如何对 Atlas 集群中的数据执行语义搜索
本教程介绍如何对 Atlas 集群上的 collection 中的 字段中的向量执行 ANN 搜索。plot_embeddingssample_mflix.embedded_movies为了演示这一点，它将带您完成以下步骤：
在
sample_mflix.embedded_movies集合中对名为plot_embeddings的数字字段创建 Atlas Vector Search 索引。针对
sample_mflix.embedded_movies集合中的plot_embeddings字段运行 Atlas Vector Search 查询。
先决条件
如要完成本教程，您必须具备以下条件：
运行 MongoDB 版本 6.0.11、v7.0.2 或更高版本（包括RC ）的 Atlas 集群。
将样本数据加载到 Atlas 集群。
用于在 Atlas 集群上运行查询的以下应用程序之一：
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。
创建 Atlas Vector Search 索引
本部分演示如何在sample_mflix.embedded_moviescollection中的plot_embeddings字段上创建 Atlas Vector Search 索引，以便对该字段运行查询。
必需的访问权限
要创建 Atlas Vector Search 索引，您必须对项目具有Project Data Access Admin或更高访问权限。
步骤
1
在 AtlasClusters 中，转到项目的 页面。
如果尚未显示，请选择包含所需项目的组织导航栏中的Organizations菜单。
如果尚未显示，请从导航栏的Projects菜单中选择所需的项目。
如果 Clusters（数据库部署）页面尚未出现，请单击侧边栏中的 Database（数据库）。
此时会显示“集群”页面。
2
转到集群的 Atlas Search 页面。
您可以从侧边栏、 Data Explorer或集群详细信息页面转到 Atlas Search 页面。
在侧边栏中，单击Services标题下的Atlas Search 。
从Select data source下拉列表中，选择您的集群并单击Go to Atlas Search 。
显示Atlas Search页面。
单击集群的对应 Browse Collections 按钮。
展开数据库并选择集合。
单击集合的Search Indexes标签页。
显示Atlas Search页面。
单击集群名称。
单击 Atlas Search 标签页。
显示Atlas Search页面。
3
4
定义 Atlas Vector Search 索引。
将默认定义替换为以下索引定义。
此索引定义指定在vectorSearch类型的索引中对以下字段建立索引：
plot_embedding字段作为向量类型。plot_embedding字段包含使用 OpenAI 的text-embedding-ada-002嵌入模型创建的嵌入。 索引定义指定了1536向量维度，并使用euclidean来衡量相似性。genres字段作为筛选器类型，用于按字段中的字符串值对数据进行预筛选。year字段作为筛选器类型，用于按字段中的数值对数据进行预筛选。
1 { 2 "fields": [ 3 { 4 "type": "vector", 5 "path": "plot_embedding", 6 "numDimensions": 1536, 7 "similarity": "euclidean" 8 }, 9 { 10 "type": "filter", 11 "path": "genres" 12 }, 13 { 14 "type": "filter", 15 "path": "year" 16 } 17 ] 18 }
使用$vectorSearch 聚合管道阶段运行查询
➤ 使用 Select your language（选择语言）下拉菜单选择要用于运行本节所述示例查询的客户端。
概述
本部分演示如何使用$vectorSearch阶段查询sample_mflix.embedded_movies集合中的索引向量数据。这些样本查询还演示了各种查询和聚合管道操作符，我们可以在查询中使用这些操作符来预筛选要执行语义搜索的数据。
步骤
1
在 AtlasClusters 中，转到项目的 页面。
如果尚未显示，请选择包含所需项目的组织导航栏中的Organizations菜单。
如果尚未显示，请从导航栏的Projects菜单中选择所需的项目。
如果 Clusters（数据库部署）页面尚未出现，请单击侧边栏中的 Database（数据库）。
此时会显示“集群”页面。
2
转到Collections 页面。
单击集群的对应 Browse Collections 按钮。
显示数据浏览器。
3
4
针对索引字段运行 Atlas Vector Search 搜索查询。
以下查询使用 $vectorSearch 管道阶段搜索与指定向量嵌入匹配的电影。请注意，这些查询使用 ada-002-text 嵌入，它与 plot_embedding 字段中的向量嵌入相同。这些查询指定最多搜索 100 个最近邻，并将结果限制为仅 10 个文档。这些查询还指定了一个 $project 阶段以执行以下操作：
在结果中排除
_id字段，而仅包含title、genres、plot和year字段。添加一个名为
score的字段，以显示结果中的每个文档的向量搜索分数。
单击 </> Text（连接）。
将左窗格中的[ ]替换为对要尝试的预过滤操作符的查询。
以下查询使用向量嵌入在
plot_embedding字段中搜索字符串历史 heist 。该查询使用ada-002-text嵌入，与plot_embedding字段中的向量嵌入相同。它为每个字段指定多个比较查询运算符，以对要执行语义搜索的文档进行预过滤。该筛选器使用
$and聚合管道操作符来查找符合以下条件的电影文档：按
genres字段过滤，查找不属于drama、western或crime类型，但属于action、adventure或family类型的电影。按
year字段筛选，查找1960到2000（含）年份之间发行的电影。
