Design and Develop Medical Assistant Bot

This paper presents the design and implementation of MediBot, an AI-driven medical chatbot utilizing the Retrieval Augmented Generation (RAG) framework. MediBot is built upon a modular pipeline that integrates document ingestion, vector embedding generation, external memory retrieval, and Large Language Model (LLM) response generation. The system loads medical documents, processes them into vector embeddings using FAISS, and enables efficient retrieval of relevant information during user interaction. By leveraging a Hugging Face-hosted Mistral-7B Instruct-v0.3 model, MediBot provides contextually accurate and reliable responses in the medical domain. The paper discusses the methodologies involved, technologies used, system architecture, challenges encountered, and potential improvements for future versions. This work highlights the increasing importance of combining domain-specific data with advanced LLMs to enhance chatbot reliability and reduce hallucination in sensitive fields like healthcare.