wehub-resource-sync
33 lines
1.4 KiB
C#
33 lines
1.4 KiB
C#
// Copyright (c) Microsoft. All rights reserved.
|
|
|
|
using Microsoft.Extensions.AI;
|
|
using Microsoft.SemanticKernel;
|
|
using xRetry;
|
|
|
|
namespace Memory;
|
|
|
|
// The following example shows how to use Semantic Kernel with AWS Bedrock API for embedding generation,
|
|
// including the ability to specify custom dimensions.
|
|
public class AWSBedrock_EmbeddingGeneration(ITestOutputHelper output) : BaseTest(output)
|
|
{
|
|
/// <summary>
|
|
/// This test demonstrates how to use the AWS Bedrock API embedding generation.
|
|
/// </summary>
|
|
[RetryFact(typeof(HttpOperationException))]
|
|
public async Task GenerateEmbeddings()
|
|
{
|
|
IKernelBuilder kernelBuilder = Kernel.CreateBuilder()
|
|
.AddBedrockEmbeddingGenerator(modelId: TestConfiguration.Bedrock.EmbeddingModelId! ?? "amazon.titan-embed-text-v1");
|
|
|
|
Kernel kernel = kernelBuilder.Build();
|
|
|
|
var embeddingGenerator = kernel.GetRequiredService<IEmbeddingGenerator<string, Embedding<float>>>();
|
|
|
|
// Generate embeddings with the default dimensions for the model
|
|
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 codebase."]);
|
|
|
|
Console.WriteLine($"Generated '{embeddings.Count}' embedding(s) with '{embeddings[0].Vector.Length}' dimensions (default for current model) for the provided text");
|
|
}
|
|
}
|