新建Class：CloudServiceWiki

using Microsoft.Extensions.VectorData;

namespace VectorDataAIDemo;

internal class CloudServiceWiki
{
    [VectorStoreKey]
    public int Key { get; set; }

    [VectorStoreData]
    public string Name { get; set; }

    [VectorStoreData]
    public string Description { get; set; }

    [VectorStoreVector(
        Dimensions: 384,
        DistanceFunction = DistanceFunction.CosineSimilarity)]
    public ReadOnlyMemory<float> Vector { get; set; }
}

Microsoft.Extensions.VectorData 属性，例如 VectorStoreKeyAttribute，会影响在向量存储中使用时每个属性的处理方式。

该 Vector 属性存储生成的嵌入，表示 Description 值在矢量搜索中的语义含义。

在 Program.cs 文件中，添加以下代码以创建描述云服务知识库集合的数据集：

List<CloudServiceWiki> cloudServices =
[
    new() {
            Key = 0,
            Name = "Azure App Service",
            Description = "Host .NET, Java, Node.js, and Python web applications and APIs in a fully managed Azure service. You only need to deploy your code to Azure. Azure takes care of all the infrastructure management like high availability, load balancing, and autoscaling."
    },
    new() {
            Key = 1,
            Name = "Azure Service Bus",
            Description = "A fully managed enterprise message broker supporting both point to point and publish-subscribe integrations. It's ideal for building decoupled applications, queue-based load leveling, or facilitating communication between microservices."
    },
    new() {
            Key = 2,
            Name = "Azure Blob Storage",
            Description = "Azure Blob Storage allows your applications to store and retrieve files in the cloud. Azure Storage is highly scalable to store massive amounts of data and data is stored redundantly to ensure high availability."
    },
    new() {
            Key = 3,
            Name = "Microsoft Entra ID",
            Description = "Manage user identities and control access to your apps, data, and resources."
    },
    new() {
            Key = 4,
            Name = "Azure Key Vault",
            Description = "Store and access application secrets like connection strings and API keys in an encrypted vault with restricted access to make sure your secrets and your application aren't compromised."
    },
    new() {
            Key = 5,
            Name = "Azure AI Search",
            Description = "Information retrieval at scale for traditional and conversational search applications, with security and options for AI enrichment and vectorization."
    }
];

创建和配置 IEmbeddingGenerator 实现以将请求发送到嵌入 AI 模型：

// Load the configuration values.
IConfigurationRoot config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
string model = config["ModelName"];
string key = config["OpenAIKey"];

// Create the embedding generator.
IEmbeddingGenerator<string, Embedding<float>> generator =
    new OpenAIClient(new ApiKeyCredential(key))
      .GetEmbeddingClient(model: model)
      .AsIEmbeddingGenerator();

使用云服务知识库数据创建和填充向量存储。 使用 IEmbeddingGenerator 实现为云服务知识库数据中的每个记录创建和分配嵌入向量：

// Create and populate the vector store.
var vectorStore = new InMemoryVectorStore();
VectorStoreCollection<int, CloudServiceWiki> cloudServicesStore =
    vectorStore.GetCollection<int, CloudServiceWiki>("cloudServices");
await cloudServicesStore.EnsureCollectionExistsAsync();

foreach (CloudServiceWiki service in cloudServices)
{
    service.Vector = await generator.GenerateVectorAsync(service.Description);
    await cloudServicesStore.UpsertAsync(service);
}

嵌入是每个数据记录语义含义的数字表示形式，这使得它们与向量搜索功能兼容。

搜索查询

// Convert a search query to a vector
// and search the vector store.
string query = "Which Azure service should I use to store my Word documents?";
ReadOnlyMemory<float> queryEmbedding = await generator.GenerateVectorAsync(query);

IAsyncEnumerable<VectorSearchResult<CloudServiceWiki>> results =
    cloudServicesStore.SearchAsync(queryEmbedding, top: 1);

await foreach (VectorSearchResult<CloudServiceWiki> result in results)
{
    Console.WriteLine($"Name: {result.Record.Name}");
    Console.WriteLine($"Description: {result.Record.Description}");
    Console.WriteLine($"Vector match score: {result.Score}");
}