README.md

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Semantic Kernel Tutorial: Microsoft's AI Orchestration

Build enterprise AI applications with Microsoft's SDK for integrating LLMs.

🔷 Enterprise-Grade AI Orchestration

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🎯 What is Semantic Kernel?

Semantic Kernel^{View Repo} is Microsoft's open-source SDK for integrating LLMs into applications. It provides a unified way to orchestrate AI services, plugins, and memory, making it easy to build sophisticated AI applications.

Key Features

Feature	Description
Multi-Language	C#, Python, and Java SDKs
Plugin System	Extensible function architecture
Planners	AI-powered task planning
Memory	Vector store integrations
Connectors	OpenAI, Azure, Hugging Face
Enterprise Ready	Built for production at scale

flowchart TD
    A[Application] --> B[Semantic Kernel]
    
    B --> C[Plugins]
    B --> D[AI Services]
    B --> E[Memory]
    
    C --> F[Native Functions]
    C --> G[Semantic Functions]
    
    D --> H[OpenAI]
    D --> I[Azure OpenAI]
    D --> J[Hugging Face]
    
    E --> K[Vector Stores]
    E --> L[Conversation History]
    
    B --> M[Planners]
    M --> N[Auto Planning]
    M --> O[Step Execution]
    
    classDef app fill:#e1f5fe,stroke:#01579b
    classDef kernel fill:#0078d4,stroke:#005a9e,color:#fff
    classDef component fill:#fff3e0,stroke:#ef6c00
    classDef service fill:#e8f5e8,stroke:#1b5e20
    
    class A app
    class B kernel
    class C,D,E,M component
    class F,G,H,I,J,K,L,N,O service

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Current Snapshot (auto-updated)

• repository: microsoft/semantic-kernel
• stars: about 27.5k
• latest release: python-1.41.0 (published 2026-03-13)

Tutorial Chapters

1. Chapter 1: Getting Started - Installation, setup, and first kernel
2. Chapter 2: Plugins & Functions - Native and semantic functions
3. Chapter 3: Prompt Engineering - Prompt templates and configuration
4. Chapter 4: AI Services - OpenAI, Azure, and other connectors
5. Chapter 5: Memory & Embeddings - Vector stores and semantic memory
6. Chapter 6: Planners - Automatic task planning and execution
7. Chapter 7: Agents - Building autonomous AI agents
8. Chapter 8: Production Deployment - Enterprise patterns and scaling

What You'll Learn

• Build AI Applications with Microsoft's enterprise SDK
• Create Plugins with native and semantic functions
• Engineer Prompts with templates and variables
• Connect AI Services from multiple providers
• Implement Memory with vector stores
• Use Planners for complex task automation
• Deploy at Scale with enterprise patterns

Prerequisites

• Python 3.8+ or .NET 6+
• API key for OpenAI or Azure OpenAI
• Understanding of async programming
• (Optional) Azure subscription

Quick Start (Python)

# Install Semantic Kernel
pip install semantic-kernel

# Install connectors
pip install semantic-kernel[openai]
pip install semantic-kernel[azure]

Your First Kernel

import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

# Create kernel
kernel = sk.Kernel()

# Add AI service
kernel.add_service(
    OpenAIChatCompletion(
        service_id="chat",
        ai_model_id="gpt-4o"
    )
)

# Create a semantic function
prompt = """
Summarize the following text in {{$style}} style:
{{$input}}
"""

summarize = kernel.create_function_from_prompt(
    function_name="summarize",
    plugin_name="TextPlugin",
    prompt=prompt
)

# Invoke the function
result = await kernel.invoke(
    summarize,
    input="Long text here...",
    style="professional"
)

print(result)

Native Functions (Plugins)

from semantic_kernel.functions import kernel_function

class MathPlugin:
    @kernel_function(
        name="add",
        description="Adds two numbers together"
    )
    def add(self, a: float, b: float) -> float:
        return a + b
    
    @kernel_function(
        name="multiply", 
        description="Multiplies two numbers"
    )
    def multiply(self, a: float, b: float) -> float:
        return a * b

# Add plugin to kernel
kernel.add_plugin(MathPlugin(), plugin_name="math")

# Use in prompts or planners
result = await kernel.invoke(
    kernel.plugins["math"]["add"],
    a=5, b=3
)

Semantic Functions

# Create prompt template
prompt_config = sk.PromptTemplateConfig(
    template="""
    You are a helpful assistant.
    
    User: {{$user_input}}
    
    Respond in a {{$tone}} tone.
    """,
    input_variables=[
        {"name": "user_input", "description": "User's message"},
        {"name": "tone", "description": "Response tone", "default": "friendly"}
    ]
)

# Register function
chat = kernel.create_function_from_prompt(
    function_name="chat",
    plugin_name="Assistant",
    prompt_template_config=prompt_config
)

Memory (Vector Store)

from semantic_kernel.memory import SemanticTextMemory
from semantic_kernel.connectors.memory.chroma import ChromaMemoryStore

# Create memory with vector store
memory = SemanticTextMemory(
    storage=ChromaMemoryStore(persist_directory="./memory"),
    embeddings_generator=OpenAITextEmbedding(model_id="text-embedding-3-small")
)

# Save information
await memory.save_information(
    collection="docs",
    id="doc1",
    text="Semantic Kernel is Microsoft's AI SDK."
)

# Search memory
results = await memory.search(
    collection="docs",
    query="What is Semantic Kernel?",
    limit=5
)

Planners

from semantic_kernel.planners import SequentialPlanner

# Create planner
planner = SequentialPlanner(kernel)

# Generate plan from goal
plan = await planner.create_plan(
    goal="Research AI trends and write a summary email"
)

# Execute the plan
result = await plan.invoke(kernel)

.NET Example

using Microsoft.SemanticKernel;

// Create kernel
var builder = Kernel.CreateBuilder();
builder.AddOpenAIChatCompletion("gpt-4o", apiKey);
var kernel = builder.Build();

// Invoke prompt
var result = await kernel.InvokePromptAsync(
    "What is {{$topic}}?",
    new() { ["topic"] = "Semantic Kernel" }
);

Console.WriteLine(result);

Learning Path

🟢 Beginner Track

1. Chapters 1-3: Setup, plugins, and prompts
2. Build simple AI-powered applications

🟡 Intermediate Track

1. Chapters 4-6: Services, memory, and planners
2. Create sophisticated AI workflows

🔴 Advanced Track

1. Chapters 7-8: Agents and production deployment
2. Build enterprise-grade AI systems

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Ready to build with Semantic Kernel? Let's begin with Chapter 1: Getting Started!

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Full Chapter Map

1. Chapter 1: Getting Started with Semantic Kernel
2. Chapter 2: Plugins & Functions
3. Chapter 3: Prompt Engineering
4. Chapter 4: AI Services & Connectors
5. Chapter 5: Memory & Embeddings
6. Chapter 6: Planners
7. Chapter 7: Agents & Tool Use
8. Chapter 8: Production Deployment & Operations

Source References

• View Repo
• Awesome Code Docs

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