Characteristics of the Data
Data for home medical device regulations and SOP documents primarily comes from internal quality management system files, product manuals, registration application materials, and regulations issued by the National Medical Products Administration. These documents update at a relatively stable frequency, typically undergoing annual or irregular revisions during the product lifecycle due to regulatory changes or internal process optimizations. Document structures are hierarchical, featuring distinct chapters and clauses. They often include numerous tables, flowcharts, and product images. Fields and units are highly specialized, such as "sterilization cycle," "scope of application," "adverse event reporting period," and "detection accuracy ± X%," involving specific units of measurement, time units, and technical parameters.
Constraints Imposed by These Characteristics on "Document Parsing and Chunking"
The structured nature of home medical regulation documents requires accurate identification and preservation of chapter hierarchy during parsing. This prevents flattening that leads to context loss. For tables and flowcharts, visual parsing techniques are necessary to extract embedded text and associate it with adjacent content, ensuring knowledge completeness. The frequent appearance of specialized fields and units means chunking must keep this critical information within the same chunk to prevent semantic truncation. Although regulatory document updates are infrequent, each update may involve revisions to key clauses. Therefore, the parsing system needs to support incremental parsing and version management to ensure the timeliness and accuracy of the knowledge base.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Ensures the completeness of professional terminology and short flowchart descriptions, reducing context breaks. |
Overlap Length | 150–200 characters | Improves semantic continuity between paragraphs, preventing critical information from being split. |
Parsing Mode | Chunk and Preserve Structure | Prioritizes retaining the original chapter and clause hierarchy, suitable for normative texts. |
Image OCR Recognition | Enabled (Enabled) | Extracts text from flowcharts and equipment diagrams as supplementary knowledge. |
Parsing Timeout | 600 seconds | Accommodates the time required to parse PDF documents with numerous tables and complex layouts. |
Text Cleaning Rules | Remove Headers, Footers, Table of Contents | Focuses on the main body content, reducing noise impact on RAG recall. |
Three Common Mistakes
- Table information is missing from parsing results. This occurs when question-answering results cannot reference key data or steps from images. The cause is not enabling or configuring image Optical Character Recognition (OCR).
- Key clauses or specialized terms are truncated, leading to incomplete or semantically biased question-answering results. This manifests as important phrases appearing only partially in the quoted snippets. The likely cause is setting
Chunk size(Chunk Length) too small. - PDF document parsing is unresponsive or errors out for extended periods. This appears as parsing tasks remaining in a "processing" state or returning a "parsing failed" error code. The cause is a complex document combined with an insufficient
Parsing Timeoutsetting.
How to Confirm Correct Configuration
- Randomly select 10 home medical regulation documents. Check if the parsed text completely retains all chapter titles, numbers, and corresponding content.
- Verify that text information from embedded diagrams (e.g., flowcharts, structural diagrams) in the parsed results has been correctly identified and integrated into the text content via OCR technology.
- For paragraphs containing specialized terms and units of measurement, confirm that their semantic integrity is maintained after chunking, with no critical information truncated.
- Simulate questions using the parsed knowledge base. Cross-reference the cited original snippets in the answers to ensure high contextual relevance and no obvious semantic errors.
The values provided are common starting points. Measure against your own samples for optimal results.
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.