Data Characteristics in This Category
Pharmaceutical e-commerce platforms primarily source pharmacovigilance data from drug sales records, user medication feedback, online consultation logs, and drug inserts. Data updates are frequent. User feedback and sales records are generated almost in real-time. Drug inserts and product catalogs are updated periodically. Sales records are typically structured data, containing drug batch numbers, sales times, partially anonymized buyer information, and dosages. User feedback is mostly unstructured text, describing symptoms and medication experiences. Fields may include generic drug names, brand names, batch numbers, manufacturers, adverse event descriptions, patient age, gender, and medical history. Units for dosage are commonly milligrams (mg), grams (g), or milliliters (mL), and frequency is expressed as "times/day" or "times/week."
Constraints Imposed by These Characteristics on Deployment and Upgrade
High-frequency updates of user feedback and sales records require FastGPT deployments to have efficient data ingestion and index update capabilities to ensure real-time pharmacovigilance information. Unstructured user feedback text demands robust text parsing and entity recognition capabilities, which in turn require advanced model versions and computational resources. Periodically updated drug inserts and product catalogs necessitate version management and incremental update mechanisms to avoid full re-indexing. The mixture of large volumes of structured and unstructured data challenges knowledge base hierarchical storage and retrieval strategies. Data sensitivity (patient information, adverse event details) requires strict adherence to data security and privacy protection guidelines during deployment and upgrades, ensuring data integrity and confidentiality during migration and storage.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodate large PDF or image files for drug inserts. |
maxContext | 2000 characters | Adapt to the length of detailed user medication feedback and adverse event descriptions. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allow sufficient time for parsing large PDF inserts or complex structured files. |
Chunk size | 800–1200 characters | Balance semantic completeness and retrieval efficiency, especially for user feedback. |
Recall count | 10 entries | Ensure enough relevant information is retrieved from the knowledge base for analysis. |
Similarity threshold | 0.75 | Improve retrieval accuracy and filter out irrelevant pharmacovigilance information. |
Common Pitfalls
- After a knowledge base upgrade, some critical fields (e.g., drug batch numbers, adverse reaction codes) return empty results during retrieval. This often happens due to differences in data models between old and new versions, or data migration scripts failing to correctly map fields during the upgrade.
- User feedback processing efficiency significantly decreases after an upgrade, and response times increase. This may occur if the new model version demands more computational resources, but the hardware resources of the deployment environment are not upgraded concurrently, leading to performance bottlenecks.
- After upgrading the system to a new version, some existing Q&A applications fail to work, reporting "unknown parameter" errors. This is often due to a large version upgrade gap, where parameters referenced in old configuration files have been removed or renamed in the new version, requiring manual configuration adjustments.
How to Verify Correct Configuration
- Upload a file containing various drug inserts and user feedback data. Confirm all content is correctly parsed and indexed in the knowledge base.
- Randomly select several drug batch numbers and adverse reaction keywords for retrieval. Check the accuracy and completeness of the returned results, and compare if the number of retrieved items meets expectations.
- Simulate high-concurrency user consultation scenarios. Monitor system response times and resource utilization to confirm stable operation under peak load.
The values given are common starting points and should be measured against the reader's own 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.