Easy to Extend:Semantic Kernel can be connected with different data sources and services so that other services can utilize natural language processing and also utilizing live business data. Better prompting:Semantic Kernels is designed with templated prompts, so you can quickly design semantic functions...
C# Advent: Getting Started with Semantic Kernel and C# 活动类型: 博客 角色: 作者 2023年12月1日周五, 13:00 主要技术领域: Azure AI Services其他技术领域: .NET 目标受众: DeveloperWrote an article in the C# Advent blogging series on Semantic Kernel and C# and cross-posted it to the usual cha...
For this sample, we first started by importing the following packages:C# Kopiraj using Microsoft.SemanticKernel; using Microsoft.SemanticKernel.ChatCompletion; using Microsoft.SemanticKernel.Connectors.OpenAI; 2) Add AI servicesAfterwards, we add the most important part of a kernel: the AI services...
Semantic Kernel Agent Framework Semantic Kernel Agent Framework The Semantic Kernel Agent Framework provides a platform within the Semantic Kernel eco-system that allow for the creation of AI agents and the ability to incorporate agentic patterns into any application based on the same patterns and feat...
Describe the bug The test run in testrunner Step04_AgentOrchestration fails To Reproduce Steps to reproduce the behavior: Load Project Configure secrets Run tests Expected behavior Test should complete without failure Screenshots If appl...
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In the Copilot application, we create applications through Semantic Kernel / LangChain. This type of application framework is generally compatible with Azure OpenAI Service / OpenAI models, and can also support open source models on Hugging face and local models. Wh...
Getting started with AI Agents in Azure Artificial intelligence agents are everywhere—from chatbots and virtual assistants to autonomous systems making decisions on behalf of users. But what exactly is an "AI agent," and how does it differ from other AI models and systems? In...
An embedding is a vector representation of text that captures its semantic meaning through its position in a high-dimensional vector space. If the distance between two embeddings is close, that means they are similar in meaning. For example, the word apple will be closer to orange than it is...