README.md

🧠 Semantic Kernel 🌽 Python Tutorial 101 🐍

Welcome to the Semantic Kernel Python Tutorial 101! This repository provides a hands-on introduction to using Semantic Kernel with Python, demonstrating integration with various AI services. Semeantic implements a Kernel that supports Python, C#, and Java. The Kernel is an opinionated, configurable set of abstractions that are agentic in various ways. It offers a very interesting Plugins architecture that allows for LLM based and native functions within a plugin namespace for grouping more granular functions and tools - both semantic and discrete - to accomplish a larger task.

• Microsoft Semantic Kernel - github repo
• Compare / contrast LangChain, Semantic Kernel, and AutoGen
• Microsoft Learn - Intro to Semantic Kernel

🚀 Getting Started

Prerequisites

• Python 3.10+
• pip (Python package manager)
• Ollama - https://www.ollama.com/
• Groq API key - https://console.groq.com/keys

Installation

1. Clone this repository:

   git clone https://github.com/killerapp/semantic-kernel-python-tutorial.git
   cd semantic-kernel-python-tutorial
   

2. Install the required packages:

   pip install -r requirements.txt
   

🔧 Configuration

Before running the examples, you need to set up your environment variables. We provide sample .env files for different services:

• .env.ollama.example - For Ollama
• .env.groq.example - For Groq

Copy the appropriate .env.*.example file to .env and fill in your API keys and other required information.

📚 Examples

Ollama Example

Run the Ollama example with:

cd sk-ollama
python sk-ollama.py

This script demonstrates how to use Semantic Kernel with an Ollama server. Key features:

execution_settings = OllamaChatPromptExecutionSettings()

kernel = Kernel()

# Alternative using Ollama:
service_id="ollama"
kernel.add_service(
  OllamaChatCompletion(
      service_id=service_id
  )
)

Groq Example

Run the Groq example with:

cd sk-groq
python sk-groq.py

This script shows how to integrate Semantic Kernel with Groq. Notable sections:

# Use Groq:
service_id = "groq"
service = OpenAIChatCompletion(
    service_id=service_id,
    api_key=os.getenv("GROQ_API_KEY"),
    org_id=os.getenv("GROQ_ORG_ID"),
    ai_model_id=os.getenv("GROQ_MODEL")
)
service.client.base_url = os.getenv("GROQ_BASE_URL")  # this is the important line
kernel.add_service(service=service)

🧩 Plugins

This tutorial includes one sample plugin to demonstrate Semantic Kernel's capabilities:

• FunPlugin: Generates jokes, limericks, and creative excuses.

You can find plugins in the plugins/ directory.

📝 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📬 Contact

If you have any questions or feedback, please open an issue in this repository.

Happy coding with Semantic Kernel! 🎉