AI-Powered E-Commerce App with .NET 9, Angular 20 & RAG

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Free Download AI-Powered E-Commerce App with .NET 9, Angular 20 & RAG
Published 10/2025
Created by Rahul Sahay
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 70 Lectures ( 8h 21m) | Size: 4.1 GB

Build a full-stack AI-enabled store with Semantic Search, Chatbot, and RAG integration using .NET 9, Angular 20 & Azure
What you'll learn
Build a fully functional, production-grade AI-powered e-commerce application using .NET 9 and Angular 20.
Integrate semantic search with vector embeddings using Azure OpenAI or Ollama and pgvector in PostgreSQL.
Implement a chatbot assistant that understands natural-language queries and recommends products contextually.
Design and structure a modular backend following Clean Architecture principles and repository pattern.
Build dynamic, responsive Angular components using standalone architecture and the new Signals API.
Add hybrid search functionality combining traditional catalog search with semantic intelligence.
Containerize backend, database, and frontend services using Docker Compose for easy local deployment.
Configure Ocelot API Gateway for routing, service orchestration, and environment-based configuration.
Prepare your system for Retrieval-Augmented Generation (RAG) to combine retrieval and generative reasoning.
Gain real-world experience in connecting microservices, AI models, and cloud infrastructure into one cohesive solution.
Requirements
Basic understanding of C# and the .NET ecosystem.
Familiarity with Angular, TypeScript, or any frontend framework.
Knowledge of RESTful APIs, JSON, and HTTP methods.
A working knowledge of databases such as SQL Server or PostgreSQL.
Basic Git/GitHub familiarity for project versioning.
No prior AI or OpenAI experience is required - all concepts are covered step by step.
Description
Disclaimer:- This course requires you to download "Docker Desktop" from Docker website. If you are a Udemy Business user, please check with your employer before downloading software.Welcome to "AI-Powered E-Commerce App with .NET 9, Angular 20 & RAG"Have you ever imagined transforming a standard e-commerce store into an intelligent, AI-enabled platform that understands your users' intent?In this course, you'll learn to build a modern, semantic search and chatbot-powered online store that's ready for Retrieval-Augmented Generation (RAG) - using .NET 9, Angular 20, Azure OpenAI, and PostgreSQL (pgvector).In this hands-on course, you'll go far beyond theory. You'll build, run, and integrate AI capabilities step by step - from foundational architecture to advanced generative intelligence - all within a clean, scalable, production-ready system.Course PhasesPhase 1 - Building the AI-Enabled Foundation (Completed)In this phase, you'll develop a fully functional, AI-ready e-commerce system powered by .NET 9 and Angular 20.This is not a toy project - you'll build real, production-grade components and integrate intelligent features end to end.You willesign a modular backend using Clean Architecture principles and the repository pattern.Implement semantic search by generating and storing embeddings using Azure OpenAI or Ollama, backed by PostgreSQL + pgvector.Create an AI chatbot assistant capable of natural language understanding and contextual product recommendations.Integrate multiple search modes - Catalog, Semantic, and Hybrid - that deliver smart, intent-based results.Develop a dynamic Angular 20 frontend using standalone components and Signals API for responsive data binding.Add a complete basket and checkout flow with persistent data management.Configure Ocelot API Gateway for service routing and Docker Compose for containerized deployment.By the end of Phase 1, you will have a fully operational AI-driven store capable of handling real-time chat queries, intelligent product discovery, and hybrid semantic search - ready for the next phase of true RAG integration.Phase 2 - Advancing to RAG-Powered Intelligence (Coming Soon)In Phase 2, you'll take your