Data Characteristics
Market access quality documents in the biopharmaceutical sector originate from regulatory agencies (regulations, guidelines, technical review requirements) and internal company data (registration dossiers, clinical trial reports, manufacturing process documents). These documents update infrequently, typically with policy revisions or product lifecycle changes. Document structures are highly standardized, often chapter-based and clause-driven. They contain extensive specialized terminology, acronyms, tables, formulas, and statutory units of measurement. Examples include drug registration approvals, manufacturing licenses, and GMP (Good Manufacturing Practice) certificates. Content is rigorous, demanding high accuracy and consistency. Common fields include dosage, concentration, batch number, expiry date, test methods, and quality standards, with units strictly adhering to international or industry-specific standards.
Constraints on Document Parsing and Chunking
The standardized structure of market access documents requires parsers to accurately identify chapters, headings, and clauses, maintaining their logical hierarchy. Specialized terminology and acronyms demand strong contextual understanding from the parser to prevent semantic fragmentation during chunking. Numerous tables and formulas necessitate robust OCR (Optical Character Recognition) and formula recognition capabilities, directly impacting information extraction completeness. Strict statutory units of measurement require the parsing results to accurately retain unit information, preventing incorrect truncation or omission during chunking. The infrequent but critical updates mean initial parsing must be precise. Subsequent incremental updates can then focus on identifying and integrating changes.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500-800 characters | Balances clause completeness with recall accuracy. Avoids semantic dilution from overly long text and context loss from overly short text. |
Chunk Overlap Length (Overlap Length) | 100-150 characters | Ensures contextual continuity across chunks, especially at clause transitions. |
Enable Title Recognition | True | Market access documents have clear hierarchical structures. Title recognition helps maintain logical integrity during chunking. |
File Type Whitelist | pdf, docx, xml, txt | Covers common regulatory, guideline, and internal document formats, ensuring parsing scope. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large PDFs or complex structured documents, preventing timeouts. |
OCR_ENABLED | True | Ensures tables, formulas, and text from scanned images are recognized and parsed. |
Common Pitfalls
- PDF preview error "Cannot read file content": This occurs if the PDF is an image-based or encrypted file, preventing direct text layer extraction. Enable the OCR module or decrypt the file.
- Incomplete display of formulas or units in parsing results: This indicates insufficient recognition capability for complex mathematical formulas or special characters, or truncation in the middle of a unit during chunking.
- Inaccurate recall for complex clauses: This happens if the chunk length is set too short, splitting a complete clause across multiple chunks and losing critical contextual information.
Verification Steps
- Randomly select multiple typical documents. Check if the parsed text content is complete and accurate, especially for tables, formulas, and specialized terminology.
- For documents with multi-level headings, verify that chunking results adhere to the original chapter logic and hierarchical relationships.
- Test with queries containing specific units of measurement or acronyms. Evaluate the accuracy and completeness of these key details in the recall results.
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.