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GenAI for .NET: Build LLM Apps with OpenAI and Ollama · LearnSpace
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GenAI for .NET: Build LLM Apps with OpenAI and Ollama

Курс от Packt
Средний≈ 11.9 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unlock the power of AI in your .NET applications with this hands-on course. You'll gain practical knowledge of Large Language Models (LLMs), Small Language Models (SLMs), and prompt engineering, empowering you to build intelligent applications for chat, text analysis, and search. Explore real-world projects that combine the latest OpenAI models and Ollama for local AI execution, giving you a complete understanding of both cloud-based and on-premise solutions. The course guides you step-by-step through setting up your .NET AI environment, integrating AI frameworks like Microsoft.Extensions.AI, working with vector databases, and implementing retrieval-augmented generation (RAG) applications. You'll develop AI chat apps, text completion tools, semantic and vector search apps, and even image analysis applications, with hands-on exercises for each concept. You'll also learn to deploy and extend AI solutions using GitHub-hosted models, Azure AI services, and local Ollama models. Projects culminate in a fully functional EShop vector search application, integrating semantic search, chat capabilities, and advanced AI services, giving you both technical depth and practical experience. This course is perfect for .NET developers, AI enthusiasts, and software engineers looking to expand their skills in AI application development. Prerequisites include basic familiarity with .NET and programming concepts. Difficulty is Intermediate, suitable for learners ready to implement AI-powered solutions confidently. By the end of the course, you will be able to build LLM-powered chat apps, implement vector-based search and embeddings, create RAG applications, integrate AI services in .NET, and deploy AI solutions using both cloud and local models.

Навыки, которые вы освоите

.NET FrameworkVector DatabasesLarge Language ModelingOpenAIGenerative AIPrompt EngineeringArtificial IntelligenceLLM ApplicationEmbeddingsMicroservicesImage AnalysisModel DeploymentRetrieval-Augmented GenerationTool CallingCloud APIMultimodal PromptsOpenAI APIPrompt Patterns

Программа курса

10 модулей · 89 учебных материалов

01Introduction4 материалов

Introduction

IntroductionВидеоPrerequisites, Source Code and Course SlidesВидеоCourse Projects: Chat, Text Analysis, Vector Search, RAG, EShop Vector SearchВидеоFull Course ResourceЧтение
02GenAI Concepts: LLM, Token, SLM, Prompt Engineering

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Packt - Course Instructors

Преподаватель курса

GenAI for .NET: Build LLM Apps with OpenAI and Ollama
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Обучение на Coursera

≈ 11.9 ч

10 модулей

Язык: Английский

Часть программы вашего университета
7 материалов

GenAI Concepts: LLM, Token, SLM, Prompt Engineering

What Are Large Language Models (LLMs)?ВидеоHow Large Language Models (LLMs) Work?ВидеоWhat Is Token and Tokenization?ВидеоWhat Are Small Language Models (SLMs)?ВидеоWhat Is Prompt and Prompt Engineering?ВидеоUnderstanding Large Language Models and TokenizationDIALOGUEGenAI Concepts: LLM, Token, SLM, Prompt EngineeringЗадание
03.NET + AI Ecosystem: AI Development Tools and Libraries for .NET6 материалов

.NET + AI Ecosystem: AI Development Tools and Libraries for .NET

Why Choose .NET to Build AI Applications?ВидеоAI Frameworks and SDKs for .NETВидеоUnified AI Building Blocks: Microsoft.Extensions.AI (MEAI)ВидеоSemantic Kernel for .NETВидеоExploring AI Development with .NET: Libraries, Frameworks, and Real-World IntegrationDIALOGUE.NET + AI Ecosystem: AI Development Tools and Libraries for .NET - AssessmentЗадание
04Setup LLM Providers: GitHub Models, Ollama, Azure AI Foundry8 материалов

Setup LLM Providers: GitHub Models, Ollama, Azure AI Foundry

Which AI Provider Should I Use for This Course?ВидеоAzure OpenAI from Azure AI Foundry and OpenAI Platform (Paid Services)ВидеоGitHub ModelsВидеоCreate Access Token for GitHub ModelsВидеоOllama – Run LLMs LocallyВидеоDownload Ollama and llama3.2, all-minilm Models with DockerВидеоChoosing and Setting Up an AI Provider: Paid vs Free OptionsDIALOGUESetup LLM Providers: GitHub Models, Ollama, Azure AI Foundry - AssessmentЗадание
05Chat, Text Completions, Analysis and Function Calling w/ .NET12 материалов

