Genai For .Net: Build Llm Apps With Openai And Ollama

Develop Chat, Vector Search, VectorDB, RAG and EShop AI Apps using OpenAI, Ollama and Microsoft Extensions AI (MEAI)

What you'll learn

GenAI Concepts: LLM, Token, SLM, Prompt Engineering

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

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

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

Text Completion LLM with GitHub Models OpenAI gpt-4o-mini model

Classification, Summarization, Sentiment Analysis LLM Other Use Cases

Structured Output in LLM for Data Extraction Use Case

Build AI Chat App with .NET and gpt-4o-mini model

Invoke .NET functions using GH gpt-4o-mini model with Function Calling

.NET AI Vector Search using Vector Embeddings and Vector Store

Generate Embeddings and Calculate Similarity w/ CosineSimilarity

Develop .NET AI Vector Search App w/ Ollama and all-minilm embedding model

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

Build .NET Chat App w/ RAG Template w/ OpenAI gpt-4o-mini model

Build .NET Chat App w/ RAG Template using Ollama and all-minilm

Build Image Analysis App w/ .NET and GH Models - OpenAI gpt-4o-mini

Build Image Analysis App w/ .NET and Ollama llava

Build Eshop Vector Search App w/ .NET Aspire, gpt-4o-mini and Qdrant Vector DB

Add Qdrant Vector Database into .NET Aspire

Unified AI building blocks: Microsoft Extensions AI (MEAI)

Requirements

Basics of .NET Development

Description

In this hands-on course, you'll learn to integrate OpenAI, Ollama and .NET's new Microsoft-Extensions-AI (MEAI) abstraction libraries to build a wide range of GenAI applications—from chatbots and semantic search to Retrieval-Augmented Generation (RAG) and image analysis.Throughout the course, you’ll learn:.NET + AI EcosystemYou'll learn about Microsoft's new abstraction libraries like Microsoft-Extensions-AI, which makes it super easy to integrate & switch different LLM providers like OpenAI, Azure AI, Ollama and even self-hosted models.Setting Up LLM ProvidersConfigure the LLM providers—such as GitHub Models, Ollama, and Azure AI Foundry—so you can choose the best fit for your use case. Text Completion LLM w/ GitHub Models OpenAI gpt-5-mini and Ollama llama3.2 Model modelYou’ll learn how to use .NET to integrate LLM models and performs Classification, Summarization, Data extraction, Anomaly detection, Translation and Sentiment Analysis use cases.Build AI Chat App with .NET and gpt-5-mini modelYou'll develop back-and-forth conversation based messaging with LLM and user where the AI maintains context across multiple user turns.  We will use Chat Streaming features when developing AI Chat Application.Function Calling with .NET and gpt-5-mini modelDevelop a function that will trigger from OpenAI GPT-5-mini. The model returns structured JSON specifying which .NET function to invoke, along with arguments for retrieving real-time data..NET AI Vector Search using Vector Embeddings and Vector StoreWe’ll also cover Vector Search, a powerful feature that allows semantic search based on meaning—not keywords.You’ll learn how to:Generate embeddings using OpenAI’s text-embedding-3-small or Ollama’s all-MiniLM  embeddings model,Store these in a vector database like QdrantQuery the vector store with user embedding to find top matches by similarityRetrieve relevant data based on similarity searches—all in our .NET applications.RAG – Retrieval-Augmented Generation with .NETYou’ll learn how to combine vector search results with LLM responses to:Retrieve relevant data from your own sourcesBreak documents into chunks → embed them → store in vector DBAt query time, embed the question → retrieve relevant chunks → pass them along with the user’s query to the LLMGet accurate, context-specific answers using your internal data from LLMWe’ll implement the full RAG flow with real examples using .NET and Qdrant.Image Analysis with .NET AICover image recognition and analysis, showing how to send images to AI models, receive tags, captions or visual summaries and integrate those capabilities directly into your .NET appsVision models for object recognition, classification, or captioningCombining text and image processing to build more powerful, multi-modal applications for traffic cam analysis operationsFinal Project: E-Shop Semantic Search with .NET AspireYou’ll build a complete full-stack AI-powered EShop Vector Search app step by step.We’ll use:.NET Aspire for service orchestrationQdrant as our Vector Databaseand GPT-5 Mini or Ollama’s local models to generate embeddings and respond intelligently to user queriesIn this project, you’ll:Generate product embeddings with OpenAI text-embeddings or Ollama all-minilmStore them in Qdrant Vector DB  for fast similarity searchImplement a RAG flow that provides semantic search over our EShop product catalogEnable users to search products by meaning—not just keywordsThis project brings everything you learn in this course into a single, full-stack, real-world app.By the end of this course, you'll have the tools and confidence to build intelligent, GenAI-powered apps in .NET.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Prerequisites, Source Code and Course Slides

Lecture 3 Course Projects: Chat, Text Analysis, Vector Search, RAG, EShop Vector Search

Section 2: GenAI Concepts: LLM, Token, SLM, Prompt Engineering

Lecture 4 What are Large Language Models (LLMs) ?

