Data Characteristics
Data for preclinical safety evaluation (PSE) regulations primarily originates from official bodies like 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 documents such as Standard Operating Procedures (SOPs), internal management regulations, and risk assessment reports. These documents are typically in PDF or Word format, feature a rigorous structure, and contain extensive technical terminology, charts, and references.
Update frequency is relatively low, usually tied to policy revisions or technical standard updates, which can range from months to years for significant changes. Fields and units within these documents are highly standardized (e.g., toxic dose in mg/kg, administration route, observation indicators like organ coefficients, statistical significance p-value), demanding extreme precision and consistency.
Constraints Imposed by These Characteristics on Workflow Orchestration
The rigorous nature and low update frequency of PSE regulatory documents necessitate high accuracy in knowledge base retrieval within workflow orchestration. This requires fine-tuned text segmentation and retrieval strategies. The specialized terminology and standardized fields in these documents demand that semantic understanding modules within the workflow accurately identify terms to avoid ambiguity. For example, minor differences in dosage units can lead to significant assessment discrepancies.
The low update frequency means that knowledge base rebuilding and indexing operations do not need to be frequent, but each update must ensure comprehensiveness. Complex document structures, including numerous charts and cross-references, require document parsers with robust structured information extraction capabilities. This enables the workflow to accurately reference chart data or related clauses, improving the reliability of question answering.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Ensures the completeness of regulatory clauses, reduces semantic fragmentation, and prevents excessively long segments from impacting retrieval efficiency. |
Recall count (Retrieval Count) | 5 entries | Balances the contextual relevance of preclinical safety evaluation regulations; retrieving more entries ensures comprehensive information while staying within a reasonable range to reduce interference. |
Similarity threshold (Similarity Threshold) | 0.75 | Preclinical safety evaluation Q&A demands high accuracy; a high threshold filters out irrelevant or ambiguous retrieval results. |
Rerank result count (Reranked Return Count) | 3 entries | After optimization by the reranking model, focuses on the three most relevant entries to improve the precision of the final answer. |
maxContext | 4000 tokens | Ensures that the large model can understand the complete context of complex regulatory clauses, preventing loss of critical information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Preclinical safety evaluation documents are often large and complex; this provides sufficient parsing time to prevent timeout failures. |
Three Common Mistakes
- Global variables not correctly passed during external API calls, leading to logic interruption or null returns. This occurs when external platforms do not pass global variables according to the preset parameter format or name.
- AI answers lacking basis due to not citing query results from the database. This happens when the AI node in the workflow is not configured to correctly cite retrieved content, or when the retrieved content is insufficiently relevant to the question.
- The decision node always evaluates as non-empty, preventing the flow from branching as expected. This is because the decision node's rules are too broad and fail to precisely match the specific conditions or fields required for evaluation.
How to Verify Configuration
- Test the workflow with typical preclinical safety evaluation questions to confirm accurate retrieval of relevant regulatory clauses and check the completeness of the retrieved content.
- Simulate external system calls to verify that global variables are correctly passed into the workflow and observe if the process execution results meet expectations.
- Submit queries containing sensitive keywords or boundary conditions to check if the decision node accurately categorizes and triggers the appropriate branches, ensuring no misjudgments.
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.