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_embedding
sample_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_embedding
的数字字段创建 Atlas Vector Search 索引。针对
sample_mflix.embedded_movies
集合中的plot_embedding
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。
示例数据已加载到 Atlas 集群中。
注意
您可以将样本数据中的嵌入转换为 BSON 向量,以便在 Atlas 中高效存储和摄取向量。要了解更多信息,请参阅如何将原生嵌入转换为 BSON 向量。
用于在Atlas 集群上运行查询的以下客户端之一:
注意
您可以使用任何MongoDB驱动程序在 ""聚合阶段运行"" 查询。 "" 仅包含前面列表中的驱动程序的示例。
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
➤ 使用选择语言下拉菜单选择用于创建 Atlas Vector Search 索引和运行查询的客户端。
本部分演示如何在sample_mflix.embedded_movies
collection中的plot_embedding
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,您必须拥有 Project Data Access Admin
或更高的项目访问权限。
步骤
AtlasGoClusters在Atlas中,Go项目的 页面。
如果尚未显示,请从导航栏上的 Organizations 菜单中选择包含所需项目的组织。
如果尚未显示,请从导航栏的Projects菜单中选择所需的项目。
如果尚未出现,请单击侧边栏中的 Clusters(集群)。
会显示集群页面。
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 下拉菜单中选择您的集群并单击 Go to Atlas Search。
将显示 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。
单击该集合的 Search Indexes 标签页。
将显示 Atlas Search 页面。
单击集群的名称。
单击 Atlas Search 标签页。
将显示 Atlas Search 页面。
定义 Atlas Vector Search 索引。
将默认定义替换为以下索引定义。
此索引定义用于指定在 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dotProduct
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": "dotProduct" 8 }, 9 { 10 "type": "filter", 11 "path": "genres" 12 }, 13 { 14 "type": "filter", 15 "path": "year" 16 } 17 ] 18 }
通过 mongosh
连接到您的集群。
在终端窗口中打开 mongosh
并连接到您的集群。有关连接的详细说明,请参阅通过 mongosh 连接。
运行 db.collection.createSearchIndex()
方法。
以下索引定义在Atlas Vector Search索引中将 plot_embedding
字段索引为 vector
类型,并将 genres
和 year
字段索引为 filter
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
个向量维度,并使用 dotProduct
函数测量相似性。
1 db.embedded_movies.createSearchIndex( 2 "vector_index", 3 "vectorSearch", 4 { 5 "fields": [ 6 { 7 "type": "vector", 8 "path": "plot_embedding", 9 "numDimensions": 1536, 10 "similarity": "dotProduct" 11 }, 12 { 13 "type": "filter", 14 "path": "genres" 15 }, 16 { 17 "type": "filter", 18 "path": "year" 19 } 20 ] 21 } 22 );
定义索引。
创建一个名为 vector-index.go
的文件。将以下代码复制并粘贴到文件中,并替换 <connectionString>
占位符值。
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 "time" 8 9 "go.mongodb.org/mongo-driver/bson" 10 "go.mongodb.org/mongo-driver/mongo" 11 "go.mongodb.org/mongo-driver/mongo/options" 12 ) 13 14 func main() { 15 ctx := context.Background() 16 17 // Replace the placeholder with your Atlas connection string 18 const uri = "<connectionString>" 19 20 // Connect to your Atlas cluster 21 clientOptions := options.Client().ApplyURI(uri) 22 client, err := mongo.Connect(ctx, clientOptions) 23 if err != nil { 24 log.Fatalf("failed to connect to the server: %v", err) 25 } 26 defer func() { _ = client.Disconnect(ctx) }() 27 28 // Set the namespace 29 coll := client.Database("sample_mflix").Collection("embedded_movies") 30 indexName := "vector_index" 31 opts := options.SearchIndexes().SetName(indexName).SetType("vectorSearch") 32 33 type vectorDefinitionField struct { 34 Type string `bson:"type"` 35 Path string `bson:"path"` 36 NumDimensions int `bson:"numDimensions"` 37 Similarity string `bson:"similarity"` 38 } 39 40 type filterField struct { 41 Type string `bson:"type"` 42 Path string `bson:"path"` 43 } 44 45 type indexDefinition struct { 46 Fields []vectorDefinitionField `bson:"fields"` 47 } 48 49 vectorDefinition := vectorDefinitionField{ 50 Type: "vector", 51 Path: "plot_embedding", 52 NumDimensions: 1536, 53 Similarity: "dotProduct"} 54 genreFilterDefinition := filterField{"filter", "genres"} 55 yearFilterDefinition := filterField{"filter", "year"} 56 57 indexModel := mongo.SearchIndexModel{ 58 Definition: bson.D{{"fields", [3]interface{}{ 59 vectorDefinition, 60 genreFilterDefinition, 61 yearFilterDefinition}}}, 62 Options: opts, 63 } 64 65 // Create the index 66 searchIndexName, err := coll.SearchIndexes().CreateOne(ctx, indexModel) 67 if err != nil { 68 log.Fatalf("failed to create the search index: %v", err) 69 } 70 log.Println("New search index named " + searchIndexName + " is building.") 71 72 // Await the creation of the index. 73 log.Println("Polling to check if the index is ready. This may take up to a minute.") 74 searchIndexes := coll.SearchIndexes() 75 var doc bson.Raw 76 for doc == nil { 77 cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName)) 78 if err != nil { 79 fmt.Errorf("failed to list search indexes: %w", err) 80 } 81 82 if !cursor.Next(ctx) { 83 break 84 } 85 86 name := cursor.Current.Lookup("name").StringValue() 87 queryable := cursor.Current.Lookup("queryable").Boolean() 88 if name == searchIndexName && queryable { 89 doc = cursor.Current 90 } else { 91 time.Sleep(5 * time.Second) 92 } 93 } 94 95 log.Println(searchIndexName + " is ready for querying.") 96 }
此索引定义在Atlas Vector Search索引中将 plot_embedding
字段索引为 vector
类型,并将 genres
和 year
字段索引为 filter
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
个向量维度,并使用 dotProduct
函数测量相似性。
将 MongoDB Java Sync 驱动程序添加到您的项目中。
将Java驾驶员版本 5.2 或更高版本添加为项目,具体取决于您的包管理器:
如果使用 Maven,请将以下依赖项添加到项目的
pom.xml
文件的dependencies
大量中:pom.xml<dependencies> <!-- MongoDB Java Sync Driver v5.2.0 or later --> <dependency> <groupId>org.mongodb</groupId> <artifactId>mongodb-driver-sync</artifactId> <version>[5.2.0,)</version> </dependency> </dependencies> 运行包管理器以安装项目。
有关更详细的安装说明和版本兼容性信息,请参阅 MongoDB Java 驱动程序文档。
定义索引。
创建一个名为 VectorIndex.java
的文件。将以下代码复制并粘贴到文件中,并替换 <connectionString>
占位符值。
1 import com.mongodb.client.ListSearchIndexesIterable; 2 import com.mongodb.client.MongoClient; 3 import com.mongodb.client.MongoClients; 4 import com.mongodb.client.MongoCollection; 5 import com.mongodb.client.MongoCursor; 6 import com.mongodb.client.MongoDatabase; 7 import com.mongodb.client.model.SearchIndexModel; 8 import com.mongodb.client.model.SearchIndexType; 9 import org.bson.Document; 10 import org.bson.conversions.Bson; 11 12 import java.util.Arrays; 13 import java.util.Collections; 14 import java.util.List; 15 16 public class VectorIndex { 17 18 public static void main(String[] args) { 19 20 // Replace the placeholder with your Atlas connection string 21 String uri = "<connectionString>"; 22 23 // Connect to your Atlas cluster 24 try (MongoClient mongoClient = MongoClients.create(uri)) { 25 26 // Set the namespace 27 MongoDatabase database = mongoClient.getDatabase("sample_mflix"); 28 MongoCollection<Document> collection = database.getCollection("embedded_movies"); 29 30 // Define the index details with the filter fields 31 String indexName = "vector_index"; 32 Bson definition = new Document( 33 "fields", 34 Arrays.asList( 35 new Document("type", "vector") 36 .append("path", "plot_embedding") 37 .append("numDimensions", 1536) 38 .append("similarity", "dotProduct"), 39 new Document("type", "filter") 40 .append("path", "genres"), 41 new Document("type", "filter") 42 .append("path", "year"))); 43 44 // Define the index model 45 SearchIndexModel indexModel = new SearchIndexModel( 46 indexName, 47 definition, 48 SearchIndexType.vectorSearch()); 49 50 // Create the filtered index 51 try { 52 List<String> result = collection.createSearchIndexes(Collections.singletonList(indexModel)); 53 System.out.println("New search index named " + result.get(0) + " is building."); 54 } catch (Exception e) { 55 throw new RuntimeException("Error creating index: " + e); 56 } 57 58 // Wait for Atlas to build the index 59 System.out.println("Polling to check if the index is ready. This may take up to a minute."); 60 61 ListSearchIndexesIterable<Document> searchIndexes = collection.listSearchIndexes(); 62 Document doc = null; 63 while (doc == null) { 64 try (MongoCursor<Document> cursor = searchIndexes.iterator()) { 65 if (!cursor.hasNext()) { 66 break; 67 } 68 Document current = cursor.next(); 69 String name = current.getString("name"); 70 // When the index completes building, it becomes `queryable` 71 boolean queryable = current.getBoolean("queryable"); 72 if (name.equals(indexName) && queryable) { 73 doc = current; 74 } else { 75 Thread.sleep(500); 76 } 77 } catch (Exception e) { 78 throw new RuntimeException("Failed to list search indexes: " + e); 79 mongoClient.close(); 80 } 81 } 82 System.out.println(indexName + " is ready for querying."); 83 84 } catch (Exception e) { 85 throw new RuntimeException("Error connecting to MongoDB: " + e); 86 } 87 } 88 }
此索引定义在Atlas Vector Search索引中将 plot_embedding
字段索引为 vector
类型,并将 genres
和 year
字段索引为 filter
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
个向量维度,并使用 dotProduct
函数测量相似性。
运行该文件,以创建索引。
在 IDE 中运行该文件,或从命令行执行命令来运行代码。
javac VectorIndex.java java VectorIndex
Successfully created a vector index named: [vector_index] It may take up to a minute for the index to build before you can query using it. Polling to confirm the index has completed building. vector_index index is ready to query
定义索引。
创建一个名为 vector-index.js
的文件。将以下代码复制并粘贴到文件中,并替换 <connectionString>
占位符值。
