{"id":57,"date":"2023-03-17T06:00:23","date_gmt":"2023-03-17T13:00:23","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/semantic-kernel\/?p=57"},"modified":"2023-03-17T06:57:33","modified_gmt":"2023-03-17T13:57:33","slug":"hello-world","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/agent-framework\/hello-world\/","title":{"rendered":"Hello, Semantic Kernel!"},"content":{"rendered":"

\"Pattern<\/a><\/p>\n

Artificial intelligence (AI) and Large Language Models (LLM) are helping to transform the way we develop and interact with software. From chatbots to code generators, natural language is the future of user interaction, delivering delightful and intelligent “copilot” experiences. As these AI models become more prevalent and accessible, organizations and developers are looking for ways to quickly and easily integrate these capabilities into applications without needing to train or fine-tune a model from scratch.<\/p>\n

Hello, Semantic Kernel!<\/h2>\n

Semantic Kernel (SK) is a lightweight SDK that lets you mix conventional programming languages, like C# and Python, with the latest in Large Language Model (LLM) AI “prompts” with prompt templating, chaining, and planning capabilities. This enables you to build new experiences into your apps to bring unparalleled productivity for your users: like summarizing a lengthy chat exchange, flagging an important \u201cnext step\u201d that\u2019s added to your to-do list via Microsoft Graph, or planning a full vacation instead of just reserving a seat on a plane.<\/p>\n

SK is available on GitHub<\/a> as an open-source framework and is accompanied by example apps and notebooks that illustrate how to get going quickly with LLM AI. Central to Semantic Kernel\u2019s design are \u201cSkills\u201d that developers can build as semantic or native code. The design of Skills has prioritized maximum flexibility for the developer to be both lightweight and extensible, and to work seamlessly with SK\u2019s Memories (for context) and Connectors (for live data and actions). Lastly, SK\u2019s \u201cPlanner\u201d facilitates complex tasks by taking a user\u2019s \u201cask\u201d and translates it into the Skills, Memories, and Connectors needed to achieve their goal.<\/p>\n

SK supports models from OpenAI, including the newly released GPT-4, and Azure OpenAI Service, and we hope to add more model support in the future. We designed SK to take advantage of emerging capabilities of next generation models like GPT-4. For example, both the Planner and Skills architectures were built for achieving outcomes instead of just outputs \u2014 in a more goal-oriented approach to programming that\u2019s embodied by SK\u2019s underlying architecture.<\/p>\n

\"SK<\/a><\/p>\n

With SK, you can now build AI-first apps faster by design while also having a front-row peek at how the SDK is being built. SK was built as an internal incubation project at Microsoft to offer flexibility to a developer adding AI capability into their app. It is being shared as an open-source project to further the world\u2019s shared understanding of how to develop software that incorporates LLM AI. We\u2019re inviting developers around the world to collaborate with us through GitHub Issues, Discussions, and our Discord channel<\/a>.<\/p>\n

Key benefits of SK include the following:<\/p>\n