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34 changes: 34 additions & 0 deletions
34
dotnet/samples/Concepts/Memory/OpenAI_EmbeddingGeneration.cs
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| // Copyright (c) Microsoft. All rights reserved. | ||
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| using Microsoft.SemanticKernel; | ||
| using Microsoft.SemanticKernel.Embeddings; | ||
| using xRetry; | ||
|
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| #pragma warning disable format // Format item can be simplified | ||
| #pragma warning disable CA1861 // Avoid constant arrays as arguments | ||
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| namespace Memory; | ||
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| // 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); | ||
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| IKernelBuilder kernelBuilder = Kernel.CreateBuilder(); | ||
| kernelBuilder.AddOpenAITextEmbeddingGeneration( | ||
| modelId: TestConfiguration.OpenAI.EmbeddingModelId!, | ||
| apiKey: TestConfiguration.OpenAI.ApiKey!); | ||
| Kernel kernel = kernelBuilder.Build(); | ||
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| var embeddingGenerator = kernel.GetRequiredService<ITextEmbeddingGenerationService>(); | ||
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| // Generate embeddings for the specified text. | ||
| var embeddings = await embeddingGenerator.GenerateEmbeddingsAsync(["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."]); | ||
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| Console.WriteLine($"Generated {embeddings.Count} embeddings for the provided text"); | ||
| } | ||
| } |
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67 changes: 67 additions & 0 deletions
67
dotnet/samples/Concepts/Memory/VectorStoreFixtures/VectorStorePostgresContainerFixture.cs
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| // Copyright (c) Microsoft. All rights reserved. | ||
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| using Docker.DotNet; | ||
| using Npgsql; | ||
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| namespace Memory.VectorStoreFixtures; | ||
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| /// <summary> | ||
| /// Fixture to use for creating a Postgres container before tests and delete it after tests. | ||
| /// </summary> | ||
| public class VectorStorePostgresContainerFixture : IAsyncLifetime | ||
| { | ||
| private DockerClient? _dockerClient; | ||
| private string? _postgresContainerId; | ||
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| public async Task InitializeAsync() | ||
| { | ||
| } | ||
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| public async Task ManualInitializeAsync() | ||
| { | ||
| if (this._postgresContainerId == null) | ||
| { | ||
| // Connect to docker and start the docker container. | ||
| using var dockerClientConfiguration = new DockerClientConfiguration(); | ||
| this._dockerClient = dockerClientConfiguration.CreateClient(); | ||
| this._postgresContainerId = await VectorStoreInfra.SetupPostgresContainerAsync(this._dockerClient); | ||
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| // Delay until the Postgres server is ready. | ||
| var connectionString = TestConfiguration.Postgres.ConnectionString; | ||
| var succeeded = false; | ||
| var attemptCount = 0; | ||
| while (!succeeded && attemptCount++ < 10) | ||
| { | ||
| try | ||
| { | ||
| NpgsqlDataSourceBuilder dataSourceBuilder = new(connectionString); | ||
| dataSourceBuilder.UseVector(); | ||
| using var dataSource = dataSourceBuilder.Build(); | ||
| NpgsqlConnection connection = await dataSource.OpenConnectionAsync().ConfigureAwait(false); | ||
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| await using (connection) | ||
| { | ||
| // Create extension vector if it doesn't exist | ||
| await using (NpgsqlCommand command = new("CREATE EXTENSION IF NOT EXISTS vector", connection)) | ||
| { | ||
| await command.ExecuteNonQueryAsync(); | ||
| } | ||
| } | ||
| } | ||
| catch (Exception) | ||
| { | ||
| await Task.Delay(1000); | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| public async Task DisposeAsync() | ||
| { | ||
| if (this._dockerClient != null && this._postgresContainerId != null) | ||
| { | ||
| // Delete docker container. | ||
| await VectorStoreInfra.DeleteContainerAsync(this._dockerClient, this._postgresContainerId); | ||
| } | ||
| } | ||
| } |
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85
dotnet/samples/Concepts/Memory/VectorStore_VectorSearch_MultiStore_Postgres.cs
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| // Copyright (c) Microsoft. All rights reserved. | ||
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| using Azure.Identity; | ||
| using Memory.VectorStoreFixtures; | ||
| using Microsoft.Extensions.DependencyInjection; | ||
| using Microsoft.SemanticKernel; | ||
| using Microsoft.SemanticKernel.Connectors.AzureOpenAI; | ||
