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# Adventures in Semantic Kernel

Welcome to **Adventures in Semantic Kernel**, your interactive guide to exploring the functionalities of Microsoft's AI Orchestration library, Semantic Kernel. Dive into hands-on experiences ranging from memory management and plan execution, to Agent building for a dynamic chat experience. This isn't just a passive learning experience; you'll get to actively experiment with these features to understand their cohesive interactions. [Try it out here](https://adventuresinsemantickernel.azurewebsites.net/)
Welcome to **Adventures in Semantic Kernel**, your interactive guide to exploring the functionalities of Microsoft's AI Orchestration library, Semantic Kernel. Dive into hands-on experiences ranging from dynamic plan generation and Agent building for a dynamic chat experience to memory management and tokenization. This isn't just a passive learning experience; you'll get to actively experiment with these features to understand their cohesive interactions. [Try it out here](https://adventuresinsemantickernel.azurewebsites.net/)

## About Semantic Kernel

Originally developed by Microsoft, [Semantic Kernel](https://learn.microsoft.com/en-us/semantic-kernel/overview/) aims to democratize AI integration for developers. While the project benefits from open-source contributions, its core mission is to simplify AI services deployment in apps. It comes equipped with a smart set of connectors that essentially act as your app's "virtual brain", capable of storing and processing information.
Originally developed by Microsoft, [Semantic Kernel](https://learn.microsoft.com/en-us/semantic-kernel/overview/) aims to democratize AI integration for developers. While the project benefits from open-source contributions, its core mission is to simplify the integration of AI services with app code. It comes equipped with a smart set of connectors that essentially act as your app's "virtual brain", capable of executing LLM prompts, native code or external REST Apis.

## Application features

### Samples
View, modify, and execute dotnet examples. Examples are from [KernelSyntaxExamples](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/samples/KernelSyntaxExamples) with small modifications.

### Execute Function
Select a single plugin from a large variety of native, semantic and external plugins, then execute a function from that plugin.
Select a single plugin from a large variety of native, prompt and external plugins, then execute a function from that plugin.

### Build Agent
Build a simple agent by providing a persona and collection of plugins used together with OpenAI Function Calling.
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### Custom Examples

#### Web Chat Agent
Chat with the web using _Bing_ search
Chat with the web using _Bing_ search and a scrape-and-summarize plugin

#### Wikipedia Chat Agent
Chat with the web using _Wikipedia_ Rest API
Chat with the Wikipedia articles using _Wikipedia_ Rest API

#### C# REPL Agent
Use natural language prompts to generate and execute c# code
- Generate and execute a c# console application using prompts.
- Generate and execute c# line-by-line using [Roslyn c# scripting api](https://github.com/dotnet/roslyn/blob/main/docs/wiki/Scripting-API-Samples.md).

#### Dnd Story Agent
Example of a Stepwise Planner at work. Planner has access to the [D&D5e Api](https://www.dnd5eapi.co/) plugin and multiple semantic plugins. It uses these to create and execute a plan to generate a short story.
Example of a Stepwise Planner at work. Planner has access to the [D&D5e Api](https://www.dnd5eapi.co/) plugin and multiple prompt plugins. It uses these to create and execute a plan to generate a short story.
- Leverages a native plugin from a Razor Class Library `AskUserPlugin` to provide user interaction during plan execution

### SK Memory
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Play around with embeddings and similarities using your own or generated text snippets

#### SK + Custom Hdbscan Clustering
See how embeddings can be used to cluster text items, and then generate a title and topic list for each cluster using semantic plugins
See how embeddings can be used to cluster text items, and then generate a title and summmary for each cluster using prompt plugins

### Tokens

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