Data Characteristics in This Category
Medical affairs pharmacovigilance data primarily originates from post-market surveillance reports, clinical trial adverse event records, literature reviews, and real-world data. This data often exists in semi-structured or unstructured document formats, such as adverse drug reaction report forms (MedWatch, CIOMS I), patient case reports, medical literature abstracts, and drug inserts. Regulatory requirements and event-driven factors influence data update frequency, which is typically continuous, with new reports and updates constantly flowing in. While standard templates exist for document structures, reports from different sources vary in field population, narrative style, and terminology. Key fields include patient demographics, drug information (brand name, generic name, batch number), adverse event description (symptoms, signs, diagnosis), event time, outcome, and assessment results. Units for dosage are often expressed in milligrams (mg), grams (g), or international units (IU). Time units include days, hours, and minutes. All these require precise identification and standardization.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The semi-structured and unstructured nature of medical affairs pharmacovigilance data places specific demands on FastGPT deployment. Processing massive, continuously updated adverse event reports requires robust data ingestion and preprocessing capabilities to ensure accurate information extraction and conversion into queryable knowledge. The diversity of document structures means the knowledge base's document parsing module needs high flexibility and robustness to accommodate various report formats. Additionally, event reports contain extensive specialized medical terminology and abbreviations. This requires FastGPT to focus on medical domain knowledge when selecting and fine-tuning embedding models to improve semantic understanding accuracy. The continuous data flow dictates knowledge base synchronization and index rebuilding strategies. These strategies must support incremental updates to avoid performance bottlenecks from full rebuilds and ensure query result timeliness. Accurate identification and unit standardization of critical information like time and dosage also demand advanced regular expression matching or entity recognition capabilities from the preprocessing pipeline.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large medical literature or bulk imported report files. |
Chunk size (Segment Length) | 800 characters (characters) | Ensures contextual completeness of adverse event descriptions while maintaining retrieval efficiency. |
Rerank result count (Reranked Return Count) | 5 | Improves the precision of critical information recall and reduces irrelevant results. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Allows complex documents (e.g., detailed PDF case reports) sufficient time for parsing. |
Knowledge Base Sync Frequency (Knowledge Base Sync Frequency) | Calibrate by actual measurement (Determined by actual measurement) | Based on the average ingestion rate of new adverse event reports and business requirements for timeliness. |
Embedding Model | Medical domain fine-tuned model | Enhances semantic understanding of medical terminology and adverse event descriptions. |
Three Common Mistakes
- If the knowledge base is inaccessible or slow after deployment, it might be due to an incorrect
SERVER_URLconfiguration or firewall policies blocking external connections. - After a new version upgrade, if the Embedding model fails to connect to OneAPI and logs show connection timeouts, the
BASE_URLorAPI_KEYfor OneAPI might not be updated to a version-compatible configuration. - Missing or incorrectly formatted key fields (e.g., drug dosage, event time) in adverse event query results usually indicate that regular expressions or entity extraction rules during the document parsing stage do not sufficiently cover all data variations.
How to Verify Correct Configuration
- Upload a PDF file containing a typical adverse reaction report. Confirm the file parses correctly and key fields (e.g., drug name, adverse event description) are accurately extracted.
- Execute a query containing medical terminology. Check if the returned results are highly relevant to the query intent. Adjust the
Similarity threshold(Similarity Threshold) to observe result changes and determine an appropriate threshold range. - Simulate high-concurrency query scenarios. Monitor system resource utilization (CPU, memory) to ensure stable system response during peak business hours.
- Compare the results of handling specific adverse reaction queries between the new and old versions. Ensure the accuracy and comprehensiveness of knowledge recall after the upgrade are not lower than the old version.
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.