.Net: Remove IMemoryStore and references to SK from MEVD

dotnet/samples/Concepts/Memory/HuggingFace_TextEmbeddingCustomHttpHandler.cs

@@ -1,9 +1,10 @@
 // Copyright (c) Microsoft. All rights reserved.
 
 using System.Text.Json;
+using Microsoft.Extensions.VectorData;
 using Microsoft.SemanticKernel.Connectors.HuggingFace;
 using Microsoft.SemanticKernel.Connectors.Sqlite;
-using Microsoft.SemanticKernel.Memory;
+using Microsoft.SemanticKernel.Embeddings;
 
 #pragma warning disable CS8602 // Dereference of a possibly null reference.
 
@@ -31,14 +32,29 @@ public async Task RunInferenceApiEmbeddingCustomHttpHandlerAsync()
             })
         );
 
-        var sqliteMemory = await SqliteMemoryStore.ConnectAsync("./../../../Sqlite.sqlite");
+        var sqliteCollection = new SqliteVectorStoreRecordCollection<string, Record>(
+            "Data Source=./../../../Sqlite.sqlite",
+            name: "Test",
+            new() { EmbeddingGenerator = hf.AsEmbeddingGenerator() });
 
-        var skMemory = new MemoryBuilder()
-            .WithTextEmbeddingGeneration(hf)
-            .WithMemoryStore(sqliteMemory)
-            .Build();
+        await sqliteCollection.UpsertAsync(new Record
+        {
+            Id = "1",
+            Text = "THIS IS A SAMPLE",
+            Embedding = "An embedding will be generated from this text"
+        });
+    }
+
+    public class Record
+    {
+        [VectorStoreRecordKey]
+        public string Id { get; set; }
+
+        [VectorStoreRecordData]
+        public string Text { get; set; }
 
-        await skMemory.SaveInformationAsync("Test", "THIS IS A SAMPLE", "sample", "TEXT");
+        [VectorStoreRecordVector(Dimensions: 768)]
+        public string Embedding { get; set; }
     }
 
     private sealed class CustomHttpClientHandler : HttpClientHandler

dotnet/samples/Concepts/Memory/VectorStore_ConsumeFromMemoryStore_AzureAISearch.cs

@@ -3,12 +3,10 @@
 using System.Text;
 using System.Text.Json;
 using Azure;
-using Azure.Identity;
 using Azure.Search.Documents.Indexes;
 using Memory.VectorStoreFixtures;
 using Microsoft.Extensions.VectorData;
 using Microsoft.SemanticKernel.Connectors.AzureAISearch;
-using Microsoft.SemanticKernel.Connectors.AzureOpenAI;
 using Microsoft.SemanticKernel.Memory;
 
 namespace Memory;
@@ -26,41 +24,24 @@ namespace Memory;
 /// dotnet user-secrets set "AzureAISearch:Endpoint" "https://myazureaisearchinstance.search.windows.net"
 /// dotnet user-secrets set "AzureAISearch:ApiKey" "samplesecret"
 /// </remarks>
-[Obsolete("The IMemoryStore abstraction is being obsoleted")]
 public class VectorStore_ConsumeFromMemoryStore_AzureAISearch(ITestOutputHelper output, VectorStoreQdrantContainerFixture qdrantFixture) : BaseTest(output), IClassFixture<VectorStoreQdrantContainerFixture>
 {
     private const int VectorSize = 1536;
-    private const string MemoryStoreCollectionName = "memorystorecollection";
     private readonly static JsonSerializerOptions s_consoleFormatting = new() { WriteIndented = true };
 
     [Fact]
     public async Task ConsumeExampleAsync()
     {
         // Setup the supporting infra and embedding generation.
         await qdrantFixture.ManualInitializeAsync();
-        var textEmbeddingService = new AzureOpenAITextEmbeddingGenerationService(
-            TestConfiguration.AzureOpenAIEmbeddings.DeploymentName,
-            TestConfiguration.AzureOpenAIEmbeddings.Endpoint,
-            new AzureCliCredential());
-
-        // Construct a legacy MemoryStore.
-        var memoryStore = new AzureAISearchMemoryStore(
-            TestConfiguration.AzureAISearch.Endpoint,
-            TestConfiguration.AzureAISearch.ApiKey);
 
