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。
➤ 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。
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
您可以通过任何 MongoDB 驱动程序 在
$vectorSearch
聚合阶段运行 Atlas Vector Search 查询。本教程仅包含前述列表中驱动程序的示例。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
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。
开始您的索引配置。
在页面上进行以下选择,然后单击 Next。
Search Type | 选择 Vector Search 索引类型。 |
Index Name and Data Source | 指定以下信息:
|
Configuration Method | For a guided experience, select Visual Editor. To edit the raw index definition, select JSON Editor. |
定义 Atlas Vector Search 索引。
在此步骤中,您定义 vectorSearch 类型的索引以用于以下字段:
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。
Atlas会自动检测包含向量嵌入的字段及其对应的维度。对于 sample_mflix.embedded_movies
集合,plot_embedding
字段会显示。
要配置索引,请执行以下操作:
从 Similarity Method 下拉列表中选择 Dot Product。
单击 Advanced,然后从下拉菜单中选择 Scalar 量化。
在 Filter Field 部分,指定
genres
和year
字段以过滤数据。
将默认定义替换为以下索引定义。
1 { 2 "fields": [ 3 { 4 "type": "vector", 5 "path": "plot_embedding", 6 "numDimensions": 1536, 7 "similarity": "dotProduct", 8 "quantization": "scalar" 9 }, 10 { 11 "type": "filter", 12 "path": "genres" 13 }, 14 { 15 "type": "filter", 16 "path": "year" 17 } 18 ] 19 }
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 IndexService.cs
的文件。将以下代码复制并粘贴到文件中,并替换 <connection-string>
占位符值。
1 namespace query_quick_start; 2 3 using MongoDB.Bson; 4 using MongoDB.Driver; 5 using System; 6 using System.Threading; 7 8 public class IndexService 9 { 10 // Replace the placeholder with your Atlas connection string 11 private const string MongoConnectionString = "<connection-string>"; 12 public void CreateVectorIndex() 13 { 14 try 15 { 16 // Connect to your Atlas cluster 17 var client = new MongoClient(MongoConnectionString); 18 var database = client.GetDatabase("sample_mflix"); 19 var collection = database.GetCollection<BsonDocument>("embedded_movies"); 20 21 var searchIndexView = collection.SearchIndexes; 22 var name = "vector_index"; 23 var type = SearchIndexType.VectorSearch; 24 25 var definition = new BsonDocument 26 { 27 { "fields", new BsonArray 28 { 29 new BsonDocument 30 { 31 { "type", "vector" }, 32 { "path", "plot_embedding" }, 33 { "numDimensions", 1536 }, 34 { "similarity", "dotProduct" }, 35 { "quantization", "scalar"} 36 }, 37 new BsonDocument 38 { 39 {"type", "filter"}, 40 {"path", "genres"} 41 }, 42 new BsonDocument 43 { 44 {"type", "filter"}, 45 {"path", "year"} 46 } 47 } 48 } 49 }; 50 51 var model = new CreateSearchIndexModel(name, type, definition); 52 53 searchIndexView.CreateOne(model); 54 Console.WriteLine($"New search index named {name} is building."); 55 56 // Polling for index status 57 Console.WriteLine("Polling to check if the index is ready. This may take up to a minute."); 58 bool queryable = false; 59 while (!queryable) 60 { 61 var indexes = searchIndexView.List(); 62 foreach (var index in indexes.ToEnumerable()) 63 { 64 if (index["name"] == name) 65 { 66 queryable = index["queryable"].AsBoolean; 67 } 68 } 69 if (!queryable) 70 { 71 Thread.Sleep(5000); 72 } 73 } 74 Console.WriteLine($"{name} is ready for querying."); 75 } 76 catch (Exception e) 77 { 78 Console.WriteLine($"Exception: {e.Message}"); 79 } 80 } 81 }
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.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 Quantization string `bson:"quantization"` 39 } 40 41 type filterField struct { 42 Type string `bson:"type"` 43 Path string `bson:"path"` 44 } 45 46 type indexDefinition struct { 47 Fields []vectorDefinitionField `bson:"fields"` 48 } 49 50 vectorDefinition := vectorDefinitionField{ 51 Type: "vector", 52 Path: "plot_embedding", 53 NumDimensions: 1536, 54 Similarity: "dotProduct", 55 Quantization: "scalar"} 56 genreFilterDefinition := filterField{"filter", "genres"} 57 yearFilterDefinition := filterField{"filter", "year"} 58 59 indexModel := mongo.SearchIndexModel{ 60 Definition: bson.D{{"fields", [3]interface{}{ 61 vectorDefinition, 62 genreFilterDefinition, 63 yearFilterDefinition}}}, 64 Options: opts, 65 } 66 67 // Create the index 68 searchIndexName, err := coll.SearchIndexes().CreateOne(ctx, indexModel) 69 if err != nil { 70 log.Fatalf("failed to create the search index: %v", err) 71 } 72 log.Println("New search index named " + searchIndexName + " is building.") 73 74 // Await the creation of the index. 75 log.Println("Polling to check if the index is ready. This may take up to a minute.") 76 searchIndexes := coll.SearchIndexes() 77 var doc bson.Raw 78 for doc == nil { 79 cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName)) 80 if err != nil { 81 fmt.Errorf("failed to list search indexes: %w", err) 82 } 83 84 if !cursor.Next(ctx) { 85 break 86 } 87 88 name := cursor.Current.Lookup("name").StringValue() 89 queryable := cursor.Current.Lookup("queryable").Boolean() 90 if name == searchIndexName && queryable { 91 doc = cursor.Current 92 } else { 93 time.Sleep(5 * time.Second) 94 } 95 } 96 97 log.Println(searchIndexName + " is ready for querying.") 98 }