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas deployment 4 const uri = "<connectionString>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 const database = client.db("sample_mflix"); 11 const collection = database.collection("embedded_movies"); 12 13 // define your Atlas Vector Search index 14 const index = { 15 name: "vector_index", 16 type: "vectorSearch", 17 definition: { 18 "fields": [ 19 { 20 "type": "vector", 21 "numDimensions": 1536, 22 "path": "plot_embedding", 23 "similarity": "dotProduct" 24 }, 25 { 26 "type": "filter", 27 "path": "genres" 28 }, 29 { 30 "type": "filter", 31 "path": "year" 32 } 33 ] 34 } 35 } 36 37 // run the helper method 38 const result = await collection.createSearchIndex(index); 39 console.log(`New search index named ${result} is building.`); 40 41 // wait for the index to be ready to query 42 console.log("Polling to check if the index is ready. This may take up to a minute.") 43 let isQueryable = false; 44 while (!isQueryable) { 45 const cursor = collection.listSearchIndexes(); 46 for await (const index of cursor) { 47 if (index.name === result) { 48 if (index.queryable) { 49 console.log(`${result} is ready for querying.`); 50 isQueryable = true; 51 } else { 52 await new Promise(resolve => setTimeout(resolve, 5000)); 53 } 54 } 55 } 56 } 57 } finally { 58 await client.close(); 59 } 60 } 61 run().catch(console.dir);
此索引定义在Atlas Vector Search索引中将 plot_embedding
字段索引为 vector
类型,并将 genres
和 year
字段索引为 filter
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
个向量维度,并使用 dotProduct
函数测量相似性。
定义索引。
创建一个名为 vector-index.py
的文件。将以下代码复制并粘贴到文件中,并替换 <connectionString>
占位符值。
1 from pymongo.mongo_client import MongoClient 2 from pymongo.operations import SearchIndexModel 3 import time 4 5 # Connect to your Atlas deployment 6 uri = "<connectionString>" 7 client = MongoClient(uri) 8 9 # Access your database and collection 10 database = client["sample_mflix"] 11 collection = database["embedded_movies"] 12 13 # Create your index model, then create the search index 14 search_index_model = SearchIndexModel( 15 definition={ 16 "fields": [ 17 { 18 "type": "vector", 19 "path": "plot_embedding", 20 "numDimensions": 1536, 21 "similarity": "dotProduct" 22 }, 23 { 24 "type": "filter", 25 "path": "genres" 26 }, 27 { 28 "type": "filter", 29 "path": "year" 30 } 31 ] 32 }, 33 name="vector_index", 34 type="vectorSearch", 35 ) 36 37 result = collection.create_search_index(model=search_index_model) 38 print("New search index named " + result + " is building.") 39 # Wait for initial sync to complete 40 print("Polling to check if the index is ready. This may take up to a minute.") 41 predicate=None 42 if predicate is None: 43 predicate = lambda index: index.get("queryable") is True 44 45 while True: 46 indices = list(collection.list_search_indexes(result)) 47 if len(indices) and predicate(indices[0]): 48 break 49 time.sleep(5) 50 print(result + " is ready for querying.") 51 52 client.close()
此索引定义在Atlas Vector Search索引中将 plot_embedding
字段索引为 vector
类型,并将 genres
和 year
字段索引为 filter
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
个向量维度,并使用 dotProduct
函数测量相似性。
使用 $vectorSearch
聚合管道阶段运行查询
Overview
本节演示如何使用 $vectorSearch
阶段查询 sample_mflix.embedded_movies
集合中的索引向量数据。这些示例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它们来对我们要执行语义搜索的数据进行预过滤。
步骤
AtlasGoClusters在Atlas中,Go项目的 页面。
如果尚未显示,请从导航栏上的 Organizations 菜单中选择包含所需项目的组织。
如果尚未显示,请从导航栏的Projects菜单中选择所需的项目。
如果尚未出现,请单击侧边栏中的 Clusters(集群)。
会显示集群页面。
转到 Collections(快速入门)页面。
单击集群的对应 Browse Collections 按钮。
显示数据浏览器。
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。
以下查询使用字符串 historical heist 的向量嵌入搜索
plot_embedding
字段。该查询使用ada-002-text
嵌入,它与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操作符,以对要执行语义搜索的文档进行预过滤。该过滤器使用
$and
聚合管道操作符查找符合以下两个条件的电影文档:按
genres
字段过滤,以查找不属于drama
、western
或crime
类型但属于action
、adventure
或family
类型的电影。按
year
字段过滤,以查找在1960
和2000
年份(含这两个年份)之间发行的电影。
1 [ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 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 martial arts。该查询使用ada-002-text
嵌入,它与plot_embedding
字段中的向量嵌入相同。它指定了若干个聚合管道和比较查询操作符来演示如何组合使用操作符来过滤数据。该过滤器使用
$or
聚合管道操作符来查找符合以下任一条件的电影文档:按
genres
字段过滤,查找不属于crime
类型的电影。按
year
字段进行过滤可查找在2015
年或之前上映的电影,按genres
字段进行过滤可查找属于action
类型的电影。
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18 "numCandidates": 200, 19 "limit": 10 20 } 21 }, 22 { 23 "$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。
通过 mongosh
连接到您的集群。
在终端窗口中打开 mongosh
并连接到您的集群。有关连接的详细说明,请参阅通过 mongosh 连接。
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。
以下查询使用字符串 historical heist 的向量嵌入搜索 plot_embedding
字段。该查询使用 ada-002-text
嵌入,它与 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操作符,以对要执行语义搜索的文档进行预过滤。
该过滤器使用 $and
聚合管道操作符查找符合以下两个条件的电影文档:
按
genres
字段过滤,以查找不属于drama
、western
或crime
类型但属于action
、adventure
或family
类型的电影。按
year
字段过滤,以查找在1960
和2000
年份(含这两个年份)之间发行的电影。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 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 martial arts。该查询使用 ada-002-text
嵌入,它与 plot_embedding
字段中的向量嵌入相同。它指定了若干个聚合管道和比较查询操作符来演示如何组合使用操作符来过滤数据。
该过滤器使用 $or
聚合管道操作符来查找符合以下任一条件的电影文档:
按
genres
字段过滤,查找不属于crime
类型的电影。按
year
字段进行过滤可查找在2015
年或之前上映的电影,按genres
字段进行过滤可查找属于action
类型的电影。
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding", 6 "filter": { 7 "$or": [{ 8 "genres": { "$ne": "Crime" } 9 }, { 10 "$and": [{ 11 "year": { "$lte": 2015 } 12 }, { 13 "genres": { "$eq": "Action" } 14 }] 15 }] 16 }, 17 "queryVector": 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18 "numCandidates": 200, 19 "limit": 10 20 } 21 }, 22 { 23 "$project": { 24 "_id": 0, 25 "title": 1, 26 "genres": 1, 27 "plot": 1, 28 "released": 1, 29 "score": { $meta: "vectorSearchScore" } 30 } 31 } 32 ])
1 [ 2 { 3 year: 2008, 4 plot: 'A young Thai boxer learns the skills and inner meaning of martial arts.', 5 genres: [ 'Action' ], 6 title: 'Ong-bak 2', 7 score: 0.7976737022399902 8 }, 9 { 10 plot: 'A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.', 11 genres: [ 'Action', 'Drama', 'Family' ], 12 title: 'The Karate Kid', 13 year: 1984, 14 score: 0.7929348349571228 15 }, 16 { 17 plot: 'A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.', 18 genres: [ 'Action', 'Adventure', 'Fantasy' ], 19 title: 'Circle of Iron', 20 year: 1978, 21 score: 0.7852014899253845 22 }, 23 { 24 plot: 'A lionized account of the life of the martial arts superstar.', 25 genres: [ 'Action', 'Biography', 'Drama' ], 26 title: 'Dragon: The Bruce Lee Story', 27 year: 1993, 28 score: 0.7763040661811829 29 }, 30 { 31 plot: 'A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.', 32 genres: [ 'Action', 'Thriller' ], 33 title: 'Kung Fu Killer', 34 year: 2014, 35 score: 0.7731232643127441 36 }, 37 { 38 plot: 'A young woman is trained by a martial arts specialist to become a professional assassin.', 39 genres: [ 'Action', 'Crime', 'Romance' ], 40 title: 'Naked Killer', 41 year: 1992, 42 score: 0.7693743109703064 43 }, 44 { 45 plot: 'The life of the greatest karate master of a generation.', 46 genres: [ 'Documentary', 'Action', 'History' ], 47 title: 'The Real Miyagi', 48 year: 2015, 49 score: 0.768799901008606 50 }, 51 { 52 plot: 'At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.', 53 genres: [ 'Action', 'Drama', 'Sport' ], 54 title: 'Never Back Down', 55 year: 2008, 56 score: 0.768179714679718 57 }, 58 { 59 plot: 'Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.', 60 genres: [ 'Action', 'Drama' ], 61 title: 'Dragon Tiger Gate', 62 year: 2006, 63 score: 0.7679706811904907 64 }, 65 { 66 year: 2001, 67 plot: 'A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.', 68 genres: [ 'Action', 'Comedy', 'Sport' ], 69 title: 'Shaolin Soccer', 70 score: 0.7660527229309082 71 } 72 ] 73
将想要尝试的预过滤运算符的代码复制并粘贴到 AtlasVectorSearchTutorial.go
文件中。
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。
以下查询使用字符串 historical heist 的向量嵌入搜索 plot_embedding
字段。该查询使用 ada-002-text
嵌入,它与 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操作符,以对要执行语义搜索的文档进行预过滤。
该过滤器使用 $and
聚合管道操作符查找符合以下两个条件的电影文档:
按
genres
字段过滤,以查找不属于drama
、western
或crime
类型但属于action
、adventure
或family
类型的电影。按
year
字段过滤,以查找在1960
和2000
年份(含这两个年份)之间发行的电影。