         // Construct a VectorStore.
         var vectorStore = new AzureAISearchVectorStore(new SearchIndexClient(
             new Uri(TestConfiguration.AzureAISearch.Endpoint),
             new AzureKeyCredential(TestConfiguration.AzureAISearch.ApiKey)));
 
-        // Build a collection with sample data using the MemoryStore abstraction.
-        await VectorStore_ConsumeFromMemoryStore_Common.CreateCollectionAndAddSampleDataAsync(
-            memoryStore,
-            MemoryStoreCollectionName,
-            textEmbeddingService);
-
-        // Connect to the same collection using the VectorStore abstraction.
-        var collection = vectorStore.GetCollection<string, VectorStoreRecord>(MemoryStoreCollectionName);
+        // Use the VectorStore abstraction to connect to an existing collection which was previously created via the IMemoryStore abstraction
+        var collection = vectorStore.GetCollection<string, VectorStoreRecord>("memorystorecollection");
         await collection.CreateCollectionIfNotExistsAsync();
 
         // Show that the data can be read using the VectorStore abstraction.
@@ -77,7 +58,7 @@ await VectorStore_ConsumeFromMemoryStore_Common.CreateCollectionAndAddSampleData
 
     /// <summary>
     /// A data model with Vector Store attributes that matches the storage representation of
-    /// <see cref="MemoryRecord"/> objects as created by <see cref="AzureAISearchMemoryStore"/>.
+    /// <see cref="MemoryRecord"/> objects as created by <c>AzureAISearchMemoryStore</c>.
     /// </summary>
     private sealed class VectorStoreRecord
     {

dotnet/samples/Concepts/Memory/VectorStore_ConsumeFromMemoryStore_Qdrant.cs

@@ -1,10 +1,8 @@
 // Copyright (c) Microsoft. All rights reserved.
 
 using System.Text.Json;
-using Azure.Identity;
 using Memory.VectorStoreFixtures;
 using Microsoft.Extensions.VectorData;
-using Microsoft.SemanticKernel.Connectors.AzureOpenAI;
 using Microsoft.SemanticKernel.Connectors.Qdrant;
 using Microsoft.SemanticKernel.Memory;
 using Qdrant.Client;
@@ -23,37 +21,22 @@ namespace Memory;
 /// To run this sample, you need a local instance of Docker running, since the associated fixture
 /// will try and start a Qdrant container in the local docker instance to run against.
 /// </remarks>
-[Obsolete("The IMemoryStore abstraction is being obsoleted")]
 public class VectorStore_ConsumeFromMemoryStore_Qdrant(ITestOutputHelper output, VectorStoreQdrantContainerFixture qdrantFixture) : BaseTest(output), IClassFixture<VectorStoreQdrantContainerFixture>
 {
     private const int VectorSize = 1536;
-    private const string MemoryStoreCollectionName = "memorystorecollection";
     private readonly static JsonSerializerOptions s_consoleFormatting = new() { WriteIndented = true };
 
     [Fact]
     public async Task ConsumeExampleAsync()
     {
         // Setup the supporting infra and embedding generation.
         await qdrantFixture.ManualInitializeAsync();
-        var textEmbeddingService = new AzureOpenAITextEmbeddingGenerationService(
-            TestConfiguration.AzureOpenAIEmbeddings.DeploymentName,
-            TestConfiguration.AzureOpenAIEmbeddings.Endpoint,
-            new AzureCliCredential());
-
-        // Construct a legacy MemoryStore.
-        var memoryStore = new QdrantMemoryStore("http://localhost:6333", VectorSize);
 
         // Construct a VectorStore.
         var vectorStore = new QdrantVectorStore(new QdrantClient("localhost"));
 
