Semantic Kernel
Status
Semantic Kernel is a versatile software development kit (SDK) available for programming languages like Python and .NET. For those who work with Python, the SDK can be found on PyPI, ensuring it is easily accessible for developers working on Python projects. Similarly, for .NET, it's available as a NuGet package, and the version and integration statuses can be monitored via badges indicating successful CI builds across platforms like Docker and Windows.
Overview
Semantic Kernel is an innovative SDK that creatively bridges modern Large Language Models (LLMs) to traditional programming languages such as C#, Python, and Java. Through Semantic Kernel, developers can seamlessly integrate leading AI services like OpenAI, Azure OpenAI, and Hugging Face into their applications. This is managed by defining plugins that can be stringed together with minimal code. The standout feature of Semantic Kernel is its automatic orchestration ability, where through planners, it can generate and execute plans based on user goals, leveraging AI to its fullest potential.
Core Features
Semantic Kernel provides a structured environment with tools and abstractions that cater to:
- AI services like chat, converting text to images, or audio-to-text.
- Support for multiple vector databases including Chroma, Qdrant, Milvus, and Azure.
- A common plugin representation that AI can automatically orchestrate.
- Flexibility in plugin creation from various sources like OpenAPI specs, prompts, or raw code.
- Enhanced support for prompt management using popular templates like Handlebars and Liquid.
- Additional functionalities like responsible AI filters and dependency injection integration.
Enterprises greatly benefit from Semantic Kernel's flexibility and security features such as telemetry support. It ensures the delivery of responsible AI solutions on a scalable level and is designed to adapt to future technological advancements, effortlessly accommodating new AI models without requiring a complete code overhaul.
Getting Started with Semantic Kernel
For those interested in exploring Semantic Kernel, the SDK can be accessed across C#, Python, and Java. By obtaining an API key from providers like OpenAI or Azure OpenAI, developers can run simple console applications/scripts.
For C#:
- Visit the quick start guide to begin with Semantic Kernel in C#.
- Follow the steps to install the SDK and craft your first console application.
For Python:
- Head to the dedicated quick start page for Python.
- Ensure that you select Python as your code language on the guide.
For Java:
- The Java version is available through the semantic-kernel-java repository, with instructions on setup and execution provided in its build documentation.
Learning to Use Semantic Kernel
The most efficient way to grasp the ins and outs of Semantic Kernel is through C# and Python Jupyter notebooks that offer hands-on code snippets to experiment with. These resources include:
- Getting started notebooks for C# and Python.
- Detailed walkthroughs and concept explanations on Semantic Kernel's Learn site.
- Comprehensive API references for both C# and Python (with Java soon to follow).
Visual Studio Code Extension
For Visual Studio Code users, a dedicated Semantic Kernel extension simplifies the design and testing of semantic functions, facilitating interaction with existing models and datasets with remarkable ease.
Conclusion
Semantic Kernel stands out as a robust platform for developers to harness the best of LLMs within traditional coding environments, ensuring functionality, flexibility, and future readiness for AI-driven applications.