Data Characteristics
Regulatory affairs pharmacovigilance data combines structured and unstructured formats. Key data sources include clinical trial reports, post-marketing surveillance reports, adverse event (AE)/serious adverse event (SAE) reports, drug inserts, and regulatory documents. Data updates typically occur in batches during clinical phases and continuously post-market, potentially involving daily or weekly updates. Document structures vary; for example, clinical trial reports are often PDFs containing tables, charts, and free text. AE/SAE reports frequently follow standard formats like ICH E2B, with fields for patient identifiers, drug names, adverse reaction descriptions (MedDRA coding), occurrence dates, and outcomes. Units include time (days, months, years), dosage (milligrams, grams), and frequency (times/day).
Constraints on Deployment and Upgrade
The mixed structure of regulatory affairs pharmacovigilance data presents multiple challenges for FastGPT deployment. Unstructured documents like PDFs require efficient text extraction and paragraph segmentation to ensure information completeness. Structured data such as ICH E2B demands precise field mapping and parsing for data quality. Continuous data updates, especially post-marketing surveillance data, necessitate incremental indexing mechanisms to avoid full reprocessing and reduce resource consumption. Additionally, identifying and processing specialized terminology like MedDRA codes requires FastGPT to support or extend vocabularies to improve recall accuracy. These constraints collectively dictate that file processing, data synchronization, and model configuration require fine-tuned adjustments during deployment and upgrades.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large clinical trial reports and regulatory documents. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Ensures sufficient time for complex PDF parsing, preventing timeouts. |
maxContext | 4000 characters | Balances report completeness with model processing efficiency, reducing truncation risk. |
Chunk size (Segment Length) | 800–1200 characters | Optimizes long text splitting, maintains contextual coherence, and improves recall quality. |
Recall count (Recall Count) | Top 10 entries | Ensures comprehensive retrieval results, covering potentially relevant information. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Requires adjustment based on specific data distribution and query scenarios. |
Common Pitfalls
- During system plugin configuration, the
packages/plugins/registerfile is missing. This prevents specific feature extensions. This usually occurs due to incomplete volume mapping during Docker deployment or a mismatch between FastGPT and plugin versions. - The workflow configuration interface for local deployments experiences lag when entering text with many nodes. This may be due to high browser rendering load or delayed FastGPT backend response. Check server resource utilization.
- Importing large local models results in system errors or unresponsiveness. This can be related to an undersized
UPLOAD_FILE_MAX_SIZEparameter, insufficient memory, or incompatible model file formats.
Verification Steps
- Upload a clinical trial PDF report containing complex tables and charts. Verify that text is parsed and extracted correctly and completely.
- Simulate submitting a standard ICH E2B adverse event report. Confirm that corresponding fields are correctly mapped and stored in the FastGPT internal database.
- Use the FastGPT query interface to perform keyword searches on imported pharmacovigilance data. Observe the number and relevance of recall results to ensure they meet expectations.
- During peak periods or after large-volume data imports, check FastGPT server CPU, memory, and disk I/O utilization to confirm that system resources are not bottlenecked.
The values provided are common starting points and should be measured against specific 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.