chore: import upstream snapshot with attribution

dotnet/samples/GettingStartedWithVectorStores/Step1_Ingest_Data.cs

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+// 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 generate embeddings and ingest data into an in-memory vector store.
+/// </summary>
+public class Step1_Ingest_Data(ITestOutputHelper output, VectorStoresFixture fixture) : BaseTest(output), IClassFixture<VectorStoresFixture>
+{
+    /// <summary>
+    /// Example showing how to ingest data into an in-memory vector store.
+    /// </summary>
+    [Fact]
+    public async Task IngestDataIntoInMemoryVectorStoreAsync()
+    {
+        // Construct the vector store and get the collection.
+        var vectorStore = new InMemoryVectorStore();
+        var collection = vectorStore.GetCollection<string, Glossary>("skglossary");
+
+        // Ingest data into the collection.
+        await IngestDataIntoVectorStoreAsync(collection, fixture.EmbeddingGenerator);
+
+        // Retrieve an item from the collection and write it to the console.
+        var record = await collection.GetAsync("4");
+        Console.WriteLine(record!.Definition);
+    }
+
+    /// <summary>
+    /// Ingest data into the given collection.
+    /// </summary>
+    /// <param name="collection">The collection to ingest data into.</param>
+    /// <param name="embeddingGenerator">The service to use for generating embeddings.</param>
+    /// <returns>The keys of the upserted records.</returns>
+    internal static async Task<IEnumerable<string>> IngestDataIntoVectorStoreAsync(
+        VectorStoreCollection<string, Glossary> collection,
+        IEmbeddingGenerator<string, Embedding<float>> embeddingGenerator)
+    {
+        // Create the collection if it doesn't exist.
+        await collection.EnsureCollectionExistsAsync();
+
+        // Create glossary entries and generate embeddings for them.
+        var glossaryEntries = CreateGlossaryEntries().ToList();
+        var tasks = glossaryEntries.Select(entry => Task.Run(async () =>
+        {
+            entry.DefinitionEmbedding = (await embeddingGenerator.GenerateAsync(entry.Definition)).Vector;
+        }));
+        await Task.WhenAll(tasks);
+
+        // Upsert the glossary entries into the collection and return their keys.
+        await collection.UpsertAsync(glossaryEntries);
+
+        return glossaryEntries.Select(g => g.Key);
+    }
+
+    /// <summary>
+    /// Create some sample glossary entries.
+    /// </summary>
+    /// <returns>A list of sample glossary entries.</returns>
+    private static IEnumerable<Glossary> CreateGlossaryEntries()
+    {
+        yield return new Glossary
+        {
+            Key = "1",
+            Category = "Software",
+            Term = "API",
+            Definition = "Application Programming Interface. A set of rules and specifications that allow software components to communicate and exchange data."
+        };
+
+        yield return new Glossary
+        {
+            Key = "2",
+            Category = "Software",
+            Term = "SDK",
+            Definition = "Software development kit. A set of libraries and tools that allow software developers to build software more easily."
+        };
+
+        yield return new Glossary
+        {
+            Key = "3",
+            Category = "SK",
+            Term = "Connectors",
+            Definition = "Semantic Kernel Connectors allow software developers to integrate with various services providing AI capabilities, including LLM, AudioToText, TextToAudio, Embedding generation, etc."
+        };
+
+        yield return new Glossary
+        {
+            Key = "4",
+            Category = "SK",
+            Term = "Semantic Kernel",
+            Definition = "Semantic Kernel is a set of libraries that allow software developers to more easily develop applications that make use of AI experiences."
+        };
+
+        yield return new Glossary
+        {
+            Key = "5",
+            Category = "AI",
+            Term = "RAG",
+            Definition = "Retrieval Augmented Generation - a term that refers to the process of retrieving additional data to provide as context to an LLM to use when generating a response (completion) to a user’s question (prompt)."
+        };
+
+        yield return new Glossary
+        {
+            Key = "6",
+            Category = "AI",
+            Term = "LLM",
+            Definition = "Large language model. A type of artificial intelligence algorithm that is designed to understand and generate human language."
+        };
+    }
+}