运行向量搜索查询
在此页面上
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。
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 指定是运行ENN还是ANN搜索的标志。值可以是以下之一：
如果省略，则默认值为 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）、字符串或 UUID 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 搜索期间要使用的最近邻数量。值必须小于或等于 ( 我们建议您指定一个大于要返回的文档数 ( |
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 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：
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（其中0表示低相似性， 1表示高相似性）。对于cosine和dotProduct相似度，Atlas Vector Search 会对分数进行归一化，以确保分数不为负数。
对于cosine ，Atlas Vector Search 使用以下算法对分数进行归一化：
score = (1 + cosine(v1,v2)) / 2
对于dotProduct ，Atlas Vector Search 使用以下算法对分数进行归一化：
score = (1 + dotProduct(v1,v2)) / 2
这些算法表明，Atlas Vector Search 通过考虑文档向量 ( v1 ) 和查询向量 ( v2 ) 的相似度分数（范围为 [ -1 , 1 ]）对分数进行归一化。 Atlas Vector Search 将1添加到相似性分数，以将分数规范化在 [ 0 , 2 ] 范围内，然后除以2以确保值介于0和1之间。
对于euclidean相似度，Atlas Vector Search 使用以下算法对分数进行归一化，以确保该值介于0和1之间：
score = 1 / (1 + euclidean(v1,v2))
前面的算法显示，Atlas Vector Search 通过计算欧几里得距离对分数进行归一化，欧几里得距离是文档向量 ( v1 ) 和查询向量 ( v2 ) 之间的距离，其范围为 [ 0 , ∞ ]。然后，Atlas Vector Search 将此距离转换为相似度分数，方法是在该距离上加上1 ，再用1除以相似度分数，以确保该值介于0和1之间。
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 $meta 表达式。该表达式需要 vectorSearchScore 值来返回向量搜索的文档分数。
例子
1 db.<collection>.aggregate([ 2 { 3 "$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
$vectorSearchfilter选项仅匹配 BSON 布尔值、日期、objectId、数字、字符串和 UUID 值。您 必须 在 vectorSearch 类型索引定义中对要筛选数据的字段作为 筛选器 类型进行索引。筛选数据有助于缩小语义搜索的范围，并确保不会考虑所有向量进行比较。
Atlas Vector Search 支持以下$vectorSearchfilter MQL 匹配表达式的 选项：
$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）。
$vectorSearch 不能用于视图定义和后续管道阶段：
$lookup子管道$unionWith子管道$facet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。
对于相同的连续查询，您可能会看到不一致的结果。要缓解此问题，请增加$vectorSearch查询中numCandidates的值。
举例
以下查询使用$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。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding", 6 "queryVector": 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7 "numCandidates": 150, 8 "limit": 10 9 } 10 }, 11 { 12 "$project": { 13 "_id": 0, 14 "plot": 1, 15 "title": 1, 16 "score": { $meta: "vectorSearchScore" } 17 } 18 } 19 ])
1 [ 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: 0.9332506656646729 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: 0.9312690496444702 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: 0.929530143737793 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: 0.9290417432785034 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: 0.9283161759376526 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: 0.9266218543052673 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: 0.9258455038070679 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: 0.9240515828132629 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: 0.923175573348999 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: 0.9228089451789856 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$project阶段来执行以下操作：
结果中排除
_id字段，仅包含plot、title和year字段。添加名为
score的字段，显示结果中文档的Atlas Vector Search分数。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector-search-tutorial", 5 "path": "plot_embedding", 6 "filter": { 7 "$and": [ 8 { 9 "year": { "$gt": 1955 } 10 }, 11 { 12 "year": { "$lt": 1975 } 13 } 14 ] 15 }, 16 "queryVector": 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17 "numCandidates": 150, 18 "limit": 10 19 } 20 }, 21 { 22 "$project": { 23 "_id": 0, 24 "title": 1, 25 "plot": 1, 26 "year": 1, 27 "score": { $meta: "vectorSearchScore" } 28 } 29 } 30 ])
1 [ 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。
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。
1 using MongoDB.Bson; 2 using MongoDB.Bson.Serialization.Attributes; 3 using MongoDB.Bson.Serialization.Conventions; 4 using MongoDB.Driver; 5 6 public class vectorSearchBasicQuery 7 { 8 // define connection to your Atlas cluster 9 private const string MongoConnectionString = "<connection-string>"; 10 11 public static void Main(string[] args){ 12 var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() }; 13 ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true); 14 15 // connect to your Atlas cluster 16 var mongoClient = new MongoClient(MongoConnectionString); 17 18 // define namespace 19 var moviesDatabase = mongoClient.GetDatabase("sample_mflix"); 20 var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies"); 21 22 // define vector embeddings to search 23 var vector = new[] 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24 var options = new VectorSearchOptions<EmbeddedMovie>() { 25 IndexName = "vector_index", 26 NumberOfCandidates = 150 27 }; 28 29 // run query 30 var results = moviesCollection.Aggregate() 31 .VectorSearch(movie => movie.Embedding, vector, 10, options) 32 .Project(Builders<EmbeddedMovie>.Projection 33 .Include(movie => movie.Title) 34 .Include(movie => movie.Plot) 35 .Exclude(movie => movie.Id) 36 .MetaVectorSearchScore(movie => movie.Score)) 37 .ToList(); 38 39 // print results 40 foreach (var movie in results) 41 { 42 Console.WriteLine(movie.ToJson()); 43 } 44 } 45 } 46 47 [] 48 public class EmbeddedMovie 49 { 50 [] 51 public string Title { get; set; } 52 public string Plot { get; set; } 53 [] 54 public double[] Embedding { get; set; } 55 public double Score { get; set; } 56 }
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" : 0.9332506656646729 } 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" : 0.9312690496444702 } 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" : 0.929530143737793 } 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" : 0.9290417432785034 } 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" : 0.9283161759376526 } 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" : 0.9266218543052673 } 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" : 0.9258455038070679 } 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" : 0.9240515828132629 } 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" : 0.923175573348999 } 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" : 0.9228089451789856 }
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。
1 using System.Reflection.Emit; 2 using MongoDB.Bson; 3 using MongoDB.Bson.Serialization.Attributes; 4 using MongoDB.Bson.Serialization.Conventions; 5 using MongoDB.Driver; 6 using MongoDB.Driver.Search; 7 8 public 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 [] 55 public class EmbeddedMovie 56 { 57 [] 58 public string Title { get; set; } 59 public string Plot { get; set; } 60 public int Year { get; set; } 61 [] 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。
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。
1 import static com.mongodb.client.model.Aggregates.*; 2 import static com.mongodb.client.model.Projections.*; 3 import static com.mongodb.client.model.search.SearchPath.fieldPath; 4 import static java.util.Arrays.asList; 5 import com.mongodb.client.MongoClient; 6 import com.mongodb.client.MongoClients; 7 import com.mongodb.client.MongoCollection; 8 import com.mongodb.client.MongoDatabase; 9 import java.util.List; 10 import com.mongodb.client.model.search.FieldSearchPath; 11 import org.bson.Document; 12 import org.bson.conversions.Bson; 13 14 public 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, 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