Document Parsing and Chunking for CSO Registration and

Data Characteristics in This Category

Contract Sales Organizations (CSOs) prepare registration and submission materials using data from various sources. These include raw R&D data, clinical trial reports, non-clinical study reports, manufacturing process documents, quality standard documents, and draft drug inserts provided by pharmaceutical companies. This data often combines structured and unstructured documents in multiple formats, such as PDF, Word, Excel, and scanned images. Updates typically align with drug R&D and submission timelines, potentially iterating over months to years. Document structures are complex; for example, a clinical trial report might contain dozens of chapters, each subdivided into charts, text descriptions, and statistical data. Fields and units are highly specialized, covering pharmacology, toxicology, and clinical medicine. Examples include pharmacokinetic parameters (Cmax, Tmax, AUC), biostatistical indicators (P值, confidence interval), and dosage units (mg/kg, IU).

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

The nature of CSO registration and submission materials imposes several constraints on document parsing and chunking. First, diverse and heterogeneous document formats require parsers with strong compatibility to accurately identify and extract text content. This specifically includes OCR capabilities for tables and graphics within scanned documents. Second, the complex document structure and specialized fields mean that simple chunking by character count can disrupt semantic integrity, for example, by splitting a complete research result or table data. Chunking must consider semantic boundaries such as chapters, paragraphs, tables, and figure captions to ensure each chunk represents a relatively complete unit of information. Accurate identification of specialized fields and units directly impacts subsequent question-answering quality. The parser needs to distinguish between general text and specific technical terms, and even understand their contextual meaning. The uncertain update frequency requires the knowledge base to support incremental updates and version management, ensuring effective handling of new or modified sections without completely re-parsing all documents. The ability to parse text and tables within images is crucial, as many historical data or original reports exist as images, making information inaccessible through plain text parsing.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Registration and submission materials often include large clinical reports and imaging data; this ensures large files can be uploaded.
`Chunk size`	`800–1200 characters`	Balances semantic completeness and retrieval efficiency, preventing overly short chunks from losing context or overly long chunks from introducing irrelevant information.
`Chunk overlap`	`150–200 characters`	Ensures adequate contextual overlap between chunks, handling semantic dependencies that span across chunks.
`Enhanced PDF Parsing`	`True`	Registration and submission materials frequently contain PDFs with complex layouts, tables, and images; enhanced parsing improves accuracy.
`Image OCR Recognition`	`True`	Many original reports or charts are embedded as images; OCR is essential for extracting key information.
`Table Structured Extraction`	`True`	Clinical and quality control data often appear in tables; structured extraction is fundamental for precise question answering.

Three Common Mistakes

File parsing fails after upload, showing a "parsing" or "failed" status. This usually occurs because the uploaded file size exceeds the UPLOAD_FILE_MAX_SIZE limit, or the file format is not supported by the current parser, such as non-standard encoded PDFs.
Critical information is missing from the knowledge base, for example, table data or figure captions are not indexed. This happens when Enhanced PDF Parsing or Image OCR Recognition are not enabled, preventing the parser from handling complex layouts or text embedded in images.
Question-answering results show semantic fragmentation or insufficient context, meaning retrieved chunks cannot independently answer the question. This often results from setting Chunk size too short, failing to capture complete semantic units.

How to Verify Correct Configuration

Upload typical registration and submission documents (e.g., clinical trial report PDFs, Word-format non-clinical study reports) and check if chunks are correctly generated in the knowledge base. Verify that the content of each chunk is semantically complete.
For PDF files containing complex tables and images, upload them and check if table data and text from images are successfully extracted into the knowledge base. This can be verified by searching for keywords within tables or figure captions.
Select key information points from the document that span across chapters or paragraphs. Test the knowledge base's question-answering performance by asking questions, evaluating the contextual coherence and informational completeness of the retrieved chunks. Adjust Chunk size and Chunk overlap as needed.
Check parsing logs to ensure no error messages related to file size limits, unsupported file formats, or parsing timeouts appear, confirming a smooth parsing process.

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.