-        // Build a collection with sample data using the MemoryStore abstraction.
-        await VectorStore_ConsumeFromMemoryStore_Common.CreateCollectionAndAddSampleDataAsync(
-            memoryStore,
-            MemoryStoreCollectionName,
-            textEmbeddingService);
-
-        // Connect to the same collection using the VectorStore abstraction.
-        var collection = vectorStore.GetCollection<Guid, VectorStoreRecord>(MemoryStoreCollectionName);
+        // Use the VectorStore abstraction to connect to an existing collection which was previously created via the IMemoryStore abstraction
+        var collection = vectorStore.GetCollection<Guid, VectorStoreRecord>("memorystorecollection");
         await collection.CreateCollectionIfNotExistsAsync();
 
         // Show that the data can be read using the VectorStore abstraction.
@@ -68,7 +51,7 @@ await VectorStore_ConsumeFromMemoryStore_Common.CreateCollectionAndAddSampleData
 
     /// <summary>
     /// A data model with Vector Store attributes that matches the storage representation of
-    /// <see cref="MemoryRecord"/> objects as created by <see cref="QdrantMemoryStore"/>.
+    /// <see cref="MemoryRecord"/> objects as created by <c>QdrantMemoryStore</c>.
     /// </summary>
     private sealed class VectorStoreRecord
     {

dotnet/samples/Concepts/Memory/VectorStore_ConsumeFromMemoryStore_Redis.cs

@@ -1,9 +1,7 @@
 // Copyright (c) Microsoft. All rights reserved.
 
-using Azure.Identity;
 using Memory.VectorStoreFixtures;
 using Microsoft.Extensions.VectorData;
-using Microsoft.SemanticKernel.Connectors.AzureOpenAI;
 using Microsoft.SemanticKernel.Connectors.Redis;
 using Microsoft.SemanticKernel.Memory;
 using StackExchange.Redis;
@@ -22,7 +20,6 @@ namespace Memory;
 /// To run this sample, you need a local instance of Docker running, since the associated fixture
 /// will try and start a Redis container in the local docker instance to run against.
 /// </remarks>
-[Obsolete("The IMemoryStore abstraction is being obsoleted")]
 public class VectorStore_ConsumeFromMemoryStore_Redis(ITestOutputHelper output, VectorStoreRedisContainerFixture redisFixture) : BaseTest(output), IClassFixture<VectorStoreRedisContainerFixture>
 {
     private const int VectorSize = 1536;
@@ -33,26 +30,13 @@ public async Task ConsumeExampleAsync()
     {
         // Setup the supporting infra and embedding generation.
         await redisFixture.ManualInitializeAsync();
-        var textEmbeddingService = new AzureOpenAITextEmbeddingGenerationService(
-            TestConfiguration.AzureOpenAIEmbeddings.DeploymentName,
-            TestConfiguration.AzureOpenAIEmbeddings.Endpoint,
-            new AzureCliCredential());
 
-        // Construct a legacy MemoryStore.
-        var memoryStore = new RedisMemoryStore("localhost:6379", VectorSize);
-
-        // Construct a VectorStore and indicate that we want to use hashes
-        // since the legacy memory store uses hashes to store memory records.
+        // Use the VectorStore abstraction to connect to an existing collection which was previously created via the IMemoryStore abstraction.
+        // Note that we use HashSet since the legacy memory store uses hashes to store memory records.
         var vectorStore = new RedisVectorStore(
             ConnectionMultiplexer.Connect("localhost:6379").GetDatabase(),
             new() { StorageType = RedisStorageType.HashSet });
 
-        // Build a collection with sample data using the MemoryStore abstraction.
-        await VectorStore_ConsumeFromMemoryStore_Common.CreateCollectionAndAddSampleDataAsync(
-            memoryStore,
-            MemoryStoreCollectionName,
-            textEmbeddingService);
-
         // Connect to the same collection using the VectorStore abstraction.
         var collection = vectorStore.GetCollection<string, VectorStoreRecord>(MemoryStoreCollectionName);
         await collection.CreateCollectionIfNotExistsAsync();
@@ -69,7 +53,7 @@ await VectorStore_ConsumeFromMemoryStore_Common.CreateCollectionAndAddSampleData
 
