Published Paper Details:

This project, titled "Semantic and Generative AI Integration for Personalized Career Guidance," focuses on developing an advanced AI-based virtual personal assistant chatbot tailored to deliver accurate, contextually relevant, and professional career advice. The integration of Llama3 via the Ollama API ensures robust generative capabilities, enabling seamless, conversational, and context-aware responses while maintaining conversational history for stateful interactions. The system employs theLM-L6-v2 Sentence Transformer to encode a pre-characterized dataset of career-related questions into high-dimensional embeddings. By leveraging cosine similarity, the chatbot efficiently calculates semantic similarity to identify and match the most contextually relevant question-answer pairs. This approach ensures precise and personalized guidance for users. Designed with scalability and adaptability in mind, the modular architecture facilitates the integration of additional datasets and NLP models to enhance system capabilities. The chatbot incorporates Natural Language Processing (NLP) and AI-driven conversational interfaces to address real-world challenges, offering personalized career guidance in a user-friendly and accessible manner. The user interface, implemented using Streamlit, provides an intuitive platform for smooth interactions. To enhance accessibility, the system supports voice-based queries through Speech Recognition technology and delivers verbal responses via Google Text-to-Speech (TTS) or pyttsx3, creating a natural and engaging user experience. This project demonstrates the practical application of semantic similarity matching and generative AI technologies, blending theLM-L6-v2 Sentence Transformer for semantic analysis with the Llama3 model for context-aware conversational responses. By integrating these technologies, the system provides users with actionable, coherent, and highly personalized career guidance through voice-based interactions.