Data Characteristics in this Category
Preclinical safety evaluation data primarily comes from toxicology, pathology, and pharmacokinetics reports generated by GLP laboratories. These documents are typically in PDF, Word, or scanned image formats. They contain animal experiment data, observation records, dosage settings, pathological section descriptions, and statistical analysis results. Data update frequency is relatively fixed during project progression, usually concentrated at the end of each research phase. Document structure is highly standardized, adhering to regulatory requirements such as OECD GLP or NMPA GLP. Standardized chapter headings include "Study Objectives," "Materials and Methods," "Results," "Discussion," and "Conclusion." Fields include animal batch numbers, dosing amounts (unit mg/kg), administration routes, observation indicators (e.g., body weight, organ coefficients), and pathological diagnostic terms. Units are strictly unified, such as "g," "mg/kg," and "%."
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The standardized structure of preclinical safety evaluation documents allows workflows to rely on predefined templates and regular expressions for information extraction during the document parsing phase. This reduces the complexity of unstructured information processing. The highly specialized content requires the RAG knowledge base to have precise domain vocabulary matching capabilities to avoid semantic deviations. The low update frequency means incremental knowledge base update strategies can combine periodic full checks with partial updates, reducing real-time pressure. Strict unit and field requirements necessitate the introduction of numerical range and unit consistency checks during model output validation to ensure the accuracy of generated content. Furthermore, due to data sensitivity, workflow permission control and audit logging mechanisms must be stricter to ensure data security and compliance.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Size | 800–1200 characters | Balances contextual completeness and model processing efficiency, preventing truncation of critical information. |
Overlap Size | 100–200 characters | Ensures semantic continuity at chunk boundaries, improving RAG recall accuracy. |
Similarity Threshold | 0.75–0.85 | Balances recall breadth and precision, ensuring high relevance between recall results and queries. |
Recall Count | 5–8 items | Provides the model with sufficient reference information while avoiding excessive noise. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDF or Word documents can be time-consuming; this prevents parsing timeouts. |
Model Temperature | 0.3–0.5 | Reduces the randomness of generated content, ensuring rigor and reproducibility of output, in line with regulatory requirements. |
Three Common Pitfalls
- RAG retrieval results do not match expectations, leading to content discrepancies. This occurs due to improper knowledge base chunking strategies, resulting in critical information being split or insufficient context.
- Workflow execution time is too long, or timeouts occur. This typically happens when the
maxContextparameter for single request processing in the model dialogue component is set too high, or the number of RAG recall items is excessive. - Model generated results contain unexpected formats or content for dates, times, or other elements. This results from incorrect referencing of platform-provided system time variables in the prompt, or an unspecified variable format.
How to Verify Correct Configuration
- Select a typical preclinical safety evaluation report. Execute a document parsing and knowledge ingestion workflow. Check the accuracy of chunking results and metadata extraction.
- For a specific safety evaluation query, run RAG retrieval within the workflow. Check if the recalled content covers key information and compare it with the original document.
- Design queries that include critical fields like time and dosage. Execute the workflow's model dialogue. Verify the accuracy of these fields, unit consistency, and expected time formats in the generated responses.
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.