SemanticKernelPooling 1.5.0

SemanticKernelPooling

SemanticKernelPooling is a .NET library designed to facilitate seamless integration with multiple AI service providers, such as OpenAI, Azure OpenAI, HuggingFace, Google, Mistral AI, and others. It utilizes a kernel pooling approach to manage resources efficiently and provide robust AI capabilities in your .NET applications.

Features

• Kernel Pooling: Efficiently manage and reuse kernels for different AI service providers.
• Support for Multiple Providers: Integrates with various AI providers like OpenAI, Azure OpenAI, HuggingFace, Google, Mistral AI, and more.
• Extensibility: Easily extendable to support additional AI service providers.
• Customizable Configuration: Allows fine-tuning of kernel behavior and AI service integration settings.
• Logging Support: Integrated with Microsoft.Extensions.Logging for detailed logging and diagnostics.
• Error Handling and Retry Logic: Implements robust error handling using Polly for retry policies, especially useful for managing API quotas and transient errors.

Getting Started

Prerequisites

• .NET 8.0 or higher
• NuGet packages:
  • Microsoft.Extensions.DependencyInjection
  • Microsoft.Extensions.Logging
  • Microsoft.SemanticKernel
  • Polly for advanced retry logic

Installation

To install SemanticKernelPooling, you can use the NuGet package manager:

dotnet add package SemanticKernelPooling

Basic Usage

1. Configure Services

   Start by configuring the services in your Program.cs or Startup.cs file:

   using Microsoft.Extensions.DependencyInjection;
   using Microsoft.Extensions.Logging;
   using SemanticKernelPooling;
   using SemanticKernelPooling.Connectors.OpenAI;
   
   var services = new ServiceCollection();
   services.AddLogging(configure => configure.AddConsole());
   services.UseSemanticKernelPooling(); // Core service pooling registration
   services.UseOpenAIKernelPool();      // Register OpenAI kernel pool
   services.UseAzureOpenAIKernelPool(); // Register Azure OpenAI kernel pool
   
   var serviceProvider = services.BuildServiceProvider();
   

2. Configure Providers

   You need to set up configuration settings for each AI service provider you intend to use. These settings can be defined in a appsettings.json or any configuration source supported by .NET:

   {
     "AIServiceProviderConfigurations": [
       {
         "UniqueName": "OpenAI",
         "ServiceType": "OpenAI",
         "ApiKey": "YOUR_OPENAI_API_KEY",
         "ModelId": "YOUR_MODEL_ID"
       },
       {
         "UniqueName": "AzureOpenAI",
         "ServiceType": "AzureOpenAI",
         "DeploymentName": "YOUR_DEPLOYMENT_NAME",
         "ApiKey": "YOUR_AZURE_API_KEY",
         "Endpoint": "YOUR_ENDPOINT",
         "ModelId": "YOUR_MODEL_ID",
         "ServiceId": "YOUR_SERVICE_ID"
       }
       // Add more providers as needed
     ]
   }
   

3. Retrieve a Kernel and Execute Commands

   Once the service providers are configured and registered, you can retrieve a kernel from the pool and execute commands:

   var kernelPoolManager = serviceProvider.GetRequiredService<IKernelPoolManager>();
   
   // Example: Getting a kernel for OpenAI
   using var kernelWrapper = await kernelPoolManager.GetKernelAsync(AIServiceProviderType.OpenAI);
   
   // Use the kernel to perform AI operations
   var response = await kernelWrapper.Kernel.ExecuteAsync("What is Semantic Kernel?");
   Console.WriteLine(response);
   
   // Return the kernel to the pool after use
   

Advanced Usage

4. Using Retry Policies

   To handle API rate limits and transient errors, use Polly to define retry policies:

   AsyncPolicy httpTimeoutAndRetryPolicy = Policy
       .Handle<Exception>(ex => ex.IsTransientError())
       .WaitAndRetryAsync(
           retryCount: 6,
           sleepDurationProvider: retryAttempt => TimeSpan.FromSeconds(Math.Pow(2, retryAttempt)) + TimeSpan.FromMilliseconds(new Random().Next(0, 3000)),
           onRetry: (exception, timespan, retryCount, context) =>
           {
               logger.LogError($"Retry {retryCount} after {timespan.TotalSeconds} seconds due to: {exception.Message}");
           });
   

5. Adding New AI Providers

   To add support for a new AI provider, follow these steps:

   • Create a Configuration Class: Define a new configuration class inheriting from AIServiceProviderConfiguration.

   • Implement a Kernel Pool Class: Create a new kernel pool class inheriting from AIServicePool<T>.

   • Register the New Provider: Add the registration method in the ServiceExtension class to register your new provider with the DI container.

   For example, to add a new "CustomAI" provider:

   public record CustomAIConfiguration : AIServiceProviderConfiguration
   {
       public required string ModelId { get; init; }
       public required string ApiKey { get; init; }
       // Additional settings...
   }
   
   class CustomAIKernelPool(
       CustomAIConfiguration config,
       ILoggerFactory loggerFactory)
       : AIServicePool<CustomAIConfiguration>(config)
   {
       protected override void RegisterChatCompletionService(IKernelBuilder kernelBuilder, CustomAIConfiguration config, HttpClient? httpClient)
       {
           // Register service logic...
       }
   
       protected override ILogger Logger { get; } = loggerFactory.CreateLogger<CustomAIKernelPool>();
   }
   
   public static class ServiceExtension
   {
       public static void UseCustomAIKernelPool(this IServiceProvider serviceProvider)
       {
           var registrar = serviceProvider.GetRequiredService<IKernelPoolFactoryRegistrar>();
           registrar.RegisterKernelPoolFactory(
               AIServiceProviderType.CustomAI,
               (aiServiceProviderConfiguration, loggerFactory) =>
                   new CustomAIKernelPool((CustomAIConfiguration)aiServiceProviderConfiguration, loggerFactory));
       }
   }
   

Supported Providers

• OpenAI: Use OpenAIConfiguration and OpenAIKernelPool to interact with OpenAI services.
• Azure OpenAI: Use AzureOpenAIConfiguration and AzureOpenAIKernelPool for Azure OpenAI.
• HuggingFace: Use HuggingFaceConfiguration and HuggingFaceKernelPool to integrate with HuggingFace models.
• Google AI: Use GoogleConfiguration and GoogleKernelPool for Google AI services.
• Mistral AI: Use MistralAIConfiguration and MistralAIKernelPool to leverage Mistral AI services.
• Custom Providers: Easily extend to support other providers by following the extensibility guidelines.

Contributing

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request. Ensure your code adheres to the project's coding standards and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

• Special thanks to the contributors of Microsoft Semantic Kernel and all integrated AI service providers.
• Inspired by the need for efficient AI resource management in .NET applications.