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What is Semantic Kernel?
Semantic Kernel is a lightweight open-source orchestration SDK that lets you easily mix-and-match AI prompts with conventional programming languages like C# and Python.
Semantic Kernel simplifies development of AI apps
Semantic Kernel has been engineered to allow developers to flexibly integrate AI into their existing apps. To do so, Semantic Kernel provides a set of abstractions that make it easy to create and manage prompts, native functions, memories, and connectors. You can then orchestrate these components using Semantic Kernel pipelines to complete users' requests or automate actions.
Because of the abstractions Semantic Kernel provides, you can use it to orchestrate AI from any provider. For example, you can use Semantic Kernel to orchestrate AI from OpenAI, Azure, or even Hugging Face.
As a developer, you can then use these pieces individually. For example, if you just need an abstraction over OpenAI and Azure OpenAI services, you could use the SDK to run pre-configured prompts.
Orchestrating AI with Semantic Kernel
The real power of Semantic Kernel, however, comes from its ability to combine these components together. By using multiple AI models, native functions, and memory all together within Semantic Kernel, you can create sophisticated pipelines that use AI to automate complex tasks.
For example, with Semantic Kernel, you could create a pipeline that helps a user send an email to their marketing team. With memory, you could retrieve information about the project and then use planner to autogenerate the remaining steps (e.g., ground the user's ask with Microsoft Graph data, generate a response with GPT-4, and send the email). Finally, you can display a success message back to the user in your custom app.
| Step | Component | Description |
|---|---|---|
| 1 | Ask | It starts with goal being sent to Semantic Kernel as an ask by either a user or developer. |
| 2 | Kernel | The kernel orchestrates a user's ask. To do so, the kernel runs a pipeline / chain that is defined by a developer. While the chain is run, a common context is provided by the kernel so data can be shared between functions. |
| 2.1 | Memories | With a specialized plugin, a developer can recall and store context in vector databases. This allows developers to simulate memory within their AI apps. |
| 2.2 | Planner | Developers can ask Semantic Kernel to auto create chains to address novel needs for a user using planner. Planner is able to use any of the plugins that have already been loaded into the kernel to create new additional steps. |
| 2.3 | Connectors | To get additional data or to perform autonomous actions, you can use out-of-the-box connectors like the Microsoft Graph Connector kit or create a custom connector to connect to your own services. |
| 2.4 | Custom functions | As a developer, you can create custom functions that run inside of Semantic Kernel. These can either be LLM prompts (semantic functions) or native C# or Python code (native function). This allows you to add new AI capabilities and integrate your existing apps and services into Semantic Kernel. |
| 3 | Response | Once the kernel is done, you can send the response back to the user to let them know the process is complete. |
Semantic Kernel is open-source
You may be familiar with the Microsoft 365 Copilot System, the steps Microsoft uses to power its new Copilot experiences on top of GPT-4. This SDK formalizes patterns like these so building LLM-powered apps can be easier. To make sure all developers can take advantage of our learnings, we have released Semantic Kernel as an open-source project on GitHub.
Given that new breakthroughs in LLM AIs are landing on a daily basis, you should expect this SDK evolve. We're excited to see what you build with Semantic Kernel and we look forward to your feedback and contributions so we can build the best practices together in the SDK.
Contribute to Semantic Kernel
We welcome contributions and suggestions from the Semantic Kernel community! One of the easiest ways to participate is to engage in discussions in the GitHub repository. Bug reports and fixes are welcome!
For new features, components, or extensions, please open an issue and discuss with us before sending a PR. This will help avoid rejections since it will allow us to discuss the impact to the larger ecosystem.
Get started using the Semantic Kernel SDK
Now that you know what Semantic Kernel is, follow the get started link to try it out. Within minutes you can create prompts and chain them with out-of-the-box plugins and native code. Soon afterwards, you can give your apps memories with embeddings and summon even more power from external APIs.