Document Parsing and Chunking for Peptide Drug Registration

Data Characteristics

Peptide drug registration submissions draw from diverse data sources. These include pharmaceutical research (CMC), non-clinical studies, and clinical studies. The CMC section covers synthesis processes, purification, quality standards, and stability. It often involves image data like reaction equations, chromatograms, mass spectra, and infrared spectra. It also includes extensive structural formulas, sequence information, and physicochemical property data. Non-clinical and clinical data encompass pharmacodynamics, toxicology, pharmacokinetic reports, clinical trial protocols, patient medical records, and statistical analysis reports. These contain large amounts of tabular data and specialized medical terminology. Data update frequency is typically low, concentrating on key milestones during the R&D phase, such as process optimization, batch scale-up, and clinical trial results. Document structures strictly adhere to regulatory guidelines from agencies like NMPA, FDA, and EMA, featuring highly standardized chapters and sub-chapters. Field units include molar concentration mol/L, mass concentration mg/mL, temperature ℃, time h, and pH values, all requiring strict precision.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

The standardized document structure of peptide drug submissions requires parsers to accurately identify chapter hierarchies and prevent content confusion. Image data, especially chromatograms and mass spectra, may contain critical raw data and analysis results. Parsers must effectively extract text information from images or perform image semantic understanding. The large number of chemical structural formulas and peptide sequences challenges text tokenization and entity recognition. Parsers must avoid truncating complete structures or sequences. Tabular data is extensive and complex, involving multi-column relationships. Parsing must preserve the table's row and column structure to prevent data isolation. The dense use of specialized terminology means that chunking must maintain contextual integrity to prevent semantic fragmentation. The low update frequency makes high-quality, one-time parsing particularly important, reducing the need for subsequent manual intervention. Precision requirements for fields and units necessitate that the parser correctly identifies and associates values with their units during extraction.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Peptide drug submission documents often contain numerous high-resolution images and large PDF files. This ensures single file uploads are not restricted.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing images and tabular data in complex PDFs requires a longer duration. This prevents parsing timeouts.
`Chunk size`	`800 characters`	This balances the integrity of short texts like peptide sequences and chemical structural formulas while preserving the context of table rows.
`Chunk Overlap Length`	`100 characters`	This ensures contextual continuity at chunk boundaries, especially when spanning figures, tables, or paragraphs.
`ENABLE_OCR`	`true`	This enables the recognition of text information within images such as chromatograms and mass spectra, as well as content in scanned documents.
`MAX_TABLE_ROWS_PER_CHUNK`	`10 Row`	This prevents individual chunks from becoming overloaded with excessively long table data while retaining local table context.

Common Pitfalls

413 Request Entity Too Large error when uploading large PDF files: This typically occurs because the UPLOAD_FILE_MAX_SIZE parameter is set too low, causing the server to reject oversized files.
Truncated peptide sequences or chemical structural formulas after parsing, leading to incomplete matches during retrieval: This happens when Chunk size is set too small, splitting critical information into different chunks and destroying semantic integrity.
Text content within images like chromatograms and mass spectra in PDFs is not extracted, resulting in incomplete retrieval results: This usually indicates that the ENABLE_OCR function is not enabled or the OCR engine is improperly configured.

Verification Steps

Select typical submission document samples containing various data types (text, tables, images). Upload them and check if the parsed chunks completely retain critical information, especially peptide sequences, chemical structural formulas, and table row/column structures.
Perform keyword searches on the parsed chunk content. Verify if queries including image text can recall relevant chunks and if specific numerical values in tables can be accurately identified and recalled.
Review parsing logs to ensure no timeout or parsing failed errors occur. Monitor resource consumption during large file parsing.

The values provided are common starting points. They 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.