Document Parsing and Chunking for Live Attenuated Vaccine

Data Characteristics

Quality documents for live attenuated vaccines originate from R&D experimental reports, manufacturing batch records, quality control inspection reports, and registration dossiers. These documents are primarily in PDF format, with some being scanned images. Document updates are infrequent, mainly occurring during R&D and registration. Manufacturing updates are typically limited to batch-specific records. Document structures usually include standardized chapter titles, tabular data, graphical images, and detailed text descriptions. Fields include batch number, production date, expiration date, test items, test results, units (e.g., IU/mL, TCID50/mL, μg/mL), and specific parameter limits.

Constraints on Document Parsing and Chunking

Scanned images of live attenuated vaccine quality documents require OCR capabilities for text extraction. Standardized chapter structures and extensive tabular data mean chunking must prioritize table integrity, avoiding splits across rows or columns, while identifying and extracting key fields and values from tables. Embedded graphical images typically lack directly parsable text information; understanding requires combining them with nearby text descriptions. Specific biological units and parameter limits are strong signals for semantic association during chunking. Avoid splitting at points that would disrupt these associations to ensure subsequent retrieval accuracy. The low update frequency means batch processing efficiency for document parsing is not critical, but accuracy and completeness of retrieval are highly important.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates large attachments and high-resolution scanned images in a single registration dossier.
`Chunk size`	`800–1200 characters`	Preserves semantic completeness, especially for tables and experimental descriptions, avoiding excessive splitting.
`Chunk Overlap Length`	`150 characters`	Ensures contextual continuity and handles semantic dependencies across pages or paragraphs.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Manages OCR and structured parsing for large scanned PDFs, preventing timeouts due to lengthy processing.
`OCR_ENABLED`	`True`	Ensures text content in scanned PDFs is recognized and extracted.
`TABLE_EXTRACTION_ENABLED`	`True`	Accurately identifies and parses table structures in documents, extracting key data.

Common Pitfalls

Parsing timeout: Uploading large or complex scanned PDFs results in a 504 Gateway Timeout error because the parsing process takes too long. This occurs when PARSE_FILE_TIMEOUT_SECONDS is set too low and does not cover the actual time required for OCR and structured parsing.
Table content loss or corruption: Documents with complex tables result in incomplete or misaligned table data after parsing. This happens when TABLE_EXTRACTION_ENABLED is not enabled, or the table parsing algorithm fails to handle multi-level nested headers or merged cells effectively.
Semantic discontinuity: Chunked text segments lack complete context, leading to missing critical information, such as batch numbers separated from their corresponding test results. This is due to Chunk size being set too small, or Chunk Overlap Length being insufficient, failing to preserve semantically related paragraphs in vaccine quality documents.

How to Verify Configuration

Upload a typical live attenuated vaccine batch production record containing complex tables and scanned pages. Check if the parsed text completely retains the table structure.
Select paragraphs from the document that contain key biological units (e.g., TCID50/mL) and numerical values. Verify the integrity of these paragraphs after parsing.
Parse a large scanned PDF requiring OCR. Check parsing logs for timeout errors and confirm that parsing time is within the PARSE_FILE_TIMEOUT_SECONDS limit.

The values given 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.