Semantic Kernel
The latest news from the Semantic Kernel team for developers
Latest posts
Support for Azure.AI.OpenAI and OpenAI v2 is coming
Microsoft announced the official OpenAI library for .NET at the Build 2024 conference. This new SDK ensures that .NET developers have parity with other programming language libraries when accessing Azure OpenAI models. This means the new SDK will be able to support the latest OpenAI features and models as soon as they come out, such as GPT4o and Assistants v2, by providing a unified experience across OpenAI and Azure OpenAI. Since the release of the official OpenAI library for .NET, the Semantic Kernel team has been working on updating our existing OpenAI connectors to use the new V2 OpenAI library and Azure.A...
Announcing Semantic Kernel for Java 1.2.0
Hello Java AI developers! We are announcing the release of Semantic Kernel for Java v1.2.0! Since our release of v1.0 this past May, we have been working to improve and expand the capabilities we provide. Today, we are thrilled to share the latest updates, new features, and experimental features with you. What’s New in Semantic Kernel for Java v1.2.0? A new dedicated repository To get greater awareness of the Java version of Semantic Kernel, we've created a separate Java repository for it. Several folks had difficulty finding updates to the Java project, so it's now much easier to find at github.com/microso...
Customer Case Study: SimCorp’s AI Journey with Semantic Kernel
We're excited to feature the SimCorp team on the Semantic Kernel blog today. The SimCorp team will discuss their AI journey and how they’ve integrated Semantic Kernel to build out their AI solutions. Let’s dive into the details from the SimCorp team! SimCorp Background: SimCorp was founded in 1971 and has continuously innovated to create an industry-leading integrated investment management platform that’s built to turn avalanches of information into insights that lead to the decisions that put our clients ahead of the competition. SimCorp’s efficient, flexible front-to-back-offering, provides clients with S...
Planning with Semantic Kernel using Automatic Function Calling
Hello, everyone! AI planning is a powerful tool that allows to generate and execute complex workflows in applications based on specified goal. In Semantic Kernel, it's possible to implement planning logic using FunctionCallingStepwisePlanner class. Today we want to introduce a new way how to achieve the same results by using Automatic Function Calling. This approach produces the results more reliably and uses fewer tokens comparing to FunctionCallingStepwisePlanner. Setup For demonstration purposes, we will use 2 plugins to get current date/time and weather: For simplicity, let's define a helper function ...
The future of Planners in Semantic Kernel
Since the very earlier days of Semantic Kernel, we have shipped experimental “planners” that use prompts to generate multi-step plans. This was extremely powerful because it allowed developers to use LLMs (which were created to merely generate text) to begin automating business processes. Since then, the Semantic Kernel team has evolved its experimental planners over time so it could adopt the latest research from both inside and outside of Microsoft. Most notably, we began leveraging function calling. With function calling, the action planner could be replaced with a single function call request and the ReAct...
Model as a Service (MaaS) Available for Semantic Kernel
Hello! We are delighted to make an announcement regarding the availability of Azure AI Inference within Semantic Kernel (Python). This latest AI connector empowers you to experiment with a broader range of models hosted on Azure within your applications. The Sample Application: Evaluating LLMs Here is the location of the full sample. To demonstrate the capabilities of this new AI connector, we have prepared a sample application. In this application, we will execute three models against the widely recognized Measuring Massive Multitask Language Understanding (MMLU) dataset and produce benchmarkin...
Introducing Python Function Choice Behavior: Streamlining AI Model Configuration
Introducing Function Choice Behavior Today, we're excited to introduce the Function Choice Behavior, an enhancement over the existing Function Call Behavior. This new feature streamlines configuration and supports specifying multiple required functions, simplifying code and enhancing functionality. One significant advantage of Function Choice Behavior is its abstraction at the base level, allowing it to work across various AI Connectors that support function calling, not just OpenAI models. This change aims to make function calling more straightforward for our users, easily extendable for new models,...
Customer Case Study: preezie’s AI Journey with Microsoft Semantic Kernel
Today we're thrilled to feature the preezie team on the Semantic Kernel blog. The preezie team will discuss their AI journey, how they've integrated the Semantic Kernel SDK to build out their AI solutions and advice they'd give to other customers getting started on their AI journeys. We've broken each question for the preezie team into short interview clips below. Let's dive into the questions we have for the preezie team! 1.) Can you introduce yourself and your role? The below video series will feature the following individuals: 2.) What has your AI journey been like in general...
Updated Documentation for Semantic Kernel
Today we're excited to announce the updated documentation on the Semantic Kernel learn site here: Introduction to Semantic Kernel | Microsoft Learn. We've updated the docs to include in depth samples, updated pages focused on concepts and much more for your learning! Key Enhancements: We've updated the table of contents and layout of the learn site to include a concepts section focused on different features by page to understand and build on. We recommend looking at the Quick Start Guide to get started if you are starting to build out Semantic Kernel. We recommend starting on the Agents page (below i...