Document Parsing and Chunking for siRNA Nucleic Acid Drug

Data Characteristics

siRNA nucleic acid drug pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE) data, post-market surveillance reports, and safety updates from global drug regulatory agencies. This data exists in both structured formats (e.g., database exports, CSV, Excel) and unstructured formats (e.g., PDF case reports, medical literature, regulatory announcements). Update frequency is high. Post-market surveillance reports may be quarterly or annual, while individual severe adverse event reports are immediate. Document structures are complex. They contain medical terminology, dosage information, administration routes, patient characteristics, adverse event descriptions, severity assessments, and causality judgments. Standardized adverse event terms (e.g., MedDRA codes) and dosage units (e.g., mg/kg, µg/kg) are specialized and specific.

Constraints on Document Parsing and Chunking

The high update frequency of siRNA nucleic acid drug adverse event data requires efficient incremental processing capabilities to avoid redundant parsing and resource waste. Complex document structures, especially the presence of medical terminology and codes, mean simple text chunking can disrupt semantic integrity. For example, a complete adverse event description or a critical MedDRA code might be separated from its context. Dosage and unit specificity, such as mg/kg or µg/kg, requires chunking strategies that identify and preserve these critical numerical values. This prevents truncation or misinterpretation during splitting. Table and chart content within unstructured PDF documents, particularly clinical data summary tables, challenge traditional parsing tools. This requires more intelligent layout recognition and data extraction capabilities.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size`	`800–1200 characters`	Retains complete adverse event descriptions, MedDRA codes, and context. Prevents truncation of critical information.
`Chunk Overlap Length`	`100–200 characters`	Ensures contextual coherence. Handles complex medical narratives and dosage information spanning paragraphs.
`File Type Whitelist`	`['.pdf', '.csv', '.xlsx', '.txt']`	Covers common clinical reports, regulatory documents, and data export formats.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Accommodates parsing times for large clinical trial reports or PDFs with extensive tables.
`EnabledTable Parsing`	`Yes`	Extracts critical dosage and adverse event summary table data from PDFs and Excels.
`Recall count`	`Top 5–8 entries`	Balances recall rate with the context window limitations of large models. Focuses on relevant adverse reaction information.

Common Pitfalls

When parsing Excel documents, the large model returns "incomplete command or request". The Excel file is treated as plain text, not a data table. The model cannot understand its structure. This occurs because the parser fails to correctly identify the data area or header in the Excel file, or table parsing is not enabled.
After document chunking, drug dosages or adverse event codes appear incomplete or truncated in search results, for example, 20 mg/k. Critical numerical information is lost. This occurs because Chunk size is set too short. It fails to retain numerical information with specific units as a whole.
After uploading large PDF reports, the system is unresponsive for an extended period or times out. The parsing task status shows failure or stagnation. This occurs because PARSE_FILE_TIMEOUT_SECONDS is set too low. It is insufficient to process reports with many pages or complex layouts.

Verification Steps

Upload a representative siRNA nucleic acid drug clinical report (PDF format). Check if the parsed chunks completely retain adverse event descriptions, MedDRA codes, and dosage information.
Upload an Excel file containing adverse reaction summary data. Verify if the parser correctly identifies and extracts table content. Ensure the granularity of each data block meets subsequent query requirements.
Perform retrieval tests on the parsed knowledge base. Use query terms containing specific dosages, adverse event names, or drug batch numbers. Check if recall results are accurate and contextually complete.
Observe system response time when parsing large documents. Confirm tasks complete within the PARSE_FILE_TIMEOUT_SECONDS threshold without timeout errors.

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.