The Future of Document Retrieval: Harnessing the Power of OpenAI and AWS Kendra

In an era inundated with data, the quest for efficient and accurate information retrieval has become paramount for organizations. Traditional enterprise search methodologies, while foundational, grapple with challenges ranging from relevance issues to the complexities of everevolving linguistic nuances. This research delves into a transformative approach to enterprise search by integrating OpenAI's Foundation Model, a state-of-the-art AI model renowned for its profound linguistic understanding, with Amazon Web Services' Kendra, a machine learning-powered search platform. Our integrated system, showcased through a meticulously designed architecture, demonstrates a remarkable
$92 \%$
accuracy in document retrieval, outshining the
$78{{\% }}$
achieved by traditional methods. Furthermore, user feedback sessions accentuated the system's adeptness in natural language query processing and the heightened relevance of search results. This paper not only presents the fruits of melding advanced AI with robust search platforms but also paves the way for the future of intelligent enterprise search, emphasizing adaptability, continuous learning, and the promise of a search realm that's not just efficient but profoundly insightful.