Vector Models and Indexing for Rehabilitation Device

Data Characteristics for This Category

Rehabilitation device registration and declaration data comes from various sources. These sources include clinical trial reports, biocompatibility test reports, electrical safety test reports, software validation reports, risk management reports, product technical requirements, instructions, and user manuals. Documents are typically in formats such as PDF, Word, and Excel. Content updates are infrequent, mainly occurring during product iterations or regulatory changes. Document structures are complex. They contain extensive normative text, charts, test data, and legal clauses. Fields and units involve medical terminology, engineering parameters, measurement units (e.g., N, Pa, mm/s), and specific naming conventions compliant with national standards (e.g., GB 9706.1-2020) and industry standards (e.g., YY/T 0664).

Constraints Imposed by These Characteristics on Vector Models and Indexing

The complex document structure and diverse sources of rehabilitation device registration data require vector models to effectively process different types of text information. They also need some multimodal extension capability. Low update frequency means index construction should prioritize document content completeness and accuracy. Real-time requirements are not high. The large volume of specialized terminology and normative expressions may cause general vector models to deviate in semantic understanding. This requires considering domain knowledge enhancement or fine-tuning. Charts and tabular data embedded in documents are critical information carriers. This challenges the indexing system's ability to extract and associate structured data. Standardized fields and units require the vectorization process to identify and retain these key entity information for accurate retrieval and comparison.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Rehabilitation device documents are often long technical reports. This range helps maintain semantic completeness, prevents truncation of key information, and controls vectorization cost per segment.
`Overlap Length`	`100–200 characters`	Ensures contextual continuity between adjacent paragraphs. This is especially important for critical descriptions spanning pages or sections, preventing information loss due to segmentation and improving recall.
`Recall count` (Recall Count)	`Top 10–15 items`	Registration and declaration queries often require more context to answer complex questions. Increasing the recall count improves coverage of relevant information and reduces omissions.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	The rehabilitation device domain has much specialized terminology. Adjust this based on actual query results and business needs. A common starting value is `0.75`, then fine-tune based on test feedback to balance recall and precision.
`Rerank result count` (Rerank Return Count)	`Top 3–5 items`	After initial recall, use a reranking model to refine the order of results. This focuses on the most relevant content, improving the quality and accuracy of the final answer and reducing interference from irrelevant information.
`rerank_model_id`	Select a model supporting Chinese semantic understanding and domain adaptation	The specialized nature of registration and declaration documents requires the reranking model to better understand technical terms and regulatory requirements in Chinese contexts, e.g., `bge-reranker-large` or other domain-optimized reranking models.

Common Mistakes

Online recall tests of the knowledge base show the reranking model is not active. The rerank_score field is missing, or there is no noticeable sorting optimization. This may be because rerank_model_id is not configured correctly, or the reranking function was not enabled during knowledge base indexing.
Query results contain many irrelevant or low-relevance items, obscuring effective information. This happens when Recall count (recall count) is too high and Similarity threshold (similarity threshold) is set too low, failing to filter noise effectively.
Indexing large PDF documents fails or takes too long. The console displays a PARSE_FILE_TIMEOUT_SECONDS timeout error. This may be due to incorrect settings for UPLOAD_FILE_MAX_SIZE or file parsing timeout parameters, preventing the system from processing the document within the specified time.

How to Verify Correct Configuration

Use the online testing feature. Query with specific parameters (e.g., GB 9706.1-2020) or specialized terms from rehabilitation device registration documents. Check if recall results include relevant document snippets. Observe if the rerank_score field reasonably sorts the results.
Index different types and lengths of registration and declaration documents. Check indexing logs to confirm all documents are processed successfully. Verify no PARSE_FILE_TIMEOUT_SECONDS or other file processing errors occurred.
Perform precise queries for key fields (e.g., product model, clinical data, test standard numbers). Verify the system accurately recalls document snippets containing this field information. Evaluate the completeness 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.