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/bson" 9 "go.mongodb.org/mongo-driver/mongo" 10 "go.mongodb.org/mongo-driver/mongo/options" 11 ) 12 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(ctx, clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [1536]float64{ 31 -0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 0.010111088, 0.0074623227, 0.009555174, 0.008338705, -0.002240005, -0.0010603234, -0.004973792, 0.003391073, 0.021543289, 0.013341927, 0.0005980159, 0.010693162, 0.005336771, 0.016062634, 0.005768421, 0.005186347, 0.039790336, 0.0021942237, -0.0026275094, 0.010431555, 0.0042151334, -0.0050359233, 0.025768232, -0.021451725, 0.01833861, -0.01836477, -0.013433489, 0.030006256, -0.014793842, 0.017475309, 0.0020585153, -0.012975678, -0.017266022, -0.01593183, -0.014257549, 0.0010676811, -0.007887433, -0.0045911926, 0.00012303676, -0.0014976967, 0.03552615, 0.0065630507, -0.037435878, 0.011929252, -0.00939167, 0.016768971, 0.01223664, 0.007789331, -0.037200432, 0.013145722, 0.00896002, 0.021857215, 0.010333453, 0.021582529, -0.007089534, -0.007154935, -0.02485261, 0.0040254686, -0.00088864425, 0.023466095, -0.020719228, -0.006690584, -0.021006994, -0.018286288, 0.025545865, -0.0096598165, 0.008803056, -0.023021365, -0.040078104, 0.015408617, 0.017043658, -0.011242535, 0.0063537657, -0.026618453, 0.0071614753, -0.014623798, 0.00067322777, -0.00083427917, -0.028070368, 0.03714811, -0.004529061, 0.0054087127, 0.0028727653, 0.008384486, 0.010026066, -0.006190262, -0.0002493436, 0.0029953935, -0.026226042, -0.018417092, 0.009941043, 0.0036494094, -0.00982332, 0.013551212, 0.02574207, -0.0022645304, -0.0006004685, 0.012805633, -0.024303235, 0.008194821, -0.014179068, -0.02977081, 0.003095131, -0.0015941641, 0.029953934, 0.0052680993, 0.025388902, -0.031392768, -0.021386323, 0.014898485, 0.022419669, 0.00897964, 0.013243824, 0.006854088, 0.0066415328, -0.003839074, -0.01877026, 0.021216279, -0.015055449, -0.0015508354, 0.013211124, -0.008783435, 0.0052157775, -0.68938524, -0.01221702, -0.04125533, -0.016232677, 0.020039052, -0.0026422248, -0.0037050007, 0.0064682183, -0.0047579664, 0.0032749851, -0.0035382267, 0.031942144, -0.00035643874, -0.011628405, -0.043086577, -0.0196074, -0.0066088317, -0.014872325, 0.028331975, 0.010294212, -0.013930541, 0.031994462, -0.018626377, 0.017462227, 0.026343765, -0.010274592, 0.0046827546, -0.029430721, -0.011746128, 0.0024362097, 0.0023054064, 0.0027730279, -0.002406779, 0.003917556, 0.059436977, 0.008665713, -0.0018901062, 0.06037876, 0.017880797, 0.05185039, 0.0067102043, -0.020300657, 0.005604917, 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0.018652538, -0.02443404, -0.025637427, 0.006497649, -0.015447859, 0.01917575, -0.016520444, -0.008678793, -0.021072397, 0.015840268, -0.006324335, 0.025925195, -0.03594472, 0.0384823, 0.01308032, 0.0054217926, 0.00448328, -0.027207067, -0.016847452, 0.0036003583, 0.01061468, -0.019816685, -0.004659864, 0.023387613, -0.005461034, 0.004326316, 0.0037278912, -0.007540805, 0.00860031, 0.0015524705, 0.020039052, -0.0028367946, 0.0049509015, 0.009162764, 0.009705598, 0.013982862, 0.004852799, 0.0061869915, -0.0083910255, 0.012975678, -0.034558207, -0.029064473, -0.03058179, -0.019450437, 0.01062122, -0.014179068, -0.010012985, 0.007874353, -0.014126746, -0.009731758, -0.03398267, -0.000115883464, -0.0029725027, -0.024290156, 0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783, 32 } 33 34 genresFilterCondition := bson.D{ 35 {"$and", bson.A{ 36 bson.D{{"genres", bson.D{{"$nin", bson.A{"Drama", "Western", "Crime"}}}}}, 37 bson.D{{"genres", bson.D{{"$in", bson.A{"Action", "Adventure", "Family"}}}}}, 38 }}} 39 40 yearFilterCondition := bson.D{ 41 {"$and", bson.A{ 42 bson.D{{"year", bson.D{{"$gte", 1960}}}}, 43 bson.D{{"year", bson.D{{"$lte", 2000}}}}, 44 }}, 45 } 46 47 filter := bson.D{{"$and", bson.A{ 48 genresFilterCondition, yearFilterCondition, 49 }}, 50 } 51 52 vectorSearchStage := bson.D{ 53 {"$vectorSearch", bson.D{ 54 {"index", "vector_index"}, 55 {"path", "plot_embedding"}, 56 {"filter", filter}, 57 {"queryVector", queryVector}, 58 {"numCandidates", 200}, 59 {"limit", 10}, 60 }}, 61 } 62 63 projectStage := bson.D{ 64 {"$project", bson.D{ 65 {"_id", 0}, 66 {"plot", 1}, 67 {"title", 1}, 68 {"genres", 1}, 69 {"year", 1}, 70 {"score", bson.D{{"$meta", "vectorSearchScore"}}}, 71 }}} 72 73 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage}) 74 if err != nil { 75 log.Fatalf("failed to retrieve data from the server: %v", err) 76 } 77 // display the results 78 type ProjectedMovieResult struct { 79 Title string `bson:"title"` 80 Plot string `bson:"plot"` 81 Genres []string `bson:"genres"` 82 Year int32 `bson:"year"` 83 Score float64 `bson:"score"` 84 } 85 86 var results []ProjectedMovieResult 87 if err = cursor.All(ctx, &results); err != nil { 88 log.Fatalf("failed to unmarshal results to ProjectedMovieResult objects: %v", err) 89 } 90 for _, result := range results { 91 fmt.Printf("Title: %v \nPlot: %v \nGenres: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Genres, result.Year, result.Score) 92 } 93 }
以下查询使用向量嵌入在 plot_embedding
字段中搜索字符串 martial arts。该查询使用 ada-002-text
嵌入,它与 plot_embedding
字段中的向量嵌入相同。它指定了若干个聚合管道和比较查询操作符来演示如何组合使用操作符来过滤数据。
该过滤器使用 $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
字段进行过滤可查找在2015
年或之前上映的电影,按genres
字段进行过滤可查找属于action
类型的电影。
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 8 "go.mongodb.org/mongo-driver/bson" 9 "go.mongodb.org/mongo-driver/mongo" 10 "go.mongodb.org/mongo-driver/mongo/options" 11 ) 12 13 func main() { 14 ctx := context.Background() 15 16 // Replace the placeholder with your Atlas connection string 17 const uri = "<connection-string>" 18 19 // Connect to your Atlas cluster 20 clientOptions := options.Client().ApplyURI(uri) 21 client, err := mongo.Connect(ctx, clientOptions) 22 if err != nil { 23 log.Fatalf("failed to connect to the server: %v", err) 24 } 25 defer func() { _ = client.Disconnect(ctx) }() 26 27 // Set the namespace 28 coll := client.Database("sample_mflix").Collection("embedded_movies") 29 30 queryVector := [1536]float64{ 31 -0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 0.014710125, 0.0060869483, 0.016192842, 0.0026484078, 0.002417546, -0.007764761, -0.0053781047, 0.0016843157, 0.009071894, 0.0026158919, -0.016465975, 0.019054228, 0.030720878, 0.028145632, -0.0124340225, -0.011491066, -0.01273967, -0.0076542073, 0.00685432, 0.002936172, 0.03194347, -0.030798918, -0.004961903, 0.012661632, -0.024646938, 0.0085256295, -0.01133499, 0.016648063, -0.017857648, 0.01627088, -0.020823086, -0.002233832, 0.008330535, 0.024894057, 0.008558145, 0.013786677, 0.027235191, -0.0075046346, -0.015308415, -0.0031442728, 0.009136925, 0.010548109, 0.009455579, -0.0070624207, -0.001147806, 0.008928824, 0.011042348, -0.019314354, -0.0035702293, 0.019691538, 0.013929747, -0.00071615935, -0.0016477356, -0.0029556816, 0.009592146, 0.011699166, -0.045183886, 0.018533977, -0.025063138, 0.002194813, -0.013968766, 0.0037848332, -0.014775156, 0.0138777215, -0.023112195, -0.019288342, -0.02252691, -0.009813253, -0.0030792414, 0.00088036386, 0.0048285886, -0.019691538, -0.021863589, 0.0068413136, 0.01641395, -0.04494977, -0.0027475806, -0.014710125, 0.0077192388, -0.0030954992, 0.005417124, -0.008219982, 0.014046803, 0.01662205, 0.027235191, 0.0015266144, 0.036261562, 0.02006872, -0.032489736, -0.0019379386, 0.004106739, -0.006808798, 0.048825648, 0.0096766865, 0.0132794315, 0.0066722315, -0.026116649, 0.019912645, -0.017441448, -0.0014128092, -0.041880284, -0.018599007, 0.018729072, 0.00619425, -0.01947043, -0.009748221, -0.012121871, 0.0001831043, 0.037198015, 0.03451872, -0.0045684627, -0.0040742233, 0.022708999, -0.028847972, 0.027287217, 0.0008214291, 0.018156795, -0.007816786, -0.017519485, 0.00501718, -0.0013811064, -0.0148401875, 0.012752676, -0.01686917, -0.0018013725, -0.014645093, -0.0064218603, 0.016426956, 0.008558145, 0.013207897, -0.012518563, -0.00018208819, 0.022643967, 0.020745048, -0.018143788, 0.0123169655, -0.018039737, 0.019626506, -0.009162938, 0.0030255904, -0.02942025, -0.007511138, 0.0050139283, -0.006408854, 0.04065769, 0.011419531, 0.000562116, 0.032775875, -0.012043833, 0.009410057, -0.012694148, -0.019041222, 0.015646579, 0.021109223, -0.010749706, -0.01915828, -0.6842354, -0.022748018, 0.010444058, -0.013090841, 0.031241132, 0.0032792133, 0.030772904, -0.0061389734, -0.018013725, -0.0103335045, 0.0075046346, 0.012395004, -0.0035442165, -0.011256952, 0.017064264, -0.00813544, 0.014319936, -0.044559583, 0.0053065703, 0.00027028716, -0.014892213, 0.012499054, 0.002056621, -0.006860823, -0.020875111, -0.0028890243, -0.01048958, 0.0043993806, -0.004360362, 0.01922331, -0.031501256, 0.025882537, 0.0032922195, -0.0058300737, 0.053117726, 0.0013900483, -0.029784426, 0.055875063, 0.02417871, 0.038888834, -0.00365477, -0.0024793257, 0.0005182197, -0.008805265, -0.007589176, 0.013643608, 0.03685985, -0.024152698, 0.00400594, -0.0148401875, 0.01971755, 0.0002887805, -0.0050594504, -0.020120746, 0.00209564, 0.00084947393, 0.004688771, -0.013526551, 0.016140817, 0.024191717, 0.0020354858, 0.008200472, -0.014463005, -0.012453532, 0.0034596757, 0.0014843439, -0.0266369, 0.0069648735, -0.0044318964, -0.015620566, 0.0060154134, 0.018937172, -0.01588069, -0.0025882535, 0.03251575, 0.062118087, 0.017805625, -0.0098522715, -0.013968766, -0.016153824, 0.01641395, 0.0018794102, -0.019964669, 0.012290953, 0.024855038, -0.015334427, -0.012017821, 0.007647704, -0.01402079, -0.0008665447, 0.0069778794, 0.014007784, -0.009709203, -0.011022839, -0.017311385, -0.0050139283, -0.0033393675, 0.0025573636, 0.010463567, -0.028691897, -0.016752115, 0.008974346, 0.024373805, -0.003908393, -0.026142662, 0.015906705, -0.012440525, 0.006808798, 0.033998467, -0.006808798, 0.011764198, 0.0033913925, 0.003986431, 0.0067632757, 0.012154386, -0.025609404, 0.008616674, 0.0023037407, 0.028093606, -0.02762538, 0.0077127353, -0.015828667, 0.014033797, 0.009039378, 0.0032905939, 0.011829229, -0.020133752, -0.0066137034, 0.008902812, -0.020667009, 0.008655692, 0.020693023, 0.022266785, -0.01216089, 0.03620954, -0.0069973893, 0.0014347574, -0.0002489487, 