Semantic Kernel Workshop

A hands-on workshop exploring Microsoft's Semantic Kernel framework for building intelligent AI applications. This workshop provides practical experience with real-world AI application patterns using Python and Azure OpenAI.

Workshop Overview

This workshop takes you from foundational concepts to advanced implementation patterns through a series of Jupyter notebooks and practical examples. You'll learn how to:

• Build AI applications using Microsoft's Semantic Kernel framework
• Create and orchestrate AI agents with different capabilities and roles
• Construct structured AI workflows using the Process Framework
• Implement enterprise-ready AI features with security and scalability in mind

Interactive Playground Demo

Experience Semantic Kernel in action through our interactive playground! This visual demonstration allows you to directly engage with the core concepts covered in the workshop.

The playground offers a hands-on environment where you can:

• Test semantic functions in real-time
• Explore agent capabilities and interactions
• Experiment with memory and embeddings
• Try out native plugin integration
• See the Process Framework in action

No need to wait until the end of the workshop - you can start exploring the playground at any time to reinforce concepts as you learn them!

For setup instructions and details on how to run the playground, refer to the Playground README.

Prerequisites

• Python 3.10 or higher
• Azure OpenAI API access (API key, endpoint, and deployment name)
• Basic knowledge of Python programming
• Understanding of prompt engineering concepts (helpful but not required)
• UV package manager

Local Dependencies Setup

The project is managed by pyproject.toml and uv package manager.

For local execution init the .venv environment using uv package manager:

uv sync --prerelease=allow
. ./.venv/bin/activate

OBS! At the time of writing the workshop depends on the prerelease libraries.

Workshop Modules

01. Introduction to Semantic Kernel

Learn the fundamentals of Semantic Kernel:

• Core architectural components (Kernel, AI Services, Plugins)
• Building semantic functions with prompts
• Creating native functions with Python code
• Implementing memory for persistent context
• Enabling automatic function calling for AI agents

Key Notebooks:

• 01-intro.ipynb: Core concepts, services, and function creation
• 02-memory.ipynb: Implementing semantic memory with embeddings

02. Semantic Kernel Agents

Master the creation and orchestration of AI agents:

• Creating specialized agents with different personas
• Implementing multi-agent communication patterns
• Agent selection strategies and orchestration
• Building agent topologies for complex scenarios
• Integrating plugins with agents for enhanced capabilities

Key Notebooks:

• 02.1-agents.ipynb: Creating and configuring agents
• 02.2-agents-chats.ipynb: Inter-agent communication and complex patterns

03. Process Framework

Learn to build structured, event-driven AI workflows:

• Understanding the Process Framework architecture
• Defining events, steps, and state management
• Building conversational AI systems with processes
• Implementing complex business logic with AI capabilities
• Creating maintainable and testable AI workflows

Key Notebooks:

• 03.1-intro-to-processes.ipynb: Building stateful, event-driven AI processes

Project Structure

semantic-kernel-workshop/
├── 01-intro-to-semantic-kernel/    # Introduction to core concepts
│   ├── 01-intro.ipynb              # Basic concepts and functions
│   └── 02-memory.ipynb             # Memory implementation
├── 02-semantic-kernel-agents/      # Agent creation and orchestration
│   ├── 02.1-agents.ipynb           # Agent fundamentals
│   ├── 02.2-agents-chats.ipynb     # Multi-agent communication
│   └── .env.sample                 # Environment variables template
├── 03-process-framework/           # Structured AI workflows
│   └── 03.1-intro-to-processes.ipynb  # Process fundamentals
└── playground/                     # Interactive application
    ├── backend/                    # FastAPI server
    ├── frontend/                   # React application
    ├── start.sh                    # Launch script
    └── README.md                   # Playground documentation

Getting Started

1. Clone this repository

2. Create a virtual environment:

   Linux/macOS:

   # Create a virtual environment
   python -m venv venv
   
   # Activate the virtual environment
   source venv/bin/activate

   Windows:

   # Create a virtual environment
   python -m venv venv
   
   # Activate the virtual environment
   venv\Scripts\activate

3. Copy the environment variables template:

   cp .env.example .env

4. Add your Azure OpenAI credentials to the .env file:

   AZURE_OPENAI_DEPLOYMENT=your-deployment-name
   AZURE_OPENAI_API_KEY=your-api-key 
   AZURE_OPENAI_ENDPOINT=your-azure-endpoint
   AZURE_OPENAI_API_VERSION=2024-02-15-preview
   AZURE_OPENAI_EMBEDDING_DEPLOYMENT=your-embedding-deployment
   

5. Start with the first notebook:

   • Begin with 01-intro-to-semantic-kernel/01-intro.ipynb, which includes instructions for installing Semantic Kernel and other required packages.

Learning Path

For optimal learning, follow this progression:

1. Foundational Concepts: Start with 01-intro.ipynb to understand the core components
2. Memory Implementation: Explore 02-memory.ipynb to see how semantic memory works
3. Basic Agents: Create your first agents in 02.1-agents.ipynb
4. Advanced Agent Patterns: Study complex agent interactions in 02.2-agents-chats.ipynb
5. Process Framework: Learn structured workflows with 03.1-intro-to-processes.ipynb
6. Practical Application: Apply everything in the interactive playground

Advanced Topics and Resources

For advanced patterns and enterprise deployment scenarios, explore the Semantic Kernel Advanced Usage repository, which includes:

• Dapr integration for scalable, distributed systems
• Authentication and security patterns
• Natural language to SQL conversion
• Copilot Studio integration
• Microsoft Graph API integration
• Production deployment architecture

Additional Resources

• Semantic Kernel Documentation
• Azure OpenAI Service
• Microsoft Copilot Studio

License

This project is licensed under the MIT License - see the LICENSE file for details.