# ai-foundry-workshop **Repository Path**: mirrors_Azure/ai-foundry-workshop ## Basic Information - **Project Name**: ai-foundry-workshop - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-02-16 - **Last Updated**: 2026-02-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Azure AI Foundry Workshop
[📦Prerequisites](#-prerequisites) | [🚀Quick Start](#-quick-start) | [🤖Overview](#-overview) | [📔Workshop Content](#-workshop-content) | [🧩Project Structure](#-project-structure) | [❓Support](#-support) | [🤝Contributing](#-contributing)
## 🤖 Overview A hands-on workshop that guides you through building intelligent apps and AI agents on top of Azure AI Foundry, with fun examples related to health and dietary advice. You will: - Learn Azure AI Foundry fundamentals - Set up authentication and project configuration - Deploy and test AI models - Build AI agents (health advisor examples) - Implement health calculations and dietary planning - Evaluate agent performance and monitor quality attributes - Deploy an end-to-end AI native sample app that incorporates all these capabilities and design patterns > **Duration**: 4-5 hours > **Focus**: Hands-on exercises, interactive notebooks, practical examples, end-to-end project ## 🎥 Workshop Overview Video Watch our overview video to get a comprehensive understanding of the workshop: [![Azure AI Foundry Workshop Overview](https://img.youtube.com/vi/0bGLgmZJ6DE/0.jpg)](https://youtu.be/0bGLgmZJ6DE) ## 📸 Visuals ### Sections Overview *Figure 1: Overview of different sections in this workshop.* ### End-to-End Sample UI *Figure 2: User interface of the end-to-end AI native sample project.* --- ## 📦 Prerequisites Before starting the workshop, ensure you have: - [Python 3.10](https://www.python.org/downloads/) or higher installed - An active Azure subscription with access to [Azure AI Foundry](https://ai.azure.com) - [Azure CLI](https://learn.microsoft.com/en-us/cli/azure/install-azure-cli) installed - [Git](https://git-scm.com/downloads) installed - [VS Code](https://code.visualstudio.com/), [GitHub Codespaces](https://github.com/features/codespaces), or [Jupyter Notebook](https://jupyter.org/install) environment - Basic Python programming knowledge - Model deployment and [AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) connection configured in Azure AI Foundry --- ## 🚀 Quick Start 1. **Clone the repo**: ```bash git clone https://github.com/Azure/ai-foundry-workshop.git cd ai-foundry-workshop ``` 2. **Install uv**: ```bash # Unix/Linux/macOS curl -LsSf https://astral.sh/uv/install.sh | sh # Windows (PowerShell) powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" ``` 3. **Create & activate a virtual environment**: ```bash uv venv source .venv/bin/activate # Windows: .venv\Scripts\activate ``` 4. **Set up Azure AI Foundry**: a. **Create Project and Deploy Resources**: 1. Go to [Azure AI Foundry](https://ai.azure.com) 2. Create a new AI Hub and Project using the AI Foundry Wizard 3. Deploy required models: - GPT models(gpt-4o, gpt-4o-mini) for chat/completion (**set TPM to max** to avoid issues with Agents notebooks) - Embedding model for vector search - Ensure the model is deployed in `Global-Standard` or `DataZone-Standard` 4. Set up connections: - Configure [Grounding with Bing](https://learn.microsoft.com/en-us/azure/ai-services/agents/how-to/tools/bing-grounding?view=azure-python-preview&tabs=python&pivots=overview) connection - Configure Azure AI Search connection 5. Add your user account to the `Azure AI Developer` role from Azure AI Foundry Management Portal b. **Configure Environment Variables**: ```bash cp .env.example .env ``` Update `.env` with your Azure AI Foundry values: - `PROJECT_CONNECTION_STRING`: Your project connection string from Azure ML workspace - `MODEL_DEPLOYMENT_NAME`: Your model deployment name - `EMBEDDING_MODEL_DEPLOYMENT_NAME`: Your embedding model deployment name - `TENANT_ID`: Your tenant ID from Azure portal - `BING_CONNECTION_NAME`: Your Bing search connection name - `SERVERLESS_MODEL_NAME`: Your serverless model name > **Note**: The model specified in `MODEL_DEPLOYMENT_NAME` must be supported by Azure AI Agents Service or Assistants API. See [supported models](https://learn.microsoft.com/en-us/azure/ai-services/agents/concepts/model-region-support?tabs=python#azure-openai-models) for details. For Grounding with Bing Search, you need to use `gpt-4o-mini` model. 5. **Install dependencies**: ```bash # Install core Azure AI SDKs and Jupyter requirements uv pip install azure-identity azure-ai-projects azure-ai-inference[opentelemetry] azure-search-documents azure-ai-evaluation azure-monitor-opentelemetry # Install Jupyter requirements uv pip install ipykernel jupyterlab notebook # Register the kernel with Jupyter python -m ipykernel install --user --name=.venv --display-name="Python (.venv)" # Install additional requirements (optional - for deploying repo or running mkdocs) uv pip install -r requirements.txt ``` > **Note**: If you encounter kernel errors in VS Code, try: > 1. Select kernel: Click "Select Kernel" > "Python Environments" > "Python (.venv)" > 2. If kernel is not listed, run `python -m ipykernel install --user --name=.venv` again, or use the "Create New Kernel" wizard in VS Code to create a new Python environment > 3. Reload VS Code if needed 6. **Choose your notebook environment**: **Option A: VS Code** - Install [VS Code Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) - Install either: - [Jupyter extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) for .ipynb files - [Polyglot Notebooks extension](https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.dotnet-interactive-vscode) for .dib files - Open any notebook and select your Python kernel (.venv) **Option B: GitHub Codespaces** - Click "Code" > "Create codespace" on the repository - Wait for environment setup - Notebooks will be ready to run **Option C: Jupyter Lab/Notebook** ```bash # Install Jupyter if you haven't already uv pip install jupyterlab notebook # Start Jupyter Lab (recommended) jupyter lab # Or start Jupyter Notebook jupyter notebook ``` 7. **Follow the Learning Path**: 1. **Introduction** (`1-introduction/`) - `1-authentication.ipynb`: Set up your Azure credentials - `2-environment_setup.ipynb`: Configure your environment - `3-quick_start.ipynb`: Learn basic operations 2. **Main Workshop** (`2-notebooks/`) - Chat Completion & RAG (`1-chat_completion/`) - Agent Development (`2-agent_service/`) - Quality Attributes (`3-quality_attributes/`) --- ## 📔 Workshop Learning Path Follow these notebooks in sequence to complete the workshop: ### 1. Introduction (`1-introduction/`) | Notebook | Description | |----------|-------------| | [1. Authentication](1-introduction/1-authentication.ipynb) | Set up Azure credentials and access | | [2. Environment Setup](1-introduction/2-environment_setup.ipynb) | Configure your development environment | | [3. Quick Start](1-introduction/3-quick_start.ipynb) | Learn basic Azure AI Foundry operations | ### 2. Main Workshop (`2-notebooks/`) | Topic | Notebooks | |-------|-----------| | **Chat Completion & RAG** | • [Chat Completion & RaG](2-notebooks/1-chat_completion/) | | **Agent Development** | • [Agent Development](2-notebooks/2-agent_service/) | | **Quality Attributes** | • [Observability & Evaluations](2-notebooks/3-quality_attributes/) | --- ## 🤝 Contributing We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details on how to: - Submit bug reports and feature requests - Submit pull requests - Follow our coding standards - Participate in code reviews --- ## ❓ Support If you need help or have questions: ---