0.020536946, 0.0008933702, -0.021135237, -0.0017704825, -0.007875314, -0.0022078194, 0.005384608, -0.030200627, -0.010821241, -0.0011136644, -0.013383482, 0.01986062, 0.007647704, 0.010353014, -0.0065454203, 0.0041490095, 0.005170004, -0.0070949364, -0.04031953, -0.0148401875, -0.0075826724, -0.020510934, 0.0012607982, 0.006425112, -0.017441448, 0.011165908, -0.0019606997, -0.017532492, -0.017649548, 0.019548468, -0.023593428, -0.027989557, 0.014944238, -0.007589176, 0.018039737, 0.014593068, -0.000074125746, 0.029342212, -0.0027589612, 0.00043571103, -0.017207334, 0.003755569, -0.008896309, -0.027677406, -0.0058365767, -0.010665165, 0.0009974206, 0.01662205, -0.0016526129, 0.005726023, -0.012193406, 0.013526551, -0.027183166, 0.032567773, -0.01203733, -0.004792821, 0.009540121, 0.006899842, -0.010918789, 0.022006659, 0.020953149, 0.0073745716, 0.011022839, -0.025505353, 0.018937172, 0.0181698, 0.013292438, -0.038706746, -0.0021818068, -0.025050133, 0.041984335, -0.004565211, 0.00012589691, -0.004968406, -0.004835092, -0.023788521, 0.008785755, 0.013578577, -0.012473041, 0.008473604, -0.01922331, 0.012499054, 0.016218856, -0.018338881, 0.024295768, -0.0015688848, 0.012687645, 0.037198015, -0.004158764, 0.023294283, -0.010918789, -0.01405981, -0.015412465, 0.0056187212, -0.010235958, 0.007114446, 0.01170567, 0.015048289, 0.027547343, -0.039461114, 0.034856882, 0.004243305, 0.006025168, 0.012941268, 0.02853582, 0.004786318, 0.015022276, -0.0034531725, 0.04073573, -0.027313229, -0.006386093, 0.0016266003, -0.034102518, -0.0015705107, -0.020836093, -0.0064218603, 0.007933843, -0.028769935, 0.009936812, 0.0038303551, 0.022682985, 0.020797072, 0.0044253934, 0.020406883, 0.012082852, -0.0028955275, 0.007972862, -0.0057032625, 0.002684175, 0.0074526095, -0.022344822, 0.0052838093, -0.004965155, -0.0088312775, 0.023333302, -0.009566133, 0.013266426, 0.0070168986, 0.008850787, -0.0021525426, -0.0006653535, 0.016192842, -0.0039409087, -0.049527988, 0.0058008097, -0.01205684, -0.010268473, -0.016518, -0.030954992, -0.0022159482, -0.038914848, 0.010730197, -0.0006629148, 0.012902249, -0.0037880847, 0.0011136644, -0.036079474, 0.010645656, 0.012785193, -0.0056122183, 0.007888321, -0.0036059965, -0.013435507, -0.004327846, -0.02762538, -0.0006722631, 0.02428276, 0.024724975, -0.007790773, 0.00027557096, 0.00685432, -0.025440322, 0.011634135, 0.011068361, -0.003983179, 0.020901123, 0.019210305, 0.011035845, -0.006737263, -0.01405981, 0.01205684, -0.000036885052, -0.011185418, -0.02287808, -0.010340008, -0.017805625, 0.10004445, -0.0103920335, -0.0067112506, 0.0101579195, -0.019587487, -0.023658458, -0.014996263, -0.0056252247, 0.003189795, 0.011022839, -0.009462083, -0.017155308, -0.010053869, 0.0041977833, 0.0023427596, 0.01216089, -0.004285576, -0.003175163, 0.0022809797, -0.006376338, 0.028431771, -0.023112195, 0.03280189, 0.025843518, 0.028509809, 0.0067437664, 0.000053498567, 0.009527114, 0.008259, -0.03293195, -0.0032401944, 0.0038108458, 0.0053553437, 0.013734652, -0.0080443965, -0.0177536, 0.014931232, 0.006051181, 0.034128528, -0.030408729, 0.015659584, 0.04341503, 0.00082183554, -0.01831287, 0.012421016, -0.024048647, -0.01655702, 0.01627088, -0.0214734, -0.025856523, 0.019821601, 0.01191377, -0.026337756, -0.030044552, 0.0096051525, 0.026090637, -0.00149166, 0.019275336, -0.0049586515, -0.011120386, -0.0010518845, -0.020549953, 0.020927135, 0.0032922195, 0.004054714, -0.020836093, 0.00082102267, -0.015763635, -0.0068478165, -0.0015916459, -0.014124841, -0.008870296, 0.010951304, -0.019678531, 0.0029410494, -0.008070408, 0.03092898, -0.008141943, 0.0028093606, 0.017129296, -0.02671494, 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0.009975832, -0.0009982334, -0.018586002, -0.0137606645, -0.031787395, 0.02481602, 0.0059308726, -0.0011941409, -0.009598649, 0.0126811415, 0.015828667, -0.0058918535, -0.0040807263, -0.00406772, -0.0015233628, -0.01402079, -0.0052577965, 0.0018290109, -0.018182807, -0.014736137, 0.014423986, -0.001820882, -0.013799684, 0.0029085337, 0.009949819, 0.02407466, 0.01992565, -0.01986062, 0.02892601, 0.0029768168, 0.00044018193, -0.012882739, 0.022487892, 0.0061682374, 0.014528036, 0.0021785551, 0.006304804, 0.0010722068, -0.0075696665, -0.0040417076, 0.0097937435, -0.01216089, -0.0044026324, -0.018494958, 0.053898104, -0.0037100469, -0.013929747, 0.0210572, 0.014293923, 0.028613858, -0.005791055, -0.008863793, -0.021668496, 0.0011803217, 0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914, 32 } 33 34 genresFilterCondition := bson.D{{"genres", bson.D{{"$ne", "Crime"}}}} 35 36 yearAndGenresFilterCondition := bson.D{ 37 {"$and", bson.A{ 38 bson.D{{"year", bson.D{{"$lte", 2015}}}}, 39 bson.D{{"genres", bson.D{{"$eq", "Action"}}}}, 40 }}, 41 } 42 43 filter := bson.D{{"$or", bson.A{ 44 genresFilterCondition, yearAndGenresFilterCondition, 45 }}, 46 } 47 48 vectorSearchStage := bson.D{ 49 {"$vectorSearch", bson.D{ 50 {"index", "vector_index"}, 51 {"path", "plot_embedding"}, 52 {"filter", filter}, 53 {"queryVector", queryVector}, 54 {"numCandidates", 200}, 55 {"limit", 10}, 56 }}, 57 } 58 59 projectStage := bson.D{ 60 {"$project", bson.D{ 61 {"_id", 0}, 62 {"plot", 1}, 63 {"title", 1}, 64 {"genres", 1}, 65 {"year", 1}, 66 {"score", bson.D{{"$meta", "vectorSearchScore"}}}, 67 }}} 68 69 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage}) 70 if err != nil { 71 log.Fatalf("failed to retrieve data from the server: %v", err) 72 } 73 // display the results 74 type ProjectedMovieResult struct { 75 Title string `bson:"title"` 76 Plot string `bson:"plot"` 77 Genres []string `bson:"genres"` 78 Year int32 `bson:"year"` 79 Score float64 `bson:"score"` 80 } 81 82 var results []ProjectedMovieResult 83 if err = cursor.All(ctx, &results); err != nil { 84 log.Fatalf("failed to unmarshal results to ProjectedMovieResult objects: %v", err) 85 } 86 for _, result := range results { 87 fmt.Printf("Title: %v \nPlot: %v \nGenres: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Genres, result.Year, result.Score) 88 } 89 }
将 <connection-string>
替换为您的 Atlas 连接字符串,然后保存文件。
确保您的连接字符串包含数据库用户的档案。要了解详情,请参阅通过驱动程序连接。
编译并运行 AtlasVectorSearchTutorial.go
文件。
go run AtlasVectorSearchTutorial.go
1 Title: Cliffhanger 2 Plot: A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains. 3 Genres: [Action Adventure Thriller] 4 Year: 1993 5 Score: 0.7694521546363831 6 7 Title: It's a Mad, Mad, Mad, Mad World 8 Plot: The dying words of a thief spark a madcap cross-country rush to find some treasure. 9 Genres: [Action Adventure Comedy] 10 Year: 1963 11 Score: 0.7638954520225525 12 13 Title: Hudson Hawk 14 Plot: A cat burglar is forced to steal Da Vinci works of art for a world domination plot. 15 Genres: [Action Adventure Comedy] 16 Year: 1991 17 Score: 0.7538407444953918 18 19 Title: Escape from New York 20 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. 21 Genres: [Action Sci-Fi] 22 Year: 1981 23 Score: 0.7487208843231201 24 25 Title: The Romanov Stones 26 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. 27 Genres: [Action Thriller] 28 Year: 1993 29 Score: 0.7467736005783081 30 31 Title: Crime Busters 32 Plot: An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store. 33 Genres: [Action Comedy Adventure] 34 Year: 1977 35 Score: 0.7437351942062378 36 37 Title: Three Kings 38 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. 39 Genres: [Action Adventure Comedy] 40 Year: 1999 41 Score: 0.7425670623779297 42 43 Title: Black Moon Rising 44 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 ... 45 Genres: [Action Sci-Fi Thriller] 46 Year: 1986 47 Score: 0.7397696375846863 48 49 Title: Deep Rising 50 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. 51 Genres: [Action Adventure Horror] 52 Year: 1998 53 Score: 0.7392246127128601 54 55 Title: That Man from Rio 56 Plot: A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues. 57 Genres: [Action Adventure Comedy] 58 Year: 1964 59 Score: 0.7357995510101318
go run AtlasVectorSearchTutorial.go
1 Title: Ong-bak 2 2 Plot: A young Thai boxer learns the skills and inner meaning of martial arts. 3 Genres: [Action] 4 Year: 2008 5 Score: 0.7976737022399902 6 7 Title: The Karate Kid 8 Plot: A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting. 9 Genres: [Action Drama Family] 10 Year: 1984 11 Score: 0.7929348349571228 12 13 Title: Circle of Iron 14 Plot: A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life. 15 Genres: [Action Adventure Fantasy] 16 Year: 1978 17 Score: 0.7852014899253845 18 19 Title: Dragon: The Bruce Lee Story 20 Plot: A lionized account of the life of the martial arts superstar. 21 Genres: [Action Biography Drama] 22 Year: 1993 23 Score: 0.7763040661811829 24 25 Title: Kung Fu Killer 26 Plot: A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom. 27 Genres: [Action Thriller] 28 Year: 2014 29 Score: 0.7731232643127441 30 31 Title: Naked Killer 32 Plot: A young woman is trained by a martial arts specialist to become a professional assassin. 33 Genres: [Action Crime Romance] 34 Year: 1992 35 Score: 0.7693743109703064 36 37 Title: The Real Miyagi 38 Plot: The life of the greatest karate master of a generation. 39 Genres: [Documentary Action History] 40 Year: 2015 41 Score: 0.768799901008606 42 43 Title: Never Back Down 44 Plot: At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran. 45 Genres: [Action Drama Sport] 46 Year: 2008 47 Score: 0.768179714679718 48 49 Title: Dragon Tiger Gate 50 Plot: Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice. 51 Genres: [Action Drama] 52 Year: 2006 53 Score: 0.7679706811904907 54 55 Title: Shaolin Soccer 56 Plot: A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage. 57 Genres: [Action Comedy Sport] 58 Year: 2001 59 Score: 0.7660527229309082
