Document Parsing and Chunking for Pharmacovigilance in

Data Characteristics in This Domain

Pharmacovigilance data in hospital operations originates from several sources: daily audit reports by clinical pharmacists, adverse drug reaction (ADR) monitoring reports, drug instruction updates, early warning information from national drug regulatory agencies, and internal hospital drug management policies. Document update frequencies vary. ADR reports can be generated at any time. Drug instructions or management policies might update quarterly or annually.

Document structures are diverse. They include unstructured free-text descriptions, semi-structured tabular data (e.g., patient information, medication details, ADR manifestations, treatment measures), and structured fields like drug batch numbers and production dates. Field names may use abbreviations or differ across departments. Units include dosage (mg, g, ml), frequency (times/day), and time (hours, days).

Constraints Imposed by These Characteristics on "Document Parsing and Chunking"

The diversity of pharmacovigilance documents in hospital operations demands high-precision document parsing. Identifying key information in unstructured ADR reports is challenging. It requires precise extraction of ADR symptoms, medication use, and patient comorbidities from long texts.

Parsing tabular data is also difficult due to inconsistent table styles, which can lead to misalignment or field recognition errors, affecting key indicator extraction. Uncertain update frequencies require the parsing system to support incremental updates and version management.

High-accuracy recognition is crucial for specific fields like drug names and batch numbers to prevent errors in pharmacovigilance decisions. Document lengths vary significantly, from multi-page ADR reports to dozens of pages for drug instructions. This directly impacts chunking strategies, requiring a balance between information completeness and retrieval efficiency.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`Chunk size` (Chunk Length)	800–1200 characters	Balances the completeness of adverse reaction descriptions with the information density of a single chunk. Avoids redundancy from overly long chunks and loss of context from overly short ones.
`Chunk Overlap Length` (Chunk Overlap Length)	100–200 characters	Ensures contextual continuity at chunk boundaries, especially when tables or long sentences span across chunks, thereby improving recall.
`Separator`	`\n\n` or `。` `；` `！` `？`	Prioritizes paragraph-based splitting, combined with Chinese sentence-ending punctuation, to maintain semantic integrity.
`Table Parsing Mode` (Table Parsing Mode)	Smart recognition combined with region specification	Addresses inconsistent table structures. Attempts smart recognition first, then allows manual specification of table regions if recognition fails.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Provides sufficient processing time for large PDF documents or complex table parsing, preventing timeouts.
`Custom Parsing Service API`	`https://your_ocr_service/parse`	Integrates with professional OCR or table parsing services to enhance processing accuracy for complex documents.

Three Common Mistakes

Table content appears misaligned or missing in the preview after uploading a PDF file. This occurs because the system's default separators or parsing modes cannot accurately recognize complex table structures.
Document parsing takes too long or results in a timeout error. This might be due to PARSE_FILE_TIMEOUT_SECONDS being set too short, preventing the processing of very large files or documents with many images.
File parsing fails during a conversation, returning a generic error code 500. This indicates that the parsing service is not configured correctly or its internal dependent services are unresponsive.

How to Confirm Correct Configuration

Upload a typical adverse reaction report PDF. Check if the preview content matches the original, especially ensuring table data is complete and correctly aligned.
Upload a multi-page drug instruction document containing long text. Observe the parsing service's processing time to ensure it completes within the PARSE_FILE_TIMEOUT_SECONDS threshold.
Upload a document via the API interface and call the parsing service. Verify that the returned chunks comply with the configured Chunk size (Chunk Length) and Chunk Overlap Length (Chunk Overlap Length) settings. Check if key fields (e.g., drug names, symptom descriptions) are accurately extracted.

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.