chore: import upstream snapshot with attribution

dotnet/samples/GettingStartedWithTextSearch/Step4_Search_With_VectorStore.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Connectors.OpenAI;
+using Microsoft.SemanticKernel.Data;
+using Microsoft.SemanticKernel.PromptTemplates.Handlebars;
+using static GettingStartedWithTextSearch.InMemoryVectorStoreFixture;
+
+namespace GettingStartedWithTextSearch;
+
+/// <summary>
+/// This example shows how to create a <see cref="ITextSearch"/> from a
+/// <see cref="VectorStore"/>.
+/// </summary>
+[Collection("InMemoryVectorStoreCollection")]
+public class Step4_Search_With_VectorStore(ITestOutputHelper output, InMemoryVectorStoreFixture fixture) : BaseTest(output)
+{
+    /// <summary>
+    /// Show how to create a <see cref="VectorStoreTextSearch{TRecord}"/> and use it to perform a search.
+    /// </summary>
+    [Fact]
+    public async Task UsingInMemoryVectorStoreRecordTextSearchAsync()
+    {
+        // Use embedding generation service and record collection for the fixture.
+        var collection = fixture.VectorStoreRecordCollection;
+
+        // Create a text search instance using the InMemory vector store.
+        var textSearch = new VectorStoreTextSearch<DataModel>(collection);
+
+        // Search and return results as TextSearchResult items
+        var query = "What is the Semantic Kernel?";
+        KernelSearchResults<TextSearchResult> textResults = await textSearch.GetTextSearchResultsAsync(query, new() { Top = 2, Skip = 0 });
+        Console.WriteLine("\n--- Text Search Results ---\n");
+        await foreach (TextSearchResult result in textResults.Results)
+        {
+            Console.WriteLine($"Name:  {result.Name}");
+            Console.WriteLine($"Value: {result.Value}");
+            Console.WriteLine($"Link:  {result.Link}");
+        }
+    }
+
+    /// <summary>
+    /// Show how to create a default <see cref="KernelPlugin"/> from an <see cref="ITextSearch"/> and use it to
+    /// add grounding context to a Handlebars prompt.
+    /// </summary>
+    [Fact]
+    public async Task RagWithInMemoryVectorStoreTextSearchAsync()
+    {
+        // Create a kernel with OpenAI chat completion
+        IKernelBuilder kernelBuilder = Kernel.CreateBuilder();
+        kernelBuilder.AddOpenAIChatCompletion(
+                modelId: TestConfiguration.OpenAI.ChatModelId,
+                apiKey: TestConfiguration.OpenAI.ApiKey);
+        Kernel kernel = kernelBuilder.Build();
+
+        // Use embedding generation service and record collection for the fixture.
+        var embeddingGenerator = fixture.EmbeddingGenerator;
+        var collection = fixture.VectorStoreRecordCollection;
+
+        // Create a text search instance using the InMemory vector store.
+        var textSearch = new VectorStoreTextSearch<DataModel>(collection);
+
+        // Build a text search plugin with vector store search and add to the kernel
+        var searchPlugin = textSearch.CreateWithGetTextSearchResults("SearchPlugin");
+        kernel.Plugins.Add(searchPlugin);
+
+        // Invoke prompt and use text search plugin to provide grounding information
+        var query = "What is the Semantic Kernel?";
+        string promptTemplate = """
+            {{#with (SearchPlugin-GetTextSearchResults query)}}
+              {{#each this}}
+                Name: {{Name}}
+                Value: {{Value}}
+                Link: {{Link}}
+                -----------------
+              {{/each}}
+            {{/with}}
+
+            {{query}}
+
+            Include citations to the relevant information where it is referenced in the response.
+            """;
+        KernelArguments arguments = new() { { "query", query } };
+        HandlebarsPromptTemplateFactory promptTemplateFactory = new();
+        Console.WriteLine(await kernel.InvokePromptAsync(
+            promptTemplate,
+            arguments,
+            templateFormat: HandlebarsPromptTemplateFactory.HandlebarsTemplateFormat,
+            promptTemplateFactory: promptTemplateFactory
+        ));
+    }
+
+    /// <summary>
+    /// Show how to create a default <see cref="KernelPlugin"/> from an <see cref="VectorStoreTextSearch{TRecord}"/> and use it with
+    /// function calling to have the LLM include grounding context in it's response.
+    /// </summary>
+    [Fact]
+    public async Task FunctionCallingWithInMemoryVectorStoreTextSearchAsync()
+    {
+        // Create a kernel with OpenAI chat completion
+        IKernelBuilder kernelBuilder = Kernel.CreateBuilder();
+        kernelBuilder.AddOpenAIChatCompletion(
+                modelId: TestConfiguration.OpenAI.ChatModelId,
+                apiKey: TestConfiguration.OpenAI.ApiKey);
+        Kernel kernel = kernelBuilder.Build();
+
+        // Use embedding generation service and record collection for the fixture.
+        var embeddingGenerator = fixture.EmbeddingGenerator;
+        var collection = fixture.VectorStoreRecordCollection;
+
+        // Create a text search instance using the InMemory vector store.
+        var textSearch = new VectorStoreTextSearch<DataModel>(collection);
+
+        // Build a text search plugin with vector store search and add to the kernel
+        var searchPlugin = textSearch.CreateWithGetTextSearchResults("SearchPlugin");
+        kernel.Plugins.Add(searchPlugin);
+
+        // Invoke prompt and use text search plugin to provide grounding information
+        OpenAIPromptExecutionSettings settings = new() { FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() };
+        KernelArguments arguments = new(settings);
+        Console.WriteLine(await kernel.InvokePromptAsync("What is the Semantic Kernel?", arguments));
+    }
+}