将想要尝试的预过滤运算符的代码复制并粘贴到 AtlasVectorSearchTutorial.java
文件中。
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。
以下查询使用字符串 historical heist 的向量嵌入搜索 plot_embedding
字段。该查询使用 ada-002-text
嵌入,它与 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操作符,以对要执行语义搜索的文档进行预过滤。
该过滤器使用 $and
聚合管道操作符查找符合以下两个条件的电影文档:
按
genres
字段过滤,以查找不属于drama
、western
或crime
类型但属于action
、adventure
或family
类型的电影。按
year
字段过滤,以查找在1960
和2000
年份(含这两个年份)之间发行的电影。
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 6 import com.mongodb.client.MongoClient; 7 import com.mongodb.client.MongoClients; 8 import com.mongodb.client.MongoCollection; 9 import com.mongodb.client.MongoDatabase; 10 import com.mongodb.client.model.*; 11 import com.mongodb.client.model.search.VectorSearchOptions; 12 import java.util.Arrays; 13 import java.util.List; 14 import com.mongodb.client.model.search.FieldSearchPath; 15 import org.bson.Document; 16 import org.bson.conversions.Bson; 17 18 public class AtlasVectorSearchTutorial { 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 = (asList(-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 0.010111088, 0.0074623227, 0.009555174, 0.008338705, -0.002240005, -0.0010603234, -0.004973792, 0.003391073, 0.021543289, 0.013341927, 0.0005980159, 0.010693162, 0.005336771, 0.016062634, 0.005768421, 0.005186347, 0.039790336, 0.0021942237, -0.0026275094, 0.010431555, 0.0042151334, -0.0050359233, 0.025768232, -0.021451725, 0.01833861, -0.01836477, -0.013433489, 0.030006256, -0.014793842, 0.017475309, 0.0020585153, -0.012975678, -0.017266022, -0.01593183, -0.014257549, 0.0010676811, -0.007887433, -0.0045911926, 0.00012303676, -0.0014976967, 0.03552615, 0.0065630507, -0.037435878, 0.011929252, -0.00939167, 0.016768971, 0.01223664, 0.007789331, -0.037200432, 0.013145722, 0.00896002, 0.021857215, 0.010333453, 0.021582529, -0.007089534, -0.007154935, -0.02485261, 0.0040254686, -0.00088864425, 0.023466095, -0.020719228, -0.006690584, -0.021006994, -0.018286288, 0.025545865, -0.0096598165, 0.008803056, -0.023021365, -0.040078104, 0.015408617, 0.017043658, -0.011242535, 0.0063537657, -0.026618453, 0.0071614753, -0.014623798, 0.00067322777, -0.00083427917, -0.028070368, 0.03714811, -0.004529061, 0.0054087127, 0.0028727653, 0.008384486, 0.010026066, -0.006190262, -0.0002493436, 0.0029953935, -0.026226042, -0.018417092, 0.009941043, 0.0036494094, -0.00982332, 0.013551212, 0.02574207, -0.0022645304, -0.0006004685, 0.012805633, -0.024303235, 0.008194821, -0.014179068, -0.02977081, 0.003095131, -0.0015941641, 0.029953934, 0.0052680993, 0.025388902, -0.031392768, -0.021386323, 0.014898485, 0.022419669, 0.00897964, 0.013243824, 0.006854088, 0.0066415328, -0.003839074, -0.01877026, 0.021216279, -0.015055449, -0.0015508354, 0.013211124, -0.008783435, 0.0052157775, -0.68938524, -0.01221702, -0.04125533, -0.016232677, 0.020039052, -0.0026422248, -0.0037050007, 0.0064682183, -0.0047579664, 0.0032749851, -0.0035382267, 0.031942144, -0.00035643874, -0.011628405, -0.043086577, -0.0196074, -0.0066088317, -0.014872325, 0.028331975, 0.010294212, -0.013930541, 0.031994462, -0.018626377, 0.017462227, 0.026343765, -0.010274592, 0.0046827546, -0.029430721, -0.011746128, 0.0024362097, 0.0023054064, 0.0027730279, -0.002406779, 0.003917556, 0.059436977, 0.008665713, -0.0018901062, 0.06037876, 0.017880797, 0.05185039, 0.0067102043, -0.020300657, 0.005604917, 0.018704858, 0.012073136, 0.0144145135, 0.012413224, -0.0074819434, 0.015801027, -0.0061412104, 0.008613391, -0.0039077457, -0.0036232488, 0.008469507, 0.014087505, 0.0124066835, 0.019267311, -0.002573553, 0.005055544, -0.009417831, -0.009103903, 0.011150973, -0.012046975, 0.0058567133, -0.0053727417, 0.018260127, -0.005588567, 0.015591742, 0.007495024, -0.02567667, 0.024211673, 0.021386323, -0.012890656, -0.016114954, 0.009515933, 0.009679437, 0.025532786, -0.0076454473, -0.02575515, 0.008319084, -0.0068410076, -0.017082898, -0.026173722, -0.0049901423, 0.01918883, -0.008646091, -0.031759016, 0.014820003, 0.011850771, 0.01836477, 0.012700991, -0.0011437106, 0.005058814, 0.0151993325, -0.0060692686, 0.027416352, 0.0037344315, 0.0013546307, 0.018325528, -0.03152357, -0.008809595, 0.014649959, -0.008345244, 0.0066415328, -0.005523165, 0.0043492066, -0.0015892589, 0.0048855, 0.034453563, -0.03837766, 0.0068410076, -0.0042151334, -0.0067429054, 0.0055689462, -0.011733048, -0.0212032, 0.016847452, -0.0022220195, 0.0059351954, -0.00449963, 0.02251123, -0.01020265, 0.023361452, -0.0032455544, 0.016180357, 0.0049443613, -0.0064747585, -0.03259616, 0.012321662, 0.020104453, 0.009954124, -0.019411195, 0.0048102876, -0.000392614, 0.012184318, 0.0044276887, 0.005634348, -0.020562263, 0.015722545, -0.005179807, -0.0067952266, 0.0027861083, 0.0024198592, -0.0020585153, 0.0018525004, -0.045100946, -0.010176489, -0.012956058, 0.0013497255, 0.0105361985, 0.003796563, -0.0106016, -0.013126101, 0.0050359233, 0.015003128, -0.0075800456, -0.015722545, -0.01755379, -0.00978408, -0.02940456, 0.017606111, 0.016612006, -0.016912855, 0.025441224, 0.0054741143, 0.00448001, 0.009470152, 0.015382457, -0.008332164, -0.019123428, 0.024564842, 0.016860534, 0.008286383, -0.007141855, 0.006559781, 0.016625088, -0.01840401, -0.011602244, -0.00489858, -0.0073184394, -0.008809595, -0.0018459603, -0.01629808, -0.005542786, 0.0064257076, 0.010379234, 0.014663039, 0.034872133, -0.013355007, 0.027285548, 0.011654565, -0.004032009, 0.02323065, -0.02653997, -0.0009941043, 0.002946342, 0.010667001, 0.008345244, 0.018626377, 0.04821406, 0.031392768, 0.010281132, 0.026069079, 0.002735422, 0.01182461, -0.01593183, 0.006585941, -0.010071847, 0.024564842, -0.0025261368, 0.004293615, -0.0068606283, -0.0066448026, -0.0074100015, -0.0014347476, 0.021530207, -0.010418476, 0.018495573, -0.0034924455, -0.014165987, -0.004784127, -0.012472086, 0.004417878, -0.0030313642, -0.010084927, -0.010954768, 0.01508161, 0.0010047321, 0.0042347535, -0.03345946, -0.00027346043, 0.014793842, -0.019882087, 0.012772933, 0.021490967, 0.0031932332, 0.0093589695, 0.00090172456, 0.0048102876, 0.0070045115, -0.0045584915, 0.015840268, 0.024342475, -0.0091300635, 0.0039796876, 0.003796563, 0.025022654, -0.008103259, -0.025022654, 0.03021554, -0.008201361, -0.0070502926, 0.0011821339, 0.021072397, 0.004849529, -0.02495725, 0.012184318, 0.0019228071, -0.007226877, 0.020562263, 0.018861823, -0.0017593032, 0.01345965, 0.0022727058, 0.003023189, -0.026971621, -0.0030558899, 0.017723834, -0.01998673, -0.010608139, 0.011491061, -0.025179617, 0.0069652707, 0.003924096, 0.021177039, 0.0045650317, -0.0009973744, 0.007586586, -0.004032009, -0.008129419, -0.010091467, -0.04279881, 0.019790525, 0.01595799, 0.0044309585, -0.0033747226, -0.018665617, -0.012818714, -0.016206518, 0.014113666, -0.0020912162, 0.01427063, -0.020248337, -0.0112752365, -0.020588424, -0.011039791, 0.008744194, -0.015147011, 0.0022269245, -0.010438096, -0.0017772885, -0.028750544, -0.008861917, -0.016991336, 0.033668745, 0.034636687, 0.009888723, 0.0023953337, 0.006991431, -0.003346927, 0.003103306, -0.0044571194, 0.011249076, 0.0033779927, 0.00012446742, -0.0027027212, -0.025859794, -0.011942333, 0.02694546, 0.028227331, 0.0064289775, -0.03385187, -0.020719228, 0.00489531, 0.10663077, 0.041752383, -0.021700252, -0.008103259, 0.0049574412, -0.01675589, -0.020182934, -0.006585941, 0.007684688, -0.002859685, 0.027023941, 0.00856107, 0.0037017306, 0.016978256, 0.025885954, -0.010372694, 0.0025964435, 0.011706887, 0.021360163, -0.021674091, -0.024983412, 0.0034074234, 0.0032030435, 0.022262705, -0.01266829, -0.002249815, 0.032779284, -0.0034303141, -0.016101874, -0.005156916, -0.0212032, 0.005362931, 0.009077743, -0.013917461, -0.0017315074, 0.010980929, -0.019450437, 0.013865139, 0.028227331, -0.008757275, -0.0033649125, -0.012857955, 0.011039791, 0.009764459, 0.00029594224, -0.026317604, 0.025048813, 0.037749805, -0.025807472, -0.005425063, 0.021791814, -0.010012985, -0.00066995766, -0.016952096, 0.0031147513, -0.016598927, 0.0084368065, 0.004787397, -0.0064355177, 0.0015164997, -0.021216279, -0.023845425, 0.013969782, -0.011255615, 0.0042576445, -0.024250913, -0.009908343, -0.02289056, -0.023361452, -0.010987469, -0.013394248, 0.0032553647, -0.019018786, 0.021438645, 0.029587684, -0.010490417, 0.01263559, -0.018417092, -0.008731114, 0.01875718, -0.0072399573, -0.029090632, -0.017736914, -0.04031355, -0.019712042, 