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如何执行混合搜索

您可以将 Atlas Vector Search 和 Atlas Search 查询组合成混合搜索以获得统一的结果。

本教程演示如何对 sample_mflix.embedded_movies 集合运行混合搜索，其中包含有关电影的详细信息。具体来说，本教程将指导您完成以下步骤：

  1. 在 plot_embedding 字段上创建 Atlas Vector Search 索引。该字段包含表示电影情节摘要的向量嵌入。

  2. 在 sample_mflix.embedded_movies 集合上创建 Atlas Search 索引。

  3. 使用以下技术运行查询以合并 $vectorSearch 查询和 $search 查询的结果：

    • 语义提升

    • 倒数秩融合

为什么选择混合搜索？

混合搜索是针对相同查询条件的不同搜索方法（如全文搜索和语义搜索）的聚合。虽然全文搜索对于查询词的精确匹配很有效，但语义搜索还提供了一个额外好处，即使文档不包含精确的查询词，也能识别语义相似的文档。这确保了同义匹配和上下文相似的匹配项也包含在两种搜索方法的组合结果中。

相反，如果数据集中有专有名词或特定关键词的词元，这些词元在数据集中使用的上下文相同，您不希望在培训嵌入模型时考虑这些词元，则向量搜索可能会受益于与全文搜索相结合。

您还可以为每个查询的每种搜索方法设立权重。根据全文或语义搜索结果是否与查询最相关且最合适，您可以增加每个查询的搜索方法的权重。

您可以应用以下技术来实现混合搜索：

技术

语义提升

倒数排名融合

用途

通过提升与向量搜索匹配的文档来提高全文搜索结果的准确性。在检索非常大的结果集时很有用。

通过计算结果中文档的倒数排名，将不同搜索方法的结果合并到单个排名结果设立。

排序方法

  • 检索 $vectorSearch 和 $search 结果。

  • 使用每个文档在各自结果集中的排名来合并两组结果。

评分方法

通过加权向量搜索搜索分数来提高与向量搜索结果匹配的文档的全文结果分数。

使用以下公式计算分数：

1.0/{document position in the results + constant value}

然后，对于每种搜索方法应用不同的权重 (w) 以赋予该搜索方法更高的重要性。对于每个文档，将权重乘以文档的排名倒数即可计算出加权排名倒数。

weighted_reciprocal_rank = w x reciprocal_rank

搜索功能

  • 支持分面以将数据群组。

  • 支持使用 searchAfter和searchBefore 对结果进行分页。

支持在后续聚合阶段（例如 $group）中进一步处理结果。

输出

单个结果设立，其中包含以下文档： $search

包含来自两种搜索方法的文档的单个结果设立。

先决条件

在开始之前，您必须具备以下条件：

  • MongoDB 版本为 v 6.0.11或 v 7.0.2或更高版本的 Atlas 集群。

    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。

  • 将 sample_mflix 数据加载到Atlas 集群。

  • mongosh 对Atlas 集群运行查询：

  • Project Data Access Admin 访问项目以创建 Atlas Vector Search 和 Atlas Search 索引。

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_embedding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。

步骤

1

定义 Atlas Vector Search 索引。

以下索引定义在查询集合时将 plot_embedding 字段索引为 Atlas Vector Search 字段。

要为使用 mongosh v2.1.2 或更高版本的集合创建 Atlas Search 索引，请执行以下步骤：

  1. 使用 mongosh 连接到 Atlas 集群。

    要了解更多信息，请参阅通过 mongosh 进行连接。

  2. 切换到包含要创建索引的集合的数据库。

    use sample_mflix
    switched to db sample_mflix

  3. 运行 db.collection.createSearchIndex() 方法。

    1db.embedded_movies.createSearchIndex(
    2  "hybrid-vector-search", 
    3  "vectorSearch", 
    4  {
    5    "fields": [
    6      {
    7        "type": "vector",
    8        "path": "plot_embedding",
    9        "numDimensions": 1536,
    10        "similarity": "dotProduct"
    11      }
    12    ]
    13  }
    14);
2

