Document Parsing and Chunking for CDMO Quality Documents

Data Characteristics

CDMO (Contract Development and Manufacturing Organization) quality documents cover the entire lifecycle of pharmaceutical and medical device products, from R&D to production and testing. Data sources are diverse. These include Batch Records, Test Reports, Deviation Investigation Reports, Change Control Documents, Quality Agreements, and Standard Operating Procedures (SOPs). Most documents are in PDF, Word, or Excel formats. Some scanned documents or historical records are image-based. Update frequency depends on project progress, regulatory requirements, and internal process changes. For example, batch records generate with each product batch, while SOPs may revise annually or as needed. Document structures are rigorous, typically including titles, chapter numbers, tables, figures, and signature pages. Fields include batch number, product name, test item, result, unit, deviation description, and root cause analysis. Units involve mass (mg, g, kg), volume (mL, L), concentration (%, ppm), and time (min, h), often with specific measurement symbols and abbreviations.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

The standardization and diversity of CDMO quality documents impose specific requirements on document parsing and chunking. First, identifying structured tables and figures is critical. Traditional text chunking methods can incorrectly split table content or lose context. Second, documents contain numerous technical terms, abbreviations, and specific units. Chunking must preserve their complete semantics, avoiding information fragmentation due to improper split points. For example, core entity information like batch numbers and product names must remain intact within a single chunk for accurate retrieval. Third, frequent document updates require incremental parsing and version management capabilities to keep the knowledge base synchronized with the latest documents. Finally, the presence of scanned and image-based documents necessitates OCR (Optical Character Recognition) capability during parsing, processing OCR results alongside text content for comprehensive coverage.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size` (Chunk Length)	`800–1200 characters`	Ensures the completeness of core information blocks like batch records and test reports, balancing retrieval efficiency.
`overlap_size` (Overlap Length)	`100–200 characters`	Connects context between different chunks, especially for continuous texts like SOPs and deviation reports.
`parse_table` (Table Parsing)	`true`	Identifies and structurally extracts key table data from production batch records and test reports.
`ocr_enabled` (OCR Recognition)	`true`	Processes historical scanned documents and image-format quality documents, such as some older batch records or signature pages.
`max_file_size` (Maximum File Size)	`500 MB`	Accommodates large quality agreements and annual reports, ensuring successful upload.
`timeout_seconds` (Parsing Timeout)	`600 seconds`	Allows sufficient time to parse complex PDF documents containing numerous tables and images.

Three Common Mistakes

Importing non-text content, such as Java API documentation, results in empty or garbled content after parsing. The parser defaults to natural language text and cannot correctly identify and extract programming language syntax.
Uploading large PDF documents leads to a prolonged "indexing" status. This can be due to complex document content (e.g., many images and tables) or the file size exceeding system processing capacity, causing the parsing process to take too long or terminate.
Parsed chunks have incomplete semantics, leading to inaccurate retrieval. This occurs when chunk_size is set too small, splitting a complete concept or critical entity across different chunks.

How to Verify Configuration

Import CDMO quality documents of different types (PDF, Word, scanned) and complexities (containing tables, images, plain text). Check if corresponding chunks are generated in the knowledge base.
Randomly select chunks from the knowledge base. Verify their content completeness and semantic coherence, paying close attention to whether table data and technical terms are correctly parsed and retained.
Use the knowledge base's retrieval function to search for key batch numbers, product names, test items, or deviation descriptions from the documents. Check the accuracy and relevance of the returned results. Adjust recall_count and similarity_threshold based on actual retrieval needs.

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.