Data Characteristics
Respiratory system disease quality documents primarily originate from pharmaceutical Good Manufacturing Practice (GMP) systems, Clinical Trial Records (CTR), regulatory guidelines, and academic journals or clinical guidelines. Updates are infrequent, but concentrated updates occur with new drug launches, clinical protocol changes, or regulatory policy adjustments. Documents are typically hierarchical PDFs, containing numerous charts, tables, and attachments. Text is highly specialized, frequently including medical terminology, drug names, dosage units (e.g., mg/kg, IU), time units (e.g., h, d), and clinical indicators (e.g., FEV1, SpO2). Data field naming conventions are generally standardized, but minor variations may exist across different document sources.
Constraints on Document Parsing and Chunking
The specialized and structured nature of respiratory system quality documents imposes specific requirements on document parsing and chunking. Complex charts and tables require parsers to accurately extract structured information, preventing their misidentification as plain text blocks. Specialized terminology and measurement units necessitate chunking strategies that prioritize semantic completeness, ensuring each chunk contains sufficient context to understand specific terms or data. Infrequent updates mean that once parsed, data remains valid for longer periods. However, efficient incremental parsing mechanisms are crucial during concentrated update events. Hierarchical PDF structures suggest incorporating chapter and heading levels into chunking to maintain logical clarity in the knowledge base. Standardized fields and units facilitate subsequent entity recognition and relation extraction, provided the chunking process preserves their integrity.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 500–800 characters | Ensures semantic completeness within a single chunk, covering most specialized terms and short sentences. |
Chunk Overlap | 100–150 characters | Connects context between different chunks, reducing information loss. |
Parsing Strategy | By Title and Text Structure | Adapts to chapter and heading levels in PDF documents, preserving the original logical structure. |
OCR Accuracy | High | Addresses potential scanned documents or text embedded in images. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Most respiratory system quality documents are large, requiring longer parsing times. |
Table Parsing Mode | Structured Extraction | Accurately identifies and extracts complex table data from documents. |
Common Pitfalls
- Slow vectorization after uploading PDFs to the knowledge base. This occurs when documents contain too many embedded images or complex PDF structures, leading to lengthy parsing times.
- Query text sometimes triggers the document parsing process. This happens when the system attempts to match all input to the document parser by default.
- Empty data processing or empty search results during training. This occurs due to document parsing failure, where no valid text blocks are successfully extracted.
Verification Steps
- Upload a typical respiratory system quality document (e.g., a clinical trial report). Check if the parsing generates the expected number and content of text blocks.
- Randomly select parsed text blocks. Verify content completeness, absence of truncation or garbled text, and correct preservation of specialized terminology and units.
- Perform search tests using specific medical terms or key data from the document. Observe the relevance and accuracy of recall results.
- Review system logs. Confirm no significant errors or timeouts during document parsing, and that the parsing status indicates success.
The values provided are common starting points. Measure them against your 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.