Vector Model and Indexing for Home Medical Device

Data Characteristics for This Category

Home medical device registration and declaration documents come from various sources. These include product technical requirements, inspection reports, clinical evaluation data, risk management reports, instruction manuals, and labels. Documents are typically in PDF, Word, or image formats. Data update frequency is relatively low, primarily occurring during product iterations, regulatory revisions, or supplementary applications. Document structure is highly standardized, adhering to National Medical Products Administration (NMPA) template requirements. Examples include the "Medical Device Registration Application Form" and performance indicators and testing methods in product technical requirements. Fields and units exhibit strong professional and standardized characteristics, such as "Rated Voltage (V)," "Measurement Range (mmHg)," and "Accuracy (±%)." Some fields may contain charts or complex formula descriptions.

Constraints Imposed by These Characteristics on Vector Models and Indexing

The standardized structure and specialized terminology of home medical device documents require vector models to accurately capture entity relationships and professional vocabulary semantics. Documents contain numerous tables, diagrams, and complex formulas. Traditional text segmentation methods may lead to information loss or context fragmentation. This necessitates more refined text preprocessing and segmentation strategies. The standardized nature of fields and units means indexing must pay special attention to the association between values and units. This avoids recall bias due to unit differences. The low update frequency allows for stable operation for extended periods after index construction. However, updates require efficient incremental indexing or full reconstruction mechanisms. High accuracy requirements mean similarity threshold settings must be more carefully considered to ensure the precision of recall results.

Configuration Settings

Configuration Item	Suggested Value	Rationale for Value
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Balances contextual completeness with vector model processing capabilities, preventing information overload in a single segment.
`Chunk overlap` (Segment Overlap)	`100–200 characters` (characters)	Ensures continuous context at segment boundaries, improving recall, especially for cross-paragraph information retrieval.
`Recall count` (Number of Recalled Items)	`Top 5–8 entries` (top 5–8 items)	Considers both retrieval efficiency and result coverage, ensuring no critical information is missed.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adjust this value based on actual recall results and business needs to balance precision and recall.
`Rerank result count` (Number of Reranked Items)	`3–5 entries` (3–5 items)	Further refines recall results, enhancing the relevance of information presented to the user.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Accommodates large PDF files or declaration documents containing numerous image attachments.

Three Common Mistakes

Symptom: Retrieval results contain a large amount of irrelevant or low-relevance content. Reason: The Similarity threshold (Similarity Threshold) is set too low, failing to effectively filter noise.
Symptom: When uploading large declaration documents, the system times out or indicates the file is too large. Reason: PARSE_FILE_TIMEOUT_SECONDS or UPLOAD_FILE_MAX_SIZE parameters are incorrectly configured, failing to meet actual file processing requirements.
Symptom: Custom index content does not take effect; retrieval still relies on the general knowledge base. Reason: The activation status of the custom index is not correctly set to active, or the associated knowledge base is not correctly bound.

How to Confirm Proper Configuration

Upload typical home medical device registration and declaration documents. Check if all documents can be parsed and indexed normally.
Perform searches for key technical indicators, regulatory terms, and risk points within the documents. Verify the relevance and accuracy of recall results against expectations.
Test query statements of varying complexity. Verify the system's ability to accurately identify specialized terms, units, and values, and recall document snippets containing this information.
Monitor system logs to confirm no abnormal errors or performance bottlenecks occur during index construction and retrieval.

The values provided are common starting points and should be measured against specific 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.