Abstract: The rapid advancement of artificial intelligence (AI), particularly in generative models, has led to an exponential increase in the need for efficient handling of high-dimensional vector data. This paper explores the critical role of vector databases in modern AI applications, focusing on their capabilities, use cases, and the challenges they address.Vector databases have emerged as a critical component in the development of generative AI applications. This paper provides a comprehensive review of the role of vector databases in generative AI, focusing on their ability to store, manage, and retrieve high-dimensional vector data efficiently. This paper explores the critical role of vector databases in modern AI applications, focusing on their capabilities, use cases, and the challenges they address. We examine the fundamental limitations of relational databases in handling vector data, contrasting them with specialized vector databases that are optimized for high-dimensional data storage and similarity search. The paper surveys various vector database solutions, including those offered by major cloud providers like Google, AWS, and Microsoft, and highlights their integration with generative AI frameworks such as Lang Chain, Semantic Kernel, and Vertex AI. We also discuss the impact of vector databases on retrieval-augmented generation (RAG) and other AI-driven applications, emphasizing their ability to enhance the accuracy and relevance of large language models (LLMs). Additionally, the paper provides insights into future trends, including scalability improvements, integration with knowledge graphs, and ethical considerations in AI development. By addressing performance, cost, and implementation challenges, this paper aims to provide a comprehensive understanding of how vector databases are shaping the future of generative AI.

Keywords: Vector Databases, Generative AI, Retrieval-Augmented Generation (RAG), High-Dimensional Data, Machine Learning, AI Applications Vector Databases, Large Language Models

| DOI: 10.17148/IARJSET.2025.12210