build Rag for every support engineer using LlamaIndex

GITHUB: https://github.com/ronidas39/llamaindex/tree/main/tutorial41 TELEGRAM: https://t.me/ttyoutubediscussion **YouTube Description:** Welcome to *Total Technology Zone*! 🚀 In **Tutorial 41**, I’m sharing an exciting project that leverages **AI and Large Language Models (LLMs)** to create a smart troubleshooting application for field engineers. This tool is perfect for industries like manufacturing, oil & gas, healthcare, and more! 🌍 🔧 **What’s in this tutorial?** Learn how to build an AI-powered **Red-Amber-Green (RAG) Support Guide** that can: - Extract support manuals from remote URLs. - Create **vector indexes** for components from different vendors. - Dynamically route queries to the relevant manuals using **LlamaIndex tools** and **function-calling agents**. 💡 **Use Case:** Imagine being an on-site engineer troubleshooting a system with components from multiple manufacturers. Instead of waiting for vendor support, this AI-driven application provides instant troubleshooting solutions by querying the relevant manuals directly. This tutorial demonstrates the concept with three components but can scale to handle thousands of manuals for real-world applications, just like the project I built for an oil & gas client in Qatar! 📂 **Resources & Tools Used:** - **LlamaIndex** for query routing and vector indexing. - Sample manuals and source code: [Links provided in the video]. 🎯 **What You’ll Learn:** 1. How to process remote support manuals with LlamaIndex. 2. Set up AI-based query routing for context-specific answers. 3. Scale and adapt the solution for real-world industrial use cases. 📌 **Next Steps:** Take this backend solution to the next level by turning it into a full-stack app using **Streamlit** or deploying it as a cloud-based microservice. 👨‍💻 **Stay Connected!** If this tutorial helps you, don’t forget to **like**, **share**, and **subscribe** to *Total Technology Zone*! Hit the 🔔 **bell icon** to stay updated on new content. Let’s make learning and innovation accessible for everyone! Thanks for watching—see you in the next tutorial! ✨