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🛒 Welcome to Fusion Electronics - a sample full-stack, lightweight, and modern online e-commerce application, built with the MERN (MongoDB, Express, React, Node.js) stack! Also features a product recommendation service using vector search, FAISS, and the Weaviate/Pinecone vector DB!
Welcome to Fusion Electronics, a MERN-Stack E-commerce Application! This project is a working demo of a full-stack web application that was built using the MERN stack (MongoDB, Express.js, React.js, Node.js). Additionally, it also includes features such as user authentication, checkout process, product recommendations with vector search, and more!
It also aims to provide a comprehensive example of building a modern e-commerce platform, covering frontend user interface, backend server logic, database management, and integration with third-party libraries. Let's dive in!
This project is a demonstration of building an e-commerce application using the MERN stack, which consists of MongoDB (database), Express.js (server), React.js (frontend), and Node.js (runtime environment). The application allows users to browse products, add them to a shopping cart, proceed to checkout, and simulate the order processing. It includes comprehensive validations for user inputs and simulates the checkout process on the backend.
The application is designed to be user-friendly and responsive, providing a seamless shopping experience. It also includes features such as product search, user authentication, and order confirmation. Additionally, it uses Pinecone (with optional Weaviate support) for product recommendations based on vector search, enhancing the user experience by suggesting relevant products.
The application is deployed live on Vercel. You can access it at the following URL: Fusion Electronics App.
The primary backend server is deployed on Vercel and can be accessed at the following URL: Fusion Electronics API.
The backup backend server is deployed on Render and can be accessed at the following URL: Fusion Electronics API.
[!IMPORTANT] Note: The backend server may take a few seconds to wake up if it has been inactive for a while. For your information, it is hosted on the free tier of Render, with 0.1 CPU and 512 MB of memory only, so it may take a bit longer to respond to requests, especially after periods of inactivity.
[!CAUTION] Warning: The vector recommendation pipeline relies on Pinecone's serverless index (free tier). Please make sure your Pinecone project has enough credits and remains active; otherwise, recommendation calls may fall back to heuristic suggestions. You can always run the application locally and provision your own Pinecone and/or Weaviate instances—see Product Recommendations with Vector Database for setup details.