Data Characteristics for This Category
Rehabilitation equipment, a medical device subcategory, has distinct R&D document characteristics. Data sources are diverse, including design specifications, risk analysis reports, test validation reports, user manual drafts, and clinical trial data. These documents have a relatively low update frequency, primarily at key product design, development, registration, and iteration upgrade stages. Document structure typically follows medical device industry standards, such as ISO 13485, with clear chapter divisions, figures, appendices, and references. Fields and units involve numerous engineering parameters (e.g., force units N, Pa; dimension units mm, cm), physiological indicators (e.g., heart rate bpm, electromyography mV), material properties (e.g., strength MPa, density g/cm³), and compliance requirements (e.g., electrical safety standard IEC 60601). Documents often contain specialized acronyms, product model codes, and descriptions of specific test methods.
Constraints from These Characteristics on "Context and Tokens"
The characteristics of rehabilitation equipment R&D documents impose specific requirements on context and token handling. First, the professional nature of the documents, extensive engineering parameters, and acronyms necessitate a longer max_token limit. This ensures complete semantic units are captured and critical information is not truncated. Second, documents update infrequently, but single updates involve large content volumes. This emphasizes the need for historical version management and differential parsing capabilities. Each parse must efficiently identify new or modified key paragraphs. Third, structured documents following industry standards have strong internal relationships, with frequent cross-chapter references. This requires Recall count (recall count) and Rerank result count (reranked return count) configurations to cover a broader context range, capturing logical relationships across chapters. Finally, the precision of fields and units, especially for numerical data, demands a high sensitivity for Similarity threshold (similarity threshold). This avoids misjudgments or information loss due to subtle differences.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 3000–4000 characters | Rehabilitation equipment R&D document paragraphs often contain many professional terms and detailed descriptions, requiring a longer context window to understand complete semantics. |
Chunk size | 500–800 characters | Balances semantic completeness and search efficiency, preventing individual segments from being too long (information redundancy) or too short (context loss). |
Recall count | Top 8–12 entries | Information in R&D documents is highly interconnected. Increasing the recall count helps cover more potentially relevant paragraphs, improving answer accuracy. |
Similarity threshold | 0.75–0.85 | For precise information like engineering parameters and compliance requirements, a higher similarity threshold is needed to ensure the accuracy of recalled content. |
Rerank result count | Top 5–7 entries | After reranking, focus on the most relevant few pieces of information, reducing the model's processing burden while maintaining high relevance. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large R&D documents can take a long time. Provide sufficient file parsing timeout to prevent processing failures due to timeout. |
Three Common Mistakes
- Knowledge base files remain "processing" for a long time after upload, eventually reporting an error or no content. This usually happens when
PARSE_FILE_TIMEOUT_SECONDSis set too short, and large R&D documents fail to parse within the default time. - The model's answer is missing key parameters or technical indicators, or numerical errors occur. This may relate to an improper
Chunk size(segment length) setting, causing statements with complete parameter descriptions to be truncated, or aSimilarity threshold(similarity threshold) that is too low, recalling irrelevant segments. AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENare not set, leading to model call failures. This indicates incomplete AI Proxy environment configuration, preventing correct routing and authentication of model requests.
How to Confirm Proper Configuration
- Upload a typical rehabilitation equipment design specification. Check if all important chapters and key technical parameters are correctly parsed and retrievable in the knowledge base.
- For document sections containing complex charts or multi-level lists, ask questions to verify if the model can accurately extract relevant information and verify its
maxContextcoverage. - Query acronyms or specialized terms specific to the rehabilitation equipment industry. Confirm the model provides correct explanations or source citations, evaluating the effectiveness of
Similarity threshold. - Simulate a product iteration update scenario by uploading a slightly modified document version. Check if new or modified key content is accurately identified and recalled.
The values provided are common starting points and should be measured 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.