Developing an elastic chatbot application for the healthcare domain, integrating the Retrieval Augmented Model (RAG) with the Elastic ELSER Model. Implementing the application using React for the front end and Python with Elasticsearch for the back end. Utilizing Azure Open AI as a Large Language Model (LLM) for chatbot functionality. Installing Python dependencies and creating a virtual environment for backend development. Importing datasets into Elasticsearch for data storage and retrieval. Configuring environment files with essential parameters such as Elasticsearch endpoint, Azure Open Key for model identification, and version details. Ensuring seamless integration between frontend and backend components for a smooth user experience. Leveraging RAG for improved retrieval of relevant information in the healthcare domain. Implementing Elastic ELSER Model for enhanced search capabilities within the chatbot application. Employed React to create a dynamic and interactive user interface, enhancing user engagement.