the inclusion of precise numerical values and units demands high accuracy in structured parsing. Model configuration must focus on the precision of named entity recognition and relationship extraction.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances paragraph completeness in rehabilitation device documents with the efficiency of model context processing. |
Recall count (Recall Count) | Top 10 entries (top 10) | Ensures coverage of various relevant information, addressing cases where specialized terminology might be scattered across different paragraphs. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Requires adjustment according to the semantic similarity distribution of the specific document set to ensure recall relevance. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5) | Focuses on the most critical information, reduces unnecessary redundancy, and improves the precision of the final response. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses lengthy parsing times for large test reports or design specification files. |
EMBEDDING_MODEL | text-embedding-ada-002 or text-embedding-3-large | Selects models with strong semantic understanding capabilities to effectively process specialized terminology and technical descriptions. |
Common Pitfalls
- Model API call returns error code
429: This usually indicates the API request frequency exceeds the model provider's limits. Adjust concurrent request numbers or implement a retry mechanism. - Key fields (e.g., device model, measurement unit) are empty in parsing results: Documents contain multiple expressions or inconsistent formats, preventing the model from accurately identifying and extracting information.
- Model answers still refer to old information after knowledge base content updates: Index update strategy is misconfigured, failing to trigger incremental updates or re-indexing in a timely manner.
Verification of Configuration
- Upload a batch of rehabilitation device R&D documents including various types (PDF, DOCX, XLSX). Check if the expected number of knowledge segments are generated after parsing.
- Query specific specialized terminology and key parameters from the documents. Verify if the model accurately recalls relevant information and provides correct answers.
- Simulate the document update process. Modify some R&D specifications or test data, then trigger a knowledge base update. Query to confirm the model's answers reflect the latest information.
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.