Document Parsing and Chunking for CSO Pharmacovigilance

Data Characteristics

Contract Sales Organizations (CSO) in pharmacovigilance primarily use data from pharmaceutical partners. This data includes clinical trial reports, post-market surveillance data, individual case safety reports (ICSRs), and medical literature. Documents are typically in PDF, Word, or scanned image formats, containing both structured and unstructured information. Update frequency varies from daily batches to monthly summaries, depending on partner business needs and regulatory requirements. Document content often includes patient demographics, drug information, adverse event descriptions, medical assessments, and outcomes. Common specialized fields and units include medical terminology, drug batch numbers, dosage units (e.g., mg, ml), and event timestamps (down to hours or minutes).

Constraints on Document Parsing and Chunking

CSO pharmacovigilance document characteristics impose specific requirements on parsing and chunking. Diverse file formats, especially scanned images, require robust OCR capabilities for complete text extraction. High-frequency updates demand a parsing pipeline with high throughput and low latency to process new documents quickly. Documents mix structured data (like tables) and unstructured information (like free-text descriptions). This means chunking strategies cannot rely solely on paragraph splitting; they must identify and preserve table context. Specialized medical terminology requires that text chunks maintain sufficient semantic integrity to avoid splitting critical information. Precise time and dosage units require the parser to accurately recognize number-unit combinations and treat them as a single entity. This prevents loss of critical modifying information during subsequent retrieval.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates large documents like clinical trial reports, ensuring files can be uploaded.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient time for complex PDF or scanned image OCR, preventing parsing timeouts.
`Chunk Length`	`800–1200 characters`	Balances semantic completeness with retrieval efficiency. This ensures critical information, such as adverse event descriptions, is not excessively fragmented.
`Chunk Overlap Length`	`100 characters`	Maintains contextual continuity and reduces semantic loss from crucial information being cut off.
`Enable Table Parsing`	Enabled	Pharmacovigilance documents often contain important tabular data, such as dosages and patient characteristics.
`Parsing Concurrency`	Calibrate by measurement	Optimizes processing throughput based on server resources and document update frequency.

Common Pitfalls

An API file upload may report success but return no data. This usually indicates a file parsing timeout or an unsupported format, preventing the parser from extracting valid content.
Log errors related to split often indicate that the document chunking strategy failed to handle specific structures, such as nested tables or long lists. This causes the chunker to error.
Parsing speed significantly below expectations can result from insufficient concurrency configuration or a file parsing module not optimized for large numbers of scanned images or complex PDFs. This leads to a backlog in the processing queue.

Verification Steps

Upload typical pharmacovigilance documents in various formats (PDF, Word, scanned images) and sizes. Check if parsing results are complete, especially for table data and medical terminology extraction.
Upload a batch of documents via API. Monitor the parsing module's log output to confirm no timeout or chunking-related errors appear.
Perform keyword searches on parsed documents. Verify the accuracy and contextual completeness of retrieval results, particularly for snippets involving adverse event descriptions and drug dosage information.
Monitor the processing speed of the parsing queue. Ensure documents are processed promptly during peak periods, avoiding prolonged waiting times.

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.