0.012772933, -0.030320182, -0.022341188, -0.02041838, 0.011752668, 0.028829027, -0.017043658, 0.024996493, 0.006334145, -0.0024263994, -0.0077370093, 0.017802317, 0.017396826, 0.030398665, 0.011464901, 0.03016322, -0.014558396, -0.0036690298, -0.009954124, -0.006703664, -0.00035705187, -0.014519156, 0.0075342646, -0.00896656, 0.040078104, 0.024420958, -0.016886694, -0.00092543266, -0.0017494928, 0.01672973, 0.016533526, 0.002648765, 0.0187441, -0.0055460557, 0.004735076, 0.03186366, 0.0003435628, 0.007495024, 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-0.034558207, -0.029064473, -0.03058179, -0.019450437, 0.01062122, -0.014179068, -0.010012985, 0.007874353, -0.014126746, -0.009731758, -0.03398267, -0.000115883464, -0.0029725027, -0.024290156, 0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783)); 30 String indexName = "vector_index"; 31 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding"); 32 int numCandidates = 200; 33 int limit = 10; 34 List<String> genresNotInCriteria = Arrays.asList("Drama", "Western", "Crime"); 35 List<String> genresInCriteria = Arrays.asList("Action", "Adventure", "Family"); 36 Bson criteria = Filters.and( 37 Filters.nin("genres", genresNotInCriteria), 38 Filters.in("genres", genresInCriteria), 39 Filters.gte("year", 1960), 40 Filters.lte("year", 2000)); 41 VectorSearchOptions options = VectorSearchOptions 42 .approximateVectorSearchOptions(numCandidates) 43 .filter(criteria); 44 45 // define pipeline 46 List<Bson> pipeline = asList( 47 vectorSearch( 48 fieldSearchPath, 49 queryVector, 50 indexName, 51 limit, 52 options), 53 project( 54 fields(exclude("_id"), include("title"), include("plot"), include("year"), 55 include("genres"), metaVectorSearchScore("score")))); 56 57 // run query and print results 58 collection.aggregate(pipeline) 59 .forEach(doc -> System.out.println(doc.toJson())); 60 } 61 } 62 }
以下查询使用向量嵌入在 plot_embedding
字段中搜索字符串 martial arts。该查询使用 ada-002-text
嵌入,它与 plot_embedding
字段中的向量嵌入相同。它指定了若干个聚合管道和比较查询操作符来演示如何组合使用操作符来过滤数据。
该过滤器使用 $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
字段进行过滤可查找在2015
年或之前上映的电影,按genres
字段进行过滤可查找属于action
类型的电影。
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 6 import com.mongodb.client.MongoClient; 7 import com.mongodb.client.MongoClients; 8 import com.mongodb.client.MongoCollection; 9 import com.mongodb.client.MongoDatabase; 10 import com.mongodb.client.model.*; 11 import com.mongodb.client.model.search.VectorSearchOptions; 12 import java.util.List; 13 import com.mongodb.client.model.search.FieldSearchPath; 14 import org.bson.Document; 15 import org.bson.conversions.Bson; 16 17 public class AtlasVectorSearchTutorial { 18 public static void main( String[] args ) { 19 // specify connection 20 String uri = "<connection-string>"; 21 22 // establish connection and set namespace 23 try (MongoClient mongoClient = MongoClients.create(uri)) { 24 MongoDatabase database = mongoClient.getDatabase("sample_mflix"); 25 MongoCollection<Document> collection = database.getCollection("embedded_movies"); 26 27 // define $vectorSearch query options 28 List<Double> queryVector = 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29 String indexName = "vector_index"; 30 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding"); 31 int numCandidates = 200; 32 int limit = 10; 33 Bson criteria = Filters.or( 34 Filters.lte("year", 2015), 35 Filters.and( 36 Filters.lte("year", 2015), 37 Filters.eq("genres", "Action"))); 38 VectorSearchOptions options = VectorSearchOptions 39 .approximateVectorSearchOptions(numCandidates) 40 .filter(criteria); 41 42 // define pipeline 43 List<Bson> pipeline = asList( 44 vectorSearch( 45 fieldSearchPath, 46 queryVector, 47 indexName, 48 limit, 49 options), 50 project( 51 fields(exclude("_id"), include("title"), include("plot"), include("year"), 52 include("genres"), metaVectorSearchScore("score")))); 53 54 // run query and print results 55 collection.aggregate(pipeline) 56 .forEach(doc -> System.out.println(doc.toJson())); 57 } 58 } 59 }
将 <connection-string>
替换为您的 Atlas 连接字符串,然后保存文件。
确保您的连接字符串包含数据库用户的档案。要了解详情,请参阅通过驱动程序连接。
编译并运行 AtlasVectorSearchTutorial.java
文件。
javac AtlasVectorSearchTutorial.java java AtlasVectorSearchTutorial
1 { 2 "year": 1993, 3 "plot": "A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.", 4 "genres": ["Action", "Adventure", "Thriller"], 5 "title": "Cliffhanger", 6 "score": 0.7694521546363831 7 } 8 { 9 "plot": "The dying words of a thief spark a madcap cross-country rush to find some treasure.", 10 "genres": ["Action", "Adventure", "Comedy"], 11 "title": "It's a Mad, Mad, Mad, Mad World", 12 "year": 1963, 13 "score": 0.7638954520225525 14 } 15 { 16 "year": 1991, 17 "plot": "A cat burglar is forced to steal Da Vinci works of art for a world domination plot.", 18 "genres": ["Action", "Adventure", "Comedy"], 19 "title": "Hudson Hawk", 20 "score": 0.7538407444953918 21 } 22 { 23 "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.", 24 "genres": ["Action", "Sci-Fi"], 25 "title": "Escape from New York", 26 "year": 1981, 27 "score": 0.7487208843231201 28 } 29 { 30 "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.", 31 "genres": ["Action", "Thriller"], 32 "title": "The Romanov Stones", 33 "year": 1993, 34 "score": 0.7467736005783081 35 } 36 { 37 "plot": "An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.", 38 "genres": ["Action", "Comedy", "Adventure"], 39 "title": "Crime Busters", 40 "year": 1977, 41 "score": 0.7437351942062378 42 } 43 { 44 "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.", 45 "genres": ["Action", "Adventure", "Comedy"], 46 "title": "Three Kings", 47 "year": 1999, 48 "score": 0.7425670623779297 49 } 50 { 51 "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 ...", 52 "genres": ["Action", "Sci-Fi", "Thriller"], 53 "title": "Black Moon Rising", 54 "year": 1986, 55 "score": 0.7397696375846863 56 } 57 { 58 "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.", 59 "genres": ["Action", "Adventure", "Horror"], 60 "title": "Deep Rising", 61 "year": 1998, 62 "score": 0.7392246127128601 63 } 64 { 65 "plot": "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", 66 "genres": ["Action", "Adventure", "Comedy"], 67 "title": "That Man from Rio", 68 "year": 1964, 69 "score": 0.7357995510101318 70 }
javac AtlasVectorSearchTutorial.java java AtlasVectorSearchTutorial
1 { 2 "year": 2008, 3 "plot": "A young Thai boxer learns the skills and inner meaning of martial arts.", 4 "genres": ["Action"], 5 "title": "Ong-bak 2", 6 "score": 0.7976737022399902 7 } 8 { 9 "plot": "A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.", 10 "genres": ["Action", "Drama", "Family"], 11 "title": "The Karate Kid", 12 "year": 1984, 13 "score": 0.7929348349571228 14 } 15 { 16 "plot": "A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.", 17 "genres": ["Action", "Adventure", "Fantasy"], 18 "title": "Circle of Iron", 19 "year": 1978, 20 "score": 0.7852014899253845 21 } 22 { 23 "plot": "A lionized account of the life of the martial arts superstar.", 24 "genres": ["Action", "Biography", "Drama"], 25 "title": "Dragon: The Bruce Lee Story", 26 "year": 1993, 27 "score": 0.7763040661811829 28 } 29 { 30 "plot": "A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.", 31 "genres": ["Action", "Thriller"], 32 "title": "Kung Fu Killer", 33 "year": 2014, 34 "score": 0.7731232643127441 35 } 36 { 37 "plot": "A young woman is trained by a martial arts specialist to become a professional assassin.", 38 "genres": ["Action", "Crime", "Romance"], 39 "title": "Naked Killer", 40 "year": 1992, 41 "score": 0.7693743109703064 42 } 43 { 44 "plot": "The life of the greatest karate master of a generation.", 45 "genres": ["Documentary", "Action", "History"], 46 "title": "The Real Miyagi", 47 "year": 2015, 48 "score": 0.768799901008606 49 } 50 { 51 "plot": "At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.", 52 "genres": ["Action", "Drama", "Sport"], 53 "title": "Never Back Down", 54 "year": 2008, 55 "score": 0.768179714679718 56 } 57 { 58 "plot": "Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.", 59 "genres": ["Action", "Drama"], 60 "title": "Dragon Tiger Gate", 61 "year": 2006, 62 "score": 0.7679706811904907 63 } 64 { 65 "year": 2001, 66 "plot": "A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.", 67 "genres": ["Action", "Comedy", "Sport"], 68 "title": "Shaolin Soccer", 69 "score": 0.7660527229309082 70 }
复制您要尝试的预过滤运算符的示例查询,并将其粘贴到 atlas-vector-search-tutorial.js
文件中。
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。
以下查询使用字符串 historical heist 的向量嵌入搜索 plot_embedding
字段。该查询使用 ada-002-text
嵌入,它与 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操作符,以对要执行语义搜索的文档进行预过滤。
该过滤器使用 $and
聚合管道操作符查找符合以下两个条件的电影文档:
按
genres
字段过滤,以查找不属于drama
、western
或crime
类型但属于action
、adventure
或family
类型的电影。按
year
字段过滤,以查找在1960
和2000
年份(含这两个年份)之间发行的电影。
