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/Google_Projects/tree/main/GoogleCollabRAG1

🎯 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

Видео Install Local AI RAG in Google Colab for FREE канала NextNexa