Document Parsing and Chunking for CDMO Clinical Trial

Data Characteristics in This Domain

CDMO (Contract Development and Manufacturing Organization) clinical trial pre-screening involves diverse data types. These primarily originate from clinical study protocols, investigator brochures, informed consent forms, and Case Report Form (CRF) templates provided by sponsors. These documents are typically in PDF or Word format, have complex structures, and contain extensive specialized terminology, dosage units, time points, exclusion/inclusion criteria, and adverse event reporting guidelines. Data update frequency is high, especially after clinical trial protocol revisions or safety report releases. Documents often feature nested tables, images, flowcharts, and non-standardized text descriptions. Fields and units are highly specialized and standardized, for example, drug concentration units like ng/mL, µg/L, time units like h, day, and specific disease diagnostic codes and trial phase identifiers.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

The complex structure of CDMO clinical trial pre-screening documents challenges document parsing, particularly text extraction from nested tables and images. Specialized terminology and non-standardized descriptions require more refined text chunking strategies. This ensures RAG (Retrieval Augmented Generation) recall captures complete semantic information and prevents critical information from being truncated. High update frequency demands that the parsing system supports efficient incremental updates, reducing the time from document update to knowledge base availability. Multiple file formats (e.g., DOCX, PDF) mean the parser needs robust compatibility. Furthermore, accurate identification and unit handling of key fields such as drug dosage and time points are prerequisites for accurate pre-screening. Chunking must pay special attention to the completeness of this structured information.

Recommended Configuration Settings

Configuration Item	Suggested Value	Rationale for This Value
`Chunk size`	`800–1200 characters`	Paragraphs in clinical protocols are often long, containing multiple conditions and descriptions. Longer chunks help maintain semantic completeness.
`Chunk Overlap Length`	`100–200 characters`	Ensures sufficient contextual overlap between adjacent chunks, preventing critical information from being cut off at chunk boundaries.
`Parsing Strategy`	`By Title and Paragraph Chunking`	Clinical documents typically have clear chapter and sub-heading structures. Chunking by these helps preserve document logic.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Large clinical protocol documents take longer to parse. A longer timeout prevents parsing interruptions.
`ENABLE_OCR`	`true`	Ensures text in tables and flowcharts within images can be recognized. Clinical documents often contain exclusion/inclusion criteria in image format.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical study protocols are often large files. Support for uploading large documents is necessary.

Three Common Mistakes

Some critical information is missing after document parsing, such as table data or image content not being indexed. This occurs because ENABLE_OCR is not enabled or the OCR engine's recognition capability for specific table image formats is insufficient.
API calls show success but return no parsing results. This may be because PARSE_FILE_TIMEOUT_SECONDS is set too short, causing large documents to time out before parsing completes, leading to background task termination.
Semantic incoherence after chunking, where retrieval results often contain only partial conditions or descriptions. This manifests as split errors or incomplete recall. This occurs because Chunk size is too short, leading to complex clinical judgment logic being incorrectly segmented.

How to Verify Correct Configuration

Select a clinical study protocol containing complex tables and flowcharts. Manually upload it and check the parsed text content to confirm all critical structured information has been correctly extracted.
Parse several documents in different formats (PDF, DOCX). Use backend logs to confirm no timeout errors occurred during parsing and check if corresponding chunks were generated in the knowledge base.
Perform multi-round Q&A tests for complex queries, such as clinical trial exclusion/inclusion criteria. Verify the completeness and accuracy of retrieval results to ensure the chunking strategy supports effective retrieval.
Monitor the processing speed of the backend parsing queue. Compare parsing times for documents of different sizes and complexities to confirm the system's responsiveness in high-frequency update scenarios.

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