1 [ 2 { 3 "$vectorSearch": { 4 "index": "vector-search-tutorial", 5 "path": "plot_embedding", 6 "filter": { 7 "$and": [{ 8 "genres": { 9 "$nin": ["Drama", "Western", "Crime"] , 10 "$in": ["Action", "Adventure", "Family"] 11 }, 12 }, { 13 "year": { 14 "$gte": 1960, 15 "$lte": 2000 16 } 17 }] 18 }, 19 "queryVector": 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20 "numCandidates": 200, 21 "limit": 10 22 } 23 }, 24 { 25 "$project": { 26 "_id": 0, 27 "title": 1, 28 "genres": 1, 29 "plot": 1, 30 "year": 1, 31 "score": { $meta: "vectorSearchScore" } 32 } 33 } 34 ] 1 year: 1993 2 plot: "A botched mid-air heist results in suitcases full of cash being search…" 3 genres: Array (3) 4 title: "Cliffhanger" 5 score: 0.7694521546363831 6 7 genres: Array (3) 8 title: "It's a Mad, Mad, Mad, Mad World" 9 year: 1963 10 score: 0.7638954520225525 11 plot: "The dying words of a thief spark a madcap cross-country rush to find s…" 12 13 score: 0.7538407444953918 14 year: 1991 15 plot: "A cat burglar is forced to steal Da Vinci works of art for a world dom…" 16 genres: Array (3) 17 title: "Hudson Hawk" 18 19 plot: "In 1997, when the US President crashes into Manhattan, now a giant max…" 20 genres: Array (2) 21 title: "Escape from New York" 22 year: 1981 23 score: 0.7487208843231201 24 25 title: "The Romanov Stones" 26 year: 1993 27 score: 0.7467736005783081 28 plot: "Patrick and Tony are hired by the wealthy gambler to steal the pricele…" 29 genres: Array (2) 30 31 plot: "An attempted robbery turns to be an unexpected recruitment when two un…" 32 genres: Array (3) 33 title: "Crime Busters" 34 year: 1977 35 score: 0.7437351942062378 36 37 plot: "In the aftermath of the Persian Gulf War, 4 soldiers set out to steal …" 38 genres: Array (3) 39 title: "Three Kings" 40 year: 1999 41 score: 0.7425670623779297 42 43 plot: "A professional thief is hired by the FBI to steal a data tape from a c…" 44 genres: Array (3) 45 title: "Black Moon Rising" 46 year: 1986 47 score: 0.7397696375846863 48 49 plot: "A group of heavily armed hijackers board a luxury ocean liner in the S…" 50 genres: Array (3) 51 title: "Deep Rising" 52 year: 1998 53 score: 0.7392246127128601 54 55 year: 1964 56 score: 0.7357995510101318 57 plot: "A young man comes to the rescue of his girlfriend abducted by thieves …" 58 genres: Array (3) 59 title: "That Man from Rio" 以下查询使用向量嵌入在
plot_embedding字段中搜索字符串“ martives ”。该查询使用ada-002-text嵌入，与plot_embedding字段中的向量嵌入相同。它指定了聚合管道和比较查询操作符，以演示如何组合使用这些操作符来筛选数据。该筛选器使用
$or聚合管道操作符来查找符合以下任一条件的电影文档：按
genres字段过滤，查找不属于crime类型的电影。按
year字段筛选可查找2015年或之前发行的电影，按genres字段筛选可查找action类型的电影。
