Getting Started Guide - Metro Gen AI SDK#

Overview#

The Metro Gen AI SDK provides a comprehensive development environment for generative AI applications using Intel’s optimized tools and microservices. This guide demonstrates the installation process and provides a practical question-answering implementation using retrieval-augmented generation (RAG) capabilities.

Learning Objectives#

Upon completion of this guide, you will be able to:

  • Install and configure the Metro Gen AI SDK

  • Deploy generative AI microservices for document processing and question-answering

  • Understand the architecture of RAG-based applications using Intel’s AI frameworks

System Requirements#

Verify that your development environment meets the following specifications:

  • Operating System: Ubuntu 24.04 LTS or Ubuntu 22.04 LTS

  • Memory: Minimum 64GB RAM (recommended for LLM operations)

  • Storage: 100GB available disk space for models and data

  • Network: Active internet connection for package downloads

Installation Process#

Execute the automated installation script to configure the complete development environment:

curl https://raw.githubusercontent.com/open-edge-platform/edge-ai-suites/refs/heads/release-2026.0.0/metro-ai-suite/metro-sdk-manager/scripts/metro-gen-ai-sdk.sh | bash

Metro Gen AI SDK Installation

Question-Answering Application Implementation#

This section demonstrates a complete RAG (Retrieval-Augmented Generation) application workflow using the installed Gen AI components.

Step 1: Navigate to Sample Application#

Navigate to the pre-installed question-answering application directory:

cd $HOME/metro/edge-ai-libraries/sample-applications/chat-question-and-answer

Step 2: Setup Model Download Service#

Configure and start the Model Download service to manage LLM and embedding model downloads:

cd $HOME/metro/edge-ai-libraries/microservices/model-download
export REGISTRY="intel/"
export TAG=latest
export HUGGINGFACEHUB_API_TOKEN=<your-huggingface-token>
source scripts/run_service.sh up --plugins openvino --model-path $HOME/metro/models/

Note: Keep this terminal open while the model download service is running. Open a new terminal to continue with the next steps.

Update the <your-huggingface-token> to your Access Token from Hugging Face. To learn more, follow this guide.

Step 3: Configure Environment and Dependencies#

Set up the Python virtual environment and install required dependencies:

cd $HOME/metro/edge-ai-libraries/sample-applications/chat-question-and-answer
# Configure application environment variables
export HUGGINGFACEHUB_API_TOKEN=<your-huggingface-token>
export LLM_MODEL=Qwen/Qwen2.5-7B-Instruct
export EMBEDDING_MODEL_NAME=Alibaba-NLP/gte-large-en-v1.5
export RERANKER_MODEL=BAAI/bge-reranker-base
export DEVICE="CPU"
export REGISTRY="intel/"
export TAG=latest
export MODEL_DOWNLOAD_HOST=localhost
export MODEL_DOWNLOAD_PORT=8200
source setup.sh llm=OVMS embed=OVMS

Step 4: Deploy the Application#

Start the complete Gen AI application stack using Docker Compose:

export ALLOWED_HOSTS="*.intel.com,en.wikipedia.org,*.wikipedia.org,*.github.com"
docker compose up

Step 5: Verify Deployment Status#

Run below command in another terminal to check that all application components are running correctly:

docker ps

Step 6: Access the Application Interface#

Open a web browser and navigate to the application dashboard:

http://localhost:8101

Additional Resources#

Technical Documentation#

Support Channels#

  • GitHub Issues - Technical issue tracking and community support for Gen AI applications