RAG-LLM Chatbot for Science Education
Our project leverages a Retrieval-Augmented Generation (RAG) approach combined with a Large Language Model (LLM) to create an interactive chatbot. This chatbot is designed to consult PDFs of scientific articles to answer questions from the general public. The primary goal is to promote science education by making scientific knowledge more accessible and engaging to a broader audience.
Key Features:
- PDF Integration: The chatbot can process and extract information from scientific articles in PDF format.
- Natural Language Processing: Utilizes advanced LLM capabilities to understand and generate human-like responses to queries.
- Conversational Retrieval Chain: Implements a conversational retrieval chain to fetch relevant information from the documents and provide accurate answers.
- Interactive Learning: Provides an engaging platform for users to learn about scientific topics by asking questions in a conversational manner.
- Promoting Accessibility: Aims to make complex scientific information understandable and accessible to everyone, regardless of their background.
Benefits:
- Educational Tool: Serves as a valuable resource for students, educators, and science enthusiasts.
- Public Engagement: Encourages public interest in science and promotes lifelong learning.
- Accessible Knowledge: Breaks down barriers to accessing scientific literature by translating technical content into layman-friendly language.
By integrating cutting-edge AI technology with a user-friendly interface, our RAG-LLM chatbot fosters a greater appreciation for science and contributes to the democratization of knowledge.