Semantic Document Retrieval Using Generative Artificial Intelligence

This study presents a comprehensive exploration and implementation of a Generative AI system tailored for semantic document retrieval, leveraging the innovative concept of vector embeddings.

Abstract

This study presents a comprehensive exploration and implementation of a Generative AI system tailored for semantic document retrieval, leveraging the innovative concept of vector embeddings. It delves into the realm of natural language processing, where traditional methods often fall short in capturing the nuanced semantic relationships between documents. Utilizing the power of vector embeddings, specifically embedding representations like Word2Vec, Doc2Vec, and Sentence-BERT, this study aimed to create a robust semantic understanding of the document corpus. Additionally, the challenges faced, lessons learned, and avenues for future enhancements in this evolving field of AI-driven semantic document retrieval are also discussed at the end.