Data Characteristics
Stability research data primarily originates from long-term or accelerated stability study reports. These reports cover different batches and conditions during drug development. Reports are typically in PDF format and contain numerous charts, tables, and experimental data. The document structure is relatively fixed, usually including an overview, sample information, test conditions, test items, result data (e.g., content, dissolution, impurities, pH value), statistical analysis, and conclusions. Data update frequency is low. Updates typically occur after a research batch completes or a phase report releases. Fields and units are highly specialized. Examples include "Content (%)", "Dissolution (%)", "Impurities (ppm)", and "Storage Conditions (25℃/60%RH)". Units are often embedded directly within field descriptions.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The fixed structure of stability research reports requires document parsing tools to identify and extract specific sections or table content. For example, test conditions and result data often appear in tables. The tool must accurately identify table boundaries, rows, and columns, then correctly parse cell content. Highly specialized fields and units demand semantic integrity for chunking. A data chunk should contain the complete field name, value, and unit to prevent information loss or misinterpretation. Low data update frequency means real-time performance is not critical. However, traceability of historical data and version management are important. Chunking must preserve original document source and version information. Parsing chart content is challenging and requires additional image recognition or data extraction capabilities.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters | Ensures sufficient context within a single chunk, prevents critical information from being cut off. |
Overlap Length | 100–150 characters | Guarantees semantic continuity between chunks, improves recall accuracy. |
Table Extraction Mode | Structured Extraction | Stability reports contain extensive tabular data; precise parsing of row/column table structures is necessary. |
Custom Regex | Calibrate based on actual measurements | Used to extract specific formats for batch numbers, test items, or key metrics. |
Parsing Timeout | 600 seconds | Accommodates the time required to parse large PDF files, prevents parsing interruptions. |
max_tokens (LLM Parameter) | 2000 | Ensures the model can process longer chunk content and understand context. |
Common Pitfalls
- Excessive blank or incomplete data blocks after document parsing: This occurs when the table extraction mode is incorrectly configured. Table content is skipped or treated as plain text, failing to recognize row and column structures.
- Key values and units are separated, making query results difficult to understand: This happens when the chunk length is set too short, or custom regular expressions are not used to bind specific fields. Values and units are split into different chunks.
- Large amounts of duplicate content in the knowledge base, reducing recall efficiency: This results from improper document version management or a lack of effective deduplication. Unchanged content from old and new report versions gets indexed repeatedly.
How to Verify Configuration
- Select a typical stability research report. Upload it and observe the number and content of the parsed document blocks. Check if key data (e.g., content, impurities, pH value) is complete and includes units.
- Search the knowledge base for specific batch numbers or test conditions. Verify that the recalled chunks accurately link to the corresponding report sections and data tables.
- For tabular data in reports, randomly select several cell contents and query them. Confirm that the parsing tool correctly extracts and recalls them as valid information.
- Attempt to upload different versions or formats of stability reports. Ensure the parsing configuration is robust to document format variations and does not fail due to minor differences.
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.