Multi-Tiered RAG-Based Chatbot for Mental Health Support

Mental health challenges are increasing globally, and many are unable to access timely and affordable care due to financial, geographical, and societal barriers. Traditional mental health solutions often fall short in offering tailored support, re-lying on rigid, one-size-fits-all approaches that fail to address the unique needs of individuals, especially in underserved regions. To address these gaps, a multi-tiered chatbot leveraging Retrieval-Augmented Generation (RAG) provides dynamic, context-aware mental health assistance. Operating in four tiers, it offers general information, support for common issues, specialized guidance for conditions like anxiety and bipolar disorder, and personalized recommendations from user-uploaded documents. The RAG-based approach integrates the latest studies and techniques into its knowledge base, evolving in real-time without costly re-training of large language models (LLMs). This cost-effective system lowers financial barriers while improving access to quality mental health care in underserved regions. By enabling proactive and affordable mental health support, this model presents a sustainable solution to the global mental health crisis.