Data Characteristics for this Category
Stability study data primarily comes from research reports, batch production records, analytical method validation reports, retained sample observation records, and product specifications. These documents are typically in PDF, Word, or Excel formats. Data updates are infrequent, occurring mainly at critical points in a product's lifecycle, such as new drug applications, batch releases, or annual reviews. Document structures are rigorous, containing extensive tabular data, charts, and text descriptions. Key fields include batch number, production date, expiration date, storage conditions, test items, test methods, test results (e.g., content, purity, dissolution rate), deviations, conclusions, and units (e.g., %, ug/mL, ℃, RH). Some data appears as spectrograms or chromatograms, requiring image content recognition.
Constraints Imposed by these Characteristics on Document Parsing and Chunking
The characteristics of stability study data impose specific requirements on document parsing and chunking. First, complex table structures and charts in documents require enhanced parsing capabilities to ensure data completeness and accuracy, preventing the omission of critical values or batch numbers. Second, the diversity and specialized nature of field names and units demand that the parser correctly identify and associate them, for example, recognizing "relative humidity" as RH. Infrequent document updates mean that initial configuration accuracy is crucial, as subsequent adjustments are costly. Additionally, conclusive text in reports is often scattered across different sections, requiring intelligent contextual chunking to ensure the model fully understands stability trends and risk assessments. For spectrograms or chromatograms, image content recognition is necessary to convert them into retrievable text information.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 100 MB | Stability study reports often contain numerous charts and high-resolution images, leading to large file sizes. |
Chunk size (Chunk Length) | 800–1200 characters | Ensures each chunk contains sufficient contextual information, such as a complete experimental result description or table row. |
Chunk Overlap Length (Chunk Overlap Length) | 100–200 characters | Prevents critical information from being split across different chunks, improving recall, especially between table rows. |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Parsing complex PDF and Word documents requires significant time, preventing parsing interruptions. |
ENABLE_TABLE_EXTRACTION | true | Stability data is often presented in tables; table extraction effectively preserves structured information. |
IMAGE_OCR_ENABLED | true | Some stability data exists as images, such as chromatograms and gel electrophoresis images, requiring OCR recognition. |
Three Common Pitfalls
- Table data misalignment or omission in parsing results: This manifests as the model failing to correctly extract critical batch data. The cause is often insufficient parsing capability for complex table structures, leading to incorrect recognition of row and column relationships.
- The model fails to understand specialized terminology or abbreviations in stability reports: This appears as inaccurate explanations of specific test items or storage conditions in question-answering results. The reason is a lack of pre-training or specialized dictionaries for these terms in the knowledge base.
- Large file parsing failures or timeouts: Logs show
PARSE_FILE_TIMEOUT_SECONDSerrors. This occurs when uploaded report files are too large or contain numerous embedded objects, exceeding the default parsing time limit.
How to Confirm Correct Configuration
- Upload a typical stability study report and verify that the chunked content in the knowledge base fully retains key tabular data and chart descriptions from the report.
- Ask questions about specialized terminology and abbreviations from the report to confirm the model accurately understands and provides relevant information, for example, inquiring about the meaning of a specific test item.
- Upload a PDF file containing complex tables and charts, check if the parsing status is successful, and confirm that numerical values and units in the document are correctly identified.
- Randomly select several chunks and examine if chunk boundaries are reasonable, avoiding inappropriate truncation of critical information.
The values provided are common starting points and should be measured 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.