eShopLite

eShopLite is a set of reference .NET applications implementing an eCommerce site with features like Semantic Search, Model Context Protocol (MCP), Reasoning models, vector databases, and more.

> - ☁️ All scenarios in this repository use the latest version of .NET and leverage .NET Aspire to orchestrate the entire solution.
> - 🌟 Don't forget to star (🌟) this repo to find it easier later.
> - ➡️ Get your own copy by Forking this repo and find it next in your own repositories.
> - ❓ Have a question? Besides creating issues or pull requests, the best option for questions is to join the Azure AI Discord channel, where a team of AI experts can help you.

Features

This project framework provides the following features:

- Modern .NET application architecture with .NET Aspire
- Various search capabilities (keyword search, semantic search)
- Integration with multiple AI models (GPT-4o, DeepSeek-R1, etc.)
- Vector database implementations (In Memory, Azure AI Search, Chroma DB and others)
- Real-time audio capabilities
- Model Context Protocol (MCP) server and client implementation

eShopLite Scenarios

The project includes several scenarios demonstrating different capabilities:

| Scenario | Description | Key Technologies |
|----------|-------------|-----------------|
| 01 - Semantic Search | A reference .NET application implementing an eCommerce site with Search features using Keyword Search and Semantic Search. | .NET Aspire, OpenAI GPT-4.1-mini, In-memory Vector DB |
| 02 - Azure AI Search | Implements an eCommerce site with Keyword Search using SQL queries and Semantic Search with Vector Database and Azure AI Search. | Azure AI Search, OpenAI Embeddings, SQL Server |
| 03 - Realtime Audio | Extends the eCommerce site with advanced search features and real-time audio capabilities powered by the GPT-4o Realtime Audio API. | GPT-4o Realtime Audio API, Audio in Blazor, .NET Aspire |
| 04 - Chroma DB | Implements semantic search functionality using Chroma DB, an open-source database designed for AI applications. | Chroma DB, OpenAI Embeddings, .NET SDK |
| 05 - DeepSeek-R1 | Demonstrates integration of the DeepSeek-R1 model for enhanced semantic understanding and search capabilities. | DeepSeek-R1, .NET Aspire, Vector Embeddings |
| 06 - Model Context Protocol (MCP) | Implements the Model Context Protocol (MCP) for advanced AI interactions with MCP Servers and MCP Clients. | Model Context Protocol, Function Calling, SSE Events |
| 07 - Agents Concurrent | Demonstrates concurrent agent orchestration and advanced AI agent collaboration patterns. | .NET Aspire, Multi-Agent Systems, Orchestration |
| 08 - SQL Server 2025 | Demonstrates the use of vector search and vector indexes in the SQL Database Engine | SQL Server 2025, Vector Search, Vector Indexes |
| 09 - Azure App Service | Shows how to deploy a .NET Aspire multi-service eCommerce app to Azure App Service, using SQLite for data and integrating AI search. | Azure App Service, .NET Aspire, OpenAI, SQLite |
| 10 - A2A Network | Demonstrates advanced agent-to-agent (A2A) communication and orchestration patterns in .NET Aspire, including multi-agent collaboration and reasoning. | .NET Aspire, Multi-Agent Systems, A2A Protocol |
| 11 - GitHub Models | Local-first AI development using GitHub Models during local runs, with automatic switch to Azure OpenAI when deployed. | .NET Aspire, GitHub Models, Azure OpenAI |
| 12 - Azure Functions | Optional Azure Functions façade for semantic search and an alternate deployment boundary for vector search. | Azure Functions, .NET Aspire, Azure OpenAI |
| 13 - Observability Assistant with Foundry Local | Summarizes logs, traces, and incidents with a local-first observability assistant. | Aspire, OpenTelemetry, Foundry Local, Microsoft.Extensions.AI |
| 14 - Product Discovery Copilot | Turns search into natural-language product discovery with grounded explanations. | Semantic search, vector search, Microsoft.Extensions.AI |
| 15 - Store Intelligence Report | Generates daily business and operational store intelligence reports. | App data, telemetry, AI summarization, reports |
| 16 - MCP Store Operations Tools | Exposes safe store capabilities as MCP tools for agent use. | MCP, Aspire, app APIs, tool calling |
| 17 - A2A Store Operations Network | Shows specialized agents collaborating around the store app through A2A. | A2A, Microsoft Agent Framework, hosted agents |

Getting Started

Prerequisites

- .NET 10
- Docker Desktop or Podman
- Azure Developer CLI (azd) (for Azure deployment)
- Git
- Aspire CLI — install with dotnet tool install -g aspire.cli (used for setting local secrets via aspire secret set)

Installation

1. Clone the repository:

   git clone https://github.com/Azure-Samples/eShopLite.git
   

1. Navigate to the scenario directory of interest:

   cd eShopLite/scenarios/[scenario-folder]
   

1. Login to Azure:

    azd auth login
    

1. Provision and deploy all the resources:

    azd up
    

It will prompt you to provide an azd environment name (like "eShopLite"), select a subscription from your Azure account, and select a location where the necessary models, like gpt-4.1-mini and ADA-002 are available, a sample region can be "eastus2".

Quick setup — Azure OpenAI secrets

The script scripts\Set-AzureOpenAISecrets.ps1 configures all 17 scenarios at once. Run it from the repo root:

pwsh .\scripts\Set-AzureOpenAISecrets.ps1

The script interactively prompts for four values:

| Prompt | Parameter set |
|--------|---------------|
| Azure OpenAI endpoint | Parameters:AzureOpenAIEndpoint |
| Azure OpenAI API key (masked) | Parameters:AzureOpenAIApiKey |
| Chat deployment name | Parameters:AzureOpenAIDeploymentName |
| Embeddings deployment name | Parameters:AzureOpenAIEmbeddingsDeploymentName |

Use -DryRun to preview the commands without executing them:

pwsh .\scripts\Set-AzureOpenAISecrets.ps1 -DryRun

Internally, the script calls the Aspire CLI (aspire secret set) for each AppHost it discovers. To set a single value manually, use the same command directly:

aspire secret set Parameters:AzureOpenAIEndpoint "https://<your-resource>.openai.azure.com/" \
  --apphost scenarios/01-SemanticSearch/src/eShopAppHost/eShopAppHost.csproj

> Note: Scenario-specific extra parameters (e.g., Parameters:AzureOpenAIRealtimeDeploymentName in 03-RealtimeAudio, Parameters:DeepSeekEndpoint in 05-deepseek, Parameters:GitHubModelsToken in 11-GitHubModels) must still be set manually. See each scenario's README for details.

Quickstart

1. Navigate to a specific scenario folder (e.g., scenarios/01-SemanticSearch/)
2. Follow the README instructions in that scenario folder
3. Run the solution using dotnet run in the appropriate host project folder

Demo

To run the demo, follow these steps:

1. Navigate to the specific scenario folder
2. Follow the "Run the solution" instructions in that scenario's README
3. Access the application via the URLs provided in the console output

Sample Application

This is the eShopLite Aplication running, performing a Keyword Search:

This is the eShopLite Aplication running, performing a Semantic Search:

This is the eShopLite Application running the Realtime Audio feature:

This is the eShopLite Application using the DeepSeek-R1 Reasoning Model:

The Aspire Dashboard to check the running services:

The Azure Resource Group with all the deployed services:

Resources

- Generative AI for Beginners .NET

- .NET Aspire Documentation

- Azure OpenAI Service Documentation

Getting Help

If you get stuck or have questions about building AI apps, join:

If you have product feedback or errors while building, visit: