Document Parsing and Chunking for CRO Clinical Trial

Data Characteristics

Contract Research Organizations (CROs) handle data from various sources during clinical trial pre-screening. These sources include sponsor-provided study protocols, informed consent form templates, Case Report Form (CRF) designs, medical literature, and regulatory documents. Documents are typically in PDF, Word, or Excel formats, with PDFs being common. They contain extensive unstructured or semi-structured text. Data updates frequently, especially with protocol revisions, regulatory changes, or trial schedule adjustments. Document structures are complex, often featuring multiple heading levels, tables, figures, and footnotes. Field names vary and can include medical terminology, dosage units (e.g., mg/kg, IU), time units (e.g., days, weeks), and statistical indicators.

Constraints from Document Parsing and Chunking

The complex structure and high update frequency of CRO clinical trial pre-screening documents challenge document parsing. Large PDF documents require accurate text extraction, identification of heading hierarchies, table data, and key fields. Standard tokenization and entity recognition methods may not capture all information accurately due to prevalent medical terminology and specialized units. Document updates necessitate rapid identification of changes for incremental parsing, avoiding full reprocessing. Precise matching requirements in pre-screening, such as filtering subjects based on inclusion/exclusion criteria, demand fine-grained chunking. This ensures subsequent retrieval accurately targets specific clauses and avoids interference from irrelevant text in large segments. Structured data in Excel files requires preservation of row-column relationships for later extraction of specific data.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	CRO documents, such as study protocols, are often large and require support for uploading big files.
`Chunk size`	`500-800 characters`	Balances paragraph completeness with retrieval accuracy, avoiding noise from overly long paragraphs.
`Chunk overlap`	`100 characters`	Ensures critical information is not truncated at paragraph boundaries, improving recall.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Complex PDFs and large Excel files take longer to parse, requiring sufficient time.
`CHUNK_STRATEGY`	`Chunk by Title`	CRO documents often have clear heading hierarchies; chunking by title preserves semantic integrity.
`EXCEL_PARSING_MODE`	`Structured Extraction`	Inclusion/exclusion criteria and dosage tables in Excel files require table structure preservation for precise querying.

Common Pitfalls

Parsing status remains "processing" for an extended time or shows "request failed." This often indicates PARSE_FILE_TIMEOUT_SECONDS is set too short, preventing large documents from completing parsing within the default time.
Uploaded Excel file content parses into disjointed text, losing table row-column relationships. This prevents effective querying of specific fields. The cause is EXCEL_PARSING_MODE not set to structured extraction mode.
Retrieval results contain many text segments irrelevant to the query intent, or important clauses are not recalled. This occurs when Chunk size is too long or Chunk overlap is insufficient, leading to semantic unit disruption or omission of key information.

Verification Steps

Upload a typical study protocol PDF and an Excel file containing inclusion/exclusion criteria. Check parsing logs for timeout errors.
Use the knowledge base preview to review parsed chunks. Confirm accurate identification and preservation of heading hierarchies, table structures, and key medical terminology. Verify appropriate chunk granularity.
Construct queries for uploaded documents using specific inclusion/exclusion criteria, drug dosages, or trial endpoints. Validate the accuracy and completeness of retrieval results. Examples include querying for 乙肝表面抗原阴性 or Once daily 10mg.

The values provided are common starting points. Measure performance against your own samples to determine optimal settings.

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.