Showing category results for Semantic Kernel

In the dynamic field of conversational AI, managing coherent and contextually meaningful interactions between humans and digital assistants poses increasingly complex challenges. As dialogue lengths extend, maintaining full conversational context becomes problematic due to token constraints and memory limitations inherent to large language mo...

Global Expansion – "G3J Learn Semantic Kernel" Show – A Deep Dive in Korean! Localization Increases Demand Following the success of this multi-language delivery, we quickly noticed a surge in demand for localized content. Developers from different parts of the world have expressed interest in diving deeper into Semantic Kernel, and we could...

Overview of AWS Bedrock Agents AWS Bedrock Agents provide a managed service that facilitates the experimentation and rapid deployment of AI agents. Users can leverage proprietary AWS models as well as a diverse selection of models from various providers available on AWS Bedrock. Semantic Kernel's Integration with AWS Bedrock Semantic Kernel now ...

For Semantic Kernel users, integrating xAI's Grok API using the OpenAI connector is a breeze thanks to its compatibility with OpenAI's API format. This tutorial focuses on setting up Grok in your Semantic Kernel projects with minimal fuss, using C# and Python examples. Why Grok? Grok, built by xAI, is a powerful AI model, offers a 128k conte...

Following on from our blog post a couple months ago: Microsoft’s Agentic AI Frameworks: AutoGen and Semantic Kernel, Microsoft’s agentic AI story is evolving at a steady pace. Both Azure AI Foundry’s Semantic Kernel and AI Frontier’s AutoGen are designed to empower developers to build advanced multi-agent systems. The AI Frontier’s team is charging...

This post describes how to use Model Context Protocol tools with Semantic Kernel. Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. MCP standardizes the connection between AI models and various data sources and tools. The Model Context Protocol is significant because it enhances the way ...

Today the Semantic Kernel team is excited to welcome a guest author, Prabal Deb to share his work. LLMAgentOps Toolkit is repository that contains basic structure of LLM Agent based application built on top of the Semantic Kernel Python version. The toolkit is designed to be a starting point for data scientists and developers for experimentation...

Semantic Kernel Agent Framework Reaches Release Candidate 1 We're excited to announce that with the release of Semantic Kernel 1.40 (.NET) and 1.22.0 (Python), we're elevating the Semantic Kernel Agent Framework to Release Candidate 1. This marks a significant milestone in our journey toward providing a robust, versatile framework for building AI ...

As AI-driven applications continue to evolve, the need for efficient vector-based search capabilities is greater than ever. Microsoft Semantic Kernel makes it easy to integrate these capabilities with PostgreSQL databases using the connector. Whether you're leveraging cloud-hosted PostgreSQL instances on Amazon Web Services or Google Cloud, this c...

Hybrid model orchestration is a powerful technique that AI applications can use to intelligently select and switch between multiple models based on various criteria, all while being transparent to the calling code. This technique not only allows for model selection based on factors such as the prompt's input token size and each model's min/max toke...