| using Microsoft.SemanticKernel.Connectors.Postgres; | ||
| using Npgsql; | ||
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| namespace Memory; | ||
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| /// <summary> | ||
| /// An example showing how to use common code, that can work with any vector database, with a Postgres database. | ||
| /// The common code is in the <see cref="VectorStore_VectorSearch_MultiStore_Common"/> class. | ||
| /// The common code ingests data into the vector store and then searches over that data. | ||
| /// This example is part of a set of examples each showing a different vector database. | ||
| /// | ||
| /// For other databases, see the following classes: | ||
| /// <para><see cref="VectorStore_VectorSearch_MultiStore_AzureAISearch"/></para> | ||
| /// <para><see cref="VectorStore_VectorSearch_MultiStore_Redis"/></para> | ||
| /// <para><see cref="VectorStore_VectorSearch_MultiStore_InMemory"/></para> | ||
| /// | ||
| /// To run this sample, you need a local instance of Docker running, since the associated fixture will try and start a Postgres container in the local docker instance. | ||
| /// </summary> | ||
| public class VectorStore_VectorSearch_MultiStore_Postgres(ITestOutputHelper output, VectorStorePostgresContainerFixture PostgresFixture) : BaseTest(output), IClassFixture<VectorStorePostgresContainerFixture> | ||
| { | ||
| [Fact] | ||
| public async Task ExampleWithDIAsync() | ||
| { | ||
| // Use the kernel for DI purposes. | ||
| var kernelBuilder = Kernel | ||
| .CreateBuilder(); | ||
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| // Register an embedding generation service with the DI container. | ||
| kernelBuilder.AddAzureOpenAITextEmbeddingGeneration( | ||
| deploymentName: TestConfiguration.AzureOpenAIEmbeddings.DeploymentName, | ||
| endpoint: TestConfiguration.AzureOpenAIEmbeddings.Endpoint, | ||
| credential: new AzureCliCredential()); | ||
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| // Initialize the Postgres docker container via the fixtures and register the Postgres VectorStore. | ||
| await PostgresFixture.ManualInitializeAsync(); | ||
| kernelBuilder.Services.AddPostgresVectorStore(TestConfiguration.Postgres.ConnectionString); | ||
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| // Register the test output helper common processor with the DI container. | ||
| kernelBuilder.Services.AddSingleton<ITestOutputHelper>(this.Output); | ||
| kernelBuilder.Services.AddTransient<VectorStore_VectorSearch_MultiStore_Common>(); | ||
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| // Build the kernel. | ||
| var kernel = kernelBuilder.Build(); | ||
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| // Build a common processor object using the DI container. | ||
| var processor = kernel.GetRequiredService<VectorStore_VectorSearch_MultiStore_Common>(); | ||
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| // Run the process and pass a key generator function to it, to generate unique record keys. | ||
| // The key generator function is required, since different vector stores may require different key types. | ||
| // E.g. Postgres supports Guid and ulong keys, but others may support strings only. | ||
| await processor.IngestDataAndSearchAsync("skglossaryWithDI", () => Guid.NewGuid()); | ||
| } | ||
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| [Fact] | ||
| public async Task ExampleWithoutDIAsync() | ||
| { | ||
| // Create an embedding generation service. | ||
| var textEmbeddingGenerationService = new AzureOpenAITextEmbeddingGenerationService( | ||
| TestConfiguration.AzureOpenAIEmbeddings.DeploymentName, | ||
| TestConfiguration.AzureOpenAIEmbeddings.Endpoint, | ||
| new AzureCliCredential()); | ||
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| // Initialize the Postgres docker container via the fixtures and construct the Postgres VectorStore. | ||
| await PostgresFixture.ManualInitializeAsync(); | ||
| var dataSourceBuilder = new NpgsqlDataSourceBuilder(TestConfiguration.Postgres.ConnectionString); | ||
| dataSourceBuilder.UseVector(); | ||
| await using var dataSource = dataSourceBuilder.Build(); | ||
| var vectorStore = new PostgresVectorStore(dataSource); | ||
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| // Create the common processor that works for any vector store. | ||
| var processor = new VectorStore_VectorSearch_MultiStore_Common(vectorStore, textEmbeddingGenerationService, this.Output); | ||
|
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| // Run the process and pass a key generator function to it, to generate unique record keys. | ||
| // The key generator function is required, since different vector stores may require different key types. | ||
| // E.g. Postgres supports Guid and ulong keys, but others may support strings only. | ||
| await processor.IngestDataAndSearchAsync("skglossaryWithoutDI", () => Guid.NewGuid()); | ||
| } | ||
| } |
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