Document Parsing and Chunking for SMO Registration and

Data Characteristics in this Category

Site Management Organizations (SMO) prepare registration and declaration documents. The data involved primarily comes from clinical trial protocols, investigator brochures, and informed consent forms (ICF) provided by sponsors. Internal documents from the SMO cover site management, ethical review, and investigator training. These documents are often in PDF format and contain both structured and unstructured information. Structured information includes data fields from Case Report Forms (CRF) and exported data from Electronic Data Capture (EDC) systems. Unstructured information includes investigator notes, scanned ethical approval documents, and meeting minutes. Document updates are frequent, especially in multi-center clinical trials, where protocol amendments, ICF version changes, and supplementary ethical approvals are common. Fields and units typically follow medical and pharmaceutical standards, such as dose units (mg, ml), time units (days, weeks), and numerical ranges with clear upper and lower limits.

Constraints Imposed by these Characteristics on Document Parsing and Chunking

SMO documents come from diverse sources and update frequently. This requires the document parsing module to be highly robust, capable of handling various PDF formats and layouts, and effectively identifying new or modified content. The abundance of unstructured information makes traditional rule-based parsing inefficient, necessitating more intelligent text extraction and chunking strategies. For example, scanned ethical approval documents contain image-based information that requires OCR technology for text recognition. In long documents like clinical trial protocols, key information is dispersed. This demands an appropriate chunking granularity to maintain contextual completeness while avoiding excessively large chunks that could hinder retrieval efficiency. The standardization of fields and units challenges entity recognition and information extraction. Models must accurately identify medical terminology, dosage units, their associated values, and process tabular data to return padansData in a structured format.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	SMO registration and declaration materials often include multiple large PDF files. This ensures sufficient single-file upload capacity.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing time can be long for PDF files containing many images or complex tables.
`Chunk size`	`800–1200 characters`	Balances contextual completeness with retrieval efficiency, avoiding overly long or short segments.
`Similarity threshold`	`0.75`	Improves the relevance of retrieval results and reduces interference from irrelevant information.
`maxContext`	`3000 tokens`	Accommodates the specialized nature and contextual dependencies of medical texts, providing more comprehensive background information.
`Rerank result count`	`Top 5 entries`	Focuses on the few most relevant results, improving the efficiency of subsequent processing.

Three Common Mistakes

After uploading a PDF file, the knowledge base parsing status remains "parsing" for an extended period or ultimately shows "parsing failed." This usually occurs because the file content is complex or too large, exceeding the default PARSE_FILE_TIMEOUT_SECONDS setting.
When parsing PDF documents containing tables, the table content returns as mixed plain text instead of structured data. This usually happens when the parser fails to effectively recognize the table structure within the PDF, or the padansData extraction strategy is not optimized for tables.
The model cannot recognize text information within images. This occurs when the image recognition function is not enabled or incorrectly configured, preventing the OCR module from being called.

How to Verify Correct Configuration

Upload a test PDF file containing complex tables and scanned documents. Check if the parsing status eventually displays "ready."
Query the parsed document to verify if the model can accurately extract and present specific data from tables, such as padansData.
Upload a PDF file containing clear image-based text. Query for the text within the image to confirm that the model can correctly recognize and output it.

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.