chore: import upstream snapshot with attribution

dotnet/samples/Concepts/Memory/VolatileVectorStore_LoadData.cs

@@ -0,0 +1,151 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.ClientModel;
+using System.ClientModel.Primitives;
+using System.Text.Json;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel.Connectors.InMemory;
+using Microsoft.SemanticKernel.Data;
+using Resources;
+
+namespace Memory;
+
+/// <summary>
+/// Sample showing how to create an <see cref="InMemoryVectorStore"/> collection from a list of strings
+/// and then save it to disk so that it can be reloaded later.
+/// </summary>
+public class InMemoryVectorStore_LoadData(ITestOutputHelper output) : BaseTest(output)
+{
+    [Fact]
+    public async Task LoadStringListAndSearchAsync()
+    {
+        // Create a logging handler to output HTTP requests and responses
+        var handler = new LoggingHandler(new HttpClientHandler(), this.Output);
+        var httpClient = new HttpClient(handler);
+
+        // Create an embedding generation service.
+        var embeddingGenerator = new OpenAI.OpenAIClient(
+            new ApiKeyCredential(TestConfiguration.OpenAI.ApiKey),
+            new OpenAI.OpenAIClientOptions() { Transport = new HttpClientPipelineTransport(httpClient) })
+                .GetEmbeddingClient(TestConfiguration.OpenAI.EmbeddingModelId)
+                .AsIEmbeddingGenerator(1536);
+
+        // Construct an InMemory vector store.
+        var vectorStore = new InMemoryVectorStore();
+        var collectionName = "records";
+
+        // Path to the file where the record collection will be saved to and loaded from.
+        string filePath = Path.Combine(Path.GetTempPath(), "semantic-kernel-info.json");
+        if (!File.Exists(filePath))
+        {
+            // Read a list of text strings from a file, to load into a new record collection.
+            var skInfo = EmbeddedResource.Read("semantic-kernel-info.txt");
+            var lines = skInfo!.Split('\n');
+
+            // Delegate which will create a record.
+            static DataModel CreateRecord(string text, ReadOnlyMemory<float> embedding)
+            {
+                return new()
+                {
+                    Key = Guid.NewGuid(),
+                    Text = text,
+                    Embedding = embedding
+                };
+            }
+
+            // Create a record collection from a list of strings using the provided delegate.
+            var collection = await vectorStore.CreateCollectionFromListAsync<Guid, DataModel>(
+                collectionName, lines, embeddingGenerator, CreateRecord);
+
+            // Save the record collection to a file stream.
+            using (FileStream fileStream = new(filePath, FileMode.OpenOrCreate))
+            {
+                await vectorStore.SerializeCollectionAsJsonAsync<Guid, DataModel>(collectionName, fileStream);
+            }
+        }
+
+        // Load the record collection from the file stream and perform a search.
+        using (FileStream fileStream = new(filePath, FileMode.Open))
+        {
+            var vectorSearch = await vectorStore.DeserializeCollectionFromJsonAsync<Guid, DataModel>(fileStream);
+
+            // Search the collection using a vector search.
+            var searchString = "What is the Semantic Kernel?";
+            var searchVector = (await embeddingGenerator.GenerateAsync(searchString)).Vector;
+            var resultRecords = await vectorSearch!.SearchAsync(searchVector, top: 1).ToListAsync();
+
+            Console.WriteLine("Search string: " + searchString);
+            Console.WriteLine("Result: " + resultRecords.First().Record.Text);
+            Console.WriteLine();
+        }
+    }
+
+    [Fact]
+    public async Task LoadTextSearchResultsAndSearchAsync()
+    {
+        // Create an embedding generation service.
+        var embeddingGenerator = new OpenAI.OpenAIClient(TestConfiguration.OpenAI.ApiKey)
+            .GetEmbeddingClient(TestConfiguration.OpenAI.EmbeddingModelId)
+            .AsIEmbeddingGenerator(1536);
+
+        // Construct an InMemory vector store.
+        var vectorStore = new InMemoryVectorStore();
+        var collectionName = "records";
+
+        // Read a list of text strings from a file, to load into a new record collection.
+        var searchResultsJson = EmbeddedResource.Read("what-is-semantic-kernel.json");
+        var searchResults = JsonSerializer.Deserialize<List<TextSearchResult>>(searchResultsJson!);
+
+        // Delegate which will create a record.
+        static DataModel CreateRecord(TextSearchResult searchResult, ReadOnlyMemory<float> embedding)
+        {
+            return new()
+            {
+                Key = Guid.NewGuid(),
+                Title = searchResult.Name,
+                Text = searchResult.Value ?? string.Empty,
+                Link = searchResult.Link,
+                Embedding = embedding
+            };
+        }
+
+        // Create a record collection from a list of strings using the provided delegate.
+        var vectorSearch = await vectorStore.CreateCollectionFromTextSearchResultsAsync<Guid, DataModel>(
+            collectionName, searchResults!, embeddingGenerator, CreateRecord);
+
+        // Search the collection using a vector search.
+        var searchString = "What is the Semantic Kernel?";
+        var searchVector = (await embeddingGenerator.GenerateAsync(searchString)).Vector;
+        var resultRecords = await vectorSearch!.SearchAsync(searchVector, top: 1).ToListAsync();
+
+        Console.WriteLine("Search string: " + searchString);
+        Console.WriteLine("Result: " + resultRecords.First().Record.Text);
+        Console.WriteLine();
+    }
+
+    /// <summary>
+    /// Sample model class that represents a record entry.
+    /// </summary>
+    /// <remarks>
+    /// Note that each property is decorated with an attribute that specifies how the property should be treated by the vector store.
+    /// This allows us to create a collection in the vector store and upsert and retrieve instances of this class without any further configuration.
+    /// </remarks>
+    private sealed class DataModel
+    {
+        [VectorStoreKey]
+        public Guid Key { get; init; }
+
+        [VectorStoreData]
+        public string? Title { get; init; }
+
+        [VectorStoreData]
+        public string Text { get; init; }
+
+        [VectorStoreData]
+        public string? Link { get; init; }
+
+        [VectorStoreVector(1536)]
+        public ReadOnlyMemory<float> Embedding { get; init; }
+    }
+}