Mental Chatbot Application Using Retrieval Augmented Generation

The ever-increasing need for psychological support faces challenges like resource scarcity, patients’ fear of stigma, and information gaps. While access to professional help remains crucial, high costs and social anxieties may pose significant barriers. Recognizing the growing potential of large language models (LLMs) in domain-specific applications, we propose a novel mental health chatbot leveraging customized retrieval augmented generation (RAG) methodology. This paper introduces a chatbot equipped with deeply integrated mental health knowledge. By harnessing the power of RAG, it retrieves relevant information and generates tailored responses, simulating a supportive and informative dialogue. Additionally, the chatbot bridges the gap to professional care by connecting users with nearby psychiatrists or psychology centers, fostering accessible and timely assistance. Through rigorous evaluation, we aim to assess not only the chatbot’s efficacy in providing psychological consultation but also its potential to reduce barriers to professional help. This study represents a critical step toward leveraging technology to democratize access to mental health support, ultimately contributing to a future where mental well-being is within reach for all.