Document Parsing and Chunking for Stability Study

Data Characteristics

Stability study regulation documents in the biopharmaceutical industry originate from experimental records, analysis reports, and quality standards generated during drug research, development, production, and quality control. These documents are typically stored as PDFs, containing extensive tabular data, charts, and normative text. The update frequency is relatively low, usually occurring with drug registration submissions, production process changes, or regulatory adjustments. Document structure is rigorous, often including chapter titles, subtitles, and appendices. Fields involve batch numbers, production dates, expiration dates, storage conditions, test items, test methods, test results (e.g., content, purity, dissolution, pH value), and their units (e.g., %, mg/mL, °C, RH%).

Constraints Imposed by These Characteristics on Document Parsing and Chunking

The rigorous structure and extensive tabular data in stability study documents require high-precision structured information extraction capabilities from document parsing services. This prevents tabular content from being incorrectly parsed as plain text. The specialized terminology and abbreviations present challenges for chunking strategies, requiring the preservation of complete professional context. Low update frequency means that once parsed successfully, the knowledge base is stable, but the accuracy of initial parsing is critical. The combination of test result fields and their units, such as "98.5%" or "5.0 mg/mL," requires chunking to keep the numerical value and unit together as a single entity, preventing information fragmentation that leads to semantic deviation. The presence of charts necessitates considering the association between image OCR recognition and descriptive text.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Stability study reports often contain multi-page high-resolution charts and large files, requiring support for uploads.
`Chunk Length`	`800–1200 characters`	Ensures that experimental methods, results, and conclusions of stability studies are within the same chunk, maintaining semantic completeness.
`Chunk Overlap`	`100–200 characters`	Ensures key information (e.g., batch numbers, test items) has contextual relevance between adjacent chunks, addressing tables spanning multiple pages.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF documents, especially those with complex tables and charts, requires longer processing times.
`Enable PDF High-Precision Parsing`	`On`	Table and chart content in stability study reports is crucial; high-precision parsing effectively identifies structured data.
`Knowledge Base Chunking Strategy`	`By Markdown Heading`	Stability study documents typically have clear heading hierarchies; chunking by heading better preserves chapter integrity.

Three Common Mistakes

After uploading a document, the system displays "OCR Error" or "Parsing failed." This occurs because the marker_pdf service, when processing high-resolution images or complex tables, encounters insufficient backend OCR engine resources or timeouts.
In the parsed knowledge base, table data is incorrectly split into multiple disconnected text segments, leading to inaccurate query results. This happens when Chunk Length is set too small, failing to include the entire table content within a single chunk.
In a private deployment environment, after uploading a large stability report PDF, the FastGPT interface reports a parsing timeout, but parsing service logs show success. This is typically due to network latency between FastGPT and the parsing service or PARSE_FILE_TIMEOUT_SECONDS being configured shorter than the actual transmission and processing time.

How to Confirm Correct Configuration

Upload a typical stability study report PDF. Check the parsed knowledge base chunks to ensure table content is fully preserved in one or a few chunks, and numerical values and units are not separated.
Perform keyword searches for specialized terminology and abbreviations within the document. Verify that recalled chunks include complete contextual explanations.
Check parsing logs for "OCR Error" or "Parsing Timeout" messages, especially for documents close to the UPLOAD_FILE_MAX_SIZE limit.
Select a key experimental batch number or test item from the document and conduct a Q&A test. Verify that the system accurately returns the test results and relevant regulatory requirements for that batch, comparing the accuracy of the recalled chunks.

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.