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding', 22 'filter': { 23 '$and': [ 24 { 25 'genres': { 26 '$nin': [ 27 'Drama', 'Western', 'Crime' 28 ], 29 '$in': [ 30 'Action', 'Adventure', 'Family' 31 ] 32 } 33 }, { 34 'year': { 35 '$gte': 1960, 36 '$lte': 2000 37 } 38 } 39 ] 40 }, 41 'queryVector': [-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, 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54 } 55 } 56 } 57 ]; 58 59 // run pipeline 60 const result = coll.aggregate(agg); 61 62 // print results 63 await result.forEach((doc) => console.dir(JSON.stringify(doc))); 64 } finally { 65 await client.close(); 66 } 67 } 68 run().catch(console.dir);
以下查询使用向量嵌入在 plot_embedding
字段中搜索字符串 martial arts。该查询使用 ada-002-text
嵌入,它与 plot_embedding
字段中的向量嵌入相同。它指定了若干个聚合管道和比较查询操作符来演示如何组合使用操作符来过滤数据。
该过滤器使用 $or
聚合管道操作符来查找符合以下任一条件的电影文档:
按
genres
字段过滤,查找不属于crime
类型的电影。按
year
字段进行过滤可查找在2015
年或之前上映的电影,按genres
字段进行过滤可查找属于action
类型的电影。
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector_index', 21 'path': 'plot_embedding', 22 'filter': { 23 '$or': [ 24 { 25 'genres': { 26 '$ne': 'Crime' 27 } 28 }, { 29 '$and': [ 30 { 31 'year': { 32 '$lte': 2015 33 } 34 }, { 35 'genres': { 36 '$eq': 'Action' 37 } 38 } 39 ] 40 } 41 ] 42 }, 43 'queryVector': [-0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, 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-0.0011502446, 0.017688567, -0.013194891, 0.0021314074, 0.0012225922, -0.027001077, -0.00074583, 0.012271443, 0.010294486, 0.014710125, 0.01641395, 0.009540121, -0.008330535, 0.0005958511, -0.026662914, -0.0019444418, 0.024737982, 0.012147884, -0.0067892885, 0.016335912, -0.017558504, -0.0058170673, -0.0009689693, 0.0025866278, 0.0105676185, -0.02442583, -0.01170567, -0.005397614, -0.018078756, -0.019704543, 0.005498413, 0.016648063, 0.024490861, -0.013942753, 0.015867686, -0.002645156, 0.008805265, -0.007296534, 0.0083500445, -0.0025362284, -0.031553283, 0.0077517545, 0.0035832354, -0.041594144, 0.006704747, -0.011185418, 0.0037913362, -0.012011318, -0.025830511, 0.041802246, -0.01003436, 0.0097937435, 0.003641764, 0.022787036, -0.0017867404, -0.0026110145, 0.028145632, 0.011152902, 0.004311588, -0.021915615, -0.009182448, 0.02077106, -0.006171489, -0.0055309287, -0.019379387, 0.0022647218, -0.021213274, -0.014983257, 0.01158211, -0.0149572445, -0.021278305, -0.011725179, -0.021798559, 0.019691538, -0.04835742, 0.011536588, 0.029082086, -0.0006828307, -0.020003688, -0.0067957914, -0.16679278, 0.015646579, 0.037119977, -0.026480826, 0.0109317945, 0.0053488407, 0.013825696, -0.00035117008, -0.0013892354, -0.0014022416, 0.021915615, 0.008974346, -0.03274986, -0.0078688115, 0.023034155, 0.003518204, -0.017857648, 0.025037127, 0.0137606645, 0.02411368, 0.025765479, 0.0024338039, -0.00607069, -0.005114727, -0.009071894, -0.0094295675, 0.021096218, 0.016400944, -0.013175381, -0.0047440478, -0.015789647, -0.006548672, 0.017766604, 0.0077452515, 0.0044221417, 0.00838256, 0.0007568041, -0.015386452, -0.017649548, 0.014306929, -0.003066235, 0.02783348, -0.006984383, -0.02586953, 0.005108224, 0.02432178, 0.00885729, 0.0023297535, 0.007166471, -0.0021281557, -0.0052740546, -0.0035149525, 0.014085822, 0.00063893443, 0.031215118, -0.00425306, 0.014202879, -0.018351888, -0.0032158075, -0.0055081677, 0.003999437, -0.0017753599, 0.022748018, -0.023684472, -0.007946849, -0.019145273, -0.022487892, 0.018247837, -0.004535947, 0.008993856, -0.0043018335, 0.005498413, -0.0022647218, -0.023112195, 0.0055114194, 0.008818271, -0.00536835, 0.022604948, -0.020406883, -0.024386812, -0.014931232, 0.01761053, 0.0037588205, -0.0034986946, 0.01831287, 0.036001436, -0.000008446474, -0.024907064, -0.0036612733, -0.011725179, 0.023047162, -0.029758412, -0.014554049, -0.012934765, 0.015412465, 0.021850582, -0.011523581, -0.0022858572, -0.0040742233, -0.0056187212, 0.010307493, -0.019184291, 0.005192765, 0.008018384, 0.022722006, -0.0058138156, 0.03771827, 0.01028148, 0.029212149, -0.0054366332, 0.0014079319, -0.0009974206, 0.011627631, 0.026480826, 0.0025346025, 0.018937172, 0.0085971635, -0.012395004, 0.011608122, 0.00065397297, 0.040085416, 0.008883302, -0.0013339586, -0.0064413697, -0.007296534, 0.0028467537, -0.092917, -0.00801188, 0.016192842, 0.027469303, -0.033166062, 0.017662555, -0.002692304, 0.036079474, -0.0144760115, 0.020445902, -0.00036641184, -0.018442933, -0.008493113, -0.01476215, 0.018703058, 0.001552627, 0.0065876907, -0.017974706, -0.0076542073, 0.022149729, -0.010066876, 0.004932639, -0.0061292187, 0.0016436711, -0.007959855, 0.0031393955, -0.0324117, 0.037093967, 0.007114446, 0.019821601, -0.016518, -0.0034596757, 0.021668496, -0.03332214, 0.012785193, -0.007810283, -0.018833121, -0.0029963262, 0.0210572, -0.019392392, 0.0021135237, 0.015685597, 0.013116853, -0.027495317, 0.011250449, -0.0071924836, -0.019106254, 0.0107562095, 0.014007784, -0.034492705, -0.03394644, -0.024750987, -0.041047882, -0.006685238, 0.0068868357, 0.008694711, 0.019093247, 0.01261611, -0.009748221, -0.0027394518, 0.014918226, -0.0032710843, -0.0044936766, 0.01546449, 0.018208819, 0.004288827, -0.021538433, -0.026897028, 0.023879565, -0.018299863, -0.012245431, 0.015516515, -0.040891804, -0.011816223, -0.02221476, 0.012082852, -0.02417871, -0.009338523, 0.010047366, -0.027079115, -0.016010754, -0.021863589, -0.004305085, -0.027261203, 0.024100672, 0.000711282, 0.001071394, -0.0037263047, -0.007829792, -0.0049456456, -0.021603463, 0.03274986, 0.01866404, -0.032567773, -0.0044318964, 0.017935688, 0.018638028, -0.015282402, -0.009377542, 0.03012259, 0.015178352, -0.004792821, -0.048305396, 0.007075427, 0.020784067, 0.010678172, 0.0019509449, 0.0045684627, 0.02242286, -0.011374009, 0.024477856, -0.0021736778, -0.021291312, -0.009188951, 0.02692304, -0.0038628709, -0.02442583, -0.029394237, 0.019054228, 0.01606278, -0.0035051976, -0.00012284856, 0.01911926, 0.0088768, 0.007036408, 0.0015461239, -0.0030711126, 0.0034629272, -0.015152339, 0.02097916, -0.031163093, 0.0061909985, 0.012694148, -0.021902608, -0.030044552, 0.031553283, 0.0002003783, -0.037119977, 0.0003367412, 0.015347433, 0.003105254, 0.034102518, 0.001911926, -0.008102925, 0.0003306445, 0.011647142, -0.004288827, 0.019665524, -0.011907267, 0.01391674, 0.0014428863, -0.01655702, -0.0020387375, 0.009975832, -0.0009982334, -0.018586002, -0.0137606645, -0.031787395, 0.02481602, 0.0059308726, -0.0011941409, -0.009598649, 0.0126811415, 0.015828667, -0.0058918535, -0.0040807263, -0.00406772, -0.0015233628, -0.01402079, -0.0052577965, 0.0018290109, -0.018182807, -0.014736137, 0.014423986, -0.001820882, -0.013799684, 0.0029085337, 0.009949819, 0.02407466, 0.01992565, -0.01986062, 0.02892601, 0.0029768168, 0.00044018193, -0.012882739, 0.022487892, 0.0061682374, 0.014528036, 0.0021785551, 0.006304804, 0.0010722068, -0.0075696665, -0.0040417076, 0.0097937435, -0.01216089, -0.0044026324, -0.018494958, 0.053898104, -0.0037100469, -0.013929747, 0.0210572, 0.014293923, 0.028613858, -0.005791055, -0.008863793, -0.021668496, 0.0011803217, 0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914], 44 'numCandidates': 200, 45 'limit': 10 46 } 47 }, { 48 '$project': { 49 '_id': 0, 50 'title': 1, 51 'genres': 1, 52 'plot': 1, 53 'released': 1, 54 'score': { 55 '$meta': 'vectorSearchScore' 56 } 57 } 58 } 59 ]; 60 61 // run pipeline 62 const result = coll.aggregate(agg); 63 64 // print results 65 await result.forEach((doc) => console.dir(JSON.stringify(doc))); 66 } finally { 67 await client.close(); 68 } 69 } 70 run().catch(console.dir);
替换查询中的 <connection-string>
,然后保存该文件。
确保您的连接字符串包含数据库用户的档案。要了解详情,请参阅通过驱动程序连接。
查询您的集合。
运行以下命令来查询您的集合:
node atlas-vector-search-tutorial.js
'{ "year":1993, "plot":"A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.", "genres":["Action","Adventure","Thriller"], "title":"Cliffhanger", "score":0.7694521546363831 }' `{ "plot":"The dying words of a thief spark a madcap cross-country rush to find some treasure.", "genres":["Action","Adventure","Comedy"], "title":"It's a Mad, Mad, Mad, Mad World", "year":1963, "score":0.7638954520225525 }` '{ "year":1991, "plot":"A cat burglar is forced to steal Da Vinci works of art for a world domination plot.", "genres":["Action","Adventure","Comedy"], "title":"Hudson Hawk", "score":0.7538407444953918 }' '{ "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.", "genres":["Action","Sci-Fi"], "title":"Escape from New York", "year":1981, "score":0.7487208843231201 }' '{ "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.", "genres":["Action","Thriller"], "title":"The Romanov Stones", "year":1993, "score":0.7467736005783081 }' '{ "plot":"An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.", "genres":["Action","Comedy","Adventure"], "title":"Crime Busters", "year":1977, "score":0.7437351942062378 }' '{ "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.", "genres":["Action","Adventure","Comedy"], "title":"Three Kings", "year":1999, "score":0.7425670623779297 }' '{ "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 ...", "genres":["Action","Sci-Fi","Thriller"], "title":"Black Moon Rising", "year":1986, "score":0.7397696375846863 }' '{ "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.","genres":["Action","Adventure","Horror"],"title":"Deep Rising","year":1998,"score":0.7392246127128601 }' '{ "plot":"A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "genres":["Action","Adventure","Comedy"], "title":"That Man from Rio", "year":1964, "score":0.7357995510101318 }'
