Console app that explains and comments existing source code using Semantic Kernel and Azure OpenAI Services
- .NET 8 is required to run this sample.
- Install the recommended extensions
- C#
- Semantic Kernel Tools (optional)
The sample can be configured by using the command line with .NET Secret Manager to avoid the risk of leaking secrets into the repository, branches and pull requests. This sample has been tested with the following models:
| Service | Model type | Model |
|---|---|---|
| Azure OpenAI | Chat Completion | gpt-3.5-turbo |
| Azure OpenAI | Embedding | text-embedding-ada |
Using .NET Secret Manager
Configure an Azure OpenAI
Supporting Azure infrastructure can be deployed manually in Azure Portal or using Azure Developer CLI:
azd auth login
azd provision
azd down
Created infrastructure resources are used for Azure OpenAI Services in code:
cd CommentMyCode
dotnet user-secrets set "AzureOpenAI:Endpoint" ">>>YOUR ENDPOINT<<<.openai.azure.com/"
dotnet user-secrets set "AzureOpenAI:ModelType" "gpt-35-turbo"
dotnet user-secrets set "AzureOpenAI:ChatCompletionModel" "gpt-3.5-turbo"
dotnet user-secrets set "AzureOpenAI:EmbeddingModel" "text-embedding-ada"
dotnet user-secrets set "AzureOpenAI:ApiKey" ">>>YOUR API KEY<<<"
This example uses Semantic Kernel, text memory with a logger, a volatile memory store, and Azure OpenAI Text Embedding Generation Service. It imports a native function from the CodeFileFinderPlugin that finds all C# code files in the directory. Main interaction is through a chat with the AI, where the AI's role as an expert in explaining programming languages and providing valuable comments to the code.
To comment and explain source code other than C# update the CodeFileFinderPlugin line 16 with extension of the files you want to comment.
After configuring the sample, to build and run the console application just hit F5.
To build and run the console application from the terminal use the following commands:
dotnet build
dotnet runWhen the application runs, it will prompt the user to enter a directory path. The application will then search for all C# code files in the directory and subdirectories.
Warning
There is cost associated with using Azure OpenAI Services. Please review the pricing and limits before running this sample.
For each code file, it prompts the user to either skip (s) the file or comment (c) and explain the file.
Specify directory with code to comment:
C:\Code\Demo\dotnet-podcastSample output shwos console output with code comments on the left and input source file on the right