定义 Atlas Search 索引。

以下索引定义自动为集合中的所有动态可索引字段编制索引。

要为集合使用 mongosh v2.1.2 或更高版本的数据集创建 Atlas Search 索引，请执行以下步骤：

  1. 使用 mongosh 连接到 Atlas 集群。

    要了解更多信息，请参阅通过 mongosh 进行连接。

  2. 切换到包含要创建索引的集合的数据库。

    use sample_mflix
    switched to db sample_mflix

  3. 运行 db.collection.createSearchIndex() 方法。

    1db.embedded_movies.createSearchIndex(
    2  "hybrid-full-text-search", 
    3  "search", 
    4  {
    5    "mappings": {
    6      "dynamic": true
    7    }
    8  }
    9);

语义提升

本部分演示如何分别使用sample_mflix.embedded_movies plot_embeddingplot$vectorSearch和$search 管道阶段查询 集合中 和 字段中 星球大战 的数据。该查询通过使用相同文档的 分数来提高全文分数，从而增强$search $vectorSearch查询的相关性。

步骤

1

为嵌入创建文件。

  1. 创建一个名为 query-embeddings.js 的文件。

    touch query-embeddings.js

  2. 将以下嵌入复制并粘贴到该文件中。

    QUERY_EMBEDDING = 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  3. 保存并关闭文件。

