Data Characteristics
IVD diagnostic reagent pharmacovigilance data originates from clinical trial reports, post-market surveillance reports, user manuals, instructions, and regulatory guidelines. These documents update frequently. Instructions and user manuals, in particular, may revise due to product iterations, new adverse event findings, or regulatory requirements. Document structures typically include product basic information, intended use, contraindications, precautions, adverse event lists, operating procedures, and result interpretation sections. Fields often include adverse event descriptions, occurrence times, and patient information. They also frequently involve diagnostic results, reagent batch numbers, instrument serial numbers, and detection method specificity and sensitivity. Units cover international units, concentration units (e.g., mg/dL, mmol/L), and time units, often accompanied by specific reference ranges.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
IVD diagnostic reagent documents are structured and semi-structured. This demands precise document parsing. Key metadata, such as product batch numbers and instrument serial numbers, require accurate extraction and association for subsequent traceability analysis. Adverse event lists in instructions often appear as tables or enumerated lists. Precise chunk boundary identification is necessary to avoid information omission or misinterpretation. Technical parameters, like detection method specificity and sensitivity, are often embedded in complex descriptive text, challenging information extraction accuracy. Document update frequency is high, requiring the parsing system to support efficient incremental updates and handle version differences. Identifying reference ranges requires the parser to understand numerical and unit combinations and process their context, ensuring data completeness.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500-800 characters | Balances the completeness of adverse event descriptions with retrieval relevance, preventing excessive truncation. |
Chunk Overlap Length | 50 characters | Ensures contextual continuity, especially when parsing tables or lists, to avoid information loss. |
Parsing Strategy | Structured Priority | Prioritizes identification and processing of chapter titles and table structures in instructions and reports. |
Metadata Extraction Rules | Calibrate by actual measurement | Configures regular expressions or keyword rules for specific fields like batch numbers and serial numbers. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Most documents parse within 5 minutes; this reserves extra time for large or complex documents. |
MAX_CHUNK_NUM_PER_FILE | 2000 | Limits the number of chunks generated per document, preventing resource exhaustion from oversized documents. |
Common Pitfalls
- PDF documents uploaded may contain image content that is not recognized or extracted. This leads to critical data loss from charts and graphs. The default parser primarily focuses on text content and lacks sufficient optical character recognition (OCR) support for embedded image text.
- Knowledge base answer accuracy is low, especially for DOCX and Excel files. These file formats have complex internal structures. The default parser may fail to correctly identify table boundaries or merged cells, resulting in disorganized information chunks.
- Retrieval results lack critical contextual information, such as associated diagnostic results or reagent batch numbers in adverse event reports. This happens when metadata extraction rules are not configured or correctly applied during document parsing. Important associated information is not stored as independent fields.
How to Verify Configuration
- Select representative IVD diagnostic reagent instructions and reports. Upload them to the knowledge base. Check if the number of chunks for each document meets expectations.
- Perform keyword searches on the uploaded documents. Verify if retrieval results include text embedded in document images to assess OCR functionality.
- Ask questions about specific adverse events. Check if the returned answers accurately include key metadata like reagent batch numbers and diagnostic results, and verify their associations.
- Choose a clinical trial report containing complex tables. Check if table content is correctly parsed and chunked, avoiding incorrect merging of table rows or columns.
Note: 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.