Chat, Text Completions, Analysis and Function Calling w/ .NET

Create .NET Solution for Developing AI ProjectsВидеоText Completion LLM with GitHub Models OpenAI gpt-5-mini ModelВидеоStreaming LLM Text CompletionВидеоClassification, Summarization, Sentiment Analysis LLM Other Use CasesВидеоStructured Output in LLM for Data Extraction Use CaseВидеоBuild AI Chat App with .NET and gpt-5-mini ModelВидеоText Completion LLM with Local Ollama llama3.2 ModelВидеоRun LLM Use Cases with Local Ollama llama3.2 ModelВидеоFunction Calling in Large Language Models (LLMs)ВидеоInvoke .NET Functions Using GH gpt-5-mini ModelВидеоBuilding and Integrating LLMs in .NET: From Text Completion to Function CallingDIALOGUEChat, Text Completions, Analysis and Function Calling w/ .NET - AssessmentЗадание
06.NET AI Vector Search Using Vector Embeddings and Vector Store11 материалов

.NET AI Vector Search Using Vector Embeddings and Vector Store

What Are Vectors and Vector Embeddings?ВидеоGenerate Embeddings and Calculate Similarity w/ CosineSimilarityВидеоWhat Is a Vector Database?Видео.NET AI Vector Search w/ Extensions.VectorData.AbstractionsВидеоCreate VectorSearch App and Install PackagesВидеоDevelop Vector Data Entity and Populate DataВидеоGenerate Embeddings and Store in In-Memory Vector StoreВидеоCreate Query Embedding and Perform Vector SearchВидеоDevelop .NET AI Vector Search App w/ Ollama and all-minilm Embedding ModelВидеоImplementing and Exploring .NET AI Vector Search with EmbeddingsDIALOGUE.NET AI Vector Search Using Vector Embeddings and Vector Store - AssessmentЗадание
07Retrieval Augmented Generation (RAG) Application w/ .NET AI12 материалов

Retrieval Augmented Generation (RAG) Application w/ .NET AI

What Is Retrieval-Augmented Generation (RAG)?ВидеоWhy Need Retrieval-Augmented Generation (RAG)? Why Is RAG Important?ВидеоHow Does Retrieval-Augmented Generation (RAG) Work?ВидеоBuild .NET Chat App w/ RAG TemplateВидеоExplore .NET Chat App RAG SolutionВидеоRun & Test .NET Chat App RAG SolutionВидеоExtend RAG Chat App with Your Own Documents - Ingest and Embeddings Custom PDFВидеоExtend RAG Chat App Functionality with Function Calling ToolВидеоBuild .NET Chat App w/ RAG Template Using Ollama and all-minilmВидеоAdd Qdrant Vector Database into Our RAG Chat App w/ .NET AspireВидеоBuilding and Extending a .NET Retrieval Augmented Generation (RAG) Chat ApplicationDIALOGUERetrieval Augmented Generation (RAG) Application w/ .NET AI - AssessmentЗадание
08Image Analysis Apps w/ .NET AI7 материалов

Image Analysis Apps w/ .NET AI

Image Recognition with .NET and MEAIВидеоBuild Image Analysis App w/ .NET and GH Models - OpenAI gpt-5-miniВидеоAnalysis Traffic Cams with Structured OutputВидеоDownload Llava Vision Model for OllamaВидеоBuild Image Analysis App w/ .NET and Ollama LlavaВидеоBuilding Multimodal Image Analysis Apps with .NET and AI Vision ModelsDIALOGUEImage Analysis Apps w/ .NET AI - AssessmentЗадание
09Build Eshop Vector Search App w/ .NET Aspire, gpt-5-mini and Qdrant Vector DB19 материалов

Build Eshop Vector Search App w/ .NET Aspire, gpt-5-mini and Qdrant Vector DB

Introduction - EShop Vector Search w/ .NET Aspire, gpt-5-mini, QdrantВидеоStarting Point: EShop Distributed Microservices Architecture with .NET AspireВидеоAdd Chat Capability in EShop Integrate GitHub Models OpenAI gpt-5-mini ModelВидеоDevelop ProductAIService.cs Class for Business Layer - Customer Support Chat AIВидеоDevelop ProductEndpoints Support Query Endpoint for Chat Q&AВидеоDevelop Chat UI Pages to Consume Support EndpointВидеоAdd Qdrant Vector Database into .NET Aspire Distributed Environment (Hosting)ВидеоClient Integration Packages for Qdrant and Extensions.VectorDataВидеоRegister Qdrant Client Services into .NET DI Services in Catalog/Program.csВидеоRegister EmbeddingGenerator Services in Catalog/Program.csВидеоDevelop ProductVector Domain Entity for Storing Vector DataВидеоDevelop Traditional Search in ProductService.cs Business ClassВидеоDevelop ProductAIService.cs for Semantic Search ImplementationВидеоDevelop Search Endpoints in ProductEndpoints.cs for Semantic SearchВидеоTest Search Endpoints from Catalog MicroservicesВидеоBlazor FrontEnd Search Page DevelopmentВидеоTest WebApp Blazor Search Page for Semantic SearchВидеоImplementing Semantic Search in .NET Microservices with Vector DatabasesDIALOGUEBuild Eshop Vector Search App w/ .NET Aspire, gpt-5-mini and Qdrant Vector DB - AssessmentЗадание
10Thanks3 материалов

Thanks

ThanksВидеоFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание