I Built a Full-Stack AI Platform with Django & LangChain | RAG Chatbot + Personal Document Assistant

Описание: 🚀 AskRAG — A Full-Stack AI-Powered Knowledge Platform

In this video, I showcase AskRAG — a complete AI-powered platform I built from scratch using Django, LangChain, and ChromaDB. This project combines RAG (Retrieval-Augmented Generation) technology with real-time messaging and a full blogging system.

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📌 FEATURES

🤖 Website RAG Chatbot
→ Answers questions about blog content using AI
→ Semantic search with vector embeddings
→ No hallucinations — answers only from actual content

📄 Personal Document Assistant
→ Upload PDFs, Word docs, and text files
→ Chat with your documents using AI
→ Each user gets isolated vector storage — 100% private
→ Generate MCQs, summaries, and study notes

💬 Real-Time Messaging
→ WebSocket-powered instant chat
→ Private conversations between users
→ Async consumers for performance

📝 Blogging Platform
→ Full CRUD operations
→ Categories and nested comments
→ AI-powered blog summarization

🔐 User Management
→ JWT authentication
→ Email verification
→ Follow system with notifications

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🛠️ TECH STACK

• Backend: Django + Django REST Framework
• AI/LLM: LangChain + Groq API (LLaMA 3.3 70B)
• Vector DB: ChromaDB
• Embeddings: HuggingFace (sentence-transformers/all-MiniLM-L6-v2)
• Real-Time: Django Channels + WebSocket
• Authentication: JWT (SimpleJWT)
• Database: PostgreSQL
• API Docs: Swagger (drf-spectacular)

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🔗 LINKS

GitHub Repository: https://github.com/Andrew-Velox/Mega-...

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💡 WHAT I LEARNED

→ Building production-ready RAG pipelines
→ User-isolated vector database architecture
→ Async WebSocket consumers in Django
→ Secure multi-tenant API design

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📢 CONNECT WITH ME

GitHub: https://github.com/Andrew-Velox
LinkedIn:   / mohabbatvlx  

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#Django #Python #AI #RAG #LangChain #MachineLearning #FullStack #WebDevelopment #ChromaDB #LLM #Groq #Portfolio #CodingProject