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Atlas Vector Search

运行向量搜索查询

在此页面上

  • 定义
  • ANN搜索
  • 新奥搜索
  • 语法
  • 字段
  • 行为
  • Atlas Vector Search 索引
  • Atlas Vector Search 分数
  • Atlas Vector Search 预筛选
  • 限制
  • 支持的客户端
  • 跨分段执行并行查询
  • 举例
  • ANN示例
  • ENN示例

Atlas Vector Search 查询采用聚合管道阶段的形式。 对于$vectorSearch查询， Atlas Vector Search返回语义Atlas Search的结果。

注意

Atlas Vector Search 支持在运行 MongoDB v 6的集群上进行ANN搜索。 0 。 11 ，v 7 。 0 。 2或更高版本， ENN在运行 MongoDB v 6的集群上搜索。 0 。 16 ，v 7 。 0 。 10 ，v 7 。 3 。 2或更高版本。

定义

$vectorSearch阶段对指定字段中的向量执行ANN或ENN搜索。

ANN 搜索

对于 ANN 搜索，Atlas Vector Search 会根据向量嵌入在多维空间中的接近度以及它考虑的邻域数量，在数据中查找与查询中的向量嵌入最接近的向量嵌入。它使用 Hierarchical Navigable Small Worlds 算法，找到与查询中的向量嵌入最相似的向量嵌入，而无需扫描每个向量。因此， ANN 搜索非常适合查询大型数据集，而无需进行大量筛选。

新奥 搜索

对于ENN搜索，Atlas Vector Search 通过计算所有嵌入之间的距离来详尽搜索所有索引的向量嵌入，并为查询中的向量嵌入找到精确的最近邻。此操作属于计算密集型，可能会对查询延迟产生负面影响。因此，我们建议对以下使用案例进行新奥搜索：

  • 您希望使用 ENN 查询的理想精确结果来确定 ANN 查询的查全率和准确性。

  • 您希望查询少于10000个文档，而不必调整要考虑的最近邻的数量。

  • 您希望在查询中包含选择性预筛选，以针对符合给定预筛选条件的数据少于5 % 的集合。

语法

您要搜索的字段必须在 vectorSearch 索引类型内作为 Atlas Vector Search 向量 类型进行索引。

$vectorSearch

$vectorSearch 管道采用以下原型形式：

{
  "$vectorSearch": {
    "exact": true | false,
    "filter": {<filter-specification>},
    "index": "<index-name>",
    "limit": <number-of-results>,
    "numCandidates": <number-of-candidates>,
    "path": "<field-to-search>",
    "queryVector": [<array-of-numbers>]
  }
}

字段

$vectorSearch 阶段采用包含以下字段的文档：

字段
类型
必要性
说明
exact
布尔
Optional

如果省略numCandidates ，则这是必需的。

指定是运行ENN还是ANN搜索的标志。值可以是以下之一：

  • false - 运行ANN搜索

  • true - 运行新奥搜索

如果省略，则默认值为 false。

运行 MongoDB 6版本的 Atlas 集群支持ENN搜索。 0 。 16 , 7 。 0 。 10 、 7 。 32或更高版本。

filter
文档
Optional
任何将索引字段与布尔值、日期、objectId、数字（非小数）或字符串进行比较以用作预筛选器的MQL匹配表达式。要了解 Atlas Vector Search 在筛选器中支持哪些查询和聚合管道运算符，请参阅Atlas Vector Search 预筛选。
index
字符串
必需

要使用的 Atlas Vector Search 索引的名称。

如果您写错索引名称，或者指定的索引在集群上尚不存在，则 Atlas Vector Search 不会返回结果。

limit
数字
必需
要在结果中返回的文档数量（仅限int类型）。如果指定numCandidates ，则此值不能超过numCandidates的值。
numCandidates
数字
Optional

如果exact为false或被省略，则此字段为必填项。

搜索期间要使用的最近邻数量。值必须小于或等于 (<=) 10000。指定的数字不能小于要返回的文档数量 (limit)。

我们建议您指定一个大于要返回的文档数 ( limit ) 的数字以提高准确性，尽管这可能会影响延迟。例如，对于仅限制一个文档的情况，我们建议将最近邻的比率设置为 10:20。这种过度请求模式是在ANN搜索中平衡延迟和召回的推荐方法，建议根据特定数据集进行调整。

path
字符串
必需
要搜索的索引 vectorEmbedding 类型字段。要了解更多信息，请参阅路径构造。
queryVector
数字数组
必需

表示查询向量的 BSON double 类型的数字数组。数组大小必须与字段的索引定义中指定的向量 dimensions 的数量相匹配。

注意

您必须使用用于嵌入数据的同一模型来嵌入查询。

行为

$vectorSearch 必须是其所在的任何管道中的第一阶段。

Atlas Vector Search 索引

您必须使用$vectorSearch vectorSearch 类型索引定义中的 阶段对要搜索的字段进行索引。您可以在 Atlas Vector Search vectorSearch 类型索引定义中对以下类型的字段进行索引：

  • 包含向量嵌入作为向量类型的字段。

  • 包含布尔值、日期、objectId、数字和字符串值作为筛选器类型的字段，用于对预筛选数据启用向量搜索。

要了解有关这些 Atlas Vector Search 字段类型的更多信息，请参阅如何为向量搜索的字段进行索引。

Atlas Vector Search 分数

Atlas Vector Search 为其返回的每个文档分配一个分数，该分数在 0 到 1 之间的固定范围内。对于 cosine 和 dotProduct 的相似度，Atlas Vector Search 使用以下算法对分数进行归一化：

score = (1 + cosine/dot_product(v1,v2)) / 2

分配给返回文档的分数是文档元数据的一部分。要包括每个返回文档的分数以及结果集，则在聚合管道中使用 $project 阶段。

要检索 Atlas Vector Search 查询结果的得分，请使用vectorSearchScore 作为 $meta 表达式中的值。也就是说，在 $vectorSearch 阶段之后的 $project 阶段，score 字段使用 $meta 表达式。该表达式需要 vectorSearchScore 值来返回向量搜索的文档分数。

例子

1db.<collection>.aggregate([
2  {
3    "$vectorSearch": {
4      <query-syntax>
5    }
6  },
7  {
8    "$project": {
9      "<field-to-include>": 1,
10      "<field-to-exclude>": 0,
11      "score": { "$meta": "vectorSearchScore" }
12    }
13  }
14])

注意

对数据进行预过滤不会影响 Atlas Vector Search 使用 $vectorSearchScore 进行 $vectorSearch 查询时返回的分数。

Atlas Vector Search 预筛选

$vectorSearchfilter选项仅匹配 BSON 布尔值、日期、objectId、字符串和数值。您 必须 在 vectorSearch 类型索引定义中对要筛选数据的字段作为 筛选器 类型进行索引。筛选数据有助于缩小语义搜索的范围，并确保不会考虑所有向量进行比较。

Atlas Vector Search$vectorSearchfilter 支持以下 MQL 匹配表达式的 选项，但存在某些 限制：

$vectorSearch filter 选项仅支持以下聚合管道操作符：

注意

$vectorSearch filter选项不支持其他查询和聚合管道操作符。

限制

$vectorSearch 仅适用于运行以下 MongoDB 版本的 Atlas 集群：

  • V 6 。 0 。 11

  • v7.0.2 及更高版本（包括 RC）。

$vectorSearchfilterobjectId 类型字段上的 选项不支持以下 MQL 匹配表达式：

$vectorSearch 不能用于视图定义和后续管道阶段：

您可以将 $vectorSearch 的结果传递到该阶段。

支持的客户端

您可以使用$vectorSearch AtlasData Explorer 、 、mongosh 和以下驱动程序运行 查询：

您还可以将 Atlas Vector Search 与通过 Atlas CLI 创建的本地 Atlas 部署结合使用。要了解详情，请参阅创建本地 Atlas 部署。

跨分段执行并行查询

我们建议使用专用搜索节点来隔离向量搜索查询处理。您可能会发现专用搜索节点上的查询性能有所提高。请注意，高 CPU 系统可能会提供更多的性能改进。当 Atlas Vector Search 在搜索节点上运行时，Atlas Vector Search 会跨数据段并行执行查询。

