AI-Enabled Semantic Web

The paper presents a comprehensive study on the development of an AI-enabled Semantic Search application using a Full Stack approach. The project integrates cutting-edge technologies, such as Next.js, Langchain, Pinecone, and ChatGPT, to create a robust and efficient information retrieval system. The methodology begins by setting up the essential imports and utility functions for Langchain and Pinecone. Diverse data types are then loaded seamlessly using specialized loaders. The project's core involves the creation of a Pinecone index and successfully uploading documents, which establishesa powerful and accessible database. The application's user interface can be enriched with advanced UI features. These features enhance the aesthetic appeal and interactivity of the platform, facilitating user navigation and query formulation. The implemented technique discusses the challenges of developing an AI-enabled Semantic Search application, such as the need to deal with large volumes of data and the complexity of natural language processing, and suggests solutions to these challenges, such as using distributed computing and machine learning techniques. The culmination of these efforts is a comprehensive Semantic Search application that leverages the capabilities of AI and modern web technologies to deliver an immersive, efficient, and personalized information retrieval experience. This research contributes to the advancement of full-stack development practices and demonstrates the potential of integrating AI technologies to enhance user interactions with data-rich platforms.