SEMANTIC SEARCH THROUGH VECTOR STORES: SIGNIFICANCE IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS OF NLP MODELS

Abstract

Semantic search has emerged as a pivotal technology in artificial intelligence (AI), enabling systems to understand and retrieve information based on meaning rather than mere keyword matching. This paper explores the significance of vector stores in enhancing semantic search capabilities within AI systems, with a particular focus on Natural Language Processing (NLP) models. We discuss how vector representations of text, powered by advanced NLP techniques such as transformer-based architectures, facilitate efficient and accurate information retrieval. The integration of vector stores with AI not only improves search precision but also opens new avenues for applications in knowledge management, question-answering systems, and beyond. This study aims to elucidate the critical role of these technologies in modern AI frameworks.