Showing category results for .NET

The Architectural Imperative: Why Multi-Agent Orchestration is Essential In modern enterprise AI systems, the scope and complexity of real-world business challenges quickly exceed the capabilities of a single, monolithic AI Agent. Facing tasks like end-to-end customer journey management, multi-source data governance, or deep human-in-the-loop revi...

In previous versions of the Semantic Kernel, the encoding of template arguments was performed automatically if the argument type was a . The encoding was not applied for custom types, anonymous types, or collections. With the latest changes, we've introduced stricter rules: if automatic encoding is enabled (the default behavior), an exception wi...

Smarter SK Agents with Contextual Function Selection In today's fast-paced AI landscape, developers are constantly seeking ways to make AI interactions more efficient and relevant. The new Contextual Function Selection feature in the Semantic Kernel Agent Framework is here to address this need. By dynamically selecting and advertising only the mos...

This is Part 2 of our series on integrating Microsoft.Extensions.AI with Semantic Kernel. In Part 1, we explored the relationship between these technologies and how they complement each other. Now, let's dive into practical examples showing how to use Microsoft.Extensions.AI abstractions with Semantic Kernel in non-agent scenarios. Getting Start...

This is the start of a series highlighting the integration between Microsoft Semantic Kernel and Microsoft.Extensions.AI. Future parts will provide detailed examples of using Semantic Kernel with Microsoft.Extensions.AI abstractions.  The most common questions are:  This blog post will address these questions and offer guidance ...

As Semantic Kernel shifts its foundational abstractions to Microsoft.Extensions.AI, we are obsoleting and moving away from our experimental embeddings interfaces to the new standardized abstractions that provide a more consistent and powerful way to work with AI services across the .NET ecosystem. The Evolution of Embedding Generation in Seman...

We’re excited to announce the release of Microsoft.Extensions.VectorData.Abstractions, a foundational library providing exchange types and abstractions for vector stores when working with vector data in AI-powered applications. This release is the result of a close collaboration between the Semantic Kernel and .NET teams, combining expertise in AI ...

Semantic Kernel: Package Previews, Graduations & Deprecations We are excited to share a summary of recent updates and continuous clean-up efforts across the Semantic Kernel .NET codebase. These changes focus on improving maintainability, aligning with the latest APIs, and ensuring a consistent experience for users. Below you’ll find details on...

Today we’re thrilled to announce support for Hybrid search with Semantic Kernel Vector Stores for .NET. What is Hybrid Search? Hybrid search performs two parallel searches on a vector database.  The union of the results of these two searches are then returned to callers with a combined rank, based on the rankings from each of the constituent sear...

OpenAI's gpt-4o-audio-preview is a powerful multimodal model that enables audio input and output capabilities, allowing developers to create more natural and accessible AI interactions. This model supports both speech-to-text and text-to-speech functionalities in a single API call through the Chat Completions API, making it suitable for building vo...