Knowledge Base Retrieval and Recall for Rehabilitation

Data Characteristics

Rehabilitation equipment, a critical component of medical devices, requires highly standardized and specialized quality documentation. Data sources include product technical requirements, registration certificates, production process specifications, inspection procedures, risk management reports, clinical evaluation reports, user manuals, maintenance manuals, and regulatory compliance declarations. These documents exist as PDFs, Word files, or scanned images. Updates typically align with product lifecycles, regulatory changes, or technological iterations; for example, product registration certificates renew every five years, and technical requirements may adjust with national standard revisions. Documents feature rigorous structures, often including chapter numbering, tables, diagrams, and extensive specialized terminology. Fields and units strictly follow medical device industry norms, such as power in watts (W), voltage in volts (V), and dimensions in millimeters (mm), often accompanied by tolerance ranges.

Constraints on Knowledge Base Retrieval and Recall

The specialized and standardized nature of rehabilitation equipment quality documents imposes specific requirements on knowledge base retrieval and recall. Extensive professional terminology and abbreviations demand accurate semantic understanding from the model to prevent incorrect or missed recalls. Documents containing tables and diagrams (especially scanned images) require robust OCR capabilities for text extraction; otherwise, important information is lost. Update cycles are relatively fixed, but each update may involve partial revisions to numerous documents, requiring the knowledge base to support incremental updates and maintain version traceability. The rigorous document structure and chapter numbering mean retrieval results must precisely point to specific paragraphs in the original text, supporting accurate localization. The strictness of fields and units necessitates considering unit conversions and tolerance ranges for numerical information retrieval, preventing retrieval failures due to unit inconsistencies.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`500–800 characters`	Rehabilitation equipment document paragraphs are typically long, containing complete product descriptions or technical parameters. This range helps maintain semantic integrity and prevents key information from being split.
`Automatic Chunking`	Enabled	Quality documents often have complex chapter structures. Automatic chunking helps intelligently identify logical boundaries, improving recall efficiency.
`Number of Retrieved Chunks`	`8–15 chunks`	Considering the professional depth and interconnectedness of the documents, retrieving more items increases coverage and ensures no potentially relevant information is missed.
`Similarity Threshold`	`0.75`	Rehabilitation equipment professional terminology demands high precision. Setting a higher threshold ensures retrieved results are highly relevant to the query intent, reducing noise.
`Number of Reranked Chunks`	`Top 5 chunks`	After high-similarity retrieval, further refine to the most relevant items, prioritizing the most critical answers.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDFs or multi-page scanned images takes a long time. Increasing the timeout prevents parsing failures due to excessively large files.

Common Pitfalls

The knowledge base fails to recognize text content in scanned PDFs, preventing retrieval of some technical parameters and chart text. This typically results from not enabling or configuring an OCR engine, or the OCR engine having insufficient processing capability for low-quality scans.
Retrieval results contain numerous irrelevant paragraphs, requiring engineers to spend extra time filtering effective information. This may occur if the Similarity Threshold is set too low, or if the Chunk Length is too long, causing a single chunk to contain too much irrelevant information.
Queries for product models or specific components fail to recall relevant documents, even if the documents explicitly mention them. This might be because documents lacked effective metadata tagging during upload, or the knowledge base failed to extract structured data from tables within the documents.

Verification Steps

Select multiple typical queries, covering different types (e.g., technical parameters, troubleshooting, regulatory requirements), and verify that retrieval results include the correct documents and precise paragraphs.
Upload test documents containing scanned images and check if the knowledge base correctly extracts text from the images, then test queries against the extracted content.
Simulate product update scenarios by uploading revised document versions. Verify that the knowledge base recognizes incremental updates and ensures the accuracy of both new and old information retrieval.
Check the knowledge base logs for error messages such as PARSE_FILE_TIMEOUT or OCR_FAILED to determine if file parsing and text extraction processes are functioning correctly.

Note: The values provided are common starting points. Measure against your own samples for optimal configuration.

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.