在很多情况下，查询处理的并行化可以缩短响应时间，例如对大型数据集的查询。在 Atlas Vector Search 查询处理期间使用查询内并行会占用更多资源，但有助于改善每个单独查询的延迟问题。

注意

Atlas Vector Search 不保证每个查询都会同时运行。例如，当排队的并发查询过多时，Atlas Vector Search 可能会回退到单线程执行。

举例

以下查询使用$vectorSearch阶段搜索示例sample_mflix.embedded_movies集合。查询搜索plot_embedding字段，其中包含使用 OpenAI 的text-embedding-ada-002嵌入模型创建的嵌入。如果已将样本集合添加到 Atlas 集群并为该集合创建了样本索引，则可以对该集合运行以下查询。

注意

将示例代码中的queryVector粘贴到终端可能需要一些时间，具体取决于您的机器。

➤ 使用选择语言下拉菜单设置此页面中示例的语言。

ANN 示例

以下查询使用$vectorSearch阶段，使用向量嵌入搜索plot_embedding字段，查找字符串时间旅行。它最多考虑150个最近邻，并在结果中返回10文档。该查询还指定了一个$project阶段来执行以下操作：

  • 在结果中排除 _id 字段并仅包含 plot 和 title 字段。

  • 添加名为score的字段，显示结果中每个文档的向量搜索分数。

1db.embedded_movies.aggregate([
2  {
3    "$vectorSearch": {
4      "index": "vector_index",
5      "path": "plot_embedding",
6      "queryVector": 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2  {
3    plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
4    title: 'Thrill Seekers',
5    score: 0.7892671227455139
6  },
7  {
8    plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.',
9    title: 'About Time',
10    score: 0.7843604683876038
11  },
12  {
13    plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.',
14    title: 'The Time Machine',
15    score: 0.7801066637039185
16  },
17  {
18    plot: "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
19    title: 'Crusade in Jeans',
20    score: 0.7789170742034912
21  },
22  {
23    plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.',
24    title: 'Timecop',
25    score: 0.7771612405776978
26  },
27  {
28    plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...',
29    title: 'A.P.E.X.',
30    score: 0.7730885744094849
31  },
32  {
33    plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
34    title: 'Men in Black 3',
35    score: 0.7712380886077881
36  },
37  {
38    plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.',
39    title: 'Tomorrowland',
40    score: 0.7669923901557922
41  },
42  {
43    plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
44    title: 'Love Story 2050',
45    score: 0.7649372816085815
46  },
47  {
48    plot: 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...',
49    title: 'The Portal',
50    score: 0.7640786170959473
51  }
52]

以下查询筛选在一月 01, 1955 和 01, 1975 之间上映的电影的文档，然后再对样本向量数据执行语义Atlas Search 。 它使用$and操作符对指定日期执行逻辑AND操作。 然后，它使用向量嵌入string在筛选后的文档中的 plot_embedding 字段以查找 150 个最近邻，并在结果中返回 10 个文档。 该查询还指定了一个$project阶段来执行以下操作：

  • 结果中排除_id字段，仅包含plot 、 title和year字段。

  • 添加名为 score 的字段，显示结果中文档的Atlas Vector Search分数。

1db.embedded_movies.aggregate([
2  {
3    "$vectorSearch": {
4      "index": "vector-search-tutorial",
5      "path": "plot_embedding",
6      "filter": {
7        "$and": [
8          {
9            "year": {  "$gt": 1955  }
10          }, 
11          {
12            "year": {  "$lt": 1975 }
13          }
14        ]
15      },
16      "queryVector": 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2  {
3    plot: 'In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...',
4    title: 'Peter Pan',
5    year: 1960,
6    score: 0.748110830783844
7  },
8  {
9    plot: 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.',
10    title: 'Chitty Chitty Bang Bang',
11    year: 1968,
12    score: 0.7442465424537659
13  },
14  {
15    plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.',
16    title: 'That Man from Rio',
17    year: 1964,
18    score: 0.7416020035743713
19  },
20  {
21    plot: 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.',
22    title: 'The Little Prince',
23    year: 1974,
24    score: 0.7378944158554077
25  },
26  {
27    plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.',
28    title: 'The Red Balloon',
29    year: 1956,
30    score: 0.7342712879180908
31  },
32  {
33    plot: 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.',
34    title: 'Willy Wonka & the Chocolate Factory',
35    year: 1971,
36    score: 0.7342107892036438
37  },
38  {
39    plot: 'An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.',
40    title: 'Bedknobs and Broomsticks',
41    year: 1971,
42    score: 0.7339356541633606
43  },
44  {
45    plot: "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.",
46    title: 'Pastoral Hide and Seek',
47    year: 1974,
48    score: 0.733299970626831
49  },
50  {
51    plot: 'A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.',
52    title: 'The Three Musketeers',
53    year: 1973,
54    score: 0.7331198453903198
55  },
56  {
57    plot: 'A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.',
58    title: 'Frosty',
59    year: 1964,
60    score: 0.7318308353424072
61  }
62]

Atlas Vector Search 根据介于1955和1975之间的year字段值过滤文档。它返回总结了1955和1975之间上映的电影情节中的儿童冒险经历的文档。

提示

另请参阅：其他筛选器示例

如何对 Atlas 集群中的数据执行语义搜索教程演示了针对sample_mflix.embedded_movies集合中的嵌入式数据的语义搜索查询中的其他预筛选器。

以下查询使用$vectorSearch阶段，使用向量嵌入搜索plot_embedding字段，查找字符串时间旅行。它最多考虑150个最近邻，并在结果中返回10文档。该查询还指定了一个$project阶段来执行以下操作：

  • 在结果中排除 _id 字段并仅包含 plot 和 title 字段。

  • 添加名为score的字段，显示结果中每个文档的向量搜索分数。

1using MongoDB.Bson;
2using MongoDB.Bson.Serialization.Attributes;
3using MongoDB.Bson.Serialization.Conventions;
4using MongoDB.Driver;
5using MongoDB.Driver.Search;
6
7public class vectorSearchBasicQuery 
8{
9  // define connection to your Atlas cluster
10  private const string MongoConnectionString = "<connection-string>";
11
12  public static void Main(string[] args){
13    var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
14    ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
15
16    // connect to your Atlas cluster
17    var mongoClient = new MongoClient(MongoConnectionString);
18
19    // define namespace
20    var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
21    var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
22
23    // define vector embeddings to search
24    var vector = new[] 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25    var options = new VectorSearchOptions<EmbeddedMovie>() {
26        IndexName = "vector_index",
27        NumberOfCandidates = 150
28    };
29
30    // run query
31    var results = moviesCollection.Aggregate()
32                .VectorSearch(m => m.Embedding, vector, 10, options)
33                .Project(Builders<EmbeddedMovie>.Projection
34                  .Include(m => m.Title)
35                  .Include(m => m.Plot)
36                  .MetaVectorSearchScore(m => m.Score))
37                .ToList();
38
39    // print results
40    foreach (var movie in results)
41      {
42        Console.WriteLine(movie.ToJson());
43      }
44  }
45}
46
47[BsonIgnoreExtraElements]
48public class EmbeddedMovie
49{
50    [BsonIgnoreIfDefault]
51    public string Title { get; set; }
52    public string Plot { get; set; }
53    [BsonElement("plot_embedding")]
54    public double[] Embedding { get; set; }
55    public double Score { get; set; }
56}
1{ "_id" : ObjectId("573a13a0f29313caabd04a4f"), "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : 0.78926712274551392 }
2{ "_id" : ObjectId("573a13d8f29313caabda6557"), "plot" : "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title" : "About Time", "score" : 0.78436046838760376 }
3{ "_id" : ObjectId("573a13a5f29313caabd13b4b"), "plot" : "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title" : "The Time Machine", "score" : 0.78010666370391846 }
4{ "_id" : ObjectId("573a13aef29313caabd2e2d7"), "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : 0.77891707420349121 }
5{ "_id" : ObjectId("573a1399f29313caabceec0e"), "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : 0.77716124057769775 }
6{ "_id" : ObjectId("573a1399f29313caabcee36f"), "plot" : "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title" : "A.P.E.X.", "score" : 0.77308857440948486 }
7{ "_id" : ObjectId("573a13c6f29313caabd715d3"), "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : 0.77123808860778809 }
8{ "_id" : ObjectId("573a13d4f29313caabd98c13"), "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title" : "Tomorrowland", "score" : 0.76699239015579224 }
9{ "_id" : ObjectId("573a13b6f29313caabd477fa"), "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : 0.76493728160858154 }
10{ "_id" : ObjectId("573a13e5f29313caabdc40c9"), "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal", "score" : 0.76407861709594727 }

