chore: import upstream snapshot with attribution

2026-07-13 13:21:23 +08:00
commit b957a53def
5423 changed files with 863745 additions and 0 deletions
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+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Extensions.AI;
+using Microsoft.SemanticKernel;
+using xRetry;
+
+#pragma warning disable format // Format item can be simplified
+#pragma warning disable CA1861 // Avoid constant arrays as arguments
+
+namespace Memory;
+
+// The following example shows how to use Semantic Kernel with OpenAI.
+public class OpenAI_EmbeddingGeneration(ITestOutputHelper output) : BaseTest(output)
+{
+    [RetryFact(typeof(HttpOperationException))]
+    public async Task RunEmbeddingAsync()
+    {
+        Assert.NotNull(TestConfiguration.OpenAI.EmbeddingModelId);
+        Assert.NotNull(TestConfiguration.OpenAI.ApiKey);
+
+        IKernelBuilder kernelBuilder = Kernel.CreateBuilder();
+        kernelBuilder.AddOpenAIEmbeddingGenerator(
+                modelId: TestConfiguration.OpenAI.EmbeddingModelId!,
+                apiKey: TestConfiguration.OpenAI.ApiKey!);
+        Kernel kernel = kernelBuilder.Build();
+
+        var embeddingGenerator = kernel.GetRequiredService<IEmbeddingGenerator<string, Embedding<float>>>();
+
+        // Generate embeddings for the specified text.
+        var embeddings = await embeddingGenerator.GenerateAsync(["Semantic Kernel is a lightweight, open-source development kit that lets you easily build AI agents and integrate the latest AI models into your C#, Python, or Java codebase."]);
+
+        Console.WriteLine($"Generated {embeddings.Count} embeddings for the provided text");
+    }
+}