The Launch of "AI Agents for Beginners": Your Gateway to Building Intelligent Systems
🌱 Getting Started Each lesson covers fundamental aspects of building AI Agents. Whether you're a novice or have some experience, you'll find valuable insights and practical knowledge. We also support multiple languages, so you can learn in your preferred language. To see the available languages, click here. If this is your first time working with Generative AI models, we highly recommend our "Generative AI For Beginners" course, which includes 21 lessons on building with GenAI. Remember to star (🌟) this repository and fork it to run the code! 📋 What You Need The course includes code examples that you can find in the code_samples folder. Feel free to fork this repository to create your own copy. The exercises utilize Azure AI Foundry and GitHub Model Catalogs for interacting with Language Models: Github Models - Free / Limited Azure AI Foundry - Azure Account Required We also leverage the following AI Agent frameworks and services from Microsoft: Azure AI Agent Service Semantic Kernel AutoGen For more information on running the code for this course, visit the Course Setup. 🙏 Want to Help? We welcome contributions from the community! If you have suggestions or spot any errors, please raise an issue or create a pull request. If you encounter any difficulties or have questions about building AI Agents, join our Azure AI Community on Discord. 📂 Each Lesson Includes A written lesson located in the README (Videos Coming March 2025) Python code samples supporting Azure AI Foundry and Github Models (Free) Links to extra resources to continue your learning 🗃️ Lessons Overview Intro to AI Agents and Use Cases Exploring Agentic Frameworks Understanding Agentic Design Patterns Tool Use Design Pattern Agentic RAG Building Trustworthy AI Agents Planning Design Pattern Multi-Agent Design Pattern Metacognition Design Pattern AI Agents in Production 🌐 Multi-Language Support We offer translations in several languages and will updating these on a regular basis. 🚀 Go Fork or Clone this repo and get started on your AI Agents journey 🤖 at https://aka.ms/ai-agents-beginnersLLM based development tools: PromptFlow vs LangChain vs Semantic Kernel
Globally, developers, data scientists, and engineers created new applications or advanced their existing applications to take advantage of LLMs. While building a Question-and-Answer chatbot is simple and you may not need advanced tools, in other more complex scenarios, the AI orchestrator come in and make the process easier. At the center of LLM applications is the AI orchestration layer that allows developers to build their own Copilot experiencesMicrosoft Semantic Kernel and AutoGen: Open Source Frameworks for AI Solutions
Explore Microsoft’s open-source frameworks, Semantic Kernel and AutoGen. Semantic Kernel enables developers to create AI solutions across various domains using a single Large Language Model (LLM). AutoGen, on the other hand, uses AI Agents to perform smart tasks through agent dialogues. Discover how these technologies serve different scenarios and can be used to build powerful AI applications.IA y NET LATAM - Episodio 6
Buenas, Es un placer para nosotros, Bruno y Pablito Piova compartir con ustedes nuestras impresiones sobre el episodio 6 de la serie AI + .NET LATAM que tuvimos el honor de presentar el 6 de Diciembre junto con Jose Luis Latorre y Luis Beltran En el episodio número 6 de nuestra serie en Microsoft Reactor, exploramos cómo la inteligencia artificial (IA) está transformando el panorama tecnológico a través de herramientas innovadoras como Agentes autónomos, Semantic Kernel y otras tecnologías avanzadas. Además, discutimos las tendencias clave de IA que marcarán el 2025 y pudimos revisar algunas noticias frescas posteriores al gran evento Microsoft Ignite 2024. A continuación, destacamos algunos de los puntos más interesantes que se mencionaron en la charla y compartimos los enlaces de referencia: 6 AI trends you’ll see more of in 2025 Un repaso a las tendencias que marcan la hoja de ruta de la IA para el futuro próximo, desde modelos más potentes y accesibles, hasta el auge de los agentes inteligentes. Microsoft Ignite 2024 Book of News Un resumen completo de todos los anuncios más relevantes presentados en Ignite, incluyendo nuevos servicios, herramientas y mejoras para desarrolladores y profesionales de TI. Introducing Microsoft Copilot actions, new agents, and tools to empower IT| Microsoft 365 Blog Copilot va más allá del simple chat; ahora incluye agentes y acciones que automatizan tareas y mejoran la productividad empresarial. Ignite 2024: Announcing the Azure AI Foundry SDK Un nuevo SDK que unifica y facilita el despliegue y la orquestación de soluciones de IA en Azure, acelerando los ciclos de desarrollo. Introducing Azure AI Agent Service Nuevas funcionalidades que facilitan la creación y administración de agentes de IA capaces de interactuar con otras herramientas y servicios. New Copilot Prompt Gallery helps you discover, save, and share your favorite prompts | Microsoft Community Hub Una galería para descubrir, guardar y compartir prompts, facilitando el trabajo con modelos generativos. Ideal para estandarizar y reutilizar buenas prácticas. Unlocking the Power of Memory: Announcing General Availability of Semantic Kernel’s Memory Packages Una galería para descubrir, guardar y compartir prompts, facilitando el trabajo con modelos generativos. Ideal para estandarizar y reutilizar buenas prácticas. eShopLite-SemanticSearch | eShopLite-SemanticSearch-AzureAISearch Ejemplos prácticos sobre cómo incorporar búsqueda semántica e IA en aplicaciones, utilizando .NET y Azure. Azure AI Content Understanding Servicio en vista previa para procesar y comprender contenidos complejos (texto, imágenes, audio, video) y extraer información relevante. Estamos muy entusiasmados con la creciente participación e interés de la comunidad. Seguiremos comprometidos en ofrecer contenido de alta calidad que promueva el conocimiento y la innovación. Los invitamos a dejar sus comentarios, compartir sus opiniones y contarnos qué más les gustaría ver en futuros episodios. Agradecemos su apoyo y esperamos verlos en el próximo episodio, el 10 de enero de 2025. Registro: https://aka.ms/IAyNET-LATAM Redes de LinkedIn de Microsoft-Reactor: https://www.linkedin.com/showcase/microsoft-reactor/ Un saludo, Bruno y PablitoBuilding Intelligent Applications with Local RAG in .NET and Phi-3: A Hands-On Guide
Let's learn how to do Retrieval Augmented Generation (RAG) using local resources in .NET! In this post, we’ll show you how to combine the Phi-3 language model, Local Embeddings, and Semantic Kernel to create a RAG scenario.
DocAider: Automated Documentation Maintenance for Open-source GitHub Repositories
Code–level documentation of a software system provides explanations of the code functionality and usages. Documentation is crucial for giving clear insights into the code for end–users and future developers. However, creating and updating documentation manually is a demanding task, requiring significant resources and labour. With the advancement of generative AI, there is a potential to reduce human labour in documentation tasks significantly. We propose DocAider, an automation tool powered by GPT–4 that integrates the processes of documentation generation and update. DocAider can generate comprehensive and structured documentation in markdown format and update it in response to any changes made in pull requests. The mission of DocAider is to reduce developers’ burden on maintaining documentation for GitHub repositories.
