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Atlas Vector Search 教程

如何执行混合搜索

在此页面上

  • 先决条件
  • 创建 Atlas Vector Search 和 Atlas Search 索引
  • 必需的访问权限
  • 步骤
  • 运行组合语义搜索和全文搜索查询
  • 关于查询
  • 步骤
  • 通过观看学习

本教程演示如何使用倒数排名融合将 $vectorSearch查询的语义搜索结果与$search查询的全文搜索结果组合起来。 倒数排名融合是一种将不同类型搜索的结果合并为单个结果的方法。 本教程将指导您完成以下步骤：

  1. 在sample_mflix.embedded_movies集合中的plot_embeddings字段上创建 Atlas Vector Search 索引。

  2. 对sample_mflix.embedded_movies集合中的title字段创建 Atlas Search 索引。

  3. $vectorSearchplot_embeddingssample_mflix.embedded_movies使用倒数排名融合，将针对collection中的字段的$search title查询与针对字段的 查询组合并运行。

先决条件

开始之前，请确保 Atlas 集群满足先决条件中所述的要求。

注意

确保您的 Atlas 集群有足够的内存来存储 Atlas Search 和 Atlas Vector Search 索引并运行高性能查询。

创建 Atlas Vector Search 和 Atlas Search 索引

本部分演示如何在sample_mflix.embedded_moviescollection中的字段上创建以下索引：

  • plot_embeddings字段上的 Atlas Vector Search 索引，用于针对该字段运行向量查询。

  • title字段上的 Atlas Search 索引，用于对该字段运行全文搜索。

必需的访问权限

要创建 Atlas Vector Search 和 Atlas Search 索引，您必须至少拥有该项目的Project Data Access Admin访问权限。

步骤

1

在 Atlas 中，转到项目的 Clusters页面。

  1. 如果尚未显示，请从导航栏的 Organizations 菜单中选择包含所需项目的组织。

  2. 如果尚未显示该页，请从导航栏的 Projects（项目）菜单中选择所需的项目。

  3. 如果 Clusters（数据库部署）页面尚未出现，请单击侧边栏中的 Database（数据库）。

2

转到集群的 Atlas Search 页面。

  1. 单击集群的名称。

  2. 单击 Atlas Search 标签页。

3

单击 Create Index。

  1. 单击导航栏Services下的Atlas Search 。

  2. 从Select data source下拉列表中选择您的集群，然后单击Go to Atlas Search 。

4

定义 Atlas Vector Search 索引。

  1. 单击 Create Search Index（连接）。

  2. 在Atlas Vector Search下，选择JSON Editor ，然后单击Next 。

  3. 在 Database and Collection（数据库和集合）部分中找到 sample_mflix 数据库，然后选择 embedded_movies 集合。

  4. 在 Index Name 字段中输入 rrf-vector-search。

  5. 将默认定义替换为以下索引定义，然后单击Next 。

5

定义 Atlas Vector Search 索引。

  1. 将默认定义替换为以下索引定义。

    此索引定义将plot_embedding字段索引为向量类型。 plot_embedding字段包含使用 OpenAI 的text-embedding-ada-002嵌入模型创建的嵌入。 索引定义指定了1536向量维度，并使用euclidean来衡量相似性。

    1{
    2  "fields": [
    3    {
    4      "type": "vector",
    5      "path": "plot_embedding",
    6      "numDimensions": 1536,
    7      "similarity": "euclidean"
    8    }
    9  ]
    10}
6

查看索引定义，然后单击Create Search Index 。

此时将显示一个模态窗口，让您知道索引正在构建中。

7

单击Close关闭You're All Set!模式窗口，并等待索引完成构建。

构建索引大约需要一分钟时间。在构建时， Status列显示为Initial Sync 。构建完成后， Status列显示为Active 。

8

单击Create Index以定义 Atlas Search 索引。

9

选择某一 Atlas Search Configuration Method 并单击 Next。

  • 要获得引导式体验，请选择 Atlas Search Visual Editor 。

  • 要编辑原始索引定义，请选择 Atlas SearchJSON Editor 。

10

输入索引名称，并设置数据库和集合。

  1. 在 Index Name 字段中输入 rrf-full-text-search。

    注意

    如果将索引命名为 default，则在使用 $search 管道阶段时，您无需指定 index 参数。否则，您必须使用 index 参数指定索引名称。

  2. 在 Database and Collection（数据库和集合）部分中找到 sample_mflix 数据库，然后选择 embedded_movies 集合。