以下查询筛选在一月 01, 1955 和 01, 1975 之间上映的电影的文档，然后再对样本向量数据执行语义Atlas Search 。 它使用$and操作符对指定日期执行逻辑AND操作。 然后，它使用向量嵌入string在筛选后的文档中的 plot_embedding 字段以查找 150 个最近邻，并在结果中返回 10 个文档。 该查询还指定了一个$project阶段来执行以下操作：

  • 结果中排除_id字段，仅包含plot 、 title和year字段。

  • 添加名为 score 的字段，显示结果中文档的Atlas Vector Search分数。

1using System.Reflection.Emit;
2using MongoDB.Bson;
3using MongoDB.Bson.Serialization.Attributes;
4using MongoDB.Bson.Serialization.Conventions;
5using MongoDB.Driver;
6using MongoDB.Driver.Search;
7
8public class vectorSearchFilterQuery 
9{
10  // define connection to your Atlas cluster
11  private const string MongoConnectionString = "<connection-string>";
12
13  public static void Main(string[] args){
14    var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
15    ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
16
17    // connect to your Atlas cluster
18    var mongoClient = new MongoClient(MongoConnectionString);
19
20    // define namespace
21    var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
22    var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
23
24    // define vector embeddings to search
25    var vector = new[] 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26    // define filter
27    var yearGtFilter = Builders<EmbeddedMovie>.Filter.Gt("year", 1955);
28    var yearLtFilter = Builders<EmbeddedMovie>.Filter.Lt("year", 1975);
29    // define options 
30    var options = new VectorSearchOptions<EmbeddedMovie>() {
31        Filter = Builders<EmbeddedMovie>.Filter.And(yearGtFilter, yearLtFilter),
32        IndexName = "vector_index",
33        NumberOfCandidates = 150
34    };
35
36    // run query
37    var results = moviesCollection.Aggregate()
38                .VectorSearch(m => m.Embedding, vector, 10, options)
39                .Project(Builders<EmbeddedMovie>.Projection
40                  .Include(m => m.Title)
41                  .Include(movie => movie.Plot)
42                  .Include(movie => movie.Year)
43                  .MetaVectorSearchScore(m => m.Score))
44                .ToList();
45
46    // print results
47    foreach (var movie in results)
48      {
49        Console.WriteLine(movie.ToJson());
50      }
51  }
52}
53
54[BsonIgnoreExtraElements]
55public class EmbeddedMovie
56{
57    [BsonIgnoreIfDefault]
58    public string Title { get; set; }
59    public string Plot { get; set; }
60    public int Year { get; set; }
61    [BsonElement("plot_embedding")]
62    public double[] Embedding { get; set; }
63    public double Score { get; set; }
64}
1{ "_id" : ObjectId("573a1395f29313caabce143b"), "plot" : "In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...", "title" : "Peter Pan", "year" : 1960, "score" : 0.74811083078384399 }
2{ "_id" : ObjectId("573a1396f29313caabce34d3"), "plot" : "A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.", "title" : "Chitty Chitty Bang Bang", "year" : 1968, "score" : 0.74424654245376587 }
3{ "_id" : ObjectId("573a1395f29313caabce23cb"), "plot" : "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "title" : "That Man from Rio", "year" : 1964, "score" : 0.74160200357437134 }
4{ "_id" : ObjectId("573a1396f29313caabce5648"), "plot" : "A pilot, stranded in the desert, meets a little boy who is a prince on a planet.", "title" : "The Little Prince", "year" : 1974, "score" : 0.73789441585540771 }
5{ "_id" : ObjectId("573a1394f29313caabce000d"), "plot" : "A red balloon with a life of its own follows a little boy around the streets of Paris.", "title" : "The Red Balloon", "year" : 1956, "score" : 0.73427128791809082 }
6{ "_id" : ObjectId("573a1396f29313caabce482f"), "plot" : "A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.", "title" : "Willy Wonka & the Chocolate Factory", "year" : 1971, "score" : 0.7342107892036438 }
7{ "_id" : ObjectId("573a1396f29313caabce43e1"), "plot" : "An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.", "title" : "Bedknobs and Broomsticks", "year" : 1971, "score" : 0.7339356541633606 }
8{ "_id" : ObjectId("573a1396f29313caabce54e6"), "plot" : "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", "title" : "Pastoral Hide and Seek", "year" : 1974, "score" : 0.73329997062683105 }
9{ "_id" : ObjectId("573a1396f29313caabce582d"), "plot" : "A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.", "title" : "The Three Musketeers", "year" : 1973, "score" : 0.73311984539031982 }
10{ "_id" : ObjectId("573a1395f29313caabce243e"), "plot" : "A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.", "title" : "Frosty", "year" : 1964, "score" : 0.73183083534240723 }

Atlas Vector Search 根据介于1955和1975之间的year字段值过滤文档。它返回总结了1955和1975之间上映的电影情节中的儿童冒险经历的文档。

提示

另请参阅：其他筛选器示例

如何对 Atlas 集群中的数据执行语义搜索教程演示了针对sample_mflix.embedded_movies集合中的嵌入式数据的语义搜索查询中的其他预筛选器。

以下查询使用$vectorSearch阶段，使用向量嵌入搜索plot_embedding字段，查找字符串时间旅行。它最多考虑150个最近邻，并在结果中返回10文档。该查询还指定了一个$project阶段来执行以下操作：