Lecture 5 How Large Language Models (LLMs) works ?

Lecture 6 What is Token And Tokenization ?

Lecture 7 What are Small Language Models (SLMs) ?

Lecture 8 What is Prompt and Prompt Engineering ?

Section 3: .NET + AI ecosystem: AI Development Tools and Libraries for .NET

Lecture 9 Why choose .NET to build AI applications ?

Lecture 10 AI frameworks and SDKs for .NET

Lecture 11 Unified AI building blocks: Microsoft.Extensions.AI (MEAI)

Lecture 12 Semantic Kernel for .NET

Section 4: Setup LLM Providers: GitHub Models, Ollama, Azure AI Foundry

Lecture 13 Which AI provider should I use for this course ?

Lecture 14 Azure OpenAI from Azure AI Foundry and OpenAI platform (Paid Services)

Lecture 15 GitHub Models

Lecture 16 Create Access Token for GitHub Models

Lecture 17 Ollama – Run LLMs Locally

Lecture 18 Download Ollama and llama3.2, all-minilm models with Docker

Section 5: Chat, Text Completions, Analysis and Function Calling w/ .NET

Lecture 19 Create .NET Solution for Developing AI Projects

Lecture 20 Text Completion LLM with GitHub Models OpenAI gpt-5-mini model

Lecture 21 Streaming LLM Text Completion

Lecture 22 Classification, Summarization, Sentiment Analysis LLM Other Use Cases

Lecture 23 Structured Output in LLM for Data Extraction Use Case

Lecture 24 Build AI Chat App with .NET and gpt-5-mini model

Lecture 25 Text Completion LLM with Local Ollama llama3.2 Model

Lecture 26 Run LLM Use Cases with Local Ollama llama3.2 Model

Lecture 27 Function Calling in Large Language Models (LLMs)

Lecture 28 Invoke .NET functions using GH gpt-5-mini model

Section 6: .NET AI Vector Search using Vector Embeddings and Vector Store

Lecture 29 What are Vectors and Vector Embeddings ?

Lecture 30 Generate Embeddings and Calculate Similarity w/ CosineSimilarity

Lecture 31 What is a Vector Database ?

Lecture 32 .NET AI Vector Search w/ Extensions.VectorData.Abstractions

Lecture 33 Create VectorSearch App and Install Packages

Lecture 34 Develop Vector Data Entity and Populate Data

Lecture 35 Generate Embeddings and Store in In-memory Vector Store

Lecture 36 Create Query Embedding and Perform Vector Search

Lecture 37 Develop .NET AI Vector Search App w/ Ollama and all-minilm embedding model

Section 7: Retrieval Augmented Generation (RAG) Application w/ .NET AI

Lecture 38 What is Retrieval-Augmented Generation (RAG) ?

Lecture 39 Why Need Retrieval-Augmented Generation (RAG) ? Why is RAG Important ?

Lecture 40 How Does Retrieval-Augmented Generation (RAG) Work ?

Lecture 41 Build .NET Chat App w/ RAG Template

Lecture 42 Explore .NET Chat App RAG Solution

Lecture 43 Run & Test .NET Chat App RAG Solution

Lecture 44 Extend RAG Chat App with Your Own Documents - Ingest and Embeddings Custom Pdf

Lecture 45 Extend RAG Chat App Functionality with Function Calling Tool

Lecture 46 Build .NET Chat App w/ RAG Template using Ollama and all-minilm

Lecture 47 Add Qdrant Vector Database into our RAG Chat App w/ .Net Aspire

Section 8: Image Analysis Apps w/ .NET AI

Lecture 48 Image Recognition with .NET and MEAI

Lecture 49 Build Image Analysis App w/ .NET and GH Models - OpenAI gpt-5-mini

Lecture 50 Analysis Traffic Cams with Structured Output

Lecture 51 Download llava vision model for Ollama

Lecture 52 Build Image Analysis App w/ .NET and Ollama llava

Section 9: Build Eshop Vector Search App w/ .NET Aspire, gpt-5-mini and Qdrant Vector DB

Lecture 53 Introduction-EShop Vector Search w/ .NET Aspire, gpt-4o-mini, Qdrant

Lecture 54 Starting Point: EShop Distributed Microservices Architecture with .NET Aspire

Developers and Architects who curios about developing LLM Applications with .NET