     /// <summary>
     /// A data model with Vector Store attributes that matches the storage representation of
-    /// <see cref="MemoryRecord"/> objects as created by <see cref="RedisMemoryStore"/>.
+    /// <see cref="MemoryRecord"/> objects as created by <c>RedisMemoryStore</c>.
     /// </summary>
     private sealed class VectorStoreRecord
     {

dotnet/samples/Concepts/Memory/VectorStore_DataIngestion_MultiStore.cs

@@ -55,16 +55,16 @@ public async Task ExampleWithDIAsync(string databaseType)
         if (databaseType == "Redis")
         {
             await redisFixture.ManualInitializeAsync();
-            kernelBuilder.AddRedisVectorStore("localhost:6379");
+            kernelBuilder.Services.AddRedisVectorStore("localhost:6379");
         }
         else if (databaseType == "Qdrant")
         {
             await qdrantFixture.ManualInitializeAsync();
-            kernelBuilder.AddQdrantVectorStore("localhost");
+            kernelBuilder.Services.AddQdrantVectorStore("localhost");
         }
         else if (databaseType == "InMemory")
         {
-            kernelBuilder.AddInMemoryVectorStore();
+            kernelBuilder.Services.AddInMemoryVectorStore();
         }
 
         // Register the DataIngestor with the DI container.

dotnet/samples/Concepts/Memory/VectorStore_MigrateFromMemoryStore_Redis.cs

@@ -1,5 +1,7 @@
 // Copyright (c) Microsoft. All rights reserved.
 
+#if DISABLED
+
 using System.Text.Json;
 using Azure.Identity;
 using Memory.VectorStoreFixtures;
@@ -189,3 +191,5 @@ All methods to upsert or get records use strongly typed model classes. The prope
             key: "33333333-3333-3333-3333-333333333333");
     }
 }
+
+#endif

dotnet/samples/Concepts/Memory/VectorStore_VectorSearch_MultiStore_AzureAISearch.cs

@@ -43,7 +43,7 @@ public async Task ExampleWithDIAsync()
             credential: new AzureCliCredential());
 
         // Register the Azure AI Search VectorStore.
-        kernelBuilder.AddAzureAISearchVectorStore(
+        kernelBuilder.Services.AddAzureAISearchVectorStore(
             new Uri(TestConfiguration.AzureAISearch.Endpoint),
             new AzureKeyCredential(TestConfiguration.AzureAISearch.ApiKey));
 

dotnet/samples/Concepts/Memory/VectorStore_VectorSearch_MultiStore_InMemory.cs

@@ -36,7 +36,7 @@ public async Task ExampleWithDIAsync()
             credential: new AzureCliCredential());
 
         // Register the InMemory VectorStore.
-        kernelBuilder.AddInMemoryVectorStore();
+        kernelBuilder.Services.AddInMemoryVectorStore();
 
         // Register the test output helper common processor with the DI container.
         kernelBuilder.Services.AddSingleton<ITestOutputHelper>(this.Output);

dotnet/samples/Concepts/Memory/VectorStore_VectorSearch_MultiStore_Qdrant.cs

@@ -41,7 +41,7 @@ public async Task ExampleWithDIAsync()
 
         // Initialize the Qdrant docker container via the fixtures and register the Qdrant VectorStore.
         await qdrantFixture.ManualInitializeAsync();
-        kernelBuilder.AddQdrantVectorStore("localhost");
+        kernelBuilder.Services.AddQdrantVectorStore("localhost");
 
         // Register the test output helper common processor with the DI container.
         kernelBuilder.Services.AddSingleton<ITestOutputHelper>(this.Output);

dotnet/samples/Concepts/Memory/VectorStore_VectorSearch_MultiStore_Redis.cs

@@ -46,7 +46,7 @@ public async Task ExampleWithDIAsync(RedisStorageType redisStorageType)
 
         // Initialize the Redis docker container via the fixtures and register the Redis VectorStore with the preferred storage type.
         await redisFixture.ManualInitializeAsync();
-        kernelBuilder.AddRedisVectorStore("localhost:6379", new() { StorageType = redisStorageType });
+        kernelBuilder.Services.AddRedisVectorStore("localhost:6379", new() { StorageType = redisStorageType });
 
         // Register the test output helper common processor with the DI container.
         kernelBuilder.Services.AddSingleton<ITestOutputHelper>(this.Output);