Chatbot Integrated with Gen AI

Conversational AI systems powered by Large Language Models (LLMs) have improved natural human–computer interaction but remain limited by static training data and frequent inaccuracies. To address these challenges, this project implements a Retrieval-Augmented Generation (RAG) chatbot that grounds responses in user-uploaded documents. The system uses a modular full-stack architecture with a React frontend, Node.js/Express backend, and a Python microservice for document processing, embedding generation, and semantic retrieval through FAISS. A commercial LLM (Cohere) generates responses only after relevant context is retrieved, ensuring privacy since raw documents are never exposed for training. Testing confirmed that the chatbot delivers domain-specific, reliable answers while minimizing hallucinations and safeguarding sensitive data. The prototype establishes a scalable and secure framework for enterprise and educational use. Future work includes expanding to multimodal data, federated learning, and integration with knowledge graphs for greater adaptability and transparency