  • 在结果中排除 _id 字段并仅包含 plot 和 title 字段。

  • 添加名为score的字段，显示结果中每个文档的向量搜索分数。

1import static com.mongodb.client.model.Aggregates.*;
2import static com.mongodb.client.model.Projections.*;
3import static com.mongodb.client.model.search.SearchPath.fieldPath;
4import static java.util.Arrays.asList;
5import com.mongodb.client.MongoClient;
6import com.mongodb.client.MongoClients;
7import com.mongodb.client.MongoCollection;
8import com.mongodb.client.MongoDatabase;
9import java.util.List;
10import com.mongodb.client.model.search.FieldSearchPath;
11import org.bson.Document;
12import org.bson.conversions.Bson;
13
14public class BasicQuery {
15  public static void main( String[] args ) {
16    // specify connection
17    String uri = "<connection-string>";
18
19    // establish connection and set namespace
20    try (MongoClient mongoClient = MongoClients.create(uri)) {
21      MongoDatabase database = mongoClient.getDatabase("sample_mflix");
22      MongoCollection<Document> collection = database.getCollection("embedded_movies");
23      
24  	  // define $vectorSearch query options 
25	    List<Double> queryVector = (asList(-0.0016261312d, -0.028070757d, -0.011342932d, -0.012775794d, -0.0027440966d, 0.008683807d, -0.02575152d, -0.02020668d, -0.010283281d, -0.0041719596d, 0.021392956d, 0.028657231d, -0.006634482d, 0.007490867d, 0.018593878d, 0.0038187427d, 0.029590257d, -0.01451522d, 0.016061379d, 0.00008528442d, -0.008943722d, 0.01627464d, 0.024311995d, -0.025911469d, 0.00022596726d, -0.008863748d, 0.008823762d, -0.034921836d, 0.007910728d, -0.01515501d, 0.035801545d, -0.0035688248d, -0.020299982d, -0.03145631d, -0.032256044d, -0.028763862d, -0.0071576433d, -0.012769129d, 0.012322609d, -0.006621153d, 0.010583182d, 0.024085402d, -0.001623632d, 0.007864078d, -0.021406285d, 0.002554159d, 0.012229307d, -0.011762793d, 0.0051682983d, 0.0048484034d, 0.018087378d, 0.024325324d, -0.037694257d, -0.026537929d, -0.008803768d, -0.017767483d, -0.012642504d, -0.0062712682d, 0.0009771782d, -0.010409906d, 0.017754154d, -0.004671795d, -0.030469967d, 0.008477209d, -0.005218282d, -0.0058480743d, -0.020153364d, -0.0032805866d, 0.004248601d, 0.0051449724d, 0.006791097d, 0.007650814d, 0.003458861d, -0.0031223053d, -0.01932697d, -0.033615597d, 0.00745088d, 0.006321252d, -0.0038154104d, 0.014555207d, 0.027697546d, -0.02828402d, 0.0066711367d, 0.0077107945d, 0.01794076d, 0.011349596d, -0.0052715978d, 0.014755142d, -0.019753495d, -0.011156326d, 0.011202978d, 0.022126047d, 0.00846388d, 0.030549942d, -0.0041386373d, 0.018847128d, -0.00033655585d, 0.024925126d, -0.003555496d, -0.019300312d, 0.010749794d, 0.0075308536d, -0.018287312d, -0.016567878d, -0.012869096d, -0.015528221d, 0.0078107617d, -0.011156326d, 0.013522214d, -0.020646535d, -0.01211601d, 0.055928253d, 0.011596181d, -0.017247654d, 0.0005939711d, -0.026977783d, -0.003942035d, -0.009583511d, -0.0055248477d, -0.028737204d, 0.023179034d, 0.003995351d, 0.0219661d, -0.008470545d, 0.023392297d, 0.010469886d, -0.015874773d, 0.007890735d, -0.009690142d, -0.00024970944d, 0.012775794d, 0.0114762215d, 0.013422247d, 0.010429899d, -0.03686786d, -0.006717788d, -0.027484283d, 0.011556195d, -0.036068123d, -0.013915418d, -0.0016327957d, 0.0151016945d, -0.020473259d, 0.004671795d, -0.012555866d, 0.0209531d, 0.01982014d, 0.024485271d, 0.0105431955d, -0.005178295d, 0.033162415d, -0.013795458d, 0.007150979d, 0.010243294d, 0.005644808d, 0.017260984d, -0.0045618312d, 0.0024725192d, 0.004305249d, -0.008197301d, 0.0014203656d, 0.0018460588d, 0.005015015d, -0.011142998d, 0.01439526d, 0.022965772d, 0.02552493d, 0.007757446d, -0.0019726837d, 0.009503538d, -0.032042783d, 0.008403899d, -0.04609149d, 0.013808787d, 0.011749465d, 0.036388017d, 0.016314628d, 0.021939443d, -0.0250051d, -0.017354285d, -0.012962398d, 0.00006107364d, 0.019113706d, 0.03081652d, -0.018114036d, -0.0084572155d, 0.009643491d, -0.0034721901d, 0.0072642746d, -0.0090636825d, 0.01642126d, 0.013428912d, 0.027724205d, 0.0071243206d, -0.6858542d, -0.031029783d, -0.014595194d, -0.011449563d, 0.017514233d, 0.01743426d, 0.009950057d, 0.0029706885d, -0.015714826d, -0.001806072d, 0.011856096d, 0.026444625d, -0.0010663156d, -0.006474535d, 0.0016161345d, -0.020313311d, 0.0148351155d, -0.0018393943d, 0.0057347785d, 0.018300641d, -0.018647194d, 0.03345565d, -0.008070676d, 0.0071443142d, 0.014301958d, 0.0044818576d, 0.003838736d, -0.007350913d, -0.024525259d, -0.001142124d, -0.018620536d, 0.017247654d, 0.007037683d, 0.010236629d, 0.06046009d, 0.0138887605d, -0.012122675d, 0.037694257d, 0.0055081863d, 0.042492677d, 0.00021784494d, -0.011656162d, 0.010276617d, 0.022325981d, 0.005984696d, -0.009496873d, 0.013382261d, -0.0010563189d, 0.0026507939d, -0.041639622d, 0.008637156d, 0.026471283d, -0.008403899d, 0.024858482d, -0.00066686375d, -0.0016252982d, 0.027590916d, 0.0051449724d, 0.0058647357d, -0.008743787d, -0.014968405d, 0.027724205d, -0.011596181d, 0.0047650975d, -0.015381602d, 0.0043718936d, 0.002159289d, 0.035908177d, -0.008243952d, -0.030443309d, 0.027564257d, 0.042625964d, -0.0033688906d, 0.01843393d, 0.019087048d, 0.024578573d, 0.03268257d, -0.015608194d, -0.014128681d, -0.0033538956d, -0.0028757197d, -0.004121976d, -0.032389335d, 0.0034322033d, 0.058807302d, 0.010943064d, -0.030523283d, 0.008903735d, 0.017500903d, 0.00871713d, -0.0029406983d, 0.013995391d, -0.03132302d, -0.019660193d, -0.00770413d, -0.0038853872d, 0.0015894766d, -0.0015294964d, -0.006251275d, -0.021099718d, -0.010256623d, -0.008863748d, 0.028550599d, 0.02020668d, -0.0012962399d, -0.003415542d, -0.0022509254d, 0.0119360695d, 0.027590916d, -0.046971202d, -0.0015194997d, -0.022405956d, 0.0016677842d, -0.00018535563d, -0.015421589d, -0.031802863d, 0.03814744d, 0.0065411795d, 0.016567878d, -0.015621523d, 0.022899127d, -0.011076353d, 0.02841731d, -0.002679118d, -0.002342562d, 0.015341615d, 0.01804739d, -0.020566562d, -0.012989056d, -0.002990682d, 0.01643459d, 0.00042527664d, 0.008243952d, -0.013715484d, -0.004835075d, -0.009803439d, 0.03129636d, -0.021432944d, 0.0012087687d, -0.015741484d, -0.0052016205d, 0.00080890034d, -0.01755422d, 0.004811749d, -0.017967418d, -0.026684547d, -0.014128681d, 0.0041386373d, -0.013742141d, -0.010056688d, -0.013268964d, -0.0110630235d, -0.028337335d, 0.015981404d, -0.00997005d, -0.02424535d, -0.013968734d, -0.028310679d, -0.027750863d, -0.020699851d, 0.02235264d, 0.001057985d, 0.00081639783d, -0.0099367285d, 0.013522214d, -0.012016043d, -0.00086471526d, 0.013568865d, 0.0019376953d, -0.019020405d, 0.017460918d, -0.023045745d, 0.008503866d, 0.0064678704d, -0.011509543d, 0.018727167d, -0.003372223d, -0.0028690554d, -0.0027024434d, -0.011902748d, -0.012182655d, -0.015714826d, -0.0098634185d, 0.00593138d, 0.018753825d, 0.0010146659d, 0.013029044d, 0.0003521757d, -0.017620865d, 0.04102649d, 0.00552818d, 0.024485271d, -0.009630162d, -0.015608194d, 0.0006718621d, -0.0008418062d, 0.012395918d, 0.0057980907d, 0.016221326d, 0.010616505d, 0.004838407d, -0.012402583d, 0.019900113d, -0.0034521967d, 0.000247002d, -0.03153628d, 0.0011038032d, -0.020819811d, 0.016234655d, -0.00330058d, -0.0032289368d, 0.00078973995d, -0.021952773d, -0.022459272d, 0.03118973d, 0.03673457d, -0.021472929d, 0.0072109587d, -0.015075036d, 0.004855068d, -0.0008151483d, 0.0069643734d, 0.010023367d, -0.010276617d, -0.023019087d, 0.0068244194d, -0.0012520878d, -0.0015086699d, 0.022046074d, -0.034148756d, -0.0022192693d, 0.002427534d, -0.0027124402d, 0.0060346797d, 0.015461575d, 0.0137554705d, 0.009230294d, -0.009583511d, 0.032629255d, 0.015994733d, -0.019167023d, -0.009203636d, 0.03393549d, -0.017274313d, -0.012042701d, -0.0009930064d, 0.026777849d, -0.013582194d, -0.0027590916d, -0.017594207d, -0.026804507d, -0.0014236979d, -0.022032745d, 0.0091236625d, -0.0042419364d, -0.00858384d, -0.0033905501d, -0.020739838d, 0.016821127d, 0.022539245d, 0.015381602d, 0.015141681d, 0.028817179d, -0.019726837d, -0.0051283115d, -0.011489551d, -0.013208984d, -0.0047017853d, -0.0072309524d, 0.01767418d, 0.0025658219d, -0.010323267d, 0.012609182d, -0.028097415d, 0.026871152d, -0.010276617d, 0.021912785d, 0.0022542577d, 0.005124979d, -0.0019710176d, 0.004518512d, -0.040360045d, 0.010969722d, -0.0031539614d, -0.020366628d, -0.025778178d, -0.0110030435d, -0.016221326d, 0.0036587953d, 0.016207997d, 0.003007343d, -0.0032555948d, 0.0044052163d, -0.022046074d, -0.0008822095d, -0.009363583d, 0.028230704d, -0.024538586d, 0.0029840174d, 0.0016044717d, -0.014181997d, 0.031349678d, -0.014381931d, -0.027750863d, 0.02613806d, 0.0004136138d, -0.005748107d, -0.01868718d, -0.0010138329d, 0.0054348772d, 0.010703143d, -0.003682121d, 0.0030856507d, -0.004275259d, -0.010403241d, 0.021113047d, -0.022685863d, -0.023032416d, 0.031429652d, 0.001792743d, -0.005644808d, -0.011842767d, -0.04078657d, -0.0026874484d, 0.06915057d, -0.00056939584d, -0.013995391d, 0.010703143d, -0.013728813d, -0.022939114d, -0.015261642d, -0.022485929d, 0.016807798d, 0.007964044d, 0.0144219175d, 0.016821127d, 0.0076241563d, 0.005461535d, -0.013248971d, 0.015301628d, 0.0085171955d, -0.004318578d, 0.011136333d, -0.0059047225d, -0.010249958d, -0.018207338d, 0.024645219d, 0.021752838d, 0.0007614159d, -0.013648839d, 0.01111634d, -0.010503208d, -0.0038487327d, -0.008203966d, -0.00397869d, 0.0029740208d, 0.008530525d, 0.005261601d, 0.01642126d, -0.0038753906d, -0.013222313d, 0.026537929d, 0.024671877d, -0.043505676d, 0.014195326d, 0.024778508d, 0.0056914594d, -0.025951454d, 0.017620865d, -0.0021359634d, 0.008643821d, 0.021299653d, 0.0041686273d, -0.009017031d, 0.04044002d, 0.024378639d, -0.027777521d, -0.014208655d, 0.0028623908d, 0.042119466d, 0.005801423d, -0.028124074d, -0.03129636d, 0.022139376d, -0.022179363d, -0.04067994d, 0.013688826d, 0.013328944d, 0.0046184794d, -0.02828402d, -0.0063412455d, -0.0046184794d, -0.011756129d, -0.010383247d, -0.0018543894d, -0.0018593877d, -0.00052024535d, 0.004815081d, 0.014781799d, 0.018007403d, 0.01306903d, -0.020433271d, 0.009043689d, 0.033189073d, -0.006844413d, -0.019766824d, -0.018767154d, 0.00533491d, -0.0024575242d, 0.018727167d, 0.0058080875d, -0.013835444d, 0.0040719924d, 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26	    String indexName = "vector_index";
27	    FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
28	    int numCandidates = 150;
29      int limit = 10;
30      
31      // define pipeline 
32      List<Bson> pipeline = asList(
33          vectorSearch(
34           fieldSearchPath,
35           queryVector,
36           indexName,
37           numCandidates,
38           limit),
39          project(
40           fields(exclude("_id"), include("title"), include("plot"), 
41           metaVectorSearchScore("score"))));
42      
43	    // run query and print results
44      collection.aggregate(pipeline)
45        .forEach(doc -> System.out.println(doc.toJson()));	
46    }
47  }
48}
1{"plot": "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title": "Thrill Seekers", "score": 0.7892671227455139}
2{"plot": "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title": "About Time", "score": 0.7843604683876038}
3{"plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title": "The Time Machine", "score": 0.7801066637039185}
4{"plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title": "Crusade in Jeans", "vectorSearchScore": 0.7789170742034912}
5{"plot": "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title": "Timecop", "score": 0.7771612405776978}
6{"plot": "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title": "A.P.E.X.", "vectorSearchScore": 0.7730885744094849}
7{"plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title": "Men in Black 3", "score": 0.7712380886077881}
8{"plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title": "Tomorrowland", "score": 0.7669923901557922}
9{"plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title": "Love Story 2050", "vectorSearchScore": 0.7649372816085815}
10{"plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title": "The Portal", "score": 0.7640786170959473}

