Install Local AI RAG in Google Colab for FREE

Build a Complete RAG (Retrieval-Augmented Generation) System for FREE using Google Colab, Ollama, Phi-3, and FAISS! In this step-by-step tutorial, you'll learn how to create your own AI-powered Retrieval-Augmented Generation (RAG) pipeline directly inside Google Colab without installing anything on your computer. ✅ What You'll Learn: • What Google Colab is and why it's perfect for AI projects • How to access FREE CPU, GPU, and TPU resources • Install Ollama inside Google Colab • Download and run the Phi-3 language model • Generate embeddings locally using Ollama • Build a FAISS vector database for semantic search • Retrieve relevant documents from your dataset • Combine retrieval + generation to create a complete RAG workflow • Query your data using natural language 📂 GitHub Repository: https://github.com/nextnexa2025-max/G... 🎯 Challenge for Viewers: In this tutorial, when context is not found, the system returns a message instead of querying the LLM directly. Can you modify the code to automatically switch to the LLM when no relevant context exists? Post your solution in the comments below! 👇 💡 By the end of this tutorial, you'll have a fully functional RAG system capable of searching your own documents and generating context-aware AI responses. #RAG #Ollama #Phi3 #GoogleColab #AI #LLM #MachineLearning #GenerativeAI #Python #FAISS #VectorDatabase #LocalAI #OpenSourceAI #ArtificialIntelligence #NextNexa