microsoft-foundry

# Microsoft Foundry Skill

This skill helps developers work with Microsoft Foundry resources, covering model discovery and deployment, RAG (Retrieval-Augmented Generation) applications, AI agent creation, evaluation workflows, and troubleshooting.

## When to Use This Skill

Use this skill when the user wants to:

- **Discover and deploy AI models** from the Microsoft Foundry catalog
- **Build RAG applications** using knowledge indexes and vector search
- **Create AI agents** with tools like Azure AI Search, web search, or custom functions
- **Evaluate agent performance** using built-in evaluators
- **Set up monitoring** and continuous evaluation for production agents
- **Troubleshoot issues** with deployments, agents, or evaluations

## Prerequisites

### Azure Resources
- An Azure subscription with an active account
- Appropriate permissions to create Microsoft Foundry resources (e.g., Azure AI Owner role)
- Resource group for organizing Foundry resources

### Tools
- **Azure CLI** installed and authenticated (`az login`)
- **Azure Developer CLI (azd)** for deployment workflows (optional but recommended)

### Language-Specific Requirements

For SDK examples and implementation details in specific programming languages, refer to:
- **Python**: See [language/python.md](language/python.md) for Python SDK setup, authentication, and examples

## Core Workflows

### 1. Getting Started - Model Discovery and Deployment

#### Use Case
A developer new to Microsoft Foundry wants to explore available models and deploy their first one.

#### Step 1: List Available Resources

First, help the user discover their Microsoft Foundry resources.

**Using Azure CLI:**

##### Bash
```bash
# List all Microsoft Foundry resources in subscription
az resource list \
  --resource-type "Microsoft.CognitiveServices/accounts" \
  --query "[?kind=='AIServices'].{Name:name, ResourceGroup:resourceGroup, Location:location}" \
  --output table

# List resources in a specific resource group
az resourc