Document Parsing and Chunking for Regulatory Affairs and

Data Characteristics

Regulatory affairs and pharmacovigilance data primarily originate from clinical trial reports, non-clinical safety evaluation reports, post-marketing adverse event (AE/SAE) reports, and regulatory guidelines. These documents are typically in PDF, Word, or scanned image formats. Updates are driven by drug development cycles and regulatory requirements. For example, clinical trial reports are submitted after trials conclude, and annual post-marketing reports are updated regularly. Document structures vary, often including numerous tables, figures, and nested sections. Key fields include medical terms, drug generic names, batch numbers, dosages, adverse event names, occurrence times, and severity. Units involved include dosage (mg, g), frequency (times/day), and time (days, weeks, years).

Constraints on Document Parsing and Chunking

The complexity of regulatory affairs and pharmacovigilance documents demands advanced parsing capabilities. The diverse sources and unstructured nature make traditional rule-based parsing inefficient. Extensive table and figure content requires specialized parsing to prevent data loss or misalignment. The specialized and varied nature of fields, such as medical terminology for adverse events and drug batch numbers, requires high-accuracy recognition to avoid semantic misinterpretation. Uncertain update frequencies and the need for historical document archiving add to data management complexity, requiring parsing systems to support batch processing and incremental updates. Strict accuracy is required for parsing results; any error can lead to non-compliance in submissions, posing regulatory risks.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size`	`800–1200 characters`	Regulatory documents are dense and contain multiple key pieces of information. This length retains contextual semantics well while preventing information overload from overly long segments.
`Chunk Overlap Length`	`100–200 characters`	Ensures semantic continuity between paragraphs, preventing critical information from being cut off, especially near table or figure descriptions.
`Parsing Mode`	`Smart Parsing`	Handles various document formats like PDF, Word, and scanned images, particularly reports with complex tables and multi-column layouts.
`OCR Recognition Accuracy`	`High`	For scanned and image-based reports, ensures accurate extraction of critical information such as medical terms and batch numbers.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Large clinical trial or annual reports can be substantial in size, requiring longer parsing times. This prevents timeouts during processing.
`MAX_EXTRACT_TABLE_ROWS`	`500 Row`	Many adverse event report tables can contain numerous entries. This setting ensures that most table data is fully extracted.

Common Pitfalls

Key fields (e.g., drug batch number, adverse event occurrence time) in parsing results are empty or incorrectly formatted. This can happen if the document parsing node fails to correctly identify table structures or if regular expressions for specific fields do not match.
After uploading a large PDF document, the system remains unresponsive for an extended period or returns a 504 Gateway Timeout error. This is often due to the PARSE_FILE_TIMEOUT_SECONDS parameter being set too low, not allowing enough time for the parser to process the file.
After front-end packaging and deployment, the document parsing node fails to access file content and reports a 404 Not Found error. This can be caused by file storage path configuration issues or insufficient permissions, preventing the parsing service from accessing uploaded files.

Verification Steps

Select various typical regulatory submission documents (e.g., clinical trial reports, SAE reports). Parse them and check the accuracy of key field extraction.
Upload PDF documents containing complex tables and figures. Verify that table data and figure descriptions are fully retained after parsing.
Push files of different sizes through the API for parsing. Observe the parsing time to confirm it is within an acceptable range.
Check parsing logs to ensure no error messages such as OCR_FAILURE or PARSE_ERROR appear.

The values provided are common starting points and should be measured against specific 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.