The Nexus of AI and Vector Databases: Revolutionizing NLP with LLMs

Abstract

Vector databases play a critical role in the efficiency and functionality of large language models (LLMs), providing scalable and efficient storage and retrieval of high-dimensional vectors. This paper explores the significance of vector databases in the context of LLMs, highlighting their role in information retrieval, similarity search, training, and adaptation processes. Despite the challenges posed by high-dimensional data, vector databases offer invaluable benefits in enhancing the capabilities of LLMs and driving advancements in natural language processing (NLP). Future research and development in this area promise to further optimize the integration and performance of vector databases, fueling continued innovation in LLM applications. Keywords: Vector databases, Large language models (LLMs), Natural language processing (NLP), Information retrieval, Similarity search, Training, Adaptation, Scalability, Efficiency.