Build an E-Learning Website with the MERN Stack

In this project, we'll build a full-stack e-learning platform using the MERN stack (MongoDB, Express.js, React.js, Node.js) with an intelligent AI-powered chatbot for course assistance. The platform features a complete course catalog, real-time search functionality, and a retrieval-augmented generation (RAG) chatbot powered by Google Gemini and LangChain that answers user questions about courses using embedded project data. We'll develop both backend REST APIs and a responsive React frontend, demonstrating modern full-stack web development practices.

We'll start by setting up the Node.js backend with Express.js, connecting to MongoDB for NoSQL database storage, and defining Mongoose schemas for course data. We'll create RESTful API endpoints to retrieve course information and implement a vector store for the LLM-powered chatbot using LangChain framework. Next, we'll build React components for displaying the course catalog, implement a dynamic search bar with real-time filtering, and create a chatbot interface that queries the Google Gemini API for context-aware responses based on document embeddings.

Finally, we'll connect the React frontend to the Express backend through HTTP requests, integrate all components into a cohesive single-page application, and test the complete workflow including course browsing, search functionality, and AI chatbot interactions. By the end, you'll have a production-ready MERN application demonstrating MongoDB database design, Express API development, React component architecture, Node.js server configuration, LangChain integration, and RAG implementation applicable to any educational platform or content management system.