以下查询筛选在一月 01, 1955 和 01, 1975 之间上映的电影的文档，然后再对样本向量数据执行语义Atlas Search 。 它使用$and操作符对指定日期执行逻辑AND操作。 然后，它使用向量嵌入string在筛选后的文档中的 plot_embedding 字段以查找 150 个最近邻，并在结果中返回 10 个文档。 该查询还指定了一个$project阶段来执行以下操作：

  • 结果中排除_id字段，仅包含plot 、 title和year字段。

  • 添加名为 score 的字段，显示结果中文档的Atlas Vector Search分数。

1import static com.mongodb.client.model.Aggregates.*;
2import static com.mongodb.client.model.Projections.*;
3import static com.mongodb.client.model.search.SearchPath.fieldPath;
4import static com.mongodb.client.model.search.VectorSearchOptions.vectorSearchOptions;
5import static java.util.Arrays.asList;
6
7import com.mongodb.client.MongoClient;
8import com.mongodb.client.MongoClients;
9import com.mongodb.client.MongoCollection;
10import com.mongodb.client.MongoDatabase;
11import com.mongodb.client.model.*;
12import com.mongodb.client.model.search.VectorSearchOptions;
13import java.util.List;
14import com.mongodb.client.model.search.FieldSearchPath;
15import org.bson.Document;
16import org.bson.conversions.Bson;
17
18public class FilterQuery {
19  public static void main( String[] args ) {
20    // specify connection
21    String uri = "<connection-string>";
22
23    // establish connection and set namespace
24    try (MongoClient mongoClient = MongoClients.create(uri)) {
25      MongoDatabase database = mongoClient.getDatabase("sample_mflix");
26      MongoCollection<Document> collection = database.getCollection("embedded_movies");
27      
28  	  // define $vectorSearch query options 
29	    List<Double> queryVector = 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30	    String indexName = "vector_index";
31	    FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
32	    int numCandidates = 150;
33      int limit = 10;
34      Bson criteria = Filters.and(Filters.gt("year", 1955), Filters.lt("year", 1975));
35      VectorSearchOptions options = vectorSearchOptions().filter(criteria);
36      
37      // define pipeline 
38      List<Bson> pipeline = asList(
39          vectorSearch(
40           fieldSearchPath,
41           queryVector,
42           indexName,
43           numCandidates,
44           limit,
45           options),
46          project(
47           fields(exclude("_id"), include("title"), include("plot"), include("year"),
48           metaVectorSearchScore("score"))));
49      
50	    // run query and print results
51      collection.aggregate(pipeline)
52        .forEach(doc -> System.out.println(doc.toJson()));	
53    }
54  }
55}
1{"plot": "In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...", "title": "Peter Pan", "year": 1960, "score": 0.748110830783844}
2{"plot": "A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.", "title": "Chitty Chitty Bang Bang", "year": 1968, "score": 0.7442465424537659}
3{"plot": "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "title": "That Man from Rio", "year": 1964, "score": 0.7416020035743713}
4{"plot": "A pilot, stranded in the desert, meets a little boy who is a prince on a planet.", "title": "The Little Prince", "year": 1974, "score": 0.7378944158554077}
5{"plot": "A red balloon with a life of its own follows a little boy around the streets of Paris.", "title": "The Red Balloon", "year": 1956, "score": 0.7342712879180908}
6{"plot": "A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.", "title": "Willy Wonka & the Chocolate Factory", "year": 1971, "score": 0.7342107892036438}
7{"plot": "An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.", "title": "Bedknobs and Broomsticks", "year": 1971, "score": 0.7339356541633606}
8{"plot": "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", "title": "Pastoral Hide and Seek", "year": 1974, "score": 0.733299970626831}
9{"plot": "A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.", "title": "The Three Musketeers", "year": 1973, "score": 0.7331198453903198}
10{"plot": "A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.", "title": "Frosty", "year": 1964, "score": 0.7318308353424072}

Atlas Vector Search 根据介于1955和1975之间的year字段值过滤文档。它返回总结了1955和1975之间上映的电影情节中的儿童冒险经历的文档。

提示

另请参阅：其他筛选器示例

如何对 Atlas 集群中的数据执行语义搜索教程演示了针对sample_mflix.embedded_movies集合中的嵌入式数据的语义搜索查询中的其他预筛选器。

