Data Characteristics
Preclinical safety evaluation regulation data originates from regulations, guidelines, and technical review requirements published by agencies such as the National Medical Products Administration (NMPA) and the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). It also includes internal Standard Operating Procedures (SOPs) and quality management documents. These documents have a relatively low update frequency, typically quarterly or annually, but undergo immediate revisions during significant policy changes. Document formats are primarily PDF, Word, and HTML. They feature a rigorous structure, containing extensive specialized terminology, dosage units (e.g., mg/kg, µg/mL), time units (e.g., hours, days), and complex experimental flowcharts. Internal SOPs usually detail animal management, administration methods, sample collection, and data analysis. Field definitions are clear, emphasizing compliance and traceability.
Constraints on Deployment and Upgrades
The rigor and specialized nature of preclinical safety evaluation regulation data require FastGPT to prioritize accurate text parsing during deployment. Extensive specialized terminology and complex charts challenge the model's semantic understanding. Ensure the segmentation strategy effectively preserves contextual integrity, preventing critical information from being fragmented. The low update frequency means knowledge base reconstruction or incremental update cycles can be extended, but each update must align with the latest regulations. Common dosage and time units in documents require the model to precisely identify and interpret them during Q&A, avoiding misunderstandings due to unit confusion. The detailed processes and field definitions in internal SOPs demand robust structured information extraction capabilities to support precise process Q&A and data queries.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for Value |
|---|---|---|
maxContext | 8192 | Preclinical safety evaluation documents have strong contextual relevance. A longer context is needed to understand complex processes and specialized terminology. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic completeness and segment length. Avoids redundancy from excessive length and fragmentation of critical information from insufficient length. |
Overlap Length | 100 characters (characters) | Ensures contextual continuity at segment boundaries, improving retrieval recall, especially for procedural descriptions. |
Recall count (Recall Count) | Top 5 entries (top 5) | Preclinical safety evaluation Q&A typically requires multiple relevant regulatory clauses for comprehensive support. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures precision of recalled content, filtering out irrelevant regulatory clauses to avoid interference. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Parsing large PDF documents can be time-consuming. Increasing the timeout prevents parsing failures. |
Common Mistakes
- When uploading large PDF documents, a
File Parsing Timeout(file parsing timeout) error appears. This occurs because thePARSE_FILE_TIMEOUT_SECONDSconfiguration value is too low, not allowing the model enough time to process complex document structures. - When users ask about specific dosage unit regulations, the answer shows unit confusion or omission. This happens because text segmentation did not adequately preserve contextual information containing units, leading to inaccurate model understanding.
- When creating a general knowledge base, the image understanding model option is missing from the interface. This typically indicates that the local deployment version has not correctly configured or enabled the relevant multimodal plugin service.
Verification Steps
- Upload a preclinical safety evaluation SOP document containing complex flowcharts and dosage units. Verify that the parsed content in the knowledge base fully retains key information and units.
- Ask multiple questions regarding specific regulatory clauses or experimental operating procedures. Check if the system's responses accurately cite the original text and correctly explain specialized terminology.
- Simulate real-world business scenarios by inquiring about the safety evaluation requirements for a specific drug. Verify if the system can integrate multiple regulatory documents to provide comprehensive and consistent answers, and assess the compliance of the responses.
The values provided are common starting points and should be measured against specific 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.