2

使用 mongosh 连接到集群。

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。

3

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
4

加载要在查询中使用的嵌入。

将 query-embeddings.js文件加载到 mongosh 中。

load('<path-to-query-embeddings.js-file>');
5

对 embedded_movies 集合运行以下 Atlas Search 查询。

1vectorWeight = 1;  
2vectorCutoff = 0.7; 
3numResults = 20;
4numCandidates = numResults * 10;  
5
6// Execute the vector search  
7vectorResults = db.embedded_movies.aggregate([  
8    {  
9        $vectorSearch: {  
10            index: "hybrid-vector-search", 
11            path: "plot_embedding",
12            queryVector: QUERY_EMBEDDING,   
13            numCandidates: numCandidates,   
14            limit: numResults  
15        }  
16    },  
17    {  
18        $project: {  
19            _id: 1,  
20            title: 1,  
21            plot: 1,
22            vectorScore: { $meta: "vectorSearchScore" }  
23        }  
24    },  
25    {  
26        $match: {  
27            vectorScore: { $gte: vectorCutoff }  
28        }  
29    }  
30]).toArray();  
31
32// Create a map for fast lookup  
33vectorScoresMap = {};  
34vectorResults.forEach(result => {  
35    vectorScoresMap[result._id] = result.vectorScore * vectorWeight;  
36});  
37
38// Execute the text search with boosting based just on ID  
39textResults = db.embedded_movies.aggregate([  
40    {  
41        $search: {  
42            index: "hybrid-full-text-search",  
43            compound: {  
44                should: [  
45                    {  
46                        text: {  
47                            query: "star wars",  
48                            path: "plot"  
49                        }  
50                    },  
51                    ...Object.keys(vectorScoresMap).map(id => ({  
52                        equals: {  
53                            path: "_id",  
54                            value: id,  
55                            score: { boost: { value: vectorScoresMap[id] } }  
56                        }  
57                    }))  
58                ]  
59            }  
60        }  
61    },  
62    {
63        $limit: 20
64    },
65    {  
66        $project: {  
67            _id: 1,  
68            title: 1,  
69            plot: 1,
70            score: { $meta: "searchScore" }  
71        }  
72    }  
73]).toArray();  
[
  {
    _id: ObjectId('573a139df29313caabcfa90b'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    score: 4.401333808898926
  },
  {
    _id: ObjectId('573a1397f29313caabce6f53'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    score: 4.401333808898926
  },
  {
    _id: ObjectId('573a1398f29313caabce9851'),
    plot: 'Johnathan Cabot is a champion gymnast. In the tiny, yet savage, country of Parmistan, there is a perfect spot for a "star wars" site. For the US to get this site, they must compete in the ...',
    title: 'Gymkata',
    score: 4.284864902496338
  },
  {
    _id: ObjectId('573a1394f29313caabcdf65b'),
    plot: 'A fantastic tale of war, love, family and ambition set in the midst of the Japanese Civil Wars of the sixteenth century.',
    title: 'Ugetsu',
    score: 2.858368396759033
  },
  {
    _id: ObjectId('573a13a4f29313caabd1137f'),
    plot: "A producer's film is endangered when his star walks off, so he decides to digitally create an actress to substitute for the star, becoming an overnight sensation that everyone thinks is a real person.",
    title: 'S1m0ne',
    score: 2.842216730117798
  },
  {
    _id: ObjectId('573a139af29313caabcefe79'),
    plot: 'An all star baseball player becomes the unhealthy focus of a down on his luck salesman.',
    title: 'The Fan',
    score: 2.6598150730133057
  },
  {
    _id: ObjectId('573a13a3f29313caabd0c992'),
    plot: 'The vampire Lestat becomes a rock star whose music wakes up the queen of all vampires.',
    title: 'Queen of the Damned',
    score: 2.6598150730133057
  },
  {
    _id: ObjectId('573a13a8f29313caabd1da86'),
    plot: 'During the Napoleonic Wars, a brash British captain pushes his ship and crew to their limits in pursuit of a formidable French war vessel around South America.',
    title: 'Master and Commander: The Far Side of the World',
    score: 2.643709897994995
  },
  {
    _id: ObjectId('573a139af29313caabcf0e7d'),
    plot: 'A puppeteer discovers a portal that leads literally into the head of the movie star, John Malkovich.',
    title: 'Being John Malkovich',
    score: 2.612804889678955
  },
  {
    _id: ObjectId('573a13b6f29313caabd4614f'),
    plot: 'A former pop star who now writes commercial jingles for a living experiences a mid-life crisis.',
    title: 'The Good Night',
    score: 2.612804889678955
  },
  {
    _id: ObjectId('573a13cbf29313caabd7fbc9'),
    plot: 'Bertrand Tavernier is in top form with this gripping, superbly mounted drama set against the savage Catholic/Protestant wars that ripped France apart in the 16th century. Based on a novella...',
    title: 'The Princess of Montpensier',
    score: 2.4938809871673584
  },
  {
    _id: ObjectId('573a139bf29313caabcf2d8e'),
    plot: 'Updated version of the classic Rodgers and Hammerstein musical of the classic fairy-tale, with an all-star, multi-racial cast.',
    title: 'Cinderella',
    score: 2.44028377532959
  },
  {
    _id: ObjectId('573a1399f29313caabcec853'),
    plot: 'An action movie star researching a role is allowed to tag along with a hardboiled New York policeman, who finds him superficial and irritating.',
    title: 'The Hard Way',
    score: 2.325138807296753
  },
  {
    _id: ObjectId('573a13b4f29313caabd3f550'),
    plot: 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.',
    title: 'Divergence',
    score: 2.325138807296753
  },
  {
    _id: ObjectId('573a13aff29313caabd2fe20'),
    plot: 'An intimate story set during the 1860s in which a young Irish woman Sarah and her family find themselves on both sides of the turbulent wars between British and Maori during the British colonization of New Zealand.',
    title: 'River Queen',
    score: 2.298485040664673
  },
  {
    _id: ObjectId('573a13aef29313caabd2df95'),
    plot: 'Story of two sisters that grew up in a small Serbian village in the beginning of the 1930s. The village is torn up by wars and years long blood oath. There are no men left in the village. ...',
    title: 'Tears for Sale',
    score: 2.268857479095459
  },
  {
    _id: ObjectId('573a13b8f29313caabd4d8e1'),
    plot: "Thomas Michael, Paolo Mancini, Chris Klein and Joe Mantegna star in director Matthiew Klinck's holiday-themed comedy concerning two downsized Easter bunnies whose friendship starts to ...",
    title: 'Hank and Mike',
    score: 2.254227876663208
  },
  {
    _id: ObjectId('573a139af29313caabcf124d'),
    plot: 'As the Clone Wars near an end, the Sith Lord Darth Sidious steps out of the shadows, at which time Anakin succumbs to his emotions, becoming Darth Vader and putting his relationships with Obi-Wan and Padme at risk.',
    title: 'Star Wars: Episode III - Revenge of the Sith',
    score: 2.2399840354919434
  },
  {
    _id: ObjectId('573a13c1f29313caabd633ac'),
    plot: "Black Dynamite is the greatest African-American action star of the 1970s. When his only brother is killed by The Man it's up to him to find justice.",
    title: 'Black Dynamite',
    score: 2.1875147819519043
  },
  {
    _id: ObjectId('573a13dbf29313caabdaf598'),
    plot: "Just as Clint Eastwood's star-making spaghetti Western A Fistful of Dollars was inspired by Akira Kurosawa's Yojimbo, Japanese-Korean filmmaker Sang-il Lee (Villain) has decided to ...",
    title: 'Unforgiven',
    score: 2.1875147819519043
  }
]

