Data Characteristics in this Domain
Pharmacovigilance documents include laws, regulations, guidelines, and technical specifications published by national and regional drug regulatory authorities. They also include internal pharmacovigilance management systems, Standard Operating Procedures (SOPs), Risk Management Plans (RMPs), and safety reports (e.g., PSURs, DSURs). These documents are primarily in PDF format, with some in Word or plain text. Laws and guidelines are updated infrequently, typically annually or ad-hoc for major events. Internal SOPs are revised more frequently, usually quarterly or semi-annually, due to regulatory updates or internal process optimizations. Document structures are complex, containing specialized terminology, abbreviations, tables, figures, and cross-references. Fields include drug names, active ingredients, indications, adverse reactions, report types, timestamps, dosage units, and reporter information. Units are often international standard units or medical professional conventions.
Constraints from Document Parsing and Chunking
The complex structure and update frequency of pharmacovigilance documents demand robust document parsing. Extensive specialized terminology and abbreviations require the parser to accurately identify and maintain context, preventing semantic loss from improper chunking. Frequently revised SOPs necessitate support for rapid incremental updates and version management to ensure knowledge base timeliness. Common tables and figures in documents require special handling to preserve their structured information. Frequent cross-references and long paragraphs mean that simple paragraph-based or fixed-length chunking can sever critical information chains. Additionally, for pharmaceutical and medical units like mg/kg or IU/mL, chunking must ensure the integrity of values and units, preventing their separation into different chunks and impacting subsequent retrieval accuracy.
Configuration Strategy
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_strategy | semantic | Pharmacovigilance documents are highly specialized; semantic chunking better preserves contextual integrity. |
max_chunk_size | 800–1200 characters | Balances the integrity of long paragraphs with the information density of a single chunk, avoiding the cutting of critical regulatory clauses. |
overlap_size | 100–150 characters | Ensures sufficient contextual overlap between adjacent chunks, addressing cross-references and long sentences. |
table_parsing_mode | strict | Tables in pharmacovigilance documents contain critical data and require strict parsing to retain structure. |
pdf_ocr_enabled | true | Handles scanned regulatory documents or older SOPs, ensuring text extractability. |
document_update_interval | Calibrate based on actual measurements | Dynamically adjust knowledge base synchronization cycles based on enterprise SOP and regulatory update frequency. |
Common Pitfalls
- Table data misalignment or omission in parsing results. AI responses about table content are inaccurate. This occurs when table parsing mode is not enabled or configured incorrectly, leading to loss of table structural information.
- Retrieved chunks lack context. AI cannot understand complex logical relationships in pharmacovigilance regulations. This occurs when chunk length is too short or overlap is insufficient, severing critical semantic chains.
- Timeout or out-of-memory errors when parsing large PDF documents. File uploads fail or parsing status remains stuck for extended periods. This occurs when
PARSE_FILE_TIMEOUT_SECONDSis set too low or system resources are insufficient to handle high-complexity documents.
Verification Steps
- Upload a typical pharmacovigilance SOP document and check the chunk preview to confirm the integrity of table content and long paragraphs.
- Select multiple documents containing cross-references, perform test queries, and observe if AI responses accurately link contextual information.
- Upload different revised versions of regulatory documents to verify if the incrementally updated knowledge base content reflects the latest version.
The values provided are common starting points and should be measured 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.