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18 "numCandidates": 200, 19 "limit": 10 20 } 21 }, 22 { 23 "$project": { 24 "_id": 0, 25 "title": 1, 26 "genres": 1, 27 "plot": 1, 28 "released": 1, 29 "score": { $meta: "vectorSearchScore" } 30 } 31 } 32 ] 1 title: "Ong-bak 2" 2 score: 0.7976737022399902 3 year: 2008 4 plot: "A young Thai boxer learns the skills and inner meaning of martial arts…" 5 genres: Array (1) 6 7 score: 0.7929348349571228 8 plot: "A handyman/martial arts master agrees to teach a bullied boy karate an…" 9 genres: Array (3) 10 title: "The Karate Kid" 11 year: 1984 12 13 plot: "A young martial artist embarks on an adventure, encountering other mar…" 14 genres: Array (3) 15 title: "Circle of Iron" 16 year: 1978 17 score: 0.7852014899253845 18 19 plot: "A lionized account of the life of the martial arts superstar." 20 genres: Array (3) 21 title: "Dragon: The Bruce Lee Story" 22 year: 1993 23 score: 0.7763040661811829 24 25 score: 0.7731232643127441 26 plot: "A martial arts instructor from the police force gets imprisoned after …" 27 genres: Array (2) 28 title: "Kung Fu Killer" 29 year: 2014 30 31 plot: "A young woman is trained by a martial arts specialist to become a prof…" 32 genres: Array (3) 33 title: "Naked Killer" 34 year: 1992 35 score: 0.7693743109703064 36 37 plot: "The life of the greatest karate master of a generation." 38 genres: Array (3) 39 title: "The Real Miyagi" 40 year: 2015 41 score: 0.768799901008606 42 43 plot: "At his new high school, a rebellious teen is lured into an underground…" 44 genres: Array (3) 45 title: "Never Back Down" 46 year: 2008 47 score: 0.768179714679718 48 49 score: 0.7679706811904907 50 plot: "Three young martial arts masters emerge from the back streets of Hong …" 51 genres: Array (2) 52 title: "Dragon Tiger Gate" 53 year: 2006 54 55 title: "Shaolin Soccer" 56 score: 0.7660527229309082 57 year: 2001 58 plot: "A young Shaolin follower reunites with his discouraged brothers to for…" 59 genres: Array (3)
注意
Pipeline Output窗格会显示查询结果。
5
展开查询结果。
Data Explorer可能不会显示其返回的文档中的所有值。 要查看在查询路径中搜索的字段的所有值，请展开文档中的字段。
1
连接到mongosh 中的集群。
在终端窗口中打开mongosh并连接到集群。有关连接的详细说明，请参阅通过 mongosh 连接。
3
针对索引字段运行语义Atlas Search查询。
以下查询使用 $vectorSearch 管道阶段搜索与指定向量嵌入匹配的电影。请注意，这些查询使用 ada-002-text 嵌入，它与 plot_embedding 字段中的向量嵌入相同。这些查询指定最多搜索 100 个最近邻，并将结果限制为仅 10 个文档。这些查询还指定了一个 $project 阶段以执行以下操作：
在结果中排除
_id字段，而仅包含title、genres、plot和year字段。添加一个名为
score的字段，以显示结果中的每个文档的向量搜索分数。
以下查询使用向量嵌入在plot_embedding字段中搜索字符串历史 heist 。该查询使用ada-002-text嵌入，与plot_embedding字段中的向量嵌入相同。它为每个字段指定多个比较查询运算符，以对要执行语义搜索的文档进行预过滤。
该筛选器使用$and聚合管道操作符来查找符合以下条件的电影文档：
按
genres字段过滤，查找不属于drama、western或crime类型，但属于action、adventure或family类型的电影。按
year字段筛选，查找1960到2000（含）年份之间发行的电影。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector-search-tutorial", 5 "path": "plot_embedding", 6 "filter": { 7 "$and": [{ 8 "genres": { 9 "$nin": ["Drama", "Western", "Crime"] , 10 "$in": ["Action", "Adventure", "Family"] 11 }, 12 }, { 13 "year": { 14 "$gte": 1960, 15 "$lte": 2000 16 } 17 }] 18 }, 19 "queryVector": 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20 "numCandidates": 200, 21 "limit": 10 22 } 23 }, 24 { 25 "$project": { 26 "_id": 0, 27 "title": 1, 28 "genres": 1, 29 "plot": 1, 30 "year": 1, 31 "score": { $meta: "vectorSearchScore" } 32 } 33 } 34 ])
