Soul-Buddy: A Student Mental Health AI Assistant Based on Large Language Models

Soul-Buddy is a mental health AI application for students, combining Large Language Models (LLM), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG) technologies. It aims to address issues such as limited resources, difficult appointments, and privacy concerns in traditional psychological counseling, providing personalized, context-aware psychological support services. The original author of the project is CodeWithSwastik-java-webdev, published on GitHub, with an update date of 2026-06-14.

Nowadays, students face psychological pressures such as academic competition, social anxiety, and uncertainty about future planning. Traditional psychological counseling has issues like limited resources, difficult appointments, and privacy concerns, so many students cannot get timely support. Soul-Buddy provides always-available, private, secure, and personalized psychological support through AI technology, which is a supplement to the traditional model and an exploration of AI solving social pain points.

Soul-Buddy adopts a full-stack architecture, with core technologies including: 1. Large Language Models (LLM) as the dialogue engine to understand complex natural language and generate empathetic responses; 2. Retrieval-Augmented Generation (RAG) technology combined with a psychology knowledge base to improve the accuracy and credibility of answers; 3. Natural Language Processing (NLP) for sentiment analysis and intent recognition, triggering intervention mechanisms in times of crisis.

The functional features of Soul-Buddy include: personalized dialogue experience (remembering history and preferences), multi-modal interaction support (voice input and output), privacy protection mechanisms (end-to-end encryption and local storage), and crisis intervention functions (identifying self-harm tendencies and triggering emergency resources).

The backend is built based on Python, using the FastAPI framework to provide API interfaces; the frontend uses the React tech stack to ensure cross-platform compatibility; the database uses a vector database to store the psychology knowledge base, supporting vector retrieval for the RAG architecture to quickly find relevant content to support LLM responses.

Soul-Buddy demonstrates the application potential of AI in the field of social public welfare; its anonymity breaks the stigma of mental health issues. It should be clear that the AI assistant cannot replace professional counselors and is suitable as a primary support tool to guide users to seek professional help when necessary.

Soul-Buddy combines LLM, RAG, and NLP technologies to provide a feasible solution for the digital mental health field, which has reference value and practical inspiration for developers and researchers in AI social applications, mental health technology, and educational technology.