A Comprehensive RAG-Based LLM for AI-Driven Mental Health Chatbot

Mental health challenges affect millions worldwide, with approximately 970 million people experiencing conditions such as anxiety and depression. In the United States alone, 57.8 million adults struggle with mental illness. Despite the growing need for support, many face barriers such as stigma, limited access to care, and long waiting times, especially in underserved areas with few mental health resources. AI-powered chatbots are an emerging intervention option that offers available, accessible, and confidential services. The study below describes the development of an AI chatbot to support people in distress, using a fine-tuned version of the Llama model, which was trained on actual mental health counseling conversations. The chatbot uses Retrieval-Augmented Generation (RAG) to improve the relevance of its responses and make interactions more personalized. It also leverages LangChain’s ConversationBufferMemory to recall past conversations, allowing for more natural and meaningful dialogue. Facebook AI Similarity Search (FAISS) and Sentence Transformer embeddings also allow the retrieval of relevant materials to provide practical, real-time coping strategies. This chatbot uses natural language processing (NLP) and machine learning (ML) to bridge the gaps in mental health care, making it more affordable, accessible, and free of stigma. The current paper discusses the effectiveness of chatbots, the challenges in AI-driven mental health support, and future improvements in deeper personalization, improved response refinement, and integration with multimedia resources for a more holistic user experience.