Data Characteristics
GMP-compliant pharmacovigilance data sources include Adverse Drug Reaction (ADR) reports, post-market safety study data, regulatory agency warnings, drug labels, and relevant regulatory documents. This data combines structured formats (e.g., CIOMS I forms, MedDRA codes) and unstructured formats (e.g., free-text descriptions, medical literature). Update frequency depends on regulatory requirements and event reporting rates. For example, serious ADR reports typically have strict submission deadlines, while non-serious reports have periodic summary requirements. Document structures vary, from standardized report templates to PDF guidelines, Word document regulatory interpretations, and structured database records. For fields and units, drug dosages are often in milligrams (mg) or grams (g), report times are datetime stamps, and adverse event descriptions use medical terminology or natural language, often with MedDRA codes.
Constraints on Referencing and Tracing
The heterogeneous nature of GMP-compliant pharmacovigilance data sources challenges unified reference management. Different document formats require diverse parsers to ensure effective content extraction. Regulatory bodies impose strict data traceability requirements. Each reference must clearly point to the original report or file and include version information for audit compliance. For example, adverse events with MedDRA codes must trace back to specific report numbers and original text descriptions. Frequent data updates require the knowledge base to quickly synchronize the latest information. The RAG (Retrieval-Augmented Generation) system must prioritize recalling the newest, most authoritative regulatory documents when generating responses. Furthermore, medical terms and professional abbreviations in free-text descriptions demand higher accuracy from text segmentation and embedding models to prevent citation errors due to semantic misunderstanding.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | ADR reports and regulatory clauses often contain dense key information. This length helps capture the full context. |
Recall count (Number of Retrieved Chunks) | Top 5 | Pharmacovigilance demands high comprehensiveness. Retrieving multiple chunks increases coverage and reduces the omission of critical information. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures semantic relevance of retrieved content, preventing irrelevant or low-quality references from affecting compliance judgments. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Allows sufficient parsing time for large PDF regulatory guidelines or historical report documents. |
Chunk size (Chunk Length) | 300 characters | Balances text block completeness with RAG retrieval efficiency, adapting to paragraph structures of different document formats. |
Rerank result count (Number of Reranked Chunks) | 3 | Focuses on the most relevant and authoritative references, reducing user burden while retaining some diversity. |
Common Pitfalls
- Symptom: The AI response does not mention knowledge base content, but the reference list shows relevant documents. Reason: The
Similarity threshold(Similarity Threshold) is set too high. This causes the model to deem retrieved documents insufficiently relevant for inclusion in the answer, even if they are retrieved. Alternatively,maxContextis set too low, preventing the effective context of retrieved content from being accommodated. - Symptom: Reference source links point to incorrect or inaccessible pages. Reason: The original URL or file path was not correctly recorded during document upload. Alternatively, the file storage path changed after a knowledge base update, invalidating reference traceability metadata.
- Symptom: For questions about specific drugs or adverse reactions, the AI response contains outdated information and does not include the latest regulatory updates. Reason: The knowledge base update frequency is insufficient. It fails to timely import newly released regulatory warnings, guidelines, or revised drug labels, leading the RAG system to retrieve outdated information.
Verification Steps
- For typical pharmacovigilance questions, test whether AI responses include citations from the knowledge base. Verify that the cited documents' dates and content are the latest versions.
- Randomly select a citation from an AI response. Click its traceability link to verify accurate navigation to the corresponding location or page in the original document. Check the document's version information.
- Simulate an audit scenario. Pose questions requiring strict compliance verification. Check if the AI-provided citations sufficiently support the answer. Confirm the completeness and authority of the citations, such as the inclusion of report numbers and publishing organizations.
Note: The values provided are common starting points. Measure them against your own samples for optimal performance.
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.