Build RAG applications with LlamaIndex on a Google Cloud C4A virtual machine

About this Learning Path

Who is this for?

This is an introductory topic for DevOps engineers, AI engineers, ML engineers, and software developers who want to build Retrieval-Augmented Generation (RAG) applications using LlamaIndex on SUSE Linux Enterprise Server (SLES) Arm64, integrate vector databases, and query custom documents using local LLMs.

What will you learn?

Upon completion of this Learning Path, you will be able to:

• Install and configure LlamaIndex on Google Cloud C4A Axion processors for Arm64
• Build indexing and retrieval pipelines using LlamaIndex
• Integrate ChromaDB vector databases with local LLMs using Ollama
• Build and test a browser-based RAG application using FastAPI

Prerequisites

Before starting, you will need the following:

• A Google Cloud Platform (GCP) account with billing enabled
• Basic familiarity with Python and AI/LLM concepts