Data Characteristics
Pharmacovigilance data comes from clinical trial reports, real-world evidence (RWE), spontaneous adverse event reporting systems, and medical literature. This data updates frequently. Adverse event reports can arrive in real-time. Document types vary, including structured Case Report Forms (CRFs), semi-structured medical records, unstructured free-text reports, and academic papers. Fields include patient demographics, medication history, and diagnosis results. Specific drug-related fields include drug names (generic and brand), batch numbers, adverse reaction descriptions (MedDRA coding), severity, outcome, and causality assessment. Units include dosage (mg, g, IU), frequency (times/day, week), and duration (days, months).
Deployment and Upgrade Constraints from Data Characteristics
The real-time nature and diversity of pharmacovigilance data create deployment and upgrade challenges. High-frequency updates require efficient incremental update mechanisms and version management for the knowledge base to ensure information timeliness. Complex document structures demand robust document parsers capable of handling various medical text formats and languages, accurately extracting key information. Converging multi-source data requires flexible data ingestion interfaces and preprocessing workflows to clean, standardize, and integrate data from different origins. The large number of specialized fields and coding systems, such as MedDRA, necessitates effective recognition and utilization of these professional terms during knowledge embedding and retrieval to ensure consultation accuracy. Archiving historical data and seamlessly integrating new data also require high system stability and scalability.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates uploading large medical literature and clinical reports |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles the longer parsing time for complex medical documents |
maxContext | 4096 characters | Ensures detailed adverse event descriptions and context are captured |
Chunk size | 800–1200 characters | Balances semantic completeness and retrieval efficiency, adapting to medical terminology density |
Recall count | Top 10 entries | Increases recall rate to cover various potentially relevant information in pharmacovigilance consultations |
Similarity threshold | Calibrate based on actual measurements | Ensures retrieved results are highly relevant to the queried drug and symptoms |
Rerank result count | Top 5 entries | Focuses on the most relevant information, improving final answer accuracy |
Common Pitfalls
- The interface displays a timeout when uploading large files to the knowledge base, but the file continues to upload. This leads to users repeating the operation because the frontend timeout setting is shorter than the backend processing time for large medical documents.
- Workflows do not support multi-turn conversations, while simple applications do. This indicates a missing component or logic for context transfer or session state management in the workflow configuration.
- The built-in AI model is unusable after deployment, requiring additional model integration. This typically occurs because the
OPENAI_API_KEYorCUSTOM_MODELSenvironment variables are incorrectly configured or do not point to a valid model service.
Verification Steps
- Upload a PDF document containing MedDRA codes and detailed adverse event descriptions. Verify the knowledge base correctly parses and extracts key fields.
- Conduct multi-turn Q&A tests for a specific drug's adverse reactions. Check if the AI provides consistent and accurate consultations based on historical conversations.
- Simulate high-concurrency knowledge base queries. Monitor system resource usage and response times to evaluate system stability under heavy load.
- Search the knowledge base for a specific drug batch number or adverse event report ID. Confirm the accuracy and completeness of the recall results.
The values provided are common starting points. Measure them against specific samples to determine optimal settings.
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.