Abstract : The rising rates of stress, anxiety, and depression, along with the lack of instantaneous help with mental health problems, call for the development of a rational and empathic digital mental health assistant. The suggested design is an artificial intelligence-based multimodal mental health assistant by the name of EmoAI, that will help patients cope with their mental health issuesinstantly, providing assistance with text, speech, and video conversations. Moreover, the suggested mental health assistant is based on the use of the Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) techniques. To put it shortly, the suggested design is a web-based platform for chat, speech, and video communications, an RAG model that can extract the right empathic responses from a filtered dataset of mental health responses and an LLM for generation of the short and soothing messages. Some of the additional features offered are speech recognition, text-to-speech transformation, and video conversation enabled with the help of AI and video avatar. The data processing flow uses semantic alignment and context alignment by referencing conversational data concerning mental health disorders to provide solutions for mental disorders like generalized anxiety disorder, depression, panic disorders, and social anxiety disorders in a manner that is more emotionally meaningful. The tests conducted during this project demonstrate enhanced fluidity in conversations.

Keyword : AI mental health assistant, empathetic conversational AI, Large Language Models (LLMs), multimodal interaction, preventive mental healthcare, real-time emotional support, Retrieval-Augmented Generation (RAG)