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Functions are a key component of Semantic Kernel. As an AI Orchestrator, Semantic Kernel coordinates function execution together with Large Language Model (LLM) inference to allow the model return better responses or take action. Semantic Kernel groups related functions as plugins and provides capabilities to generate plugins from native code (e.g....

Hello Java AI developers! We are announcing the 1.4.0 release of the Semantic Kernel for Java. Please see the full changelog here. You can find our updates on our GitHub repository and the artifacts on Maven Central. What's New in Semantic Kernel for Java 1.4.0? Vector Store Functionality Goes GA The central focus of this release is for the V...

Large Language Models (LLMs) operate with a defined limit on the number of tokens they can process at once, referred to as the context window. Exceeding this limit can have significant cost and performance implications. Therefore, it is essential to manage the size of the input sent to the LLM, particularly when using chat completion models. Th...

One of the principal features of Semantic Kernel is its ability to integrate various AI services seamlessly. We are pleased to announce that this capability now extends to AWS Bedrock. With AWS Bedrock, you can access foundational models such as the Amazon Titan models. The new connector supports Chat Completion, Text Generation, and Text Embedd...

Microsoft Ignite 2024 is almost here!  This year the in-person conference will be held in Chicago Illinois USA November 19th - 22nd and the Semantic Kernel team will be on site answering questions and sharing best practices and if you can't attend in-person you can also sign up for free and watch it online or On Demand. I’m thrilled to announce ...

We are thrilled to announce the launch of Microsoft.Extensions.VectorData for .NET! Our collaboration with the .NET team since the debut of Semantic Kernel has resulted in a powerful new feature that aligns with the best practices of both current and upcoming .NET releases.  This is the second new package as part of this collaboration.  You can lea...

Diving into Function Calling and its JSON Schema in Semantic Kernel .NET Function-calling is one of the most exciting features of certain Large Language Models (LLMs), enabling developers to execute code directly in response to user queries. In Semantic Kernel, we streamline the process by allowing you to use built-in plugins or integrate your own...

As we continue to empower developers in embedding AI into their workflows, we are thrilled to announce significant updates to the Microsoft Semantic Kernel's Process Framework. Building on our introductory blog, we’ve made enhancements that further streamline business process automation and AI integration and help you to be successful with the Sema...

We are thrilled to announce the launch of Microsoft.Extensions.AI for .NET! Our collaboration with the .NET team since the debut of Semantic Kernel has resulted in a powerful new feature that aligns with the best practices of both current and upcoming .NET releases. With the introduction of Microsoft.Extensions.AI, Independent Software Vendors (...

In one of the previous posts, we demonstrated how to use JSON Schema to get Structured Output with OpenAI and Python version of Semantic Kernel: Using JSON Schema for Structured Output in Python for OpenAI Models. In this post, we will explore how to implement a JSON Schema-based structured output using .NET version of Semantic Kernel. For mo...