chore: import upstream snapshot with attribution

2026-07-13 13:21:23 +08:00
commit b957a53def
5423 changed files with 863745 additions and 0 deletions
@@ -0,0 +1,174 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using MCPServer;
+using MCPServer.ProjectResources;
+using MCPServer.Prompts;
+using MCPServer.Resources;
+using MCPServer.Tools;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Agents;
+using Microsoft.SemanticKernel.Connectors.InMemory;
+using ModelContextProtocol.Protocol;
+using ModelContextProtocol.Server;
+
+var builder = Host.CreateEmptyApplicationBuilder(settings: null);
+
+// Load and validate configuration
+(string embeddingModelId, string chatModelId, string apiKey) = GetConfiguration();
+
+// Register the kernel
+IKernelBuilder kernelBuilder = builder.Services.AddKernel();
+
+// Register SK plugins
+kernelBuilder.Plugins.AddFromType<DateTimeUtils>();
+kernelBuilder.Plugins.AddFromType<WeatherUtils>();
+kernelBuilder.Plugins.AddFromType<MailboxUtils>();
+
+// Register SK agent as plugin
+kernelBuilder.Plugins.AddFromFunctions("Agents", [AgentKernelFunctionFactory.CreateFromAgent(CreateSalesAssistantAgent(chatModelId, apiKey))]);
+
+// Register embedding generation service and in-memory vector store
+kernelBuilder.Services.AddSingleton<VectorStore, InMemoryVectorStore>();
+kernelBuilder.Services.AddOpenAIEmbeddingGenerator(embeddingModelId, apiKey);
+
+// Register MCP server
+builder.Services
+    .AddMcpServer()
+    .WithStdioServerTransport()
+
+    // Add all functions from the kernel plugins to the MCP server as tools
+    .WithTools()
+
+    // Register the `getCurrentWeatherForCity` prompt
+    .WithPrompt(PromptDefinition.Create(EmbeddedResource.ReadAsString("getCurrentWeatherForCity.json")))
+
+    // Register vector search as MCP resource template
+    .WithResourceTemplate(CreateVectorStoreSearchResourceTemplate())
+
+    // Register the cat image as a MCP resource
+    .WithResource(ResourceDefinition.CreateBlobResource(
+        uri: "image://cat.jpg",
+        name: "cat-image",
+        content: EmbeddedResource.ReadAsBytes("cat.jpg"),
+        mimeType: "image/jpeg"));
+
+await builder.Build().RunAsync();
+
+/// <summary>
+/// Gets configuration.
+/// </summary>
+static (string EmbeddingModelId, string ChatModelId, string ApiKey) GetConfiguration()
+{
+    // Load and validate configuration
+    IConfigurationRoot config = new ConfigurationBuilder()
+        .AddUserSecrets<Program>()
+        .AddEnvironmentVariables()
+        .Build();
+
+    if (config["OpenAI:ApiKey"] is not { } apiKey)
+    {
+        const string Message = "Please provide a valid OpenAI:ApiKey to run this sample. See the associated README.md for more details.";
+        Console.Error.WriteLine(Message);
+        throw new InvalidOperationException(Message);
+    }
+
+    string embeddingModelId = config["OpenAI:EmbeddingModelId"] ?? "text-embedding-3-small";
+
+    string chatModelId = config["OpenAI:ChatModelId"] ?? "gpt-4o-mini";
+
+    return (embeddingModelId, chatModelId, apiKey);
+}
+static ResourceTemplateDefinition CreateVectorStoreSearchResourceTemplate(Kernel? kernel = null)
+{
+    return new ResourceTemplateDefinition
+    {
+        Kernel = kernel,
+        ResourceTemplate = new()
+        {
+            UriTemplate = "vectorStore://{collection}/{prompt}",
+            Name = "Vector Store Record Retrieval",
+            Description = "Retrieves relevant records from the vector store based on the provided prompt."
+        },
+        Handler = async (
+            RequestContext<ReadResourceRequestParams> context,
+            string collection,
+            string prompt,
+            [FromKernelServices] IEmbeddingGenerator<string, Embedding<float>> embeddingGenerator,
+            [FromKernelServices] VectorStore vectorStore,
+            CancellationToken cancellationToken) =>
+        {
+            // Get the vector store collection
+            VectorStoreCollection<Guid, TextDataModel> vsCollection = vectorStore.GetCollection<Guid, TextDataModel>(collection);
+
+            // Check if the collection exists, if not create and populate it
+            if (!await vsCollection.CollectionExistsAsync(cancellationToken))
+            {
+                static TextDataModel CreateRecord(string text, ReadOnlyMemory<float> embedding)
+                {
+                    return new()
+                    {
+                        Key = Guid.NewGuid(),
+                        Text = text,
+                        Embedding = embedding
+                    };
+                }
+
+                string content = EmbeddedResource.ReadAsString("semantic-kernel-info.txt");
+
+                // Create a collection from the lines in the file
+                await vectorStore.CreateCollectionFromListAsync<Guid, TextDataModel>(collection, content.Split('\n'), embeddingGenerator, CreateRecord);
+            }
+
+            // Generate embedding for the prompt
+            ReadOnlyMemory<float> promptEmbedding = (await embeddingGenerator.GenerateAsync(prompt, cancellationToken: cancellationToken)).Vector;
+
+            // Retrieve top three matching records from the vector store
+            var result = vsCollection.SearchAsync(promptEmbedding, top: 3, cancellationToken: cancellationToken);
+
+            // Return the records as resource contents
+            List<ResourceContents> contents = [];
+
+            await foreach (var record in result)
+            {
+                contents.Add(new TextResourceContents()
+                {
+                    Text = record.Record.Text,
+                    Uri = context.Params!.Uri!,
+                    MimeType = "text/plain",
+                });
+            }
+
+            return new ReadResourceResult { Contents = contents };
+        }
+    };
+}
+
+static Agent CreateSalesAssistantAgent(string chatModelId, string apiKey)
+{
+    IKernelBuilder kernelBuilder = Kernel.CreateBuilder();
+
+    // Register the SK plugin for the agent to use
+    kernelBuilder.Plugins.AddFromType<OrderProcessingUtils>();
+
+    // Register chat completion service
+    kernelBuilder.Services.AddOpenAIChatCompletion(chatModelId, apiKey);
+
+    // Using a dedicated kernel with the `OrderProcessingUtils` plugin instead of the global kernel has a few advantages:
+    // - The agent has access to only relevant plugins, leading to better decision-making regarding which plugin to use.
+    //   Fewer plugins mean less ambiguity in selecting the most appropriate one for a given task.
+    // - The plugin is isolated from other plugins exposed by the MCP server. As a result the client's Agent/AI model does
+    //   not have access to irrelevant plugins.
+    Kernel kernel = kernelBuilder.Build();
+
+    // Define the agent
+    return new ChatCompletionAgent()
+    {
+        Name = "SalesAssistant",
+        Instructions = "You are a sales assistant. Place orders for items the user requests and handle refunds.",
+        Description = "Agent to invoke to place orders for items the user requests and handle refunds.",
+        Kernel = kernel,
+        Arguments = new KernelArguments(new PromptExecutionSettings() { FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() }),
+    };
+}