Semantic Kernel
Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code.
What makes Semantic Kernel special, however, is its ability to automatically orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.
It provides:
- abstractions for AI services (such as chat, text to images, audio to text, etc.) and memory stores
- implementations of those abstractions for services from OpenAI, Azure OpenAI, Hugging Face, local models, and more, and for a multitude of vector databases, such as those from Chroma, Qdrant, Milvus, and Azure
- a common representation for plugins, which can then be orchestrated automatically by AI
- the ability to create such plugins from a multitude of sources, including from OpenAPI specifications, prompts, and arbitrary code written in the target language
- extensible support for prompt management and rendering, including built-in handling of common formats like Handlebars and Liquid
- and a wealth of functionality layered on top of these abstractions, such as filters for responsible AI, dependency injection integration, and more.
Semantic Kernel is utilized by enterprises due to its flexibility, modularity and observability. Backed with security enhancing capabilities like telemetry support, and hooks and filters so you’ll feel confident you’re delivering responsible AI solutions at scale. Semantic Kernel was designed to be future proof, easily connecting your code to the latest AI models evolving with the technology as it advances. When new models are released, you’ll simply swap them out without needing to rewrite your entire codebase.