Data Characteristics
Preclinical safety evaluation documents primarily include study protocols, raw data records, and analysis reports (e.g., toxicology reports, pharmacokinetic reports). Data sources typically include Laboratory Information Management Systems (LIMS), Electronic Lab Notebooks (ELN), and paper or electronic reports from Contract Research Organizations (CROs). Document update frequency is relatively low, usually occurring when research milestones are reached or final reports are published. Document structures are highly standardized, adhering to GLP (Good Laboratory Practice) or ICH guidelines, and include clear chapter titles, figures, tables, and appendices. Field characteristics are distinct, such as dosage (mg/kg), administration route (po, iv), observation indicators (body weight, blood routine, organ coefficients), and statistical P-values. Units are precise and specialized.
Constraints on "Reference and Traceability" Imposed by These Characteristics
The high standardization of preclinical safety evaluation documents requires references to be precise down to specific chapters, figures, tables, or data points to support regulatory compliance and traceability. Low update frequency means that once a document is ingested, its content is relatively stable. However, when regulatory updates or supplementary studies occur, version iteration and incremental updates must be handled to ensure that the latest approved version is always referenced. The rigor of specialized fields and units requires accurate matching of values and units during referencing to avoid safety risks due to semantic ambiguity. The large number of tables and figures in documents demands high structural analysis capabilities, requiring accurate identification and extraction of key data rows and columns from tables, or trend information from figures, and treating this information as the smallest referenceable unit.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Ensures each segment contains a complete experimental result description or method detail, aiding contextual understanding. |
Recall count (Recall Count) | Top 8 | Balances retrieval efficiency and coverage, ensuring key toxicology or pharmacokinetic data points are recalled. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Reduces false recall rate, focusing on safety evaluation data or conclusions highly relevant to the query intent. |
Rerank result count (Reranked Return Count) | Top 3 | Further selects the most relevant core evidence from recalled results, reducing interference from irrelevant information. |
maxContext | 4000–6000 tokens | Accommodates sufficient original text fragments from safety evaluation reports, supporting deep understanding and reasoning by the AI model. |
Common Pitfalls
- AI conversation results do not provide specific data source page numbers or chapter information because position metadata is not retained during knowledge base chunking.
- After calling the knowledge base in the workflow, AI answers contain incorrect dosages or units because document parsing fails to accurately identify numerical values and corresponding units in tables, leading to incomplete field extraction.
- Knowledge base query response times are too long, causing the entire workflow to stall. This happens when
Recall count(Recall Count) andmaxContextare set too high, increasing the input length for the large language model and the burden on knowledge base retrieval.
Validation
- Randomly select 10 queries. Check if the document snippets cited in the AI's answers accurately point to specific paragraphs or table rows in the original report, and verify that the cited data matches the original text.
- Use queries containing specific technical terms and numerical values. Verify if the AI's answers can accurately extract and present this information, such as compound names, dosages, administration routes, and observation results.
- Simulate user questions about the conclusions and supporting data for a specific toxicology indicator. Check if the AI's answers can provide clear citation sources (e.g., report name, version, specific page number).
The values given 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.