node atlas-search-tutorial.js
'{ "year":2008, "plot":"A young Thai boxer learns the skills and inner meaning of martial arts.", "genres":["Action"], "title":"Ong-bak 2", "score":0.7976737022399902 }' '{ "plot":"A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.", "genres":["Action","Drama","Family"], "title":"The Karate Kid", "year":1984, "score":0.7929348349571228 }' '{ "plot":"A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life."," genres":["Action","Adventure","Fantasy"], "title":"Circle of Iron", "year":1978, "score":0.7852014899253845 }' '{ "plot":"A lionized account of the life of the martial arts superstar.", "genres":["Action","Biography","Drama"], "title":"Dragon: The Bruce Lee Story", "year":1993, "score":0.7763040661811829 }' '{ "plot":"A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.", "genres":["Action","Thriller"], "title":"Kung Fu Killer", "year":2014, "score":0.7731232643127441 }' '{ "plot":"A young woman is trained by a martial arts specialist to become a professional assassin.", "genres":["Action","Crime","Romance"], "title":"Naked Killer", "year":1992, "score":0.7693743109703064 }' '{ "plot":"The life of the greatest karate master of a generation.", "genres":["Documentary","Action","History"], "title":"The Real Miyagi", "year":2015, "score":0.768799901008606 }' '{ "plot":"At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.", "genres":["Action","Drama","Sport"], "title":"Never Back Down", "year":2008, "score":0.768179714679718 }' '{ "plot":"Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.", "genres":["Action","Drama"], "title":"Dragon Tiger Gate", "year":2006, "score":0.7679706811904907 }' '{ "year":2001, "plot":"A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.", "genres":["Action","Comedy","Sport"], "title":"Shaolin Soccer", "score":0.7660527229309082 }'
将想要尝试的预过滤运算符的查询复制并粘贴到 atlas-vector-search-tutorial.py
文件中。
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。
以下查询使用字符串 historical heist 的向量嵌入搜索 plot_embedding
字段。该查询使用 ada-002-text
嵌入,它与 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操作符,以对要执行语义搜索的文档进行预过滤。
该过滤器使用 $and
聚合管道操作符查找符合以下两个条件的电影文档:
按
genres
字段过滤,以查找不属于drama
、western
或crime
类型但属于action
、adventure
或family
类型的电影。按
year
字段过滤,以查找在1960
和2000
年份(含这两个年份)之间发行的电影。
1 import pymongo 2 3 # connect to your Atlas cluster 4 client = pymongo.MongoClient("<connection-string>") 5 6 # define pipeline 7 pipeline = [ 8 { 9 '$vectorSearch': { 10 'index': 'vector_index', 11 'path': 'plot_embedding', 12 'filter': { 13 '$and': [ 14 { 15 'genres': { 16 '$nin': [ 17 'Drama', 'Western', 'Crime' 18 ], 19 '$in': [ 20 'Action', 'Adventure', 'Family' 21 ] 22 } 23 }, { 24 'year': { 25 '$gte': 1960, 26 '$lte': 2000 27 } 28 } 29 ] 30 }, 31 'queryVector': [-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, 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-0.019816685, -0.004659864, 0.023387613, -0.005461034, 0.004326316, 0.0037278912, -0.007540805, 0.00860031, 0.0015524705, 0.020039052, -0.0028367946, 0.0049509015, 0.009162764, 0.009705598, 0.013982862, 0.004852799, 0.0061869915, -0.0083910255, 0.012975678, -0.034558207, -0.029064473, -0.03058179, -0.019450437, 0.01062122, -0.014179068, -0.010012985, 0.007874353, -0.014126746, -0.009731758, -0.03398267, -0.000115883464, -0.0029725027, -0.024290156, 0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783], 32 'numCandidates': 200, 33 'limit': 10 34 } 35 }, { 36 '$project': { 37 '_id': 0, 38 'title': 1, 39 'genres': 1, 40 'plot': 1, 41 'year': 1, 42 'score': { 43 '$meta': 'vectorSearchScore' 44 } 45 } 46 } 47 ] 48 49 # run pipeline 50 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 51 52 # print results 53 for i in result: 54 print(i)
以下查询使用向量嵌入在 plot_embedding
字段中搜索字符串 martial arts。该查询使用 ada-002-text
嵌入,它与 plot_embedding
字段中的向量嵌入相同。它指定了若干个聚合管道和比较查询操作符来演示如何组合使用操作符来过滤数据。
该过滤器使用 $or
聚合管道操作符来查找符合以下任一条件的电影文档:
按
genres
字段过滤,查找不属于crime
类型的电影。按
year
字段进行过滤可查找在2015
年或之前上映的电影,按genres
字段进行过滤可查找属于action
类型的电影。
1 import pymongo 2 3 # connect to your Atlas cluster 4 client = pymongo.MongoClient("<connection-string>") 5 6 # define pipeline 7 pipeline = [ 8 { 9 '$vectorSearch': { 10 'index': 'vector_index', 11 'path': 'plot_embedding', 12 'filter': { 13 '$or': [ 14 { 15 'genres': { 16 '$ne': 'Crime' 17 } 18 }, { 19 '$and': [ 20 { 21 'year': { 22 '$lte': 2015 23 } 24 }, { 25 'genres': { 26 '$eq': 'Action' 27 } 28 } 29 ] 30 } 31 ] 32 }, 33 'queryVector': [-0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 0.014710125, 0.0060869483, 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0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914], 34 'numCandidates': 200, 35 'limit': 10 36 } 37 }, { 38 '$project': { 39 '_id': 0, 40 'title': 1, 41 'genres': 1, 42 'plot': 1, 43 'year': 1, 44 'score': { 45 '$meta': 'vectorSearchScore' 46 } 47 } 48 } 49 ] 50 51 # run pipeline 52 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 53 54 # print results 55 for i in result: 56 print(i)
替换查询中的 <connection-string>
,然后保存该文件。
确保您的连接字符串包含数据库用户的档案。要了解详情,请参阅通过驱动程序连接。
运行命令以查询您的集合。
python atlas-vector-search-tutorial.py
{ 'year': 1993, 'plot': 'A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.', 'genres': ['Action', 'Adventure', 'Thriller'], 'title': 'Cliffhanger', 'score': 0.7694521546363831 } { 'plot': 'The dying words of a thief spark a madcap cross-country rush to find some treasure.', 'genres': ['Action', 'Adventure', 'Comedy'], 'title': "It's a Mad, Mad, Mad, Mad World", 'year': 1963, 'score': 0.7638954520225525 } { 'year': 1991, 'plot': 'A cat burglar is forced to steal Da Vinci works of art for a world domination plot.', 'genres': ['Action', 'Adventure', 'Comedy'], 'title': 'Hudson Hawk', 'score': 0.7538407444953918 } { '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.', 'genres': ['Action', 'Sci-Fi'], 'title': 'Escape from New York', 'year': 1981, 'score': 0.7487208843231201 } { '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.', 'genres': ['Action', 'Thriller'], 'title': 'The Romanov Stones', 'year': 1993, 'score': 0.7467736005783081 } { 'plot': 'An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.', 'genres': ['Action', 'Comedy', 'Adventure'], 'title': 'Crime Busters', 'year': 1977, 'score': 0.7437351942062378 } { '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.', 'genres': ['Action', 'Adventure', 'Comedy'], 'title': 'Three Kings', 'year': 1999, 'score': 0.7425670623779297 } { '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 ...', 'genres': ['Action', 'Sci-Fi', 'Thriller'], 'title': 'Black Moon Rising', 'year': 1986, 'score': 0.7397696375846863 } { '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.', 'genres': ['Action', 'Adventure', 'Horror'], 'title': 'Deep Rising', 'year': 1998, 'score': 0.7392246127128601 } { 'plot': 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.', 'genres': ['Action', 'Adventure', 'Comedy'], 'title': 'That Man from Rio', 'year': 1964, 'score': 0.7357995510101318 }
python atlas-vector-search-tutorial.py
{ 'year': 2008, 'plot': 'A young Thai boxer learns the skills and inner meaning of martial arts.', 'genres': ['Action'], 'title': 'Ong-bak 2', 'score': 0.7976737022399902 } { 'plot': 'A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.', 'genres': ['Action', 'Drama', 'Family'], 'title': 'The Karate Kid', 'year': 1984, 'score': 0.7929348349571228 } { 'plot': 'A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.', 'genres': ['Action', 'Adventure', 'Fantasy'], 'title': 'Circle of Iron', 'year': 1978, 'score': 0.7852014899253845 } { 'plot': 'A lionized account of the life of the martial arts superstar.', 'genres': ['Action', 'Biography', 'Drama'], 'title': 'Dragon: The Bruce Lee Story', 'year': 1993, 'score': 0.7763040661811829 } { 'plot': 'A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.', 'genres': ['Action', 'Thriller'], 'title': 'Kung Fu Killer', 'year': 2014, 'score': 0.7731232643127441 } { 'plot': 'A young woman is trained by a martial arts specialist to become a professional assassin.', 'genres': ['Action', 'Crime', 'Romance'], ' title': 'Naked Killer', 'year': 1992, 'score': 0.7693743109703064 } { 'plot': 'The life of the greatest karate master of a generation.', 'genres': ['Documentary', 'Action', 'History'], 'title': 'The Real Miyagi', 'year': 2015, 'score': 0.768799901008606 } { 'plot': 'At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.', 'genres': ['Action', 'Drama', 'Sport'], 'title': 'Never Back Down', 'year': 2008, 'score': 0.768179714679718 } { 'plot': 'Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.', 'genres': ['Action', 'Drama'], 'title': 'Dragon Tiger Gate', 'year': 2006, 'score': 0.7679706811904907 } { 'year': 2001, 'plot': 'A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.', 'genres': ['Action', 'Comedy', 'Sport'], 'title': 'Shaolin Soccer', ' score': 0.7660527229309082 }