Data Characteristics in this Category
Pharmacovigilance regulatory submission documents primarily include safety reports, risk management plans, and drug labels. Data sources are diverse, covering clinical trial data, post-marketing adverse event reports (e.g., CIOMS I forms, MedWatch forms), and medical literature. Data updates frequently, especially post-marketing safety data, which may be summarized and analyzed quarterly or annually. Document structures are complex, often containing large amounts of unstructured text, images (e.g., pathology slides, skin lesion photos), and structured data (e.g., laboratory test results, basic patient information). Fields and units vary, such as dosage units (mg, g, IU), time units (days, weeks, months), and medical terminology (ICD-10 codes, MedDRA terms).
Constraints Imposed by these Characteristics on "Workflow Orchestration"
High-frequency updates of safety data require workflows with flexible data ingestion and incremental processing capabilities to avoid reprocessing historical data. Complex document structures and mixed data types mean a single text processing model is insufficient; visual models and structured data parsing capabilities must be integrated. Converting image data to a processable format (e.g., Base64 encoding) is a prerequisite. Diverse fields and units challenge data cleaning and standardization, requiring dedicated preprocessing modules to ensure consistent input for subsequent AI models. Additionally, submission documents are typically voluminous, making workflow parallel processing capabilities and efficient file transfer mechanisms critical to avoid long waits or transfer failures.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 32000 | Handles lengthy safety reports and risk management plans |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates submission documents with many images and text |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Ensures sufficient parsing time for large PDFs or multimedia files |
Chunk size | 800–1200 characters | Balances context completeness with model processing efficiency |
Recall count | Top 15 entries | Improves the comprehensiveness of retrieving relevant safety information from the knowledge base |
Similarity threshold | 0.75 | Ensures retrieved results are highly relevant to pharmacovigilance terms and concepts |
Three Common Mistakes
- Image files are uploaded but not correctly identified or processed. This often happens because the workflow lacks an image preprocessing step or a Base64 encoding conversion module.
- File uploads fail when calling the workflow API, returning a 4xx error code. This is usually due to incorrect file encoding in the API request body or the file size exceeding the
UPLOAD_FILE_MAX_SIZElimit. - Knowledge base content does not take effect in API calls, even when set as a global variable. This can be due to incorrect knowledge base ID passing in API call parameters or improper knowledge base permission configuration.
How to Confirm Proper Configuration
- Successfully upload and process a comprehensive submission document containing both images and text. Check if the output includes parsed image content.
- Monitor workflow execution logs to confirm that file upload, conversion (e.g., Base64 encoding), and AI model inference steps complete without errors.
- Use the API to call the workflow, providing test files and a knowledge base ID. Verify that the returned results effectively utilize knowledge base content for answers or analysis.
- Randomly select processed safety reports and manually cross-check the accuracy of key field extraction and risk assessment conclusions.
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.