A Retrieval-Augmented Generation (RAG) based chatbot built using ReactJS. This chatbot leverages advanced AI techniques to provide accurate and context-aware responses. The RAG model combines the strengths of retrieval-based and generation-based approaches to deliver more relevant and coherent answers. The frontend of the chatbot is developed using ReactJS, providing a responsive and interactive user interface. The backend is powered by Node.js and Python, with TensorFlow handling the AI processing. This architecture ensures that the chatbot can handle a large number of user queries efficiently. The chatbot is designed to be easily integrated into various platforms, including websites and mobile applications. It can be customized to suit different business needs, making it a versatile solution for customer support, information retrieval, and more.
This project involves the development of a chatbot that uses RAG to enhance its response generation capabilities. The frontend is built with ReactJS, while the backend leverages Node.js and Python with TensorFlow for AI processing.