mRNA Vaccine Pharmacovigilance Deployment and Upgrade

Data Characteristics

mRNA vaccine pharmacovigilance data originates from global drug regulatory adverse event reporting systems (e.g., WHO VigiBase, FDA FAERS, EMA EudraVigilance), clinical trial data, academic literature, and social media. This data exists as a mix of unstructured text (e.g., patient descriptions, medical terminology), semi-structured data (e.g., report form fields), and structured data (e.g., ICD-10 codes, MedDRA terms). Update frequency is high; regulatory reporting systems typically update daily or weekly, and literature searches are continuous. Document structures vary, including PDF clinical study reports, XML ICSR (Individual Case Safety Report) files, and plain text patient narratives. Fields and units are highly specialized, such as dosage units μg, mg, time units hours, days, and MedDRA Preferred Term and System Organ Class codes for adverse reactions.

Constraints on Deployment and Upgrade from Data Characteristics

The wide range and heterogeneity of data sources require deployment solutions with robust multi-source data ingestion capabilities, compatible with structured databases, unstructured documents, and API interfaces. High update frequency means the knowledge base needs to support real-time or near real-time incremental update mechanisms to ensure information timeliness. Document diversity challenges parsing modules, requiring accurate identification and extraction of key information from PDFs, XMLs, and plain text, such as patient information, medication history, adverse reaction descriptions, and severity. The presence of specialized fields and coding systems necessitates standardized medical terminology mapping during data processing, for example, mapping free-text descriptions to MedDRA terms. This directly impacts the accuracy of subsequent RAG retrieval. When processing this specialized data, setting FastGPT's maxContext parameter and chunking strategy is crucial to avoid losing critical medical information.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical study reports or regulatory guidelines can contain large amounts of data and charts, leading to large file sizes.
`maxContext`	`3000 Tokens`	Ensures comprehensive coverage of critical medical context in adverse reaction reports, such as patient descriptions, medication history, and diagnoses, reducing the risk of information loss.
`Chunk size`	`500 characters`	Balances the completeness of adverse reaction descriptions with retrieval efficiency, preventing excessively long chunks from introducing irrelevant information.
`Recall count`	`10 entries`	Given the complexity and potential interconnections of medical information, increasing the number of recalled items helps cover more relevant knowledge points.
`Similarity threshold`	`0.75`	High accuracy is required for medical terminology matching; a threshold that is too low may introduce significant noise, while one that is too high may miss critical information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large PDF or XML files can be time-consuming, requiring sufficient timeout duration.

Common Pitfalls

Knowledge base query results fail to accurately match specialized medical terms in adverse reaction reports, leading to empty or irrelevant retrieval results. This occurs because the model does not correctly identify MedDRA codes or generic drug names.
When processing large-scale incremental data, the system encounters out of memory errors or file deadlock states. This happens due to improper configuration of UPLOAD_FILE_MAX_SIZE and PARSE_FILE_TIMEOUT_SECONDS, leading to exhaustion of file processing resources.
After FastGPT deployment, an invalid token prompt appears when integrating with the OneAPI platform. This occurs because the API Key generated by the OneAPI platform is not configured correctly, or the OPENAI_API_KEY environment variable on the FastGPT side is not updated.

Verification of Configuration

Upload an adverse reaction report dataset containing complex medical terminology and various file formats (PDF, XML, TXT). Check if the knowledge base correctly parses and generates chunks.
Query for specific mRNA vaccine adverse reactions using search terms that include MedDRA terms. Verify if FastGPT's returned answers accurately cite relevant report snippets from the knowledge base. Check the actual effect of Recall count and Similarity threshold.
Simulate high-concurrency data updates. Monitor system resource usage to ensure no timeout or connection errors occur when processing incremental reports.

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.