Data Characteristics in this Category
Registration and declaration materials in the health management sector come from diverse sources and have varying update frequencies. These materials primarily include user health records, physical examination reports, disease risk assessments, intervention plans, and relevant medical guidelines and regulatory documents. User health records and physical examination reports typically exist as structured or semi-structured documents (e.g., PDF reports, scanned images). Fields include physiological indicators (blood pressure, blood sugar), lifestyle habits, and family medical history, with units involving both SI units and common medical units. Medical guidelines and regulatory documents are mostly unstructured text, updated less frequently, but contain highly specialized terminology and numerous tables and figures.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The characteristics of health management data impose specific requirements on document parsing and chunking. First, PDF-formatted physical examination reports and health records contain dense tabular data. Parsing these can lead to misalignment, affecting data integrity. Second, professional terminology and concepts in unstructured medical guidelines require semantic completeness during chunking to prevent critical information from being truncated. Third, some materials may include scanned images, demanding high accuracy from OCR recognition. Furthermore, data privacy compliance requires the document parsing process to occur in a localized environment, preventing sensitive health data leakage. Upon parsing failure, the system must precisely identify the problem, such as exceeding file size limits or unsupported formats.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large physical examination reports or compressed archives containing multiple files. |
Chunk size (Chunk Length) | 800–1200 characters (characters) | Balances semantic completeness and retrieval efficiency. Avoids loss of context from overly short chunks and noise from overly long chunks. |
Delimiter | \n\n or 。 | Prioritizes natural paragraph breaks to ensure the independence of semantic units in Chinese contexts. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Provides sufficient time for OCR recognition and parsing of complex PDFs or scanned images. |
max_tokens | 4000 tokens | Adapts to the health management domain's characteristic of extensive professional terminology and longer contexts, ensuring no information loss. |
enable_local_parsing | true | Ensures sensitive health data is parsed locally, complying with privacy requirements. |
Three Common Pitfalls
- Table content in PDF files appears misaligned or data is lost after parsing. This occurs because the parser fails to correctly identify the table structure or the chunking strategy is not optimized for tables.
- Uploading large health record files results in the parsing process being unresponsive for extended periods or reporting timeout errors. This is typically due to file size or parsing time exceeding system configuration limits.
- File parsing fails during a conversation, and the system returns a non-specific error message. This may stem from backend service restrictions on file types, sizes, or concurrent parsing quantities.
How to Confirm Correct Configuration
- Upload a health report PDF containing complex tables. Check if the parsed chunks fully retain table data and are correctly aligned.
- Upload a file exceeding the
UPLOAD_FILE_MAX_SIZElimit. Confirm if the system returns a clear file size exceeded error. - Upload a medical guideline containing extensive professional terminology. Check if the retrieved chunks in the knowledge base maintain the integrity and contextual relevance of the terminology. Experiment with adjusting the
Similarity threshold(similarity threshold) to observe changes in retrieval effectiveness.
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.