AI assistant to the next level by introducing Retrieval-Augmented Generation (RAG), Voice Assistant Integration, and Web Search Augmentation.You will:Implement a RAG pipeline that combines vector search, document retrieval, and generative AI for context-aware answers.Add voice input and output, enabling users to interact naturally through speech.Extend the chatbot with web search fallback - if a product isn't in the store, the assistant will fetch live recommendations from the internet.Integrate context memory, allowing the assistant to maintain awareness across multiple turns in the conversation.Add analytics and telemetry dashboards to monitor user queries, AI accuracy, and engagement trends.By the end of Phase 2, your application will evolve into a fully RAG-powered conversational shopping assistant that can reason, retrieve, and respond like a true AI companion.Tech StackBackend: .NET 9, ASP.NET Core Minimal APIs, C#Frontend: Angular 20 with Standalone Components & Signals APIAI Integration: Azure OpenAI, Ollama, pgvector (PostgreSQL)Gateway: Ocelot API GatewayContainerization: Docker & Docker ComposeHosting: Local or Cloud-based deployment (Azure-ready)Who Is This Course ForDevelopers who want to integrate AI capabilities into real-world applications..NET and Angular engineers looking to master semantic search and RAG-based intelligence.Architects designing next-generation, AI-enabled microservices and e-commerce platforms.Learners eager to gain hands-on experience in building full-stack, AI-powered systems.Course Stats8+ hours of in-depth, project-based learning (Phase 1).70+ practical coding sessions, all demonstrated step-by-step.Lifetime access, free updates, and new features with every phase.Real-world architecture you can extend, deploy, and showcase.Why This CourseThis isn't a basic chatbot tutorial. By the end of this course, you'll have:Built a production-grade AI e-commerce system powered by .NET 9 and Angular 20.Implemented semantic search, vector-based intelligence, and chatbot interaction.Deployed a containerized AI stack ready for RAG, voice, and web-integrated intelligence.Gained the expertise to design and scale AI-first enterprise applications.Your journey to building an AI-Powered E-Commerce Platform starts here.Enroll today and learn to combine software engineering, AI integration, and full-stack development - all in one real-world project.Happy Learning
Who this course is for
.NET developers who want to add AI and RAG features to enterprise-grade applications.
Angular developers aiming to integrate modern AI-based search and chatbot capabilities.
Full-stack developers interested in building intelligent, production-ready web application
Software architects designing scalable, AI-enabled microservice ecosystems.
Backend engineers curious about semantic search, vector databases, and LLM integration.
Cloud engineers exploring Docker, containerization, and Azure OpenAI Service integration.
Students and AI enthusiasts who want hands-on exposure to real-world GenAI systems.
Professionals looking to transition into AI-driven full-stack development roles.
Product engineers and technical leads working on modern e-commerce or SaaS platforms.
Anyone who wants to master practical AI + RAG integration using familiar .NET and Angular tools.
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4.09 GB | 21min 15s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

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FileName :1 - Introduction.mp4 | Size: (26.84 MB)
FileName :2 - Understanding Embeddings, Vector Search & RAG.mp4 | Size: (37.4 MB)
FileName :3 - Solution Strategy.mp4 | Size: (44.81 MB)
FileName :4 - Github Strategy.mp4 | Size: (44.94 MB)
FileName :5 - About the Blog.mp4 | Size: (16.72 MB)
FileName :6 - Course Demo.mp4 | Size: (83.56 MB)
FileName :1 - Introduction.mp4 | Size: (54.29 MB)
FileName :2 - What we are going to build.mp4 | Size: (32.77 MB)
FileName :3 - Creating Solution.mp4 | Size: (21.52 MB)
FileName :4 - Understanding the Ollama Model.mp4 | Size: (20.58 MB)
