Data Characteristics
Retail chain pharmacovigilance data originates from daily sales records, customer feedback, pharmacist-submitted adverse event reports, and regulatory warnings. Data updates frequently. Sales records generate in real-time. Adverse event reports are typically submitted within 24 hours of an event. Document structures vary. Sales data primarily uses structured databases, including fields like drug batch, sales time, anonymized customer information, and symptom descriptions. Adverse event reports are often unstructured text, covering patient complaints, medication history, adverse reaction manifestations, and treatment measures. Regulatory warnings are issued as announcements, guidelines, or structured data packages, potentially including drug names, batch numbers, risk levels, and specific recommendations. Common special fields in the data include generic drug name, brand name, batch number, manufacturer, patient age group, gender, adverse reaction type (e.g., rash, nausea), severity grading (mild, moderate, severe), and reporter profession.
Constraints from These Characteristics on Deployment and Upgrades
The high update frequency and heterogeneous nature of retail chain pharmacovigilance data impose real-time and compatibility requirements on FastGPT deployment. Frequent data ingestion demands efficient synchronization mechanisms to prevent outdated data from delaying warnings. The mix of unstructured text and structured data requires FastGPT to process and integrate different data sources effectively, performing knowledge extraction. For example, both symptom descriptions in sales records and free text in adverse event reports require precise parsing to identify potential adverse drug reaction signals.
Additionally, widespread chain pharmacies generate large data volumes. FastGPT's deployment environment must support high-concurrency processing and scalability to handle daily data peaks. During upgrades, new versions optimizing data models or indexing mechanisms require evaluation of their impact on existing knowledge bases. Smooth data migration or reconstruction must be ensured to avoid business disruption. The system also needs to support historical data analysis to leverage the existing knowledge base for trend analysis and risk assessment after upgrades.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 100 MB | Accommodates large regulatory warning documents and guidelines that may contain many images or charts, ensuring successful uploads. |
maxContext | 800–1200 characters | Balances the detail of adverse reaction reports with recall efficiency, ensuring critical information is not truncated. |
Chunk size | 300 characters | Adapts to the length of symptom descriptions and treatment processes in adverse reaction reports, improving segmentation granularity. |
Recall count | Top 5 entries | Reduces unnecessary recall while maintaining relevance, improving response speed. |
Similarity threshold | Calibrate by measurement | Dynamically adjusts based on the accuracy of identifying similar adverse reactions in actual business scenarios. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient time to process large PDF or Word format drug inserts and regulatory documents. |
Common Pitfalls
- The chat interface displays a blank screen on iOS devices. This usually occurs due to incorrect reverse proxy or HTTPS certificate configuration during deployment, leading to frontend resource loading failures.
- Docker deployment fails to access or reports errors. This may be due to incorrect container port mapping or firewall rules blocking external access.
- New data is not retrieved after a knowledge base update. This may be because the knowledge base index was not rebuilt in time or incremental update configuration is incorrect.
Verification Steps
- Upload a regulatory warning document containing various formats (e.g., PDF, Word, TXT). Ensure all files are successfully parsed and integrated into the knowledge base.
- Simulate submitting a detailed adverse reaction report. Observe if FastGPT accurately identifies key fields such as drugs, symptoms, and severity from the report.
- In the FastGPT chat interface, ask questions about specific drug adverse reactions. Verify that the returned results include the latest updated regulatory information and historical report data.
Note: 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.