Smart Emotional Chatbot

This project presents the development of a mental health support chatbot that leverages large language models (LLMs) and domain-specific knowledge to provide reliable, empathetic responses. The system integrates Groq's high-performance LPU-powered LLMs with a retrieval-augmented generation (RAG) pipeline, enabling the chatbot to access curated mental health literature stored in a Chroma vector database. Documents such as "Defining Mental Health and Mental Illness" are pre-processed using text splitting and integrated with Hugging Face models to ensure efficient semantic search. When users enter a query, the retriever identifies relevant situations or cases, which are then combined with the user's question and passed to the LLM through a structured prompt. The chatbot is deployed on a Gradio interface, providing an understandable conversational platform that supports for example queries and personalized interaction. Crucially, the system prioritizes user safety by incorporating a proactive alert mechanism. If distress patterns are detected, the system automatically sends an email notification to a pre-registered trusted contact. The knowledge base emphasizes key mental health topics including the definitions of mental health and illness, the scale of adolescent mental health problems, the impact of stigma, and evidence-based interventions. By combining the strengths of LLMs with domain-related documents, this system aims to enhance mental health literacy, reduce misinformation, and provide accessible, Honorable, and meaningful free guidance.