Knowledge Base Retrieval and Recall for Rehabilitation

Data Characteristics

Data for rehabilitation device clinical trial pre-screening originates from product manuals, technical white papers, past clinical research reports, adverse event reports, regulatory documents (e.g., NMPA, FDA guidelines), and standards (e.g., ISO 13485, GB 9706). Update frequencies vary: product manuals update with new versions, regulatory documents may revise annually, and clinical research reports release according to project cycles. Document structures differ. Manuals typically include sections on device principles, indications, contraindications, usage, and technical parameters. Clinical reports contain trial protocols, subject information, statistical analysis, results, and discussion. Common fields and units include device model, serial number, power (W), frequency (Hz), pressure (Pa), temperature (°C), dimensions (mm), and biomedical indicators (e.g., EMG signal mV, joint range of motion °). Datasets often include numerous charts and tables describing device performance curves or clinical data statistics.

Constraints on Knowledge Base Retrieval and Recall

The diverse sources and varying structural complexity of rehabilitation device data require robust multi-format file parsing capabilities in the knowledge base. For example, PDF technical white papers and Word clinical reports need accurate text extraction, especially embedded table data, to avoid information loss. Standardized fields and units, such as power and frequency, demand precise matching during retrieval to prevent misjudgments due to unit inconsistencies. The rigor and specificity of regulatory documents necessitate maintaining contextual integrity during text segmentation to avoid fragmenting critical clauses. The update frequency of clinical trial data limits the real-time nature of offline knowledge bases, requiring a regular update mechanism. Documents containing extensive specialized terminology and abbreviations require enhancing the domain understanding of vectorization models to improve recall accuracy.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Clinical reports and technical white papers can be large; relax the file size limit accordingly.
`Chunk size` (Segment Length)	`500 characters` (characters)	Balances the integrity of regulatory clauses with the semantic completeness of clinical report paragraphs, reducing information fragmentation.
`Overlap Length`	`100 characters` (characters)	Ensures contextual continuity, especially when processing specialized terminology and complex descriptions.
`Recall count` (Recall Count)	`8 entries` (items)	Increases the coverage of retrieval results, raising the probability of selecting relevant information.
`Similarity threshold` (Similarity Threshold)	`0.78`	Clinical trial pre-screening requires high accuracy; a higher threshold reduces irrelevant results.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Processing large PDF files can be time-consuming; prevents parsing timeouts.

Common Pitfalls

Knowledge base files remain in an indexing state for an extended period. This may occur if files are too large or contain complex tables, causing parsing time to exceed the default PARSE_FILE_TIMEOUT_SECONDS.
Chinese queries fail to recall English documents from the knowledge base. This may be due to the text embedding model not being optimized for mixed-language content, or improper segmentation of Chinese and English content.
Recall results contain a large amount of irrelevant or duplicate information. This typically happens when the Similarity threshold (Similarity Threshold) is set too low or Recall count (Recall Count) is too high, leading to the retrieval of low-relevance documents.

Verification Steps

Upload rehabilitation device documents in various formats (PDF, Word, TXT) and sizes. Verify that all files parse and index correctly.
Query for key technical parameters from documents (e.g., "maximum output power 20W", "frequency range 1-100Hz"). Check if recall results include this information and evaluate contextual completeness.
Use mixed Chinese and English query statements. Verify that the knowledge base accurately recalls documents containing English technical terms and check the language matching of the recall results.

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.