Graph Based Retrieval-Augmented Generation for Personalized Dietary Guidance with LLMs

This paper presents a personalized diet recommendation system that leverages a graph database and retrieval-augmented generation (RAG) with large language models (LLMs) to provide accurate and tailored nutritional plans. The system focuses on vegetarian Indian dishes and analyzes individual health metrics such as physical activity levels, weight age and height to generate customized diet plans. A Neo4j graph database, built from scraped recipe data, stores dish names, ingredients, and nutritional values, enabling precise information retrieval via LLMs. Additionally, the system provides nutritional details and overall health assessments of the dishes. To enhance overall health, the system also suggests basic workout plans based on users' physical metrics and dietary recommendations. This approach aims to provide a low-cost, accessible alternative to traditional diet consultations while mitigating hallucinations in AI outputs through RAG and structured data.