Document Parsing and Chunking for Peptide Drug Quality

Data Characteristics

Peptide drug quality documents primarily originate from analytical reports, production batch records, stability study reports, and regulatory submission documents generated during drug development. These documents are typically in PDF format and contain extensive structured and semi-structured data. Update frequency correlates with the drug's development stage and lifecycle; new drug development phases see frequent updates, while post-market documents are relatively stable. Common sections include quality standards, test methods, test results, impurity profiles, and degradation product analysis. Fields and units are highly specialized, such as "Main Component Content (%)", "Peptide Purity (%)", "Molecular Weight (Da)", "Specific Rotation (°)", "pH Value", and "Retention Time (min)". Complex chemical formulas, chromatograms, and mass spectra are often embedded within these documents.

Constraints Imposed by These Characteristics on "Document Parsing and Chunking"

The specialized nature of peptide drug quality documents necessitates high-precision text recognition for document parsing, especially for chemical structures, specialized terminology, and special symbols. The presence of semi-structured data, such as test results and limit values in tables, requires the parser to accurately identify table boundaries, rows, and columns, and extract corresponding data. The challenge of document update frequency lies in the subtle but critical differences that may exist between old and new versions. The system needs to effectively identify and process these version changes. Furthermore, embedded images like chromatograms and mass spectra, while not directly text-searchable, contain critical information in their legends and related descriptive text. These associated texts must not be overlooked and need to maintain a logical connection with the image content to avoid information fragmentation.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Peptide drug quality documents can contain numerous charts and images, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex PDF document parsing can be time-consuming; allow sufficient processing time.
`Chunk size`	`800–1200 characters`	Balances contextual completeness and retrieval efficiency, accommodating the density of specialized peptide content.
`Chunk overlap`	`100 characters`	Ensures critical information at paragraph boundaries is not lost due to chunking.
`ocr`	`Enabled`	Ensures text in scanned documents or images, such as tables and legends, can be recognized.
`Table Parsing Mode`	`Structured`	Accurately extracts tabular data from quality standards for subsequent structured queries.

Common Pitfalls

System prompts "request failed" after uploading a large PDF document, usually due to PARSE_FILE_TIMEOUT_SECONDS being set too low, causing the parsing process to time out.
Some fields in tabular data are empty after uploading to the knowledge base. This may be because Table Parsing Mode was not set to Structured, preventing correct identification of table boundaries or cell contents.
Key detection limit values are missing from retrieval results. This is caused by Chunk size being too long or too short, leading to limit values being incorrectly separated from or merged with their corresponding test items.

Verification Steps

Upload a typical peptide drug quality document containing complex tables and legends. Check if the parsed knowledge chunks completely retain the row and column data of the tables.
Verify if the parser correctly identifies and extracts specialized terminology, chemical formulas, and values with special units from the document.
For a document with revision history, upload different versions and check if the system can distinguish and accurately parse key differences between versions.
Check if the legend text next to embedded images like chromatograms and mass spectra is correctly extracted and maintains a logical association with the related text.

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.