Document Parsing and Chunking for SMO Products

Data Characteristics in this Category

Documents for Site Management Organization (SMO) products primarily originate from clinical trial protocols, investigator brochures, informed consent forms, ethics committee approvals, and contractual agreements. These documents have a relatively low update frequency, typically occurring during project initiation, protocol amendments, or regulatory updates. Document structures are complex, containing extensive specialized terminology, abbreviations, and tables. Examples include drug dosage units (mg/kg, g/m²), trial durations (weeks, days), and visit schedules (day N, week N). File formats vary, with PDF, Word, and Excel being common. Data often embeds medical images or charts, requiring special processing to extract effective information.

Constraints Imposed by these Characteristics on Document Parsing and Chunking

The complex structure and specialized nature of SMO documents demand advanced document parsing capabilities. Extensive professional vocabulary and abbreviations require dictionary assistance to ensure accurate tokenization. Excel files may contain multi-dimensional data, making traditional text chunking methods inadequate for maintaining data integrity. Charts and images in PDF documents cannot be directly parsed as text, necessitating image recognition or OCR, which increases processing time and resource consumption. Document update frequency is low, but single updates can be substantial, making incremental parsing efficiency and version management critical. Accurate identification of fields and units is essential for subsequent knowledge retrieval and inference; incorrect parsing leads to information deviation.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters`	Balances contextual completeness and retrieval efficiency, preventing overly long or short chunks.
`Chunk Overlap Length` (Chunk Overlap Length)	`100–150 characters`	Ensures contextual continuity, especially at the boundaries of specialized terms and tables.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	SMO documents are often large and complex, requiring a longer parsing time to avoid timeouts.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Supports uploading large clinical trial protocols and investigator brochures.
`Parsing Strategy`	`Table Recognition Priority`	Tables in Excel and PDF are core information carriers; their structure and content require correct parsing.
`EnabledOCR` (Enable OCR)	`True`	PDFs often contain scanned documents or image-based text; OCR ensures this text is recognized.

Three Common Mistakes

Parsing takes too long, resulting in request failure or timeout. This occurs when PARSE_FILE_TIMEOUT_SECONDS is set too low for complex documents.
Excel file parsing results in disorganized or lost table data. This happens when the parsing strategy is not optimized for table structures, treating table content as plain text.
Retrieval results contain a large amount of irrelevant or duplicate information. This is due to Chunk size (Chunk Length) being too small, leading to excessive context splitting, or Chunk Overlap Length (Chunk Overlap Length) being inappropriate.

How to Verify Correct Configuration

Upload a PDF file containing complex tables and multiple pages. Check if the parsed text chunks retain table structures and key information.
Upload a large Excel file. Review parsing logs to confirm no HTTP 504 Gateway Timeout or Parsing Failed errors occurred.
Randomly select several documents containing specialized terms and units. Use keyword retrieval to verify that relevant chunks are accurately recalled.

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.