以下查询使用$vectorSearch阶段，使用向量嵌入搜索plot_embedding字段，查找字符串时间旅行。它最多考虑150个最近邻，并在结果中返回10文档。该查询还指定了一个$project阶段来执行以下操作：

  • 在结果中排除 _id 字段并仅包含 plot 和 title 字段。

  • 添加名为score的字段，显示结果中每个文档的向量搜索分数。

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5    
6const client = new MongoClient(uri);
7
8async function run() {
9    try {
10        await client.connect();
11
12        // set namespace
13        const database = client.db("sample_mflix");
14        const coll = database.collection("embedded_movies");
15
16        // define pipeline
17        const agg = [
18            {
19              '$vectorSearch': {
20                'index': 'vector_index', 
21                'path': 'plot_embedding', 
22                'queryVector': [
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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136
24                ], 
25                'numCandidates': 150, 
26                'limit': 10
27              }
28            }, {
29              '$project': {
30                '_id': 0, 
31                'plot': 1, 
32                'title': 1, 
33                'score': {
34                  '$meta': 'vectorSearchScore'
35                }
36              }
37            }
38          ];
39        // run pipeline
40        const result = coll.aggregate(agg);
41
42        // print results
43        await result.forEach((doc) => console.dir(JSON.stringify(doc)));
44    } finally {
45        await client.close();
46    }
47}
48run().catch(console.dir);
1'{"plot":"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.","title":"Thrill Seekers","score":0.7892671227455139}'
2'{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.7843604683876038}'
3'{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.7801066637039185}'
4`{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
5'{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.7771612405776978}'
6'{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.7730885744094849}'
7`{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
8'{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}'
9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'

以下查询筛选在一月 01, 1955 和 01, 1975 之间上映的电影的文档，然后再对样本向量数据执行语义Atlas Search 。 它使用$and操作符对指定日期执行逻辑AND操作。 然后，它使用向量嵌入string在筛选后的文档中的 plot_embedding 字段以查找 150 个最近邻，并在结果中返回 10 个文档。 该查询还指定了一个$project阶段来执行以下操作：

  • 结果中排除_id字段，仅包含plot 、 title和year字段。

  • 添加名为 score 的字段，显示结果中文档的Atlas Vector Search分数。

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5    
6const client = new MongoClient(uri);
7
8async function run() {
9  try {
10    await client.connect();
11
12    // set namespace
13    const database = client.db("sample_mflix");
14    const coll = database.collection("embedded_movies");
15
16    // define pipeline
17    const agg = [
18      {
19        '$vectorSearch': {
20          'index': 'vector_index', 
21          'path': 'plot_embedding', 
22          'filter': {
23            '$and': [
24              {
25                'year': {
26                  '$gt': 1955
27                }, 
28                'year': {
29                  '$lt': 1975
30                }
31              }
32            ]
33          }, 
34          'queryVector': [0.02421053, -0.022372592, -0.006231137, -0.02168502, -0.020375984, 0.037552103, -0.010505334, -0.027026938, 0.0070674648, -0.020032197, 0.01783725, 0.016303431, 0.014584498, -0.018736385, 0.009031017, -0.0045981496, 0.02750295, -0.028322749, 0.010624337, -0.024236975, -0.0048659067, 0.015153068, -0.000490888, -0.022161031, -0.0024560927, -0.007411252, 0.009745035, -0.01886861, 0.02112967, -0.011939983, 0.015153068, 0.0025800543, 0.017824028, -0.02410475, -0.016633997, -0.0018214093, -0.008323609, -0.009222744, 0.009388026, -0.0028296304, 0.0017536436, 0.0065517845, -0.011635863, -0.028454976, -0.018934723, 0.012951509, -0.0032015154, -0.005880739, -0.03115238, 0.012951509, 0.0057749585, 0.009202911, -0.0069352393, 0.00205611, 0.0063732797, 0.0039700773, -0.007100521, -0.0077087595, 0.011596196, -0.010207825, -0.007100521, -0.0051006074, -0.01670011, 0.012773004, -0.035304267, -0.0074971984, 0.0025800543, -0.006118745, 0.030253245, -0.0010751605, 0.039456155, 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0.03001524, -0.00046609566, 0.015615858, -0.02935411, -0.0009925193, 0.033796895, -0.019040504, -0.014901839, 0.009533474, -0.010121879, 0.026458368, -0.038054563, 0.009956597, -0.0030048296, -0.0019519823, 0.016872002, -0.001142926, -0.014941507, -0.02930122, 0.004611372, -0.029512782, 0.030887928, -0.0018015754, 0.010624337, -0.0044791466, -0.007993045, -0.0056790947, 0.0019602464, 0.011173073, 0.0023222142, -0.00022499033, 0.0024511344, 0.015443964, 0.018987615, -0.02349651, -0.008740121, 0.00029730127, -0.004750209, -0.017969476, -0.06442037, 0.006816236, 0.0019833858, 0.0063038613, -0.0054675336, -0.01161603, 0.032818425, -0.030094575, 0.009685534, -0.0012520123, -0.0013090346, 0.0085285595, 0.015959645, -0.0006574098, -0.00688896, -0.019133061, -0.0008057505, 0.009672312, 0.019913195, 0.008145104, -0.012290381, 0.0016139803, 0.026405476, 0.014875393, -0.002168502, 0.012792839, -0.011840814, 0.003464314, 0.0069682957, -0.0073781954, 0.018842166, -0.03165484, -0.017242234, 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-0.018339708, -0.041307315, -0.012296992, 0.0070542423, 0.0019371068, 0.0117945345, -0.020032197, 0.017797582, -0.015443964, 0.00537167, -0.00015474542, -0.0117747, -0.011140017, 0.017334793, 0.016250541, 0.006019576, 0.017612467, -0.017982699, 0.010366497, 0.0029949127, 0.015086955, -0.000027813887, -0.008660785, -0.008713675, -0.0050873845, -0.0117945345, -0.016118316, -0.0022015583, 0.006518728, -0.0047766543, 0.0055501745, 0.039747052, -0.034061346, 0.049425974, 0.0023883271, -0.0035601775, 0.00863434, -0.003897353, 0.016237319, 0.006436087, -0.00037828952, -0.017797582, -0.019450404, 0.0009809496, 0.0036461244, 0.013176293, 0.0036461244, -0.01094829, -0.018260373, 0.00035246418, 0.012885396, -0.006796402, -0.015972868, 0.027899627, -0.0077021485, 0.027608732, 0.01696456, -0.0014486981, -0.017969476, 0.015642302, -0.00477996, -0.0048890463, -0.020058641, 0.008323609, 0.013017623, -0.01886861, -0.008204606, 0.016303431, -0.010029321, -0.001018138, -0.0332151, 0.010525168, 0.032871313, 0.011549917, 0.010928456, -0.014253933, -0.011384634, 0.00894507, 0.034616694, -0.016872002, -0.010987958, -0.011953205], 
35          'numCandidates': 150, 
36          'limit': 10
37        }
38      }, {
39        '$project': {
40          '_id': 0, 
41          'title': 1, 
42          'plot': 1, 
43          'year': 1, 
44          'score': {
45            '$meta': 'vectorSearchScore'
46          }
47        }
48      }
49    ];
50  // run pipeline
51  const result = coll.aggregate(agg);
52
53  // print results
54    await result.forEach((doc) => console.dir(JSON.stringify(doc)));
55  } finally {
56    await client.close();
57  }
58}
59run().catch(console.dir);
1'{"plot":"In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...","title":"Peter Pan","year":1960,"score":0.748110830783844}'
2'{"plot":"A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.","title":"Chitty Chitty Bang Bang","year":1968,"score":0.7442465424537659}'
3'{"plot":"A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.","title":"That Man from Rio","year":1964,"score":0.7416020035743713}'
4'{"plot":"A boy raised by wolves tries to adapt to human village life.","title":"Jungle Book","year":1942,"score":0.7387760877609253}'
5'{"plot":"A pilot, stranded in the desert, meets a little boy who is a prince on a planet.","title":"The Little Prince","year":1974,"score":0.7378944158554077}'
6'{"plot":"A red balloon with a life of its own follows a little boy around the streets of Paris.","title":"The Red Balloon","year":1956,"score":0.7342712879180908}'
7'{"plot":"A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.","title":"Willy Wonka & the Chocolate Factory","year":1971,"score":0.7342107892036438}'
8'{"plot":"An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.","title":"Bedknobs and Broomsticks","year":1971,"score":0.7339356541633606}'
9'{"plot":"Arriving home to find his native land under the yoke of corrupt merchants, an adventurer named Sadko sets sail in search of a mythical bird of happiness.","title":"Sadko","year":1953,"score":0.7339220643043518}'
10`{"plot":"A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.","title":"Pastoral Hide and Seek","year":1974,"score":0.733299970626831}`