关于查询

示例查询会根据$search 结果分数提升$vectorSearch 结果，以返回最相关的文档。对于语义提升，请执行以下步骤：

  1. 运行 $vectorSearch 查询。

    使用 over 请求值指定要考虑的候选数量超过指定限制。通过over请求，您可以完成以下操作：

    • 在初始搜索中考虑大量且广泛的文档。

    • 完善后续阶段的评分计算。

    该查询使用以下计算来确定向量搜索期间要考虑的候选数量：

    numCandidates = limit * over request

    结果根据最低分数阈值 (vectorCutoff) 进行筛选。

  2. 缩放向量搜索分数以调整向量搜索结果对文档评分和排名的影响。

    使用 vectorWeight 参数设立权重，并将向量搜索分数乘以 vectorWeight。这控制了向量搜索结果的重要性，并确保只考虑语义上最相关的文档。

  3. 运行 $search 查询并提高与向量搜索结果匹配的文档的分数。

倒数排名融合

本部分演示如何使用 $vectorSearch 和 $search 管道阶段查询 sample_mflix.embedded_movies 集合中 plot_embedding 和 title 字段中的 star wars 数据，并结合两个阶段中每个文档的分数，对结果中的文档进行重新排序。这可确保同时出现在两个搜索结果中的文件都出现在组合结果的顶部。

步骤

1

设置要在查询中使用的嵌入。

  1. 创建一个名为 query-embeddings.js 的文件。

  2. 将以下嵌入复制并粘贴到文件中。

  3. 保存并关闭文件。

2

使用 mongosh 连接到集群。

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。

3

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
4

加载要在查询中使用的嵌入。

运行以下命令以加载嵌入：

load('<path-to-query-embeddings.js');

要验证嵌入是否已成功加载，运行以下命令：

STAR_WARS_EMBEDDING.length
1536
5

针对 集合运行以下Atlas Search和Atlas Vector Search查询。embedded_movies

1var vector_weight = 0.1;
2var full_text_weight = 0.9;
3db.embedded_movies.aggregate([
4  {
5    "$vectorSearch": {
6      "index": "hybrid-vector-search",
7      "path": "plot_embedding",
8      "queryVector": 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67        }, {
68          "$addFields": {
69            "fts_score": {
70              "$multiply": [
71                full_text_weight, {
72                  "$divide": [
73                    1.0, {
74                      "$add": ["$rank", 60]
75                    }
76                  ]
77                }
78              ]
79            }
80          } 
81        },
82        {
83          "$project": {
84            "fts_score": 1,
85            "_id": "$docs._id",
86            "title": "$docs.title"
87          }
88        }
89      ]
90    }
91  },
92  {
93    "$group": {
94      "_id": "$_id",
95      "title": {"$first": "$title"},
96      "vs_score": {"$max": "$vs_score"},
97      "fts_score": {"$max": "$fts_score"}
98    }
99  },
100  {
101    "$project": {
102      "_id": 1,
103      "title": 1,
104      "vs_score": {"$ifNull": ["$vs_score", 0]},
105      "fts_score": {"$ifNull": ["$fts_score", 0]}
106    }
107  },
108  {
109    "$project": {
110      "score": {"$add": ["$fts_score", "$vs_score"]},
111      "_id": 1,
112      "title": 1,
113      "vs_score": 1,
114      "fts_score": 1
115    }
116  },
117  {"$sort": {"score": -1}},
118  {"$limit": 10}
119])
{
  _id: new ObjectId('573a13c0f29313caabd62f62'),
  title: 'Star Wars: The Clone Wars',
  vs_score: 0.0015625,
  fts_score: 0.015,
  score: 0.0165625
}
{
  _id: new ObjectId('573a1397f29313caabce68f6'),
  title: 'Star Wars: Episode IV - A New Hope',
  vs_score: 0.0016666666666666668,
  fts_score: 0.014754098360655738,
  score: 0.016420765027322405
}
{
  _id: new ObjectId('573a139af29313caabcf0f5f'),
  title: 'Star Wars: Episode I - The Phantom Menace',
  vs_score: 0.0016393442622950822,
  fts_score: 0.014516129032258065,
  score: 0.016155473294553146
}
{
  _id: new ObjectId('573a1397f29313caabce77d9'),
  title: 'Star Wars: Episode V - The Empire Strikes Back',
  vs_score: 0.0016129032258064516,
  fts_score: 0.014285714285714285,
  score: 0.015898617511520736
}
{
  _id: new ObjectId('573a1397f29313caabce8cdb'),
  title: 'Star Wars: Episode VI - Return of the Jedi',
  vs_score: 0.0015873015873015873,
  fts_score: 0.0140625,
  score: 0.015649801587301587
}
{
  _id: new ObjectId('573a139af29313caabcf124d'),
  title: 'Star Wars: Episode III - Revenge of the Sith',
  vs_score: 0.0013698630136986301,
  fts_score: 0.013846153846153847,
  score: 0.015216016859852476
}
{
  _id: new ObjectId('573a139af29313caabcf1258'),
  title: 'Star Wars: Episode II - Attack of the Clones',
  vs_score: 0.0014925373134328358,
  fts_score: 0.013636363636363637,
  score: 0.015128900949796473
}
{
  _id: new ObjectId('573a1397f29313caabce6f53'),
  title: 'Message from Space',
  vs_score: 0.0015384615384615387,
  fts_score: 0,
  score: 0.0015384615384615387
}
{
  _id: new ObjectId('573a139df29313caabcfa90b'),
  title: 'Message from Space',
  vs_score: 0.0015151515151515154,
  fts_score: 0,
  score: 0.0015151515151515154
}
{
  _id: new ObjectId('573a13d9f29313caabda92ff'),
  title: 'Guardians of the Galaxy',
  vs_score: 0.0014705882352941176,
  fts_score: 0,
  score: 0.0014705882352941176
}

