Atlas Vector Search

在此页面上

Overview
支持的向量嵌入类型
数组表示
二进制向量表示
二进制向量数据序列化
向量搜索查询示例
例子
更多信息
API 文档

Overview

您可以使用Atlas Vector Search对Atlas中存储的数据执行向量搜索。向量搜索允许您根据语义而不仅仅是关键字匹配查询数据，这有助于检索更多相关的搜索结果。它使您的AI应用程序能够支持语义搜索、混合搜索和生成搜索（包括检索增强生成 (RAG)）等使用案例。

通过将 Atlas 用作矢量数据库，您可无缝地为矢量数据以及 Atlas 中的其他数据创建索引。如此一来，您便可对集合中的字段进行过滤，并对向量数据执行向量搜索查询。您还可将向量搜索与全文搜索查询相结合，以便为您的用例返回最相关的结果。您可将 Atlas Vector Search 与主流 AI 框架和服务相集成，以在您的应用程序中轻松实现向量搜索。

要学习；了解有关Atlas Vector Search 的更多信息，请参阅MongoDB Atlas文档中的Atlas Vector Search指南。

支持的向量嵌入类型

向量嵌入是用于表示数据的向量。这些嵌入捕获数据中有意义的关系，并启用语义搜索和检索等任务。

.NET/ C#驱动程序支持多种类型的向量嵌入。以下各部分介绍支持的向量嵌入类型。

数组表示

.NET/ C#驱动程序支持向量嵌入中大量类型的以下表示形式：

BsonArray
Memory
ReadOnlyMemory
float[] 和 double[]

以下示例显示了具有上述类型属性的类：

public class BsonArrayVectors
{
    public BsonArray BsonArrayVector { get; set; }
    public Memory<float> MemoryVector { get; set; }
    public ReadOnlyMemory<float> ReadOnlyMemoryVector { get; set; }
    public float[] FloatArrayVector { get; set; }
}

二进制向量表示

.NET/ C#驱动程序支持向量嵌入中的以下二进制向量表示：

BinaryVectorFloat32（大端架构不支持）
BinaryVectorInt8
BinaryVectorPackedBit
Memory<float>, Memory<byte>, Memory<sbyte>
ReadOnlyMemory<float>, ReadOnlyMemory<byte>, ReadOnlyMemory<sbyte>
float[], byte[], sbyte[]

注意

指定 Memory<T>、ReadOnlyMemory<T> 或大量类型的二元向量表示时，必须使用 BinaryVector 属性。

以下示例显示了具有上述类型属性的类：

public class BinaryVectors
{
    public BinaryVectorInt8 ValuesInt8 { get; set; }
    public BinaryVectorPackedBit ValuesPackedBit { get; set; }
    public BinaryVectorFloat32 ValuesFloat { get; set; }
    [BinaryVector(BinaryVectorDataType.Int8)]
    public Memory<byte> ValuesByte { get; set; }
    [BinaryVector(BinaryVectorDataType.Float32)]
    public float[] ValuesFloat { get; set; }
}

二进制向量数据序列化

您可以将 Int8 二进制向量类型数据序列化为 byte 或 sbyte。您还可以将 Float32 二进制向量类型数据序列化为 float。以下示例将序列化 Int8 和 Float32 二进制向量数据：

[BinaryVector(BinaryVectorDataType.Int8)]
public Memory<byte> ValuesByte { get; set; }
[BinaryVector(BinaryVectorDataType.Int8)]
public Memory<sbyte> ValuesSByte { get; set; }
[BinaryVector(BinaryVectorDataType.Float32)]
public float[] ValuesFloat { get; set; }

仅当向量数据的填充值为 0 时，才能将 PackedBit 向量数据反序列化为二进制向量表示byte数据类型。如果向量数据的填充值不等于 0，则只能将其反序列化为 BsonVectorPackedBit。

向量搜索查询示例

您可以通过聚合管道中的 VectorSearch() 方法来执行向量搜索查询。要对集合执行向量搜索，必须首先拥有一个带有包含向量数据的字段和涵盖该字段的向量搜索索引的集合。

提示

要学习；了解有关为向量搜索配置集合的更多信息，请参阅MongoDB Atlas文档中的Atlas Vector Search指南。

您可以使用 ToQueryVector() 方法将 BinaryVectorFloat32、BinaryVectorInt8 和 BinaryVectorPackedBit 数据转换为 BsonBinaryData 类型，以便在向量搜索查询中使用。以下示例将 BinaryVectorInt8 转换为 BsonBinaryData对象：

var binaryVector = new BinaryVectorInt8(new sbyte[] { 0, 1, 2, 3, 4 });
var queryVector = binaryVector.ToQueryVector();

您可以将数组表示的向量数据指定为 QueryVector 类的实例，以在向量搜索查询中使用。以下示例创建了一个 ReadOnlyMemory<float> 值大量作为 QueryVector对象，用于向量搜索查询：

QueryVector v = new QueryVector(new ReadOnlyMemory<float>([1.2f, 2.3f]));

例子

此示例执行以下步骤，对包含矢量数据和 PlotEmbedding字段上的向量搜索索引的集合运行Atlas Vector Search查询：

创建一个大量，其中包含要搜索的以数组表示的向量数据
指定一个 VectorSearchOptions对象，其中包含索引名称以及搜索期间要使用的最近邻数量
创建一个聚合管道，该管道使用 VectorSearch() 阶段执行向量搜索查询，并使用 Project() 阶段过滤结果
打印查询结果

        QueryVector vector = new[] { -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 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        var options = new VectorSearchOptions<Movie>()
        {
            IndexName = "vector_index",
            NumberOfCandidates = 150
        };
        // run query
        var results = collection.Aggregate()
              .VectorSearch(movie => movie.PlotEmbedding, vector, 10, options)
              .Project(Builders<Movie>.Projection
                .Include(movie => movie.Title)
                .Include(movie => movie.Plot))
              .ToList();
        // print results
        foreach (var movie in results)
        {
            Console.WriteLine(movie.ToJson());
        }