Document Parsing and Chunking for Peptide Drug Pharmacovigilance

Data Characteristics

Peptide drug pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE) data, drug inserts, medical literature, and adverse drug reaction (ADR) reporting systems. This data updates frequently, especially post-market launch, with a continuous influx of ADR reports. Document structures vary. Clinical trial reports are typically structured PDFs or Word documents containing detailed patient information, treatment regimens, adverse event descriptions, and laboratory results. Some content appears in tables. Medical literature often consists of unstructured research papers, with adverse reaction information scattered throughout the text, figures, and appendices. ADR reports are usually semi-structured XML or text files, including fields such as patient identifiers, drug names, dosages, administration routes, adverse event terms (e.g., MedDRA codes), occurrence times, and outcomes. Dosage units frequently involve milligrams (mg), micrograms (µg), and units (U). Reports often contain medical abbreviations and specialized terminology.

Constraints Imposed by Data Characteristics on Document Parsing and Chunking

The wide range of peptide drug data sources leads to diverse document formats, impacting the efficiency of unified parsing. Scanned PDFs and image-based reports, in particular, require high-quality Optical Character Recognition (OCR) processing to ensure text content integrity. The high frequency of incoming ADR reports necessitates real-time processing capabilities to prevent information lag. Complex table structures within documents, such as adverse event lists in clinical trial reports, require the parser to accurately identify table boundaries, row and column relationships, and extract cell content. The abundance of medical terminology and abbreviations challenges the semantic integrity of chunks, requiring that critical medical concepts are not fragmented. Furthermore, unique peptide drug dosage units (e.g., U) and administration route descriptions require the parser to retain this key information during chunking for subsequent entity recognition and relationship extraction.

Configuration Settings

Common Pitfalls

• After uploading a scanned PDF, knowledge base search results are empty. This occurs because OCR is not enabled, preventing image content from being recognized as text.
• After knowledge base chunking, peptide drug dosage information (e.g., "100 U") is fragmented across different chunks. This happens when Chunk size is set too small, and Overlap Length is insufficient to cover key phrases.
• After uploading multiple ADR report files, the system indicates that some files failed to parse with status code 504 Gateway Timeout. This is because PARSE_TIMEOUT_SECONDS is set too short, unable to handle the parsing time for long files or complex structures.

Verification Steps

• Randomly select peptide drug clinical trial reports, drug inserts, and ADR reports. Upload them to the knowledge base and verify that each document successfully parses and generates chunks.
• Parse PDF files containing complex tables. Check that table content is correctly extracted and that key data within tables (e.g., adverse event incidence, dosage) is retained in the chunks.
• Search the knowledge base for specific peptide drug dosages, adverse reaction terms (e.g., "nausea," "vomiting"), and administration routes. Verify that the recalled chunks contain this key information and maintain semantic integrity.
• Upload documents containing medical abbreviations (e.g., "QD," "BID"). Verify that the parsed chunks correctly identify and retain these abbreviations or expand them into full terms.

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.