chore: import upstream snapshot with attribution

dotnet/samples/Demos/OnnxSimpleRAG/Program.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.IO;
+using System.Linq;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.Configuration;
+using Microsoft.Extensions.VectorData;
+using Microsoft.ML.OnnxRuntimeGenAI;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.ChatCompletion;
+using Microsoft.SemanticKernel.Connectors.InMemory;
+using Microsoft.SemanticKernel.Connectors.Onnx;
+using Microsoft.SemanticKernel.Data;
+using Microsoft.SemanticKernel.PromptTemplates.Handlebars;
+
+Console.OutputEncoding = System.Text.Encoding.UTF8;
+
+// Ensure you follow the preparation steps provided in the README.md
+var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
+
+// Path to the folder of your downloaded ONNX PHI-3 model
+var chatModelPath = config["Onnx:ModelPath"]!;
+var chatModelId = config["Onnx:ModelId"] ?? "phi-3";
+
+// Path to the file of your downloaded ONNX BGE-MICRO-V2 model
+var embeddingModelPath = config["Onnx:EmbeddingModelPath"]!;
+
+// Path to the vocab file your ONNX BGE-MICRO-V2 model
+var embeddingVocabPath = config["Onnx:EmbeddingVocabPath"]!;
+
+// If using Onnx GenAI 0.5.0 or later, the OgaHandle class must be used to track
+// resources used by the Onnx services, before using any of the Onnx services.
+using var ogaHandle = new OgaHandle();
+
+// Load the services
+var builder = Kernel.CreateBuilder()
+    .AddOnnxRuntimeGenAIChatCompletion(chatModelId, chatModelPath)
+    .AddBertOnnxEmbeddingGenerator(embeddingModelPath, embeddingVocabPath);
+
+// Build Kernel
+var kernel = builder.Build();
+
+// Get the instances of the services
+using var chatService = kernel.GetRequiredService<IChatCompletionService>() as OnnxRuntimeGenAIChatCompletionService;
+var embeddingService = kernel.GetRequiredService<IEmbeddingGenerator<string, Embedding<float>>>();
+
+// Create a vector store and a collection to store information
+var vectorStore = new InMemoryVectorStore(new() { EmbeddingGenerator = embeddingService });
+var collection = vectorStore.GetCollection<string, InformationItem>("ExampleCollection");
+await collection.EnsureCollectionExistsAsync();
+
+// Save some information to the memory
+var collectionName = "ExampleCollection";
+foreach (var factTextFile in Directory.GetFiles("Facts", "*.txt"))
+{
+    var factContent = File.ReadAllText(factTextFile);
+    await collection.UpsertAsync(new InformationItem()
+    {
+        Id = Guid.NewGuid().ToString(),
+        Text = factContent
+    });
+}
+
+// Add a plugin to search the database with.
+var vectorStoreTextSearch = new VectorStoreTextSearch<InformationItem>(collection);
+kernel.Plugins.Add(vectorStoreTextSearch.CreateWithSearch("SearchPlugin"));
+
+// Start the conversation
+while (true)
+{
+    // Get user input
+    Console.ForegroundColor = ConsoleColor.White;
+    Console.Write("User > ");
+    var question = Console.ReadLine()!;
+
+    // Clean resources and exit the demo if the user input is null or empty
+    if (question is null || string.IsNullOrWhiteSpace(question))
+    {
+        // To avoid any potential memory leak all disposable
+        // services created by the kernel are disposed
+        DisposeServices(kernel);
+        return;
+    }
+
+    // Invoke the kernel with the user input
+    var response = kernel.InvokePromptStreamingAsync(
+        promptTemplate: @"Question: {{input}}
+        Answer the question using the memory content:
+        {{#with (SearchPlugin-Search input)}}
+          {{#each this}}
+            {{this}}
+            -----------------
+          {{/each}}
+        {{/with}}",
+        templateFormat: "handlebars",
+        promptTemplateFactory: new HandlebarsPromptTemplateFactory(),
+        arguments: new KernelArguments()
+        {
+            { "input", question },
+            { "collection", collectionName }
+        });
+
+    Console.Write("\nAssistant > ");
+
+    await foreach (var message in response)
+    {
+        Console.Write(message);
+    }
+
+    Console.WriteLine();
+}
+
+static void DisposeServices(Kernel kernel)
+{
+    foreach (var target in kernel
+        .GetAllServices<IChatCompletionService>()
+        .OfType<IDisposable>())
+    {
+        target.Dispose();
+    }
+}
+
+/// <summary>
+/// Information item to represent the embedding data stored in the memory
+/// </summary>
+internal sealed class InformationItem
+{
+    [VectorStoreKey]
+    [TextSearchResultName]
+    public string Id { get; set; } = string.Empty;
+
+    [VectorStoreData]
+    [TextSearchResultValue]
+    public string Text { get; set; } = string.Empty;
+
+    [VectorStoreVector(Dimensions: 384)]
+    public string Embedding => this.Text;
+}