Data Characteristics
Drug safety data in regulatory affairs originates from clinical trial reports, non-clinical study reports, post-market surveillance data, safety update reports (PSUR/PBRER), risk management plans (RMP), and regulatory guidelines from global agencies. Data updates follow fixed cycles, typically aligned with regulatory submission schedules or periodic update requirements. Document structures are highly standardized, such as ICH-E2B for Individual Case Safety Reports (ICSRs) and CTD for submission documents. Fields and units adhere to strict standardization; for example, adverse event terms use MedDRA coding, drug dosages are precise to milligrams (mg) or International Units (IU), and timestamps follow a unified format.
Constraints on Knowledge Base Retrieval
The highly standardized nature of regulatory affairs data requires precise identification and processing of structured information during indexing to avoid semantic ambiguity. For instance, the specificity of MedDRA codes means fuzzy matching can lead to severe false positives. Regular updates necessitate incremental update and version management capabilities to ensure retrieval results are always based on the latest regulations and safety information. Rigorous document structures and field definitions allow for more precise filtering and sorting during retrieval using document metadata, such as by drug name, indication, or adverse event type. The extensive use of specialized terminology and abbreviations in reports demands advanced tokenization and embedding models for accurate contextual understanding.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Regulatory document paragraphs are often long and contain multiple pieces of information. Shorter segments lose context, while longer ones introduce noise. |
Recall count (Retrieval Count) | 8–12 items | This covers multiple aspects of safety information, preventing omission of critical regulatory clauses or adverse event reports, while managing model input volume. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | High precision is required for domain-specific terminology. A higher threshold ensures strong relevance of retrieved content and reduces false positives. |
Rerank result count (Reranked Return Count) | 3–5 items | This improves the accuracy of the final results, focusing on the most relevant regulatory or report segments. |
maxContext | 3000–4000 tokens | This ensures that critical retrieved information can be fully presented within the limited context window, avoiding truncation. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Submission documents are often large, requiring sufficient upload limits to support the ingestion of complete documents. |
Common Pitfalls
- Retrieval results contain excessive irrelevant information, leading to redundant model output. This occurs when the
Similarity threshold(Similarity Threshold) is set too low, failing to effectively filter out low-relevance knowledge blocks. - Model responses contain repetitive content or logical contradictions. This occurs when the
Recall count(Retrieval Count) is set too high and effective reranking is not performed, sending duplicate or conflicting information to the model. - Knowledge base search results display directly on the interface, interfering with subsequent AI model generation. This occurs when the knowledge base search component's output is not correctly configured for internal passing, but is instead directly bound to frontend display.
Verification Steps
- Test knowledge base retrieval against typical regulatory affairs questions, such as "contraindications of drug X in pregnant women," to verify accurate recall of relevant regulatory clauses and clinical trial report segments.
- Check if recalled knowledge blocks include critical MedDRA codes, dosage units, and other fields, and validate their completeness.
- Adjust the
Similarity threshold(Similarity Threshold) to observe changes in retrieval count and relevance, finding a balance that ensures high-precision recall. - Use query statements of varying lengths and complexities to verify stable retrieval performance across different query scenarios.
The values provided are common starting points and should be measured against your 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.