Data Characteristics
Preclinical safety assessment data originates primarily from toxicology and pharmacokinetic study reports. These reports are typically in PDF format and contain detailed experimental methods, raw data, statistical analysis results, charts, and expert review comments. Data update frequency is low, with data generated in batches after a series of experiments. Report internal structures are rigorous, often adhering to GLP (Good Laboratory Practice) requirements, with clear section divisions such as test article information, experimental animal information, dosing regimens, observation indicators, results, discussion, and conclusions. Fields include dosage, administration route, animal species, sex, body weight, various physiological and biochemical indicator values (e.g., complete blood count, liver and kidney function indicators), and pathological findings. Units are diverse, including mg/kg, mL/kg, µmol/L, percentages, and various International Units (IU). Charts and tables contain high data density, sometimes including complex statistical symbols.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The PDF format and rigorous structure of preclinical safety assessment reports require tool calling and plugins to have robust text and table parsing capabilities to accurately extract key information. Low update frequency means that knowledge bases are built by importing a large number of documents at once, with few subsequent incremental updates. Therefore, real-time performance is not critical, but the completeness and accuracy of initial parsing are paramount. The diverse units of measurement and complex statistical symbols in reports challenge models to understand context and perform unit conversions, requiring plugins to recognize and process this specialized information. Unstructured text, particularly pathological descriptions, requires models to perform deep semantic understanding to extract key pathological changes. Furthermore, chart data in reports, if not directly parsable, requires OCR or manual annotation, increasing data preprocessing complexity. Considering knowledge base reference limits, document chunking strategies must be optimized to ensure complete recall of relevant information.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters (characters) | Preclinical safety assessment report paragraphs are typically long, containing complete experimental descriptions or results. Chunking too short loses context; chunking too long increases recall difficulty. |
Overlap Length | 100 characters (characters) | Ensures continuity of information at paragraph boundaries, preventing critical information from being cut off. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Parsing large PDF reports takes a long time. This provides sufficient time to prevent parsing interruptions. |
maxContext | 32000 token | Ensures the model can handle complex queries involving details from multiple safety assessment reports, covering long text contexts. |
Recall count (Recall Count) | Top 5 entries (top 5) | Preclinical safety assessment queries typically require detailed and precise background information. Increasing the recall count appropriately improves accuracy. |
Similarity threshold (Similarity Threshold) | 0.78 | Preclinical safety assessment terminology and data are highly specialized. A higher threshold helps filter out highly relevant knowledge snippets. |
Three Common Pitfalls
- Empty or incomplete results when calling the knowledge base: The PDF parser fails to correctly identify tables or complex layouts in the report, leading to critical data not being extracted.
- Incorrect dosages or units in AI responses: The model or plugin fails to correctly parse or convert various units of measurement in the report, for example, misreading milligrams as micrograms.
- Low relevance in results when querying specific pathological changes: The document chunking strategy fails to fully preserve the complete context of pathological descriptions, or the embedding model's understanding of medical terminology is insufficient.
How to Verify Configuration
- Select a structurally complex preclinical safety assessment report. Perform the knowledge base import process. Verify that the parsed text completely retains the original report's tabular data and paragraph structure.
- For experimental results in the report that contain multiple units of measurement, submit queries to verify if the AI response accurately identifies and uses the correct units.
- Select descriptions of specific toxic effects or pathological findings from the report. Submit relevant queries. Evaluate whether the recalled knowledge snippets accurately point to the corresponding content in the original report and check the accuracy of the AI response.
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.