Welcome to the Interactive Medical Education LLM (Lifelong Learning Model) Chatbot project. This project aims to enhance medical education by providing an interactive learning experience through a novel approach called Retrieval Augmented Generation (RAG). The chatbot allows medical students to quiz themselves using flashcards based on textbook information to the Llama 2 model, providing a unique and effective learning experience.
final.demo.medical.Ai.mp4
The core feature of this project is the implementation of a Retrieval Augmented Generation (RAG) study LLM. This allows medical students to quiz themselves using textbooks, promoting active engagement with the study material. The RAG model leverages the power of retrieval-based learning and generative models to enhance the overall learning experience.
To facilitate efficient retrieval and linking of textbook information to the Llama 2 model, a vector database has been implemented. The vector database plays a crucial role in storing and retrieving information, ensuring a smooth and seamless learning experience for the users.
The project integrates with Llama 2, a specialized model designed for lifelong learning. This integration enhances the chatbot's capability to provide a unique learning experience by combining the power of retrieval-based learning, generative models, and lifelong learning methodologies.
The chatbot utilizes Chroma DB as a vector database, further optimizing the storage and retrieval of information. Chroma DB's capabilities enhance the efficiency of the vector database, contributing to the overall speed and performance of the chatbot.
This project is licensed under the MIT License, allowing for open collaboration and use.
We would like to express our gratitude to the Boston University community and all contributors who have played a role in the development of this project.
Happy learning!