Build an AI Chatbot with Django & LangChain — Llama 3.3 + RAG + Memory

A fully functional AI-powered product chatbot built from scratch using Django, LangChain, and Meta's Llama 3.3 70B!

TechStore is a professional electronics store with an intelligent shopping assistant that answers product questions, checks prices, and delivers personalized recommendations — all powered by a real AI language model running in a real Django web application.

What's covered:
→ Setting up a Django project with multiple apps
→ Building a professional dark-themed UI with HTML & CSS
→ Using LangChain chains, memory, and prompts
→ RAG (Retrieval Augmented Generation) over a CSV product catalog
→ Storing conversation history with Django sessions
→ Connecting Llama 3.3 70B to a Django backend
→ Building a real-time chat interface with JavaScript fetch API

Tech Stack:
→ Python & Django 4.2
→ LangChain
→ Meta Llama 3.3 70B
→ HTML, CSS, JavaScript
→ SQLite & Django Sessions
→ CSV product catalog with 50 electronics products

A complete end-to-end project — from zero to a working AI chatbot running on a local machine. Perfect for developers looking to integrate large language models into real web applications.

Tags:
Django AI chatbot, LangChain tutorial, Llama 3.3, RAG tutorial, Django LangChain, AI web app, Python chatbot, LangChain Django, Llama chatbot, retrieval augmented generation, Django project, LangChain memory, AI shopping assistant

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