Data Characteristics
R&D documents in health management originate from clinical trial reports, health assessment questionnaires, intervention plan designs, user health data analysis reports, and scientific literature. These documents update frequently, especially clinical trials and user health data analysis, which may generate new data daily or weekly. Document structures typically include structured chapter headings, tabular data, chart descriptions, and extensive unstructured text. Fields and units vary, with common metrics like blood pressure (mmHg), blood glucose (mmol/L or mg/dL), BMI (kg/m²), heart rate (beats/minute), and medication dosage (mg, IU) often appearing with multiple unit systems. Documents also contain numerous medical terms, abbreviations, and domain-specific jargon.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
High-frequency updates require an efficient, automated document parsing system to quickly ingest and process newly uploaded documents. Diverse document structures necessitate flexible parsing strategies that can identify standard sections and extract critical information embedded in unstructured text, such as experimental results or user feedback. Varying fields and units demand that the parser accurately identify numerical values and their corresponding units, supporting unit conversion or standardization to prevent ambiguity. The abundance of medical terms and abbreviations challenges tokenization and entity recognition, requiring pre-configured or learned domain-specific vocabularies to ensure semantic integrity of chunks. Additionally, due to data sensitivity, parsing processes must strictly adhere to data security and privacy regulations.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances semantic completeness and recall efficiency, preventing individual chunks from being overloaded or too fragmented. |
Chunk overlap | 50–100 characters | Ensures contextual continuity and prevents critical information from being truncated at chunk boundaries. |
Parsing Mode | Smart Chunking | Prioritizes using the document's inherent structural information for chunking to improve accuracy. |
OCR识别 | Enabled | Processes scanned or image-based R&D reports to ensure completeness. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates potentially long processing times for large clinical reports or complex charts. |
maxContext | 3000 Tokens | Ensures the model receives sufficient context to handle complex medical concepts. |
Common Pitfalls
- A "cannot read file content" message during preview typically indicates an incompatible file encoding format or corrupted document content.
- PPT or PDF files failing to synchronize after knowledge base configuration often means the backend OCR service is not correctly started or configured, preventing content extraction from these non-text formats.
- AI model issues arising after file parsing, while normal chat functions correctly, usually stem from poor quality parsed text containing excessive noise or chaotic formatting, making it difficult for the model to understand.
Verification Steps
- Upload various types (PDF, DOCX, TXT) of health management R&D documents. Check if the chunk preview displays content correctly and if chunk boundaries align with semantic logic.
- For documents containing tables and charts, examine OCR recognition results to ensure tabular data and chart captions are accurately extracted.
- Randomly select parsed chunks and compare them against the original document. Confirm that key medical terms, numerical values, and their units are fully preserved without significant errors or omissions.
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.