Deployment and Upgrades for Pharmacovigilance in Academic

Data Characteristics in This Category

Pharmacovigilance data for academic promotion primarily originates from clinical trial reports, real-world evidence (RWE) data, medical literature, drug labels, and various adverse event reporting systems (e.g., MedDRA). Data update frequency varies by source. Clinical trial data typically generates in bulk after study completion, while adverse event reports are continuous and high-frequency. Document structures are complex and diverse, including structured database records, semi-structured XML files (like ICSR reports), and extensive unstructured text (e.g., medical reports, patient feedback). Fields include drug generic name, active ingredient, dosage form, indication, adverse event name, occurrence time, severity, patient characteristics (e.g., age, gender), and concomitant medications. Units commonly use milligrams (mg) or grams (g) for dosage, days, months, or years for time, and percentages or incidence rates for frequency.

Constraints on Deployment and Upgrades from These Characteristics

The complex and diverse document structures and data types require FastGPT to have robust heterogeneous data parsing capabilities during preprocessing, especially for extracting and structuring unstructured text. Continuous, high-frequency updates to adverse event reports demand high requirements for real-time knowledge base synchronization and incremental indexing mechanisms to prevent information lag from affecting promotion accuracy. Sensitive patient information and drug data necessitate strict data security and compliance in the deployment environment, including data anonymization, access control, and audit logs. Standardized field and unit processing is crucial for RAG recall precision and accurate content generation, requiring predefined or configurable rich entity recognition and normalization rules. For academic promotion, knowledge base timeliness and accuracy directly impact decision support for medical professionals, making rapid iteration and fault recovery capabilities critical after deployment.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Supports uploading large clinical reports and literature sets, ensuring data completeness.
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Balances context integrity and recall efficiency, suitable for medical literature length.
`Recall count` (Recall Count)	`10 entries` (items)	Increases recall scope, improving recall rate for complex medical queries.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Calibrate using test datasets based on semantic similarity characteristics of adverse event reports.
`Rerank result count` (Rerank Return Count)	`5 entries` (items)	Selects the most relevant medical facts, reducing information overload for academic promoters.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Addresses potential timeouts when parsing large PDF documents or complex tables.

Three Common Mistakes

Application conversation logs are not recorded as expected or are incomplete. The symptom is that specific interaction records for a time period are not found in the backend. This is caused by a LOG_LEVEL configuration that is too high or insufficient permissions for the log storage path.
After a knowledge base update, newly uploaded professional literature content cannot be retrieved promptly. The symptom is that AI responses lack the latest information. This is caused by an excessively long Knowledge Base Indexing Cycle setting or a failed backend indexing task that did not trigger a retry.
In a multi-replica deployment environment, some request response times are too long or even time out. The symptom is slow frontend loading or 504 errors. This is caused by an unoptimized Load Balancing Strategy, leading to uneven request distribution or resource exhaustion on specific replicas.

How to Verify Correct Configuration

Upload a typical adverse event report or clinical trial data. Check if the knowledge base index status shows "completed" and attempt to retrieve content using keywords to verify recallability.
Simulate questions from academic promoters. Query drug dosages, adverse event incidence rates, etc., and verify the accuracy of key numbers and units in the AI-generated responses.
In the deployment environment, check container logs or system monitoring to confirm that FastGPT service memory and CPU usage fluctuate within normal ranges, without sustained high levels or abnormal interruptions.
Perform a small-scale incremental knowledge base update, such as adding a new medical document. Then immediately query related content to verify that the updated data is retrievable within a reasonable timeframe.

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.