Healthcare Chatbot: AI-Powered Medical Information Assistant Using Retrieval-Augmented Generation

The advent of Artificial Intelligence (AI) and Natural Language Processing (NLP) has transformed existing technology systems in the case of modern healthcare. This project aims at developing a Healthcare Chatbot based on a Retrieval-Augmented Generation (RAG) architecture to provide medically relevant and accurate responses to user inquiries. The system retrieves medical information from healthcare authoritative documents and uses a semantic vector search. An information retrieval based Large Language Model (LLM) uses the information to provide a response that is relevant and informative. The architecture uses embedding models from Hugging Face to construct semantic vectors. The Pinecone vector database is used for contextual healthcare information storage and retrieval. LangChain is used

to manage the retrieval and prompt workflows. Access to the Llama 3.3 LLM is provided via the Groq API for rapid response generation. The implementation of the chatbot is done on Flask and the deployment is managed using Docker and the AWS EC2 cloud. The comparative evaluations done demonstrate improvements in factual accuracy and reductions in hallucination and response time compared to traditional generative AI systems. This Healthcare Chatbot demonstrates the use of modern AI technology to improve information technology in the healthcare field and improve information access through a conversational chatbot to users.

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