如果您通过将第 117 行 score 的值替换为 1 来按升序对结果进行排序，Atlas Vector Search 将返回以下结果：

{
  _id: new ObjectId('573a13b0f29313caabd34a3e'),
  title: 'Cowboys & Aliens',
  vs_score: 0.0012658227848101266,
  fts_score: 0,
  score: 0.0012658227848101266
}
{
  _id: new ObjectId('573a13a3f29313caabd0ec59'),
  title: 'Planet of the Apes',
  vs_score: 0.001282051282051282,
  fts_score: 0,
  score: 0.001282051282051282
}
{
  _id: new ObjectId('573a139bf29313caabcf3d4b'),
  title: 'Planet of the Apes',
  vs_score: 0.001298701298701299,
  fts_score: 0,
  score: 0.001298701298701299
}
{
  _id: new ObjectId('573a1397f29313caabce7509'),
  title: 'Starcrash',
  vs_score: 0.0013157894736842105,
  fts_score: 0,
  score: 0.0013157894736842105
}
{
  _id: new ObjectId('573a13b0f29313caabd33d15'),
  title: 'Zathura: A Space Adventure',
  vs_score: 0.0013333333333333335,
  fts_score: 0,
  score: 0.0013333333333333335
}
{
  _id: new ObjectId('573a1398f29313caabce8d67'),
  title: 'Space Raiders',
  vs_score: 0.0013513513513513514,
  fts_score: 0,
  score: 0.0013513513513513514
}
{
  _id: new ObjectId('573a1398f29313caabce90bd'),
  title: 'The Ewok Adventure',
  vs_score: 0.001388888888888889,
  fts_score: 0,
  score: 0.001388888888888889
}
{
  _id: new ObjectId('573a1398f29313caabce9091'),
  title: 'Dune',
  vs_score: 0.0014084507042253522,
  fts_score: 0,
  score: 0.0014084507042253522
}
{
  _id: new ObjectId('573a13d4f29313caabd9887f'),
  title: 'Abiogenesis',
  vs_score: 0.0014285714285714286,
  fts_score: 0,
  score: 0.0014285714285714286
}
{
  _id: new ObjectId('573a13d5f29313caabd9c312'),
  title: 'Guardians of the Galaxy',
  vs_score: 0.0014492753623188406,
  fts_score: 0,
  score: 0.0014492753623188406
}

关于查询

示例查询从语义搜索和全文搜索中检索排序后的搜索结果，并根据结果数组中的位置为结果中的文档分配倒数排名分数。倒数排名分数的计算公式如下：

1.0/{document position in the results + 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。

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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