1 [ 2 { 3 year: 1993, 4 plot: 'A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.', 5 genres: [ 'Action', 'Adventure', 'Thriller' ], 6 title: 'Cliffhanger', 7 score: 0.7694521546363831 8 }, 9 { 10 plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.', 11 genres: [ 'Action', 'Adventure', 'Comedy' ], 12 title: "It's a Mad, Mad, Mad, Mad World", 13 year: 1963, 14 score: 0.7638954520225525 15 }, 16 { 17 year: 1991, 18 plot: 'A cat burglar is forced to steal Da Vinci works of art for a world domination plot.', 19 genres: [ 'Action', 'Adventure', 'Comedy' ], 20 title: 'Hudson Hawk', 21 score: 0.7538407444953918 22 }, 23 { 24 plot: 'In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.', 25 genres: [ 'Action', 'Sci-Fi' ], 26 title: 'Escape from New York', 27 year: 1981, 28 score: 0.7487208843231201 29 }, 30 { 31 plot: 'Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.', 32 genres: [ 'Action', 'Thriller' ], 33 title: 'The Romanov Stones', 34 year: 1993, 35 score: 0.7467736005783081 36 }, 37 { 38 plot: 'An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.', 39 genres: [ 'Action', 'Comedy', 'Adventure' ], 40 title: 'Crime Busters', 41 year: 1977, 42 score: 0.7437351942062378 43 }, 44 { 45 plot: 'In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.', 46 genres: [ 'Action', 'Adventure', 'Comedy' ], 47 title: 'Three Kings', 48 year: 1999, 49 score: 0.7425670623779297 50 }, 51 { 52 plot: 'A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...', 53 genres: [ 'Action', 'Sci-Fi', 'Thriller' ], 54 title: 'Black Moon Rising', 55 year: 1986, 56 score: 0.7397696375846863 57 }, 58 { 59 plot: 'A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.', 60 genres: [ 'Action', 'Adventure', 'Horror' ], 61 title: 'Deep Rising', 62 year: 1998, 63 score: 0.7392246127128601 64 }, 65 { 66 plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.', 67 genres: [ 'Action', 'Adventure', 'Comedy' ], 68 title: 'That Man from Rio', 69 year: 1964, 70 score: 0.7357995510101318 71 } 72 ]
以下查询使用向量嵌入在plot_embedding字段中搜索字符串“ martives ”。该查询使用ada-002-text嵌入，与plot_embedding字段中的向量嵌入相同。它指定了聚合管道和比较查询操作符，以演示如何组合使用这些操作符来筛选数据。
该筛选器使用$or聚合管道操作符来查找符合以下任一条件的电影文档：
按
genres字段过滤，查找不属于crime类型的电影。按
year字段筛选可查找2015年或之前发行的电影，按genres字段筛选可查找action类型的电影。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector-search-tutorial", 5 "path": "plot_embedding", 6 "filter": { 7 "$or": [{ 8 "genres": { "$ne": "Crime" } 9 }, { 10 "$and": [{ 11 "year": { "$lte": 2015 } 12 }, { 13 "genres": { "$eq": "Action" } 14 }] 15 }] 16 }, 17 "queryVector": 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