Data Characteristics
Preclinical safety assessment registration dossier data primarily originates from laboratory study reports, toxicology study reports, and pharmacokinetic reports. These reports are typically in PDF, Word, or image formats. Data update frequency is low, concentrating around different new drug development phases. Document structure is highly standardized, adhering to regulatory guidelines from agencies like FDA, EMA, and NMPA. They include fixed sections and subsections, such as study protocols under GLP, raw data, statistical analysis results, and conclusions. Fields cover dosage, administration route, animal species, observation indicators, and pathological findings. Units strictly follow international standards (e.g., mg/kg, mol/L, days, weeks).
Constraints on Model Integration and Configuration
The standardized document structure of preclinical safety assessment dossiers allows models to leverage predefined templates or rules for information extraction, improving accuracy. Image-format reports (e.g., pathology slides, charts) require models with multimodal processing capabilities, especially for image recognition and text extraction. Low data update frequency means models can be trained on relatively stable datasets. However, models need updates to their knowledge base when guidelines are revised. Strict field and unit requirements necessitate rigorous validation and standardization after information extraction to prevent submission failures due to unit confusion or incorrect numerical formats. The extensive use of specialized terminology and abbreviations in reports also demands advanced semantic understanding from models.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 32000 tokens | Preclinical reports are lengthy; a large context window is needed to maintain information completeness. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic integrity and vector retrieval efficiency, avoiding truncation of critical information. |
Recall count (Recall Count) | Top 5 entries (top 5) | Preclinical data is highly specialized; ensure enough relevant segments are retrieved for in-depth understanding. |
Similarity threshold (Similarity Threshold) | Calibrate based on measurements | Adjust using a test set to balance recall and accuracy, based on specific datasets and model performance. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading preclinical report PDFs containing numerous images or scanned documents. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Large file parsing and multimodal content processing require longer processing times. |
Common Pitfalls
- Application tests pass, but the model fails to correctly extract text or tabular data from images during actual use. This occurs because OCR or image understanding components are not configured correctly during model integration, or component versions are incompatible with the FastGPT version.
- The model incorrectly extracts or omits critical information like dosage and units from reports. This happens when model training data lacks sufficient, accurately labeled preclinical safety assessment-specific data, leading to inadequate recognition capabilities for particular fields.
- After OneAPI configuration, FastGPT cannot call external model services, displaying connection timeouts or authentication failures. This typically results from incorrect
API_KEYorBASE_URLconfiguration, or network firewalls restricting FastGPT server access to the OneAPI address.
Verification Steps
- Upload typical preclinical safety assessment report PDFs containing various images and complex tables. Observe if the model can fully extract all text and tabular data.
- Construct queries for key fields in reports (e.g., dosage, animal species, adverse reactions). Check if the model's returned results accurately include this information and if units are correct.
- Use FastGPT's debugging tools to review log outputs at various stages of model processing. Ensure no errors or warnings related to image processing or multimodal parsing appear.
- Within the application, test with a series of professional questions related to preclinical safety assessment. Evaluate the model's ability to understand complex semantics and specialized terminology, and verify the accuracy and completeness of its answers.
Note: 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.