In this project, I developed a Streamlit-based application that enables users to chat with website content. By leveraging LangChain’s capabilities for document loading, text splitting, and OpenAI embeddings, I created a retrieval-augmented generation (RAG) model. The application retrieves relevant information from a website, breaks the content into manageable chunks, and uses it to generate AI-powered conversational responses based on user queries. The system maintains conversation context to ensure coherent, history-aware interactions. I completed this project by integrating multiple LangChain components and building the app interface using Streamlit, providing users with a seamless chat experience with web-based information.