Build a RAG Chatbot for FREE Using Google Colab & Gemini API | RAG Explained | Part 2

Build a RAG Chatbot for FREE Using Google Colab & Gemini API | RAG Explained | Part 2 Want to build your own AI chatbot from scratch? 🤖 In this video, learn how to build a RAG chatbot using Python, Google Colab, LangChain, ChromaDB, and the Gemini API with a step-by-step practical implementation! Welcome to What Is RAG? | Part 2, where we move beyond theory and explore how to build a working Retrieval-Augmented Generation (RAG) model. Understand how RAG works in artificial intelligence and how it enables Large Language Models (LLMs) to generate relevant responses using information retrieved from a knowledge base. The best part? You can get started with Google Colab without installing software on your computer! What You’ll Learn in This Video: ✅ What is RAG in AI, and how does a RAG model work? ✅ Setting up Google Colab and configuring the Gemini API ✅ Loading and processing datasets using Pandas ✅ Creating documents and metadata for retrieval ✅ Generating embeddings using Hugging Face sentence-transformer models ✅ Storing data in a ChromaDB vector database ✅ Retrieving relevant information using semantic search ✅ Integrating Gemini with LangChain to generate responses ✅ Building a RAG chain and creating an AI chatbot interface using Gradio ✅ Understanding advanced concepts like Hybrid RAG and Agentic RAG 🛠️ Technologies Covered: Python, Google Colab, LangChain, Gemini API, ChromaDB, Hugging Face, Sentence Transformers, Pandas, Gradio, and LLMs. 🎯 Who Should Watch? ✅ Beginners exploring artificial intelligence and Generative AI ✅ Students learning RAG, LLMs, and AI development ✅ Aspiring AI/ML engineers building practical AI projects ✅ Developers interested in creating AI chatbots and RAG applications Continue your learning journey with RAG Part 1, where we explore what RAG is, how Retrieval-Augmented Generation works, its architecture, and its different types. 💬 Have questions about RAG, LangChain, embeddings, vector databases, or Agentic RAG? Share them in the comments! 👍 Like, share, and subscribe for more practical tutorials on AI, Generative AI, Python, LLMs, and AI engineering. Want to learn practical skills and stay ahead in today’s rapidly changing professional world? 🚀 At SKILL MADE EASY, we simplify complex concepts and turn them into practical, real-world knowledge that helps you learn, build, and grow. In this video, we explore valuable insights, practical skills, and industry-relevant knowledge to help you stay updated with the latest technologies and prepare for the evolving demands of the professional world. 📌 What You’ll Learn: 🔹 Generative AI (GenAI) & Artificial Intelligence 🔹 AI Tools & Emerging Technologies 🔹 Future Skills & Industry Trends 🔹 Data Science & Data Analytics 🔹 Digital Marketing & SEO 🔹 Programming & Technology Skills 🔹 Cyber Security 🎯 Who Is This Video For? ✔️ Students & Freshers looking to build practical, job-ready skills ✔️ Working Professionals looking to upskill, reskill, or grow their careers ✔️ Career Switchers exploring new opportunities ✔️ Tech Enthusiasts interested in AI, Data Science, Digital Marketing, Cyber Security, and emerging technologies About SKILL MADE EASY: SKILL MADE EASY focuses on practical learning, hands-on projects, expert mentorship, industry-relevant skills, certifications, and career guidance. 🔗 Follow SKILL MADE EASY for More Updates: 🌐 Website: https://skillmadeeasy.in/ 📲 Telegram: https://t.me/skillmadeeasy 💬 WhatsApp Channel: https://whatsapp.com/channel/0029VbDt... If you found this video useful, Like 👍, Comment 💬, Share ↗️, and Subscribe 🔔 to SKILL MADE EASY for more practical tutorials, career guidance, technology insights, and future-ready skills. #rag #artificialintelligence #generativeai #langchain #aichatbot #python #geminiai #chromadb #agenticrag #machinelearning #llm #googlecolab #skillmadeeasy #madeeasy