Comparison of Graph Databases and Relational Databases in Retrieval-Augmented Generation (RAG) Bots
Abstract
This thesis evaluates the performance of graph and relational databases as retrieval backends in production-grade Retrieval-Augmented Generation (RAG) chatbot systems. The study examines how database architecture affects retrieval performance in conversational AI systems and how database design choices influence retrieval speed and long-term system behavior under realistic workload conditions.
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