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eShopSupport
A reference .NET application using AI for a customer support ticketing system
Stars: 228
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eShopSupport is a sample .NET application showcasing common use cases and development practices for building AI solutions in .NET, specifically Generative AI. It demonstrates a customer support application for an e-commerce website using a services-based architecture with .NET Aspire. The application includes support for text classification, sentiment analysis, text summarization, synthetic data generation, and chat bot interactions. It also showcases development practices such as developing solutions locally, evaluating AI responses, leveraging Python projects, and deploying applications to the Cloud.
README:
A sample .NET application showcasing common use cases and development practices for build AI solutions in .NET (Generative AI, specifically). This sample demonstrates a customer support application for an e-commerce website using a services-based architecture with .NET Aspire. It includes support for the following AI use cases:
- Text classification, applying labels based on content
- Sentiment analysis based on message content
- Summarization of large sets of text
- Synthetic data generation, creating test content for the sample
- Chat bot interactions with chat history and suggested responses
This sample also demonstrates the following development practices:
- Developing a solution locally, using small local models
- Evaluating the quality of AI responses using grounded Q&A data
- Leveraging Python projects as part of a .NET Aspire solution
- Deploying the application, including small local models, to the Cloud (coming soon)
- A device with an Nvidia GPU (see workaround for running on the CPU)
- Clone the eShopSupport repository: https://github.com/dotnet/eshopsupport
- Install & start Docker Desktop
- Install Python 3.12.5
- Install Visual Studio 2022 version 17.10 or newer
- Select the following workloads:
-
ASP.NET and web development
workload. -
Python Development
workload. -
.NET Aspire SDK
component inIndividual components
.
-
- Select the following workloads:
-
Install the latest .NET 8 SDK
-
Install the .NET Aspire workload with the following commands:
dotnet workload update dotnet workload install aspire dotnet restore eShopSupport.sln
-
(Optionally) Install Visual Studio Code with the C# Dev Kit extension
From the Terminal, at the root of the cloned repo, run:
pip install -r src/PythonInference/requirements.txt
Note: If the above command doesn't work on Windows, use the following command:
py -m pip install -r src/PythonInference/requirements.txt
[!WARNING] Remember to ensure that Docker is started.
-
(Windows only) Run the application from Visual Studio:
- Open the
eShopSupport.sln
file in Visual Studio - Ensure that
AppHost
is your startup project - Hit Ctrl-F5 to launch .NET Aspire
- Open the
-
Or run the application from your terminal:
dotnet run --project src/AppHost
then look for lines like this in the console output in order to find the URL to open the Aspire dashboard:
Login to the dashboard at: http://localhost:17191/login?t=uniquelogincodeforyou
You may need to install ASP.NET Core HTTPS development certificates first, and then close all browser tabs. Learn more at https://aka.ms/aspnet/https-trust-dev-cert
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
The sample data is defined in seeddata. All products/descriptions/brands, manuals, customers, and support tickets names are fictional and were generated using GPT-35-Turbo using the included DataGenerator project.
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eShopSupport
eShopSupport is a sample .NET application showcasing common use cases and development practices for building AI solutions in .NET, specifically Generative AI. It demonstrates a customer support application for an e-commerce website using a services-based architecture with .NET Aspire. The application includes support for text classification, sentiment analysis, text summarization, synthetic data generation, and chat bot interactions. It also showcases development practices such as developing solutions locally, evaluating AI responses, leveraging Python projects, and deploying applications to the Cloud.
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