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/main/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: Configure Environment and Dependencies#

Set up the Python virtual environment and install required dependencies:

# 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=2.0.0
source setup.sh llm=OVMS embed=OVMS

Update the to your Access Token from Hugging Face. To know more, follow this guide.

Step 3: Deploy the Application#

Start the complete Gen AI application stack using Docker Compose:

docker compose up

Step 4: Verify Deployment Status#

Check that all application components are running correctly:

docker ps

Step 5: Access the Application Interface#

Open a web browser and navigate to the application dashboard:

http://localhost:8101

Additional Resources#

Technical Documentation#

Chat Q&A
Audio Analyzer - Comprehensive documentation for multimodal audio processing capabilities
Document Ingestion - pgvector - Vector database integration and document processing workflows
Multimodal Embedding Serving - Embedding generation service architecture and API documentation
Visual Data Preparation For Retrieval - VDMS integration and visual data management workflows
VLM OpenVINO Serving - Vision-language model deployment and optimization guidelines
Edge AI Libraries - Complete development toolkit documentation and microservice API references
Edge AI Suites - Comprehensive application suite documentation with Gen AI implementation examples

Support Channels#

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