Data Characteristics in this Category
Preclinical safety evaluation data primarily originates from pharmacology and toxicology research reports, GLP laboratory raw records, and analytical method validation reports. This data updates relatively infrequently, typically in batches after specific research phases conclude. Document structures mainly consist of structured tables, graphs, and textual descriptions, such as dose-response curves, toxicokinetic parameter tables, and organ pathology descriptions. Fields include dosage, administration route, animal species, observation indicators, and statistical results. Units involve mg/kg, mol/L, days, and times, adhering to strict specifications and professional standards. The data volume is substantial, with a single report potentially reaching hundreds of pages.
Constraints Imposed by these Characteristics on "Deployment and Upgrade"
The large volume and specialized nature of preclinical safety evaluation data demand high performance from FastGPT's knowledge base segmentation strategy, recall accuracy, and model comprehension capabilities. Charts, specialized terminology, and abbreviations within documents require additional preprocessing or stronger embedding model support to ensure accurate semantic understanding. The low data update frequency means knowledge base reconstruction or incremental update cycles can be longer, but each update involves a large amount of data. Strict unit and field specifications require the model to accurately identify and cite information during data extraction and question answering, avoiding confusion or errors. Additionally, browser compatibility and system resource consumption are significant concerns to ensure engineers can operate smoothly in various environments.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Accommodates long paragraphs and dense specialized terminology in safety evaluation reports, ensuring contextual completeness. |
Recall count | Top 5–8 entries | Ensures coverage of sufficient relevant information while avoiding excessive noise. |
Similarity threshold | 0.78–0.85 | Balances recall and precision, enabling accurate matching for highly specialized safety evaluation data. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses long parsing times for large safety evaluation reports, preventing timeouts and parsing failures. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets storage requirements for potentially large individual safety evaluation report files (including attachments). |
maxContext | 32000 token | Provides ample context for the model to handle multi-turn conversations involving complex safety evaluation issues. |
Common Pitfalls
- Knowledge base search results are directly output to the interface, leading to repetition when the AI generates further responses. This occurs when the knowledge base is configured for direct return instead of being set for AI internal reference only.
- Frequent timeout errors occur when uploading large safety evaluation report files. This is often due to
PARSE_FILE_TIMEOUT_SECONDSbeing set too low, failing to accommodate the actual file parsing time. - The knowledge base management interface loads slowly or functions abnormally in specific browser versions. This typically indicates an outdated browser version or compatibility issues with the frontend framework; check the browser console for errors.
Verification Steps
- Upload a typical preclinical safety evaluation research report. Verify successful parsing, correct segmentation, and indexing within the knowledge base.
- Simulate questions to confirm the AI platform accurately cites key data, dosage units, and research conclusions from the report. Cross-reference with the original report.
- Test knowledge base upload, query, and management functions across different browser versions (e.g., Chrome 100+, Firefox 90+). Ensure normal interface responsiveness and no error messages.
The values provided are common starting points. Measure performance 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.