Data Characteristics
Preclinical safety evaluation documents originate from internal pharmaceutical R&D reports, Contract Research Organization (CRO) study reports, and regulatory guidelines. These documents update infrequently, typically with drug development phases or regulatory changes. Documents are primarily unstructured text, containing extensive experimental data, observations, statistical analyses, and expert opinions. Common fields include animal species, dosage, administration route, observation indicators (e.g., body weight, blood routine, organ coefficients), pathological examination results, and statistical P-values. Units are diverse, including mg/kg, g, mL, kPa, min, μm, and often mix Chinese and English abbreviations.
Constraints on Context and Tokens
The unstructured nature and high density of specialized terminology in preclinical safety evaluation documents demand advanced context understanding for accurate key information identification. Infrequent updates mean model training and knowledge base construction can use relatively stable data versions. However, each update may involve extensive text revisions. Diverse fields and units, along with mixed formats of charts, tables, and text, challenge text preprocessing and information extraction. Models must effectively process different data formats and accurately tokenize specialized vocabulary and numerical units. Reports often include lengthy background introductions and methodology descriptions. These redundant details can dilute the weight of core experimental data, impacting context relevance efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000–5000 characters | Ensures coverage of key experimental results and conclusion sections in reports. |
Chunk size (Segment Length) | 800–1200 characters | Balances context completeness with retrieval efficiency, preventing excessively long segments from diluting information. |
Recall count (Recall Count) | 5–8 items | Guarantees coverage of multiple relevant experimental details, preventing omission of critical evidence. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Improves retrieval precision, filtering out low-relevance general descriptions. |
Rerank result count (Reranked Return Count) | 3–5 items | Further focuses on core information, enhancing response quality. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large safety evaluation reports, preventing file processing failures due to timeouts. |
Common Pitfalls
- An
InternalError.Algo.lnvstatus code during report parsing typically indicates complex file content or an abnormal format. This causes the parsing algorithm to encounter unexpected errors when processing specific structures. - Some key fields (e.g., "organ coefficient") are empty in application results. This can happen because the field's expression varies in the document or non-standard abbreviations exist, preventing the model from accurate extraction.
- Slow model response times and insufficient contextual information after chat queries may occur if
Chunk size(Segment Length) is set too small. This results in individual segments lacking sufficient semantic information. Alternatively,Recall count(Recall Count) might be too low, failing to cover relevant content comprehensively.
Validation Steps
- Select multiple typical preclinical safety evaluation reports. Upload them and check the knowledge base segment preview. Ensure all critical experimental data, conclusions, and methodology descriptions are fully retained.
- Query specific experimental results, drug dosages, and other key information from the reports. Verify that the model's returned context accurately points to the original text and confirm the answer's accuracy.
- Simulate complex queries, such as those involving multi-indicator correlation analysis or comparisons between different experimental batches. Evaluate the model's ability to maintain context across multi-turn conversations and observe if response speed meets expectations.
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.