chore: import upstream snapshot with attribution

dotnet/samples/GettingStartedWithVectorStores/Step2_Vector_Search.cs

@@ -0,0 +1,89 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel.Connectors.InMemory;
+
+namespace GettingStartedWithVectorStores;
+
+/// <summary>
+/// Example showing how to do vector searches with an in-memory vector store.
+/// </summary>
+public class Step2_Vector_Search(ITestOutputHelper output, VectorStoresFixture fixture) : BaseTest(output), IClassFixture<VectorStoresFixture>
+{
+    /// <summary>
+    /// Do a basic vector search where we just want to retrieve the single most relevant result.
+    /// </summary>
+    [Fact]
+    public async Task SearchAnInMemoryVectorStoreAsync()
+    {
+        var collection = await GetVectorStoreCollectionWithDataAsync();
+
+        // Search the vector store.
+        var searchResultItem = await SearchVectorStoreAsync(
+            collection,
+            "What is an Application Programming Interface?",
+            fixture.EmbeddingGenerator);
+
+        // Write the search result with its score to the console.
+        Console.WriteLine(searchResultItem.Record.Definition);
+        Console.WriteLine(searchResultItem.Score);
+    }
+
+    /// <summary>
+    /// Search the given collection for the most relevant result to the given search string.
+    /// </summary>
+    /// <param name="collection">The collection to search.</param>
+    /// <param name="searchString">The string to search matches for.</param>
+    /// <param name="embeddingGenerator">The service to generate embeddings with.</param>
+    /// <returns>The top search result.</returns>
+    internal static async Task<VectorSearchResult<Glossary>> SearchVectorStoreAsync(VectorStoreCollection<string, Glossary> collection, string searchString, IEmbeddingGenerator<string, Embedding<float>> embeddingGenerator)
+    {
+        // Generate an embedding from the search string.
+        var searchVector = (await embeddingGenerator.GenerateAsync(searchString)).Vector;
+
+        // Search the store and get the single most relevant result.
+        var searchResultItems = await collection.SearchAsync(
+            searchVector,
+            top: 1).ToListAsync();
+        return searchResultItems.First();
+    }
+
+    /// <summary>
+    /// Do a more complex vector search with pre-filtering.
+    /// </summary>
+    [Fact]
+    public async Task SearchAnInMemoryVectorStoreWithFilteringAsync()
+    {
+        var collection = await GetVectorStoreCollectionWithDataAsync();
+
+        // Generate an embedding from the search string.
+        var searchString = "How do I provide additional context to an LLM?";
+        var searchVector = (await fixture.EmbeddingGenerator.GenerateAsync(searchString)).Vector;
+
+        // Search the store with a filter and get the single most relevant result.
+        var searchResultItems = await collection.SearchAsync(
+            searchVector,
+            top: 1,
+            new()
+            {
+                Filter = g => g.Category == "AI"
+            }).ToListAsync();
+
+        // Write the search result with its score to the console.
+        Console.WriteLine(searchResultItems.First().Record.Definition);
+        Console.WriteLine(searchResultItems.First().Score);
+    }
+
+    private async Task<VectorStoreCollection<string, Glossary>> GetVectorStoreCollectionWithDataAsync()
+    {
+        // Construct the vector store and get the collection.
+        var vectorStore = new InMemoryVectorStore();
+        var collection = vectorStore.GetCollection<string, Glossary>("skglossary");
+
+        // Ingest data into the collection using the code from step 1.
+        await Step1_Ingest_Data.IngestDataIntoVectorStoreAsync(collection, fixture.EmbeddingGenerator);
+
+        return collection;
+    }
+}