11

定义 Atlas Search 索引。

以下索引定义将title字段索引为用于查询该字段的字符串类型。

您可以使用 Atlas 用户界面中的 Atlas Search Visual Editor 或 Atlas Search JSON Editor 来创建索引。

12

单击 Create Search Index。

此时将显示一个模态窗口，让您知道索引正在构建中。

13

单击Close关闭You're All Set!模式窗口，并等待索引完成构建。

构建索引大约需要一分钟时间。在构建时，Status 列显示 Initial Sync。构建完成后，Status 列显示 Active。

运行组合语义搜索和全文搜索查询

本部分演示如何使用$vectorSearch和$search管道阶段查询sample_mflix.embedded_moviescollection中的数据，并组合来自这两个阶段的每个文档的分数以对结果中的文档重新排序。这可确保两次搜索中出现的文档显示在合并结果的顶部。

关于查询

以下查询从语义搜索和全文搜索中检索排序的搜索结果，并根据结果中的文档在结果数组中的位置为这些文档分配倒数排名分数。 排名分数的倒数使用以下公式计算：

1.0/{document position in the results + vector or full-text penalty + constant value}

然后，该查询将每个文档的两次搜索分数相加，根据组合分数对文档进行排名，并对文档进行排序以返回单个结果。

查询变量

示例查询定义了以下变量以增加分数的权重，数字越小，权重越高：

  • vector_penalty

  • full_text_penalty

查询阶段

示例查询使用以下管道阶段：

  • $vectorSearch阶段，用于在plot_embeddings字段中搜索指定为查询的queryVector字段中的向量嵌入的字符串new york 。 该查询使用ada-002-text嵌入，与plot_embedding字段中的向量嵌入相同。 该查询还指定最多搜索100个最近邻，并将结果限制为仅20个文档。 此阶段从结果中返回语义搜索的排序文档。

  • $group阶段，将语义搜索结果中的所有文档分组到名为docs的字段中。

  • $unwind阶段，展开docs字段中的文档数组，并将文档在结果数组中的位置存储在名为rank的字段中。

  • $addFields阶段添加一个名为vs_score的新字段，其中包含结果中每个文档的排名分数倒数。此处，排名分数倒数的计算方法是将1.0除以rank 、 vector_penalty权重和1常量值的总和。

  • $project 阶段以仅在结果中包含以下字段：

    • vs_score

    • _id

    • title

  • $unionWith 阶段，将子管道中前面阶段的结果与以下阶段的结果相结合：

    • $search阶段，用于搜索在new york title字段中包含术语一词的电影。此阶段从结果中的全文搜索中返回已排序的文档。

    • $limit阶段将输出限制为仅20结果。

    • $group阶段将全文搜索中的所有文档分组到名为docs的字段中。

    • $unwind阶段，展开docs字段中的文档数组，并将文档在结果数组中的位置存储在名为rank的字段中。

    • $addFields阶段添加一个名为fts_score 的新字段，其中包含结果中每个文档的排名分数倒数。此处，倒数排名分数的计算方法是将1.0 rankfull_text penalty除以 值、 权重和1 常量值的总和。

