Document Parsing and Chunking for Stability Study

Data Characteristics

Stability study data originates from drug stability investigation reports, batch production records, inspection reports, and relevant regulatory documents. This data typically combines structured tables (e.g., Excel, CSV) and unstructured text (e.g., Word, PDF experimental protocols, summary reports). Update frequency aligns with batch production, annual reviews, or registration cycles, potentially quarterly, semi-annually, or annually. Document structures are complex, containing extensive specialized terminology, abbreviations, chemical formulas, and charts. Fields include temperature, humidity, batch number, production date, expiration date, test items (e.g., content, dissolution, impurities), test results, units (e.g., %, mg/mL, ppm), and statistical analysis data. Some data may exist as scanned images.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

Mixed data types in stability study documents require a document parser with multimodal processing capabilities, especially for recognizing tables and charts in scanned PDFs. The update frequency necessitates knowledge base support for incremental updates and version management to ensure the timeliness of declaration materials. Complex document structures and specialized terminology, such as "degradation products," "storage conditions," and "accelerated tests," demand chunking strategies that identify and preserve the integrity of related information, preventing semantic loss from over-segmentation. For instance, an experimental results table should not be split into multiple chunks. Accurate identification of fields and units is crucial for subsequent information extraction, particularly for numerical data, where correct association of units directly impacts result usability. Parsing detection curves or structural formulas in image format requires specific OCR and image recognition capabilities.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`800–1200 characters`	Balances semantic completeness with recall efficiency, avoiding information overload in long paragraphs.
`Chunk overlap` (Chunk Overlap)	`100–200 characters`	Ensures contextual continuity, especially when table or chart descriptions span multiple pages.
`File Types`	`PDF, DOCX, XLSX, JPG, PNG`	Covers common document and image formats in stability study reports.
`Parsing Mode`	`Smart Chunking, with Table Recognition enabled`	Addresses mixed text and table data, improving table data parsing accuracy.
`OCR Accuracy`	`High`	Improves recognition accuracy for text in scanned reports and images.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates parsing time for large or complex documents, preventing timeouts.

Common Pitfalls

Symptom: After uploading a PDF file, table data is not correctly recognized, or table rows are incorrectly split into different chunks. Reason: Table recognition is not enabled or incorrectly configured, or Chunk size (Chunk Length) is set too short, leading to table structure disruption.
Symptom: Parsing logs show an Unsupported file type error, even for common image formats. Reason: The system lacks or has not enabled the corresponding image processing plugin, or the file extension does not match the actual content.
Symptom: Text chunks containing chemical formulas or specialized abbreviations have low recall rates during retrieval. Reason: Chunk size (Chunk Length) is too short, causing complete semantic professional terms to be broken apart, dispersing semantic information during vectorization.

Verification Steps

Upload a typical stability study report PDF and examine the parsed knowledge chunks. Verify that table and chart descriptions are complete and semantically coherent.
Randomly select parsed knowledge chunks and check if numerical data and their units are correctly extracted and associated.
Perform knowledge base retrieval using key professional terms and abbreviations from the report. Evaluate the accuracy and relevance of recall results, then adjust the Similarity threshold (Similarity Threshold) based on recall performance.

The values provided are common starting points and should be measured against specific 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.