시맨틱 커널 C# 통합 시작하기

// Import Packages
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Connectors.MongoDB; 
using Microsoft.SemanticKernel.Connectors.OpenAI;
using Microsoft.SemanticKernel.Memory;
using Microsoft.SemanticKernel.Plugins.Memory;
# pragma warning disable SKEXP0010, SKEXP0020, SKEXP0001, SKEXP0050
class Program {
    static async Task Main(string[] args) {
        // Get connection string and OpenAI API Key
        var connectionString = Environment.GetEnvironmentVariable("ATLAS_CONNECTION_STRING");
        if (connectionString == null)
        {
            Console.WriteLine("You must set your 'ATLAS_CONNECTION_STRING' environment variable.");
            Environment.Exit(0);
        }
        var openAIKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY");
        if (openAIKey == null)
        {
            Console.WriteLine("You must set your 'OPENAPI_KEY' environment variable.");
            Environment.Exit(0);
        }        
        
        // Create new OpenAI API Embedding Model 
        var embeddingGenerator = new OpenAITextEmbeddingGenerationService("text-embedding-ada-002", openAIKey);
        // Initialize Kernel
        IKernelBuilder builder = Kernel.CreateBuilder();
        // Add OpenAI Chat Completion to Kernel
        builder.AddOpenAIChatCompletion(
            modelId: "gpt-3.5-turbo",
            apiKey: openAIKey
        ); 
        Kernel kernel = builder.Build();
        // Instantiate Atlas as a memory store.
        MongoDBMemoryStore memoryStore = new(connectionString, "semantic_kernel_db", indexName: "vector_index"); 
        SemanticTextMemory textMemory = new(memoryStore, embeddingGenerator);
        // Populate memory with sample data
        async Task PopulateMemoryAsync(Kernel kernel) {
            await textMemory.SaveInformationAsync(collection: "test", text: "I am a developer", id: "1");
            await textMemory.SaveInformationAsync(collection: "test", text: "I started using MongoDB two years ago", id: "2");
            await textMemory.SaveInformationAsync(collection: "test", text: "I'm using MongoDB Vector Search with Semantic Kernel to implement RAG", id: "3");
            await textMemory.SaveInformationAsync(collection: "test", text: "I like coffee", id: "4");
            
        }
        await PopulateMemoryAsync(kernel);
    }
}

dotnet run

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var results = textMemory.SearchAsync(collection: "test", query: "What is my job title?");
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await foreach (var result in results) {
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   Console.WriteLine($"Answer: {result?.Metadata.Text}, {result?.Relevance}");
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}
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Console.WriteLine("Search completed.");

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 kernel.ImportPluginFromObject(new TextMemoryPlugin(textMemory));
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 const string promptTemplate = @"
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 Answer the following question based on the given context.
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 Question: {{$input}}
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 Context: {{recall 'When did I start using MongoDB?'}}
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 ";
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 // Create and Invoke function from the prompt template
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 var settings = kernel.CreateFunctionFromPrompt(promptTemplate, new OpenAIPromptExecutionSettings());
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 var ragResults = await kernel.InvokeAsync(settings, new()
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 {
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     [TextMemoryPlugin.InputParam] = "When did I start using MongoDB?",
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     [TextMemoryPlugin.CollectionParam] = "test"
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 });
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 // Print RAG Search Results
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 Console.WriteLine("Question: When did I start using MongoDB?");
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 Console.WriteLine($"Answer: {ragResults.GetValue<string>()}");