Data Characteristics
Preclinical safety assessment data originates primarily from pharmacology and toxicology research reports, GLP (Good Laboratory Practice) raw laboratory records, analytical test reports, and regulatory submission documents. These documents typically come in PDF, Word, or Excel formats. They contain both structured and unstructured data. Update frequency is relatively fixed, usually occurring when research projects reach a milestone or are submitted for regulatory approval. Document structures are complex. They often include charts, tables, biological indicator data, pathological descriptions, and statistical analysis results. Fields involve dosage, administration route, test article batch, animal species, strain, gender, weight, observation indicators (e.g., organ coefficients, hematology, urinalysis, pathological diagnosis), and their units (e.g., mg/kg, g, dL, %).
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The complex structure of preclinical safety assessment documents demands advanced document parsing capabilities. Nested tables in PDFs and Word documents, embedded text within images, and irregular layouts can prevent traditional parsing tools from accurately extracting data. This impacts the accuracy of subsequent knowledge base retrieval. Accurate identification of biological indicator data and their units is crucial. Incorrect unit or value extraction directly leads to deviations in pre-screening results. Document update frequency is not high. However, each update can involve substantial data additions or revisions. The parsing process must effectively identify and handle these changes. This prevents knowledge base data redundancy or inconsistency. Furthermore, semantic understanding and effective chunking of unstructured text, such as pathological descriptions, are vital for accurately answering questions related to toxicity mechanisms and pathological features. This requires balancing contextual completeness.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Balances contextual completeness and retrieval efficiency. Avoids excessively long or short individual chunks. |
Overlap Length | 100 characters | Ensures semantic coherence at chunk boundaries. Captures associated information across chunks. |
File Type Whitelist | ['.pdf', '.docx', '.xlsx'] | Restricts processing to common document formats in preclinical safety assessment. Improves processing efficiency and security. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles the longer parsing times required for large toxicology reports. Prevents timeouts. |
Image OCR Enabled | Enabled | Ensures experimental data, chart titles, and annotations embedded in images are recognized and indexed. |
Table Structure Recognition | Enabled | Accurately extracts tabular data like dose-response and hematological indicators. Enhances structured information retrieval capabilities. |
Three Common Mistakes
- Knowledge base answers contain incorrect biological indicator values or unit confusion. This occurs when document parsing fails to correctly identify values and their corresponding unit columns in tables.
- Chart information in uploaded PDF documents is not retrievable. This happens when
Image OCR Enabledis not active, preventing text content within images from being extracted. - The model fails to provide complete pathological description context when answering a question about a toxicity mechanism. This is due to
Chunk Lengthbeing set too small, causing relevant descriptions to be excessively segmented.
How to Verify Configuration
- Upload a toxicology report PDF containing complex tables and charts. Check if the knowledge base correctly extracts table data and chart annotations.
- Through the FastGPT management interface, randomly select several document chunks. Verify that the chunk content is semantically complete, without obvious truncation, and includes key information.
- Execute a series of searches including keywords like biological indicators, dosage, and pathological descriptions. Evaluate the accuracy and relevance of the returned results. Adjust
Similarity Thresholdbased on actual needs. - Upload a revised research report. Observe how the knowledge base updates, specifically how new and old data are covered and replaced. Ensure data consistency.
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.