Document Parsing and Chunking for Intelligent Triage System

Characteristics of the Data Category

Data for an intelligent triage system's registration and declaration preparation comes from multiple sources. Core data includes official documents such as medical device registration certificates, product technical requirements, clinical trial reports, user operation manuals, and risk management reports. Supplementary materials might include medical literature, disease diagnosis and treatment guidelines, and expert consensuses. These documents have a low update frequency, primarily changing during product iterations or regulatory policy adjustments. Document structures are highly standardized, adhering to templates and format requirements from regulatory bodies like the National Medical Products Administration (NMPA). Documents contain extensive professional terminology, medical abbreviations, units of measurement (e.g., mg/dL, mmHg), and complex tables and figures.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

The standardized and professional nature of intelligent triage registration and declaration materials places high demands on document parsing. First, their highly structured characteristic requires precise identification of semantic boundaries for different chapters, paragraphs, and even table cells to prevent information confusion. Second, the large volume of professional terminology and abbreviations requires the parser to have strong domain vocabulary recognition capabilities; otherwise, it can lead to incorrect word segmentation or loss of critical information. For example, abbreviations like ECG and CT must be correctly identified as single entities. Third, complex tables and figures, especially those involving diagnostic criteria, treatment processes, or performance indicators, require specialized table parsing capabilities to ensure data integrity and correlation. Finally, the low update frequency necessitates the ability to manage and parse historical document versions to trace differences between versions.

Configuration Settings

Configuration Item	Suggested Value	Rationale for this Value
`Chunk size`	`800–1200 characters`	Registration documents often contain long logical paragraphs. This length maintains contextual integrity and balances recall efficiency.
`Chunk overlap`	`100–200 characters`	Ensures semantic continuity between adjacent chunks, preventing critical information from being cut at chunk boundaries.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Large clinical trial reports or technical documents take a long time to parse, requiring sufficient processing time.
`CHUNK_STRATEGY`	`By Title and Paragraph`	Registration documents have a strict structure. Chunking by title and paragraph better preserves the original document logic.
`TABLE_RECOGNITION_ENABLED`	`true`	Many diagnostic standards and performance indicators are presented in tables. Table recognition is crucial for ensuring information completeness.
`OCR_ENABLED`	`true`	Some older or scanned declaration materials may contain image-based text. OCR ensures comprehensive parsing.

Three Common Mistakes

Files remain in a "processing" state for a long time after upload, or ultimately fail to parse. This is typically due to PARSE_FILE_TIMEOUT_SECONDS being set too low, which does not accommodate the parsing time for large documents.
Knowledge base retrieval results are missing or incomplete for table data related to the query. This occurs because TABLE_RECOGNITION_ENABLED is not enabled, or the table parsing algorithm cannot effectively handle complex nested table structures.
Queries for specific medical terms do not reflect their professional context in the retrieval results. This may be because Chunk size (chunk length) is too short, causing the complete context of the term to be split across different chunks.

How to Confirm Correct Configuration

Select a registration and declaration document containing complex tables and multi-level headings. After uploading, check if specific data within tables can be retrieved from the knowledge base, and if content under each heading level is correctly chunked.
Upload a document with handwritten annotations or scanned pages. Verify if the OCR function successfully recognized and extracted text information from the images by querying keywords found in the images.
Query for unique medical terms or abbreviations within the document. Check if the retrieved chunk content fully includes the definition or related description of the term, and if chunk boundaries are reasonable.

The values provided are common starting points and should be measured against the reader's 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.