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
Originally developed by Microsoft, Semantic Kernel 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.
Current configuration will work for all the main features of the demo, and for most (though not all) plugins. However, several KernelSyntaxExamples will require config values for specific resources (e.g. Pinecone, Chroma, Weaviate, etc.) not available by default. Any service config highlighted in red is missing values that will need to be added for the associated sample to work.
You don't need to supply an OpenAI api key for most of the demo features, but if you want to use a gpt-4 model (or if you want to change the default service to Azure OAI), you will need to supply an api key in the OpenAIConfig or AzureOpenAIConfig section.
Note: All configurations added/changed are encrypted and saved to your browser's local storage so they can be loaded across sessions while remaining secure.
View, modify, and execute dotnet examples. Examples are from KernelSyntaxExamples with small modifications.
Select a single plugin from a large variety of native, prompt and external plugins, then execute a function from that plugin.
Build a simple agent by providing a persona and collection of plugins used together with OpenAI Function Calling.
Build a group chat comprised of ChatCompletionAgents using AgentGroupChat
Select plugins and functions to build and execute your own:
- OpenAI Function Calling Agent
- Handlebars planner (Still available, but usage is discouraged in favor of Function Calling. See Planners vs Function Calling for more information.)
- Stepwise planner (Still available, but usage is discouraged in favor of Function Calling. See Planners vs Function Calling for more information.)
Chat with the web using Bing search and a scrape-and-summarize plugin
Chat with the Wikipedia articles using Wikipedia Rest API
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.
Example of a Stepwise Planner at work. Planner has access to the D&D5e Api 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
AskUserPluginto provide user interaction during plan execution
Play around with embeddings and similarities using your own or generated text snippets
See how embeddings can be used to cluster text items, and then generate a title and summmary for each cluster using prompt plugins
- Generate or add text, set the text chunking parameters, and then see the Semantic Kernel
TextChunkerwork - Search over chunked text to see how the
TextChunkercan be used to improve search results
See how input text translates into tokens. Select specific tokens to set the LogitBias for a chat completion request/response.