RAG-based Architectures for Drug Side Effect Retrieval in LLMs

To overcome these significant challenges, we propose two novel architectures designed to integrate domain knowledge about drug side effects into a Llama 3 - 8B Language Model: Retrieval Augmented Generation (RAG) and GraphRAG. Our first architecture employs RAG, which enhances LLMs by retrieving relevant information from an external Pinecone vector database where drug side effect information is stored as feature vectors. The second architecture utilizes GraphRAG, which leverages a Neo4j graph database to stor e and efficiently handle more complex relationships of drug side effect associations. Both frameworks incorporate custom split functions and filtering modules to optimize user prompts for accurate retrieval. Through extensive evaluations on 19,520 associat ions between 976 marketed drugs and 3,851 unique side effect terms, we demonstrate that GraphRAG achieves near - perfect accuracy in drug side effect retrieval, significantly outperforming standalone LLMs and standard RAG approaches.