Atlas Vector Search 根据介于1955和1975之间的year字段值过滤文档。它返回总结了1955和1975之间上映的电影情节中的儿童冒险经历的文档。

提示

另请参阅：其他筛选器示例

如何对 Atlas 集群中的数据执行语义搜索教程演示了针对sample_mflix.embedded_movies集合中的嵌入式数据的语义搜索查询中的其他预筛选器。

以下查询使用$vectorSearch阶段，使用向量嵌入搜索plot_embedding字段，查找字符串时间旅行。它最多考虑150个最近邻，并在结果中返回10文档。该查询还指定了一个$project阶段来执行以下操作：

  • 在结果中排除 _id 字段并仅包含 plot 和 title 字段。

  • 添加名为score的字段，显示结果中每个文档的向量搜索分数。

1import pymongo
2import datetime
3
4# connect to your Atlas cluster
5client = pymongo.MongoClient("<connection-string>")
6
7# define pipeline
8pipeline = [
9  {
10    '$vectorSearch': {
11      'index': 'vector_index', 
12      'path': 'plot_embedding', 
13      'queryVector': [-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, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 0.01794076, 0.011349596, -0.0052715978, 0.014755142, -0.019753495, -0.011156326, 0.011202978, 0.022126047, 0.00846388, 0.030549942, -0.0041386373, 0.018847128, -0.00033655585, 0.024925126, -0.003555496, -0.019300312, 0.010749794, 0.0075308536, -0.018287312, -0.016567878, -0.012869096, -0.015528221, 0.0078107617, -0.011156326, 0.013522214, -0.020646535, -0.01211601, 0.055928253, 0.011596181, -0.017247654, 0.0005939711, -0.026977783, -0.003942035, -0.009583511, -0.0055248477, -0.028737204, 0.023179034, 0.003995351, 0.0219661, -0.008470545, 0.023392297, 0.010469886, -0.015874773, 0.007890735, -0.009690142, -0.00024970944, 0.012775794, 0.0114762215, 0.013422247, 0.010429899, -0.03686786, -0.006717788, -0.027484283, 0.011556195, -0.036068123, -0.013915418, -0.0016327957, 0.0151016945, -0.020473259, 0.004671795, 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-0.03156294, -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136], 
14      'numCandidates': 150, 
15      'limit': 10
16    }
17  }, {
18    '$project': {
19      '_id': 0, 
20      'plot': 1, 
21      'title': 1, 
22      'score': {
23        '$meta': 'vectorSearchScore'
24      }
25    }
26  }
27]
28
29# run pipeline
30result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
31
32# print results
33for i in result:
34    print(i)
35 
1{'plot': 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', 'title': 'Thrill Seekers', 'score': 0.7892671227455139}
2{'plot': 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.', 'title': 'About Time', 'score': 0.7843604683876038}
3{'plot': 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', 'title': 'The Time Machine', 'score': 0.7801066637039185}
4{'plot': "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", 'title': 'Crusade in Jeans', 'score': 0.7789170742034912}
5{'plot': 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', 'title': 'Timecop', 'score': 0.7771612405776978}
6{'plot': 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', 'title': 'A.P.E.X.', 'score': 0.7730885744094849}
7{'plot': "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 'title': 'Men in Black 3', 'score': 0.7712380886077881}
8{'plot': 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', 'title': 'Tomorrowland', 'score': 0.7669923901557922}
9{'plot': 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 'title': 'Love Story 2050', 'score': 0.7649372816085815}
10{'plot': 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 'title': 'The Portal', 'score': 0.7640786170959473}

以下查询筛选在一月 01, 1955 和 01, 1975 之间上映的电影的文档，然后再对样本向量数据执行语义Atlas Search 。 它使用$and操作符对指定日期执行逻辑AND操作。 然后，它使用向量嵌入string在筛选后的文档中的 plot_embedding 字段以查找 150 个最近邻，并在结果中返回 10 个文档。 该查询还指定了一个$project阶段来执行以下操作：

  • 结果中排除_id字段，仅包含plot 、 title和year字段。

  • 添加名为 score 的字段，显示结果中文档的Atlas Vector Search分数。

1import pymongo
2import datetime
3
4# connect to your Atlas cluster
5client = pymongo.MongoClient("<connection-string>")
6
7# define pipeline
8pipeline = [
9  {
10    '$vectorSearch': {
11      'index': 'vector_index', 
12      'path': 'plot_embedding', 
13      'filter': {
14        '$and': [
15          {
16            'year': {
17              '$gt': 1955
18            }, 
19            'year': {
20              '$lt': 1975
21            }
22          }
23        ]
24      }, 
25      'queryVector': [0.02421053, -0.022372592, -0.006231137, -0.02168502, -0.020375984, 0.037552103, -0.010505334, -0.027026938, 0.0070674648, -0.020032197, 0.01783725, 0.016303431, 0.014584498, -0.018736385, 0.009031017, -0.0045981496, 0.02750295, -0.028322749, 0.010624337, -0.024236975, -0.0048659067, 0.015153068, -0.000490888, -0.022161031, -0.0024560927, -0.007411252, 0.009745035, -0.01886861, 0.02112967, -0.011939983, 0.015153068, 0.0025800543, 0.017824028, -0.02410475, -0.016633997, -0.0018214093, -0.008323609, -0.009222744, 0.009388026, -0.0028296304, 0.0017536436, 0.0065517845, -0.011635863, -0.028454976, -0.018934723, 0.012951509, -0.0032015154, -0.005880739, -0.03115238, 0.012951509, 0.0057749585, 0.009202911, -0.0069352393, 0.00205611, 0.0063732797, 0.0039700773, -0.007100521, -0.0077087595, 0.011596196, -0.010207825, -0.007100521, -0.0051006074, -0.01670011, 0.012773004, -0.035304267, -0.0074971984, 0.0025800543, -0.006118745, 0.030253245, -0.0010751605, 0.039456155, 0.007821151, -0.0017189344, -0.0010801188, 0.0062575825, -0.011490415, -0.022637043, 0.004743598, -0.012601111, 0.0197413, -0.0015255542, -0.025942687, -0.03284487, 0.020389207, 0.009797926, 0.0141217075, -0.0015172901, 0.025982354, -0.011589585, -0.001138794, 0.0006131968, 0.016832335, 0.017916586, 0.014412603, -0.0027155858, 0.011854036, -0.02169824, 0.02112967, -0.020680103, -0.007391418, -0.012872174, 0.021473458, 0.0047766543, -0.0048394613, -0.024395647, 0.0065418677, 0.009797926, -0.00449898, 0.041836217, 0.0023833686, -0.021737909, 0.0136721395, 0.014148152, -0.028772317, -0.027899627, -0.015695194, -0.012521776, 0.02205525, -0.01927851, -0.022068473, 0.020971, 0.02317917, 0.030544141, -0.011827591, 0.0075170323, 0.023086611, -0.02164535, -0.01732157, 0.007510421, -0.027635176, 0.016263764, -0.0008801275, 0.033109322, -0.014505162, -0.029909458, 0.036679417, 0.0074971984, 0.0059137954, -0.031178825, -0.012634167, 0.008416167, 0.030491251, -0.016832335, -0.009507029, 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0.032871313, 0.011549917, 0.010928456, -0.014253933, -0.011384634, 0.00894507, 0.034616694, -0.016872002, -0.010987958, -0.011953205], 
26      'numCandidates': 150, 
27      'limit': 10
28    }
29  }, {
30    '$project': {
31      '_id': 0, 
32      'title': 1, 
33      'plot': 1, 
34      'year': 1, 
35      'score': {
36        '$meta': 'vectorSearchScore'
37      }
38    }
39  }
40]
41
42# run pipeline
43result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
44
45# print results
46for i in result:
47    print(i)
48 
1{'plot': 'In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...', 'title': 'Peter Pan', 'year': 1960, 'score': 0.748110830783844}
2{'plot': 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.', 'title': 'Chitty Chitty Bang Bang', 'year': 1968, 'score': 0.7442465424537659}
3{'plot': 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.', 'title': 'That Man from Rio', 'year': 1964, 'score': 0.7416020035743713}
4{'plot': 'A boy raised by wolves tries to adapt to human village life.', 'title': 'Jungle Book', 'year': 1942, 'score': 0.7387760877609253}
5{'plot': 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.', 'title': 'The Little Prince', 'year': 1974, 'score': 0.7378944158554077}
6{'plot': 'A red balloon with a life of its own follows a little boy around the streets of Paris.', 'title': 'The Red Balloon', 'year': 1956, 'score': 0.7342712879180908}
7{'plot': 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.', 'title': 'Willy Wonka & the Chocolate Factory', 'year': 1971, 'score': 0.7342107892036438}
8{'plot': 'An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.', 'title': 'Bedknobs and Broomsticks', 'year': 1971, 'score': 0.7339356541633606}
9{'plot': 'Arriving home to find his native land under the yoke of corrupt merchants, an adventurer named Sadko sets sail in search of a mythical bird of happiness.', 'title': 'Sadko', 'year': 1953, 'score': 0.7339220643043518}
10{'plot': "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", 'title': 'Pastoral Hide and Seek', 'year': 1974, 'score': 0.733299970626831}

