wd-ai-hackathon

Run locally

To run the notebooks locally, you need to have a docker runtime installed on your machine. You can install docker from here.

After installing, you can run this repository as a devcontainer by following the steps below:

1. Clone this repository to your local machine.
2. Open the repository in Visual Studio Code.
3. Open the command palette by pressing Ctrl+Shift+P or Cmd+Shift+P and search for Remote-Containers: Reopen in Container.

Finally, you need to rename the .env_template file to .env and replace the values with the following

AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="gpt-4o-mini"
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-3-large"
AZURE_OPENAI_ENDPOINT="https://models.inference.ai.azure.com"
AZURE_OPENAI_API_KEY="<YOUR API KEY>"

You can get the API key by creating a GitHub Personal Access Token. See here.

Installation

When running as devcontainer or codespace everything should be set up already.

When running locally uvneeds to be installed:

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/astral-sh/uv/releases/download/0.5.4/uv-installer.sh | sh

after installation run

uv sync

Also following vscode extensions are needed:

"ms-python.python"
"ms-toolsai.jupyter"
"ms-toolsai.jupyter-renderers"
"ms-toolsai.jupyter-keymap"
"charliermarsh.ruff"
"GitHub.copilo"
"GitHub.copilo-chat"

Sanity check

Open notebooks/00_hello_world.ipynb and select .venvas kernel/virtual environment.

Execute the notebook to make sure it runs without error.

AI Hackathon: Building Intelligent Applications with Semantic Kernel

This repository contains a series of Jupyter notebooks that guide you through building AI-powered applications using Semantic Kernel. The content progresses from basic concepts to advanced implementations, with each module containing both theoretical foundations and practical exercises.

Prerequisites

• Python 3.8 or later
• Basic understanding of Python programming
• OpenAI API key or Azure OpenAI access
• Semantic Kernel library installed

Notebooks Overview

Module 1: Introduction to Semantic Kernel

• 01_a_introduction_to_semantic_kernel.ipynb: Theory module covering:
  • What is Semantic Kernel
  • Setting up the Kernel
  • Plugins (Native and Semantic)
  • Memory capabilities
  • Practical examples including an Emoji Translator
• 01_b_Introduction_to_semantic_kernel.ipynb: Practical exercises for implementing small applications using core concepts:
  • Fusion-Chef: Recipe combination system
  • QnA Bot: Memory-based question answering
  • Roleplay Gamemaster: Interactive game system
  • Shell Meister: Natural language to shell command translator

Module 2: Planning and Execution

• 02_a_a_bad_plan_is_better_than_no_plan.ipynb: Theory module exploring:
  • AI Planning concepts
  • Types of Planners (Sequential and Function Calling Stepwise)
  • Memory integration with planners
  • Web research capabilities
• 02_b_a_bad_plan_is_better_than_no_plan.ipynb: Practical exercises including:
  • Code Documentation Assistant
  • Personal Finance Advisor
  • AI Dungeon Master
  • Research Paper Assistant

Module 3: AI Agents

• 03_a_agents.ipynb: Theory module covering:
  • Introduction to SK Agents
  • Creating basic agents
  • Agent capabilities and plugins
  • Multi-agent interactions
• 03_b_agents.ipynb: Practical exercises for building:
  • Code Review Team
  • Game Master System
  • Competitive Debate System
  • Technical Support System
  • Research Paper Collaboration System
• 03_c_battle_of_the_agents.ipynb: Advanced concepts on:
  • Agent simulation and testing
  • Conversation modeling
  • Performance evaluation

Module 4: Process Management

• 04_a_its_a_process.ipynb: Theory module on:
  • Building an AI Travel Agent
  • Process Steps and Event Handling
  • State Management
  • AI Integration
• 04_b_its_a_process.ipynb: Practical exercises for implementing:
  • Smart Home Automation System
  • Restaurant Kitchen Process System
  • Document Approval Workflow
  • Data ETL Pipeline System
  • Project Management System

Module 5: Capstone Project

• 05_a_putting_it_all_together.ipynb: Understanding Knowledge Graphs
  • Theory and foundations
  • Graph components and relationships
  • Visualization techniques
  • Best practices
• 05_b_putting_it_all_together.ipynb: Building an AI Knowledge System
  • Core Knowledge Graph System
  • Knowledge Extraction Agents
  • Query Agent System
  • Process Integration
• 05_c_putting_it_all_together.ipynb: Extended Practical Applications
  • Research Paper Assistant
  • Technical Documentation Assistant
  • Learning Path Generator
  • Troubleshooting Assistant
  • Content Recommendation System

Learning Path

The notebooks are designed to be completed in order, as each module builds upon concepts introduced in previous modules. The progression goes from basic Semantic Kernel concepts to advanced multi-agent systems and process management, culminating in a comprehensive capstone project.

Each module contains:

• Theoretical foundations
• Code examples
• Practical exercises
• Best practices and tips
• Real-world applications