Data Characteristics
Pharmacovigilance data for high-value consumables originates from medical device manufacturer product manuals, clinical trial reports, real-world study data, post-market adverse event reports, and regulatory risk alerts. Documents are primarily in PDF format, with some Word documents or structured data files. Update frequency is relatively low, typically occurring after product batch updates, regulatory policy changes, or significant adverse events.
Document structures are well-defined. Product manuals include fixed sections such as product description, indications, contraindications, warnings, precautions, adverse events, and shelf life. Clinical reports contain background, methods, results, and discussion sections. Adverse event reports are often semi-structured text, including patient information, device information, adverse event descriptions, interventions, and outcomes.
Fields and units involve device models, batch numbers, manufacturing dates, expiration dates, implantation sites, adverse event types (e.g., infection, dislocation, fracture), occurrence times, severity grades (e.g., CTCAE v5.0), dosage, dimensions, and quantities. Units are typically international standard units or industry-specific units.
Constraints on Document Parsing and Chunking
The fixed chapter structure of high-value consumable documents allows for preprocessing using structural information, such as identifying sections based on titles or headers/footers. The low update frequency enables more detailed manual proofreading after parsing and reduces resource consumption from frequent re-parsing.
PDF documents often contain images, tables, and complex layouts, requiring robust parsing tools. Extracting adverse event data from tables, in particular, demands data completeness and accuracy. Narrative text in semi-structured adverse event reports requires chunking strategies that capture the complete context of an event, preventing critical information from being cut off.
Standardized fields and units facilitate entity recognition and information extraction after chunking, such as identifying device models like XYZ-123 or adverse event codes like T83.8. This lays the foundation for subsequent knowledge graph construction. Specific versions of CTCAE grading standards must be identified during parsing to ensure compatibility across different versions.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances the completeness of adverse event descriptions with contextual relevance during recall. |
Chunk Overlap Length | 100–150 characters | Ensures semantic continuity at chunk boundaries, preventing loss of critical information. |
Parsing Mode | Structured Priority | High-value consumable documents have relatively fixed structures; prioritizing structural information improves parsing accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time for large clinical reports or manuals. |
Knowledge base ID | Calibrated by actual measurement | Corresponds to specific high-value consumables or adverse event types, facilitating management and retrieval. |
chunk_id | Auto-generated | Ensures each chunk has a unique identifier for subsequent traceability and modification. |
Common Pitfalls
- In context references, document formats are not rendered as Markdown, leading to a poor reading experience. This often occurs when the parser fails to correctly identify or convert internal format tags in specific PDF or Word documents.
- Content cannot be parsed after providing a document link, resulting in parsing failure or timeout. This may be due to document link permission issues, network connection failures, or
PARSE_FILE_TIMEOUT_SECONDSbeing set too short for large documents. - Chunk IDs or knowledge base IDs in the knowledge base cannot be copied. This impacts the efficiency of engineers in quickly obtaining identifiers during debugging or building automated workflows.
Verification Steps
- Upload typical high-value consumable product manuals and adverse event reports. Check if the parsed chunks are complete, especially data within tables and complex layouts.
- Perform retrieval in a test environment. Observe if the returned chunks contain relevant adverse event descriptions, device models, and severity grades. Evaluate contextual coherence.
- Check parser logs to ensure no timeout errors due to
PARSE_FILE_TIMEOUT_SECONDSbeing set too low, and confirm thatchunk_idis correctly generated and associated.
The values given 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.