    • $project 阶段以仅在结果中包含以下字段：

      • fts_score

      • _id

      • title

  • $group阶段，用于按title 、 vs_score和fts_score对前面阶段的结果中的文档进行分组。

  • $project 阶段以仅在结果中包含以下字段：

    • vs_score

    • fts_score

    • _id

    • title

  • $project阶段，将名为score的字段添加到结果中，其中包含vs_score和fts_score之和。

  • $sort阶段按score降序对结果进行排序。

  • $limit阶段将输出限制为仅10结果。

步骤

1

使用mongosh连接到您的集群。

在终端窗口中打开mongosh并连接到集群。有关连接的详细说明，请参阅通过mongosh连接。

2

使用sample_mflix数据库。

在mongosh提示符下运行以下命令：

use sample_mflix
switched to db sample_mflix
3

针对embedded_movies集合运行以下 Atlas Search 查询。

1var vector_penalty = 1;
2var full_text_penalty = 10;
3db.embedded_movies.aggregate([
4  {
5    "$vectorSearch": {
6      "index": "rrf-vector-search",
7      "path": "plot_embedding",
8      "queryVector": 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62              "$divide": [
63                1.0,
64                {"$add": ["$rank", full_text_penalty, 1]}
65              ]
66            }
67          }
68        },
69        {
70          "$project": {
71            "fts_score": 1,
72            "_id": "$docs._id",
73            "title": "$docs.title"
74          }
75        }
76      ]
77    }
78  },
79  {
80    "$group": {
81      "_id": "$title",
82      "vs_score": {"$max": "$vs_score"},
83      "fts_score": {"$max": "$fts_score"}
84    }
85  },
86  {
87    "$project": {
88      "_id": 1,
89      "title": 1,
90      "vs_score": {"$ifNull": ["$vs_score", 0]},
91      "fts_score": {"$ifNull": ["$fts_score", 0]}
92    }
93  },
94  {
95    "$project": {
96      "score": {"$add": ["$fts_score", "$vs_score"]},
97      "_id": 1,
98      "title": 1,
99      "vs_score": 1,
100      "fts_score": 1
101    }
102  },
103  {"$sort": {"score": -1}},
104  {"$limit": 10}
105])
[
  { _id: 'Pixels', vs_score: 0.5, fts_score: 0, score: 0.5 },
  {
    _id: 'Fighting',
    vs_score: 0.3333333333333333,
    fts_score: 0,
    score: 0.3333333333333333
  },
  {
    _id: 'A Most Violent Year',
    vs_score: 0.25,
    fts_score: 0,
    score: 0.25
  },
  {
    _id: 'Mistletoe Over Manhattan',
    vs_score: 0.2,
    fts_score: 0,
    score: 0.2
  },
  {
    _id: 'End of Days',
    vs_score: 0.16666666666666666,
    fts_score: 0,
    score: 0.16666666666666666
  },
  { _id: 'Sam', vs_score: 0.125, fts_score: 0, score: 0.125 },
  {
    _id: 'Rumble in the Bronx',
    vs_score: 0.1111111111111111,
    fts_score: 0,
    score: 0.1111111111111111
  },
  {
    _id: 'Escape from New York',
    vs_score: 0.047619047619047616,
    fts_score: 0.05555555555555555,
    score: 0.10317460317460317
  },
  { _id: 'The Other Guys', vs_score: 0.1, fts_score: 0, score: 0.1 },
  {
    _id: 'Sky Captain and the World of Tomorrow',
    vs_score: 0.09090909090909091,
    fts_score: 0,
    score: 0.09090909090909091
  }
]

如果通过将第 103 行上的score的值替换为1来对结果进行升序排序，则 Atlas Vector Search 将返回以下结果：

[
  {
    _id: '2 Days in New York',
    vs_score: 0,
    fts_score: 0.03333333333333333,
    score: 0.03333333333333333
  },
  {
    _id: 'An Englishman in New York',
    vs_score: 0,
    fts_score: 0.034482758620689655,
    score: 0.034482758620689655
  },
  {
    _id: 'New York, I Love You',
    vs_score: 0,
    fts_score: 0.03571428571428571,
    score: 0.03571428571428571
  },
  {
    _id: 'New York: A Documentary Film',
    vs_score: 0,
    fts_score: 0.037037037037037035,
    score: 0.037037037037037035
  },
  {
    _id: 'A Couch in New York',
    vs_score: 0,
    fts_score: 0.038461538461538464,
    score: 0.038461538461538464
  },
  {
    _id: 'Sherlock Holmes in New York',
    vs_score: 0,
    fts_score: 0.04,
    score: 0.04
  },
  {
    _id: 'A King in New York',
    vs_score: 0,
    fts_score: 0.041666666666666664,
    score: 0.041666666666666664
  },
  {
    _id: 'Live from New York!',
    vs_score: 0,
    fts_score: 0.043478260869565216,
    score: 0.043478260869565216
  },
  {
    _id: 'Sleepless in New York',
    vs_score: 0,
    fts_score: 0.045454545454545456,
    score: 0.045454545454545456
  },
  {
    _id: 'Gangs of New York',
    vs_score: 0,
    fts_score: 0.047619047619047616,
    score: 0.047619047619047616
  }
]

通过观看学习

观看应用程序演示，该应用程序展示了混合搜索查询，该查询将 Atlas Search 全文搜索和向量搜索相结合以返回单个合并结果集。该应用程序实施相对分数融合 (RSF) 和倒数排名融合 (RRF)，以返回使用排名融合算法创建的合并集。

时长：2.43 分钟

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