Document Parsing and Chunking for Pharmacovigilance in

Data Characteristics

Pharmacovigilance data in academic promotion primarily comes from clinical study reports, post-market surveillance reports, real-world evidence (RWE) analyses, and pharmacovigilance literature. These documents are typically in PDF, DOCX, or RTF formats. Update frequency correlates with the drug's lifecycle and regulatory requirements. New drugs have intensive clinical trial data before market approval, followed by continuous safety updates post-market. Document structures usually include standardized sections like "Adverse Event Listings," "Safety Summaries," and "Risk Management Plans." Fields cover patient demographics, adverse reaction terms (e.g., MedDRA codes), dosage information, event start/end dates, and causality assessments. Units commonly used are milligrams (mg) and grams (g) for dosage, and days, months, and years for time.

Constraints from "Document Parsing and Chunking"

Standardized chapter structures in academic promotion documents require the parser to identify and prioritize content in specific areas, such as adverse event descriptions. High update frequency necessitates knowledge base support for incremental updates and version management, ensuring parsed data timeliness. Diverse file formats (PDF, DOCX) demand robust parser compatibility to accurately extract text and table content. Tables, in particular, often contain detailed lists and statistics of adverse events. Accurate table parsing directly impacts subsequent Q&A quality. Key fields like causality assessments often appear as descriptive text. This requires a fine-grained chunking strategy to preserve context and avoid losing important semantic connections due to over-chunking. Additionally, recognizing and retaining specialized terms like MedDRA codes is crucial for accurate understanding of pharmacovigilance information.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Ensures the upload of detailed reports containing numerous charts and tables.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates complex parsing processes for large PDF or DOCX documents, preventing timeout errors.
`Chunk size`	`800–1200 characters`	Balances contextual completeness and retrieval efficiency, maintaining semantic coherence of adverse event descriptions.
`Chunk overlap`	`100 characters`	Ensures no information loss at chunk boundaries, especially in descriptive text.
`Table Parsing Mode`	`Structured Extraction`	Accurately identifies table boundaries, rows, and columns, preserving internal table structure.
`Custom Segmentation Rules`	`Enabled`	Applies more detailed chunking strategies for specific sections (e.g., "Adverse Event Summary").

Common Mistakes

When uploading large PDFs or complex DOCX files, a timeout of 360000ms exceeded error indicates that the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, not allowing enough time for the parser to process.
If knowledge base Q&A results do not return adverse reaction descriptions verbatim from the document, but rather AI-summarized versions, this often results from a Chunk size that is too small, causing key descriptions to be broken up, or a retrieval strategy that does not enforce exact text matching.
If table content in some DOCX documents cannot be correctly chunked or recognized after upload, appearing as lost table data, this usually means Table Parsing Mode was not set to Structured Extraction, or the document itself contains non-standard tables.

How to Verify Configuration

Upload a typical academic promotion report containing complex tables and detailed adverse event descriptions. Check whether the parsed knowledge base chunks fully retain table row and column information, as well as the context of key descriptive text.
Perform Q&A tests against the knowledge base to verify that the system can accurately retrieve and return the "answer" portion from the original text, without AI modification. This assesses the effectiveness of Chunk size and the retrieval strategy.
Monitor system logs for timeout errors after setting PARSE_FILE_TIMEOUT_SECONDS, especially during peak times or when processing maximum file sizes, to ensure the parser has sufficient processing time.

The values provided are common starting points and should be measured against the reader's 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.