Semantic Kernel
The latest news from the Semantic Kernel team for developers
Latest posts
Customer Case Study: Announcing the Microsoft Semantic Kernel Couchbase Connector
We're thrilled to announce the launch of the Semantic Kernel Couchbase Vector Store Connector for .NET developers, created through our strategic partnership with Microsoft's Semantic Kernel team. This powerful out-of-the-box connector transforms how developers integrate vector search capabilities into their AI applications. What sets this connector apart is how it harnesses Couchbase's distributed NoSQL platform alongside Semantic Kernel's vector store abstractions, creating an integration that prioritizes both performance and developer experience. The Semantic Kernel Couchbase Vector Store Connector elimin...
The Future of AI: Customizing AI Agents with the Semantic Kernel Agent Framework
Today we're excited to promote a recent blog from AI Platform focused on customizing AI agents with the Semantic Kernel agent framework. Read the entire blog post: here The Future of AI blog series is an evolving collection of posts from the AI Futures team in collaboration with subject matter experts across Microsoft. In this series, we explore tools and technologies that will drive the next generation of AI. Explore more at: https://aka.ms/the-future-of-ai Customizing AI agents with the Semantic Kernel agent framework AI agents are autonomous entities designed to solve complex tasks for humans...
Using Azure AI Agents with Semantic Kernel in .NET and Python
Today we’re excited to dive into Semantic Kernel and Azure AI Agents. There are additional details about using an within Semantic Kernel covered in our documentation here. Azure AI Agents are powerful tools for developers seeking to integrate AI capabilities into their applications. In this blog post, we'll explore how to utilize Azure AI Agents alongside the Semantic Kernel in both .NET and Python, showcasing the potential of these technologies to create intelligent and responsive applications. What is an Azure AI Agent? An Azure AI Agent is a specialized agent within the Semantic Kernel framework, designe...
Customer Case Story: Creating a Semantic Kernel Agent for Automated GitHub Code Reviews
Today I want to welcome a guest author to our Semantic Kernel blog, Rasmus Wulff Jensen, to cover how he's created a Semantic Kernel agent for automated GitHub code review. We'll turn it over to Rasmus to dive in. Introduction If you work in software development, you know that Code reviews are an essential part of the software development process, ensuring quality, security, and maintainability. However, they also consume a significant amount of developers’ time, often delaying feature delivery and increasing workload. Recognizing this challenge, the company Relewise, a Search and Recommendation SASS Business, ...
Customer Case Study: INCM transforms legal accessibility with an AI Search Assistant
Customer Case Study: INCM transforms legal accessibility with an AI Search Assistant The Imprensa Nacional-Casa da Moeda (INCM) is responsible for managing and publishing Portugal’s Diário da República (Official Gazette of the Republic of Portugal), which includes essential information for understanding laws, regulations, and legal processes. The quantity of information and the complex language used in these documents present significant challenges for individuals seeking to access and understand them. This complexity often acts as an obstacle, limiting access to vital legal information. These challenges not onl...
Keeping the Conversation Flowing: Managing Context with Semantic Kernel Python
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 models (LLMs). These constraints not only degrade conversational clarity but also compromise the system's ability to deliver accurate and relevant responses. Thus, effective solutions require strategies that intelligently balance context retention with efficient memory ma...
G3J Learn Semantic Kernel Show – A Deep Dive in Korean! | 세계로 뻗어갑니다: “G3J Learn Semantic Kernel” 쇼 – 한국어로 배우는 Semantic Kernel!
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 couldn’t be more excited to expand on this momentum. To address this growing interest, we are thrilled to announce the recordings of a new initiative, starting with a deep dive into Semantic Kernel in Korean. The pilot session featured above is the first of many localized f...
Integration of AWS Bedrock Agents in Semantic Kernel
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 integrates with AWS Bedrock Agents, enabling users to leverage kernel functions alongside features such as code interpretation and Retrieval-Augmented Generation (RAG) powered by the AWS knowledge base. If you manage resources on AWS and are exploring a multi-cloud AI s...
Talk to your agents! Introducing the Realtime API’s in Semantic Kernel
Introducing Realtime Agents in Semantic Kernel for Python With release 1.23.0 of the Python version of Semantic Kernel we are introducing a new set of clients for interacting with the realtime multi-modal API's of OpenAI and Azure OpenAI. They provide a abstracted approach to connecting to those services, adding your tools and running apps that leverage these very powerful and useful agents. The key addition that Semantic Kernel brings when you want to connect to these models is that we make the experience of using these models with function calling very easy, just create a Kernel and add your plugins as ...