Data Characteristics
Preclinical safety evaluation data primarily originates from regulatory guidelines, technical review requirements, and filing material requirements published by drug administration authorities. It also includes internal standard operating procedures (SOPs), test methods, and batch record templates. These documents are typically in PDF, Word, or scanned image formats. Updates are relatively stable, occurring when drug regulatory policies or technical standards change, with cycles ranging from months to years. Document structures are rigorous, often containing chapters, clauses, appendices, figures, tables, and references, with clear hierarchies. Field content includes animal experiment data (e.g., dosage, administration route, observation indicators), testing methods (e.g., chromatographic conditions, mass spectrometry parameters), and quality standards (e.g., limits, purity). Units are precise, down to milligrams, micrograms, milliliters, days, and hours.
Constraints on Document Parsing and Chunking
The rigorous structure of preclinical safety evaluation documents requires parsers to accurately identify chapter titles, lists, and table content, maintaining semantic integrity and avoiding breaks at critical information points. The presence of PDFs and scanned images necessitates OCR support and post-processing of OCR results to correct errors. The precision of fields and units requires chunking to avoid separating values from units or mixing data from different indicators. The slow update pace makes incremental knowledge base updates crucial, efficiently identifying and updating only modified sections to reduce redundant parsing. Additionally, documents often contain numerous technical terms and abbreviations, requiring the parser to provide clear, complete context for subsequent embedding models after chunking.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 500–800 characters | Preclinical safety evaluation documents have strict logic, requiring longer context to maintain semantic integrity and prevent critical information breaks. |
Overlap Length | 50–100 characters | Ensures sufficient overlap between adjacent chunks to capture logical connections across chunks, especially at clause transitions. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for large SOPs or guideline documents, preventing parsing failures due to timeouts. |
Enable OCR | True | Many preclinical safety evaluation documents exist as scanned images; OCR is essential for extracting text content. |
Table Parsing Strategy | Attempt Structured Parsing | Documents contain many experimental data tables; structured parsing helps preserve semantic information. |
Ignore Specific Headers/Footers | True | Many regulatory documents contain repetitive headers and footers; ignoring them reduces noise and improves chunking quality. |
Common Pitfalls
- Parsing status remains "parsing" for an extended period or directly shows "parsing failed." This can occur if the document is too large or contains complex formatting, leading to an insufficient
PARSE_FILE_TIMEOUT_SECONDSconfiguration. - Uploaded PDF documents contain image content, but image information is missing or cannot be referenced after parsing. This happens when OCR is not enabled or image recognition is not configured, causing the AI to process only the text layer.
- API calls return a generic "parsing failed" status for knowledge base parsing, lacking detailed error messages. This makes pinpointing the specific cause of failure difficult and may require further investigation using log systems.
Verification Steps
- Upload and parse a preclinical safety evaluation SOP document containing complex tables and multi-level headings. Verify that the parsed chunks fully retain table data and heading hierarchies.
- Upload a high-quality scanned guideline document. Verify that its text content is accurately recognized without obvious OCR errors or garbled text.
- Randomly select several parsed chunks. Check if the content is semantically coherent, especially across pages or sections, ensuring critical fields and units are not inappropriately split.
- Use the knowledge base retrieval function to query using technical terms or key clauses from the document. Verify that relevant chunks are accurately recalled and check the completeness of the recalled content's context.
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.