FileName :5 - Creating Docker Support - 1st Part.mp4 | Size: (46.02 MB)
FileName :6 - Creating Docker Support - 2nd Part.mp4 | Size: (47.47 MB)
FileName :7 - Creating PG Vector Repository Interface.mp4 | Size: (47.05 MB)
FileName :8 - Creating Models.mp4 | Size: (28.3 MB)
FileName :9 - Creating Embedding Provider.mp4 | Size: (32.83 MB)
FileName :10 - Implementing Ollama Embedding Provider - 1st Part.mp4 | Size: (102.21 MB)
FileName :11 - Implementing Ollama Embedding Provider - 2nd Part.mp4 | Size: (35.52 MB)
FileName :12 - Configuring App Settings.mp4 | Size: (51.88 MB)
FileName :13 - Modifying Docker File.mp4 | Size: (22.53 MB)
FileName :14 - Implementing Ollama Chat Provider - 1st Part.mp4 | Size: (110.72 MB)
FileName :15 - Implementing Ollama Chat Provider - 2nd Part.mp4 | Size: (65.53 MB)
FileName :1 - Introduction.mp4 | Size: (33.67 MB)
FileName :2 - Installing Required Nuget Packages.mp4 | Size: (70.23 MB)
FileName :3 - Implementing Pg Vector Repository - 1st Part.mp4 | Size: (128.89 MB)
FileName :4 - Implementing Pg Vector Repository - 2nd Part.mp4 | Size: (70.11 MB)
FileName :5 - Creating Models for Data Ingestion.mp4 | Size: (47.25 MB)
FileName :6 - Creating Semantic EndPoint.mp4 | Size: (174.47 MB)
FileName :7 - Understanding Data Ingestion.mp4 | Size: (52.65 MB)
FileName :8 - Creating Semantic Search EndPoint.mp4 | Size: (109.95 MB)
FileName :9 - Understanding the Search EndPoint.mp4 | Size: (29.41 MB)
FileName :10 - Creating Chat Interface.mp4 | Size: (72.38 MB)
FileName :11 - Implementing Web Search provider.mp4 | Size: (35.44 MB)
FileName :12 - Creating Chat Service.mp4 | Size: (125.89 MB)
FileName :13 - Understanding Chat Service.mp4 | Size: (52.6 MB)
FileName :14 - Creating Chat EndPoint.mp4 | Size: (44.14 MB)
FileName :15 - Creating Test EndPoint.mp4 | Size: (56.37 MB)
FileName :16 - Wiring up Program File - 1st Part.mp4 | Size: (64.12 MB)
FileName :17 - Wiring Up Program File - 2nd Part.mp4 | Size: (108.83 MB)
FileName :18 - Data Ingestion Demo.mp4 | Size: (143.17 MB)
FileName :19 - Search Demo.mp4 | Size: (38.93 MB)
FileName :20 - Chat Demo.mp4 | Size: (17.76 MB)
FileName :21 - Fixing Null value of Brand Name.mp4 | Size: (13.93 MB)
FileName :1 - Introduction.mp4 | Size: (36.77 MB)
FileName :2 - Creating OpenAI Embedding Provider.mp4 | Size: (103.99 MB)
FileName :3 - Understanding OpenAI Embedding Provider.mp4 | Size: (43.41 MB)
FileName :4 - Creating Open AI Chat Provider.mp4 | Size: (105.08 MB)
FileName :5 - Creating Azure Open AI Embedding Provider.mp4 | Size: (102.69 MB)
FileName :6 - Creating Chat Completion Response Model.mp4 | Size: (27.45 MB)
FileName :7 - Creating Azure Open AI Chat Provider.mp4 | Size: (84.66 MB)
FileName :8 - Getting Started with Azure AI Foundry.mp4 | Size: (56.87 MB)
FileName :9 - Creating Azure Resource Group.mp4 | Size: (17.89 MB)
FileName :10 - Setting up Azure Open AI.mp4 | Size: (41.46 MB)
FileName :11 - Deploying Azure Open AI Models.mp4 | Size: (46.44 MB)
FileName :12 - Getting Exceeded max tries Issue.mp4 | Size: (45.82 MB)
FileName :13 - Azure Open AI Demo.mp4 | Size: (49.5 MB)
FileName :1 - Introduction.mp4 | Size: (46.43 MB)
FileName :2 - Understanding the Existing Layout of Ecommerce Site.mp4 | Size: (30.87 MB)
FileName :3 - About the Blog.mp4 | Size: (20.76 MB)
FileName :4 - Modifying Navbar Search Code.mp4 | Size: (39.25 MB)
FileName :5 - Modifying the Navbar Markup and Style.mp4 | Size: (55.19 MB)
FileName :6 - Creating Semantic and Hybrid Search.mp4 | Size: (68.24 MB)
FileName :7 - Integrating Semantic and Hybrid Search.mp4 | Size: (98.98 MB)
FileName :8 - Testing Search Functionality.mp4 | Size: (71.56 MB)
FileName :9 - Fixing the Filtering Issue.mp4 | Size: (68.36 MB)
FileName :10 - Creating Chat Models.mp4 | Size: (33.97 MB)
FileName :11 - Creating Chat Service.mp4 | Size: (29.82 MB)
FileName :12 - Creating Chatbot Component.mp4 | Size: (146.33 MB)
FileName :13 - Creating Chatbot Markup.mp4 | Size: (142.21 MB)
FileName :14 - Chat Demo.mp4 | Size: (67.11 MB)
FileName :1 - Glimpse of Phase 2.mp4 | Size: (44.74 MB)

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