Atlas Vector Search 根据介于1955和1975之间的year字段值过滤文档。它返回总结了1955和1975之间上映的电影情节中的儿童冒险经历的文档。

提示

另请参阅：其他筛选器示例

如何对 Atlas 集群中的数据执行语义搜索教程演示了针对sample_mflix.embedded_movies集合中的嵌入式数据的语义搜索查询中的其他预筛选器。

ENN 示例

以下查询使用$vectorSearch阶段在plot_embedding字段中搜索字符串World War的向量嵌入。它要求完全匹配并将结果限制为仅10文档。该查询还指定了一个$project阶段来执行以下操作：

  • 结果中排除_id字段，仅包含plot 、 title和year字段。

  • 添加名为 score 的字段，显示结果中文档的Atlas Vector Search分数。

1db.embedded_movies.aggregate([
2  {
3    "$vectorSearch": {
4      "index": "vector_index",
5      "path": "plot_embedding",
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3      plot: 'It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.',
4      title: 'Red Dawn',
5      score: 0.7700583338737488
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7    {
8      plot: 'A dramatization of the World War II Battle of Iwo Jima.',
9      title: 'Sands of Iwo Jima',
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12    {
13      plot: 'Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.',
14      title: 'White Tiger',
15      score: 0.750884473323822
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17    {
18      plot: "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.",
19      title: 'P-51 Dragon Fighter',
20      score: 0.749922513961792
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22    {
23      plot: 'A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.',
24      title: 'When Trumpets Fade',
25      score: 0.7498313188552856
26    },
27    {
28      plot: 'Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.',
29      title: 'Battletruck',
30      score: 0.7497193217277527
31    },
32    {
33      plot: 'It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....',
34      title: 'Robot Jox',
35      score: 0.7495121955871582
36    },
37    {
38      plot: 'During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.',
39      title: 'The Enemy Below',
40      score: 0.746050238609314
41    },
42    {
43      plot: 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.',
44      title: 'Saints and Soldiers',
45      score: 0.7435222864151001
46    },
47    {
48      plot: 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.',
49      title: 'Saints and Soldiers',
50      score: 0.743497371673584
51    }
52]
Not yet available.
Not yet available.
1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5    
6const client = new MongoClient(uri);
7
8async function run() {
9    try {
10        await client.connect();
11
12        // set namespace
13        const database = client.db("sample_mflix");
14        const coll = database.collection("embedded_movies");
15
16        // define pipeline
17        const agg = [
18            {
19              '$vectorSearch': {
20                'index': 'vector_index', 
21                'path': 'plot_embedding', 
22                'queryVector': [
23                  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24                ], 
25                'exact': true, 
26                'limit': 10
27              }
28            }, {
29              '$project': {
30                '_id': 0, 
31                'plot': 1, 
32                'title': 1, 
33                'score': {
34                  '$meta': 'vectorSearchScore'
35                }
36              }
37            }
38          ];
39        // run pipeline
40        const result = coll.aggregate(agg);
41
42        // print results
43        await result.forEach((doc) => console.dir(JSON.stringify(doc)));
44    } finally {
45        await client.close();
46    }
47}
48run().catch(console.dir);
1{
2  "plot":"It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.",
3  "title":"Red Dawn",
4  "score":0.7700583338737488
5}
6{
7  "plot":"A dramatization of the World War II Battle of Iwo Jima.",
8  "title":"Sands of Iwo Jima",
9  "score":0.7581185102462769
10}
11{
12  "plot":"Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.",
13  "title":"White Tiger",
14  "score":0.750884473323822
15}
16{
17  "plot":"As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.",
18  "title":"P-51 Dragon Fighter",
19  "score":0.749922513961792
20}
21{
22  "plot":"A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.",
23  "title":"When Trumpets Fade",
24  "score":0.7498313188552856
25}
26{
27  "plot":"Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.",
28  "title":"Battletruck",
29  "score":0.7497193217277527
30}
31{
32  "plot":"It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....",
33  "title":"Robot Jox",
34  "score":0.7495121955871582
35}
36{
37  "plot":"During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.",
38  "title":"The Enemy Below",
39  "score":0.746050238609314
40}
41{
42  "plot":"Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.",
43  "title":"Saints and Soldiers",
44  "score":0.7435222864151001
45}
46{
47  "plot":"Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.",
48  "title":"Saints and Soldiers",
49  "score":0.743497371673584
50}
1import pymongo
2import datetime
3
4# connect to your Atlas cluster
5client = pymongo.MongoClient("<connection-string>")
6
7# define pipeline
8pipeline = [
9  {
10    '$vectorSearch': {
11      'index': 'vector_index', 
12      'path': 'plot_embedding', 
13      'queryVector': 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
14      'exact': True, 
15      'limit': 10
16    }
17  }, {
18    '$project': {
19      '_id': 0, 
20      'plot': 1, 
21      'title': 1, 
22      'score': {
23        '$meta': 'vectorSearchScore'
24      }
25    }
26  }
27]
28
29# run pipeline
30result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
31
32# print results
33for i in result:
34    print(i)
35 
1{
2  'plot': 'It is the dawn of World War III. In mid-western America, a group of teenagers bands together to defend their town, and their country, from invading Soviet forces.', 
3  'title': 'Red Dawn', 
4  'score': 0.7700583338737488
5}
6{
7  'plot': 'A dramatization of the World War II Battle of Iwo Jima.', 
8  'title': 'Sands of Iwo Jima', 
9  'score': 0.7581185102462769
10}
11{
12  'plot': 'Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.', 
13  'title': 'White Tiger', 
14  'score': 0.750884473323822
15}
16{
17  'plot': "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.", 
18  'title': 'P-51 Dragon Fighter', 
19  'score': 0.749922513961792
20}
21{
22  'plot': 'A private in the latter days of WWII on the German front struggles between his will to survive and what his superiors perceive as a battlefield instinct.', 
23  'title': 'When Trumpets Fade', 
24  'score': 0.7498313188552856
25}
26{
27  'plot': 'Post World War III futuristic tale of collapsed governments & bankrupt countries heralding a new lawless age.', 
28  'score': 0.7497193217277527
29}
30{
31  'plot': 'It is post-World War III. War is outlawed. In its place, are matches between large Robots called Robot Jox. These matches take place between two large superpowers over disputed territories....', 
32  'title': 'Robot Jox', 
33  'score': 0.7495121955871582
34}
35{
36  'plot': 'During World War II, an American destroyer meets a German U-Boat. Both captains are good ones, and the engagement lasts for a considerable time.', 
37  'title': 'The Enemy Below', 
38  'score': 0.746050238609314}
39{'plot': 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 'title': 'Saints and Soldiers', 'score': 0.7435222864151001
40}
41{
42  'plot': 'Four American soldiers and one Brit fighting in Europe during World War II struggle to return to Allied territory after being separated from U.S. forces during the historic Malmedy Massacre.', 
43  'title': 'Saints and Soldiers', 
44  'score': 0.743497371673584
45}

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