Next-Gen AI E-commerce Assistant (Django + React + RAG + FAISS) 🚀 Full Stack Projectdemo
📌 Next-Gen AI Shopping Assistant | Styling Tips + FAQ Bot 🤖🛍️
In this video, I built a Next-Gen AI E-commerce Shopping Assistant that provides product recommendations, styling tips, and instant FAQ support using AI.
The system is built with a Django backend (API layer) and a React frontend, creating a full-stack AI-powered shopping experience.
It uses RAG (Retrieval-Augmented Generation) along with a FAISS vector database to deliver fast, relevant, and context-aware responses based on product and FAQ data.
✨ Features:
👗 Personalized styling recommendations
💬 Instant FAQ chatbot support
🔍 Semantic product search using embeddings
⚡ Fast similarity search using FAISS
🧠 RAG-based intelligent response generation
🛍️ Smooth e-commerce assistant experience
🌐 Full-stack integration (Django + React)
🛠️ Tech Stack:
Backend: Django, Django REST Framework
Frontend: React.js
AI/ML: RAG, NLP, Embeddings
Vector DB: FAISS
Other: Python, REST APIs
🚀 Project Goal:
To build a real-world AI-powered shopping assistant that improves user experience through smart conversations, styling help, and fast product discovery.
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