Document Parsing and Chunking for CMC Research Registration

Data Characteristics

CMC (Chemistry, Manufacturing, and Controls) research registration and submission documents primarily use data from internal documents. These include experimental reports, quality standards, manufacturing process specifications, and stability study reports. These documents originate from drug development and manufacturing processes. They typically exist in various formats like PDF, Word, and Excel. Update frequency correlates with the R&D phase, from preclinical studies to post-market change applications.

Document structures are highly standardized. They follow ICH Q-series guidelines and national drug regulatory requirements, such as the CTD format. Data fields include chemical structure information, physicochemical properties, production batches, purity, impurity profiles, content, and dissolution rates. Units include percentages (%), milligrams (mg), micrograms (µg), degrees Celsius (°C), pH values, and time units (hours, days, months). Precision requirements are extremely high.

Constraints from these Characteristics on Document Parsing and Chunking

The standardized structure of CMC documents requires accurate identification of chapter levels and content attribution during parsing, especially for data in tables and charts. PDF documents often contain scanned images. This requires OCR technology to ensure text readability while retaining original images for contextual reference, preventing information loss. Excel files with multiple sheet structures mean the parser must differentiate and extract data from different tabs. High data precision requires parsing results to have no numerical deviations or unit confusion.

Irregular update frequencies require the system to have incremental update capabilities. It must quickly identify and process changes. Additionally, numerous technical terms and abbreviations pose challenges for word segmentation and entity recognition. This demands more refined text processing strategies to ensure accurate knowledge base recall.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Single CMC files can be large, containing high-resolution images and numerous tables.
`Chunk size`	`800–1200 characters`	Balances paragraph integrity and retrieval efficiency for CMC documents, avoiding cutting off important information.
`Chunk overlap`	`100–200 characters`	Ensures contextual continuity, especially for cross-paragraph references or explanations.
`PDF_OCR_ENABLED`	`true`	Processes scanned PDFs and embedded image text, ensuring all content is searchable.
`PARSE_TABLES_AS_TEXT`	`true`	Converts table content to text, making structured data understandable and retrievable by the model.
`SHEET_NAME_TAGGING`	`true`	Tags Excel `sheet` names to differentiate data sources and contexts from different tabs.

Three Common Mistakes

Image content is lost or only OCR text remains after document parsing. Original charts are not viewable. This occurs due to improper image retention configuration or incorrect image processing mode.
After importing an Excel file, data from different sheet pages is mixed up, or content from specific tabs cannot be retrieved individually. This happens when the parser fails to effectively identify and differentiate the multi-sheet structure of Excel files.
Document chunking is unreasonable after parsing, leading to missing context or fragmented information during retrieval. This is because the Chunk size setting is too short or does not adequately consider the chapter logic of CMC documents.

How to Verify Configuration

Randomly select various types of CMC submission documents. Upload them and check if the parsed text content is complete, especially for charts, tables, and technical terms.
Import a file containing multiple Excel sheets. Verify that the knowledge base can perform precise retrieval using sheet names.
Compare the parsed text's chapter structure and paragraph boundaries against the original document. Ensure that the Chunk size and Chunk overlap settings effectively preserve semantic integrity.
Use FastGPT's knowledge base retrieval function. Input specific data points or keywords from CMC documents. Verify that the recall results are accurate and include necessary contextual information.

Note: The values provided are common starting points. Measure them against specific 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.