Part of GPT‑RAG

GPT-RAG Orchestrator

Part of the GPT-RAG solution.

The GPT-RAG Orchestrator service is an agentic orchestration layer built on Azure AI Foundry Agent Service and the Semantic Kernel framework. It enables agent-based RAG workflows by coordinating multiple specialized agents—each with a defined role—to collaboratively generate accurate, context-aware responses for complex user queries.

How the Orchestrator Works

The orchestrator uses Azure AI Foundry Agent Service for single-agent and connected-agent flows, leveraging its managed runtime for agent lifecycle, state management, and tool orchestration. For multi-agent scenarios, it integrates the Semantic Kernel Agent Framework to compose and coordinate specialized agents working together on tasks. Custom agent strategies enable developers to add domain-specific logic without modifying the core orchestration code.

Developers can extend the orchestrator by creating a new subclass of BaseAgentStrategy, implementing the required initiate_agent_flow method (along with any additional helpers), and registering it in AgentStrategyFactory.get_strategy under a unique key.

Examples of available strategies include the Single Agent RAG Strategy and the NL2SQL Strategy.

Prerequisites

Before deploying the application, you must provision the infrastructure as described in the GPT-RAG repo. This includes creating all necessary Azure resources required to support the application runtime.

Click to view software prerequisites

The machine used to customize and or deploy the service should have:

• Azure CLI: Install Azure CLI
• Azure Developer CLI (optional, if using azd): Install azd
• Git: Download Git
• Python 3.12: Download Python 3.12
• Docker CLI: Install Docker
• VS Code (recommended): Download VS Code

Click to view permissions requirements

To customize the service, your user should have the following roles:

Resource	Role	Description
App Configuration Store	App Configuration Data Owner	Full control over configuration settings
Container Registry	AcrPush	Push and pull container images
Key Vault	Key Vault Contributor	Manage Key Vault Secrets
AI Search Service	Search Service Contributor	Create or update search service components
AI Search Service	Search Index Data Contributor	Read and write index data
Storage Account	Storage Blob Data Contributor	Read and write blob data
AI Foundry Project	Azure AI Project User	Access and work with the AI Foundry project
Cosmos DB	Cosmos DB Built-in Data Contributor	Read and write documents in Cosmos DB

To deploy the service, assign these roles to your user or service principal:

Resource	Role	Description
App Configuration Store	App Configuration Data Reader	Read config
Container Registry	AcrPush	Push images
Azure Container App	Azure Container Apps Contributor	Manage Container Apps

Ensure the deployment identity has these roles at the correct scope (subscription or resource group).

Deployment steps

Make sure you're logged in to Azure before anything else:

az login

Deploying the app with azd (recommended)

Initialize the template:

azd init -t azure/gpt-rag-orchestrator 

Important

Use the same environment name with azd init as in the infrastructure deployment to keep components consistent.

Update env variables then deploy:

azd env refresh
azd deploy 

Important

Run azd env refresh with the same subscription and resource group used in the infrastructure deployment.

Aqui está uma versão mais clara, direta e consistente da instrução:

Deploying the app with a shell script

To deploy using a script, first clone the repository, set the App Configuration endpoint, and then run the deployment script.

PowerShell (Windows)

git clone https://github.com/Azure/gpt-rag-orchestrator.git
$env:APP_CONFIG_ENDPOINT = "https://<your-app-config-name>.azconfig.io"
cd gpt-rag-orchestrator
.\scripts\deploy.ps1

Bash (Linux/macOS)

git clone https://github.com/Azure/gpt-rag-orchestrator.git
export APP_CONFIG_ENDPOINT="https://<your-app-config-name>.azconfig.io"
cd gpt-rag-orchestrator
./scripts/deploy.sh

Previous Releases

Note

For earlier versions, use the corresponding release in the GitHub repository (e.g., v1.0.0 for the initial version).

🤝 Contributing

We appreciate contributions! See CONTRIBUTING for guidelines on submitting pull requests.

Trademarks

This project may contain trademarks or logos. Authorized use of Microsoft trademarks or logos must follow Microsoft’s Trademark & Brand Guidelines. Modified versions must not imply sponsorship or cause confusion. Third-party trademarks are subject to their own policies.