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 驱动程序版本 5.2 或更高版本作为依赖项添加到您的项目中,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的依赖项。
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.MongoClient; 2 import com.mongodb.client.MongoClients; 3 import com.mongodb.client.MongoCollection; 4 import com.mongodb.client.MongoDatabase; 5 import com.mongodb.client.model.SearchIndexModel; 6 import com.mongodb.client.model.SearchIndexType; 7 import org.bson.Document; 8 import org.bson.conversions.Bson; 9 10 import java.util.Arrays; 11 import java.util.Collections; 12 import java.util.List; 13 14 public class VectorIndex { 15 16 public static void main(String[] args) { 17 18 // Replace the placeholder with your Atlas connection string 19 String uri = "<connectionString>"; 20 21 // Connect to your Atlas cluster 22 try (MongoClient mongoClient = MongoClients.create(uri)) { 23 24 // Set the namespace 25 MongoDatabase database = mongoClient.getDatabase("sample_mflix"); 26 MongoCollection<Document> collection = database.getCollection("embedded_movies"); 27 28 // Define the index details with the filter fields 29 String indexName = "vector_index"; 30 Bson definition = new Document( 31 "fields", 32 Arrays.asList( 33 new Document("type", "vector") 34 .append("path", "plot_embedding") 35 .append("numDimensions", 1536) 36 .append("similarity", "dotProduct") 37 .append("quantization", "scalar"), 38 new Document("type", "filter") 39 .append("path", "genres"), 40 new Document("type", "filter") 41 .append("path", "year"))); 42 43 // Define the index model 44 SearchIndexModel indexModel = new SearchIndexModel( 45 indexName, 46 definition, 47 SearchIndexType.vectorSearch()); 48 49 // Create the index using the defined model 50 List<String> result = collection.createSearchIndexes(Collections.singletonList(indexModel)); 51 System.out.println("Successfully created vector index named: " + result.get(0)); 52 System.out.println("It may take up to a minute for the index to leave the BUILDING status and become queryable."); 53 54 // Wait for Atlas to build the index 55 System.out.println("Polling to confirm the index has left the BUILDING status."); 56 // No special handling in case of a timeout. Custom handling can be implemented. 57 waitForIndex(collection, indexName); 58 } 59 } 60 61 /** 62 * Polls the collection to check whether the specified index is ready to query. 63 */ 64 public static <T> boolean waitForIndex(final MongoCollection<T> collection, final String indexName) { 65 long startTime = System.nanoTime(); 66 long timeoutNanos = TimeUnit.SECONDS.toNanos(60); 67 while (System.nanoTime() - startTime < timeoutNanos) { 68 Document indexRecord = StreamSupport.stream(collection.listSearchIndexes().spliterator(), false) 69 .filter(index -> indexName.equals(index.getString("name"))) 70 .findAny().orElse(null); 71 if (indexRecord != null) { 72 if ("FAILED".equals(indexRecord.getString("status"))) { 73 throw new RuntimeException("Search index has FAILED status."); 74 } 75 if (indexRecord.getBoolean("queryable")) { 76 System.out.println(indexName + " index is ready to query"); 77 return true; 78 } 79 } 80 try { 81 Thread.sleep(100); // busy-wait, avoid in production 82 } catch (InterruptedException e) { 83 Thread.currentThread().interrupt(); 84 throw new RuntimeException(e); 85 } 86 } 87 return false; 88 } 89 }
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 "quantization": "scalar" 25 }, 26 { 27 "type": "filter", 28 "path": "genres" 29 }, 30 { 31 "type": "filter", 32 "path": "year" 33 } 34 ] 35 } 36 } 37 38 // run the helper method 39 const result = await collection.createSearchIndex(index); 40 console.log(`New search index named ${result} is building.`); 41 42 // wait for the index to be ready to query 43 console.log("Polling to check if the index is ready. This may take up to a minute.") 44 let isQueryable = false; 45 while (!isQueryable) { 46 const cursor = collection.listSearchIndexes(); 47 for await (const index of cursor) { 48 if (index.name === result) { 49 if (index.queryable) { 50 console.log(`${result} is ready for querying.`); 51 isQueryable = true; 52 } else { 53 await new Promise(resolve => setTimeout(resolve, 5000)); 54 } 55 } 56 } 57 } 58 } finally { 59 await client.close(); 60 } 61 } 62 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 "quantization": "scalar" 23 }, 24 { 25 "type": "filter", 26 "path": "genres" 27 }, 28 { 29 "type": "filter", 30 "path": "year" 31 } 32 ] 33 }, 34 name="vector_index", 35 type="vectorSearch", 36 ) 37 38 result = collection.create_search_index(model=search_index_model) 39 print("New search index named " + result + " is building.") 40 41 # Wait for initial sync to complete 42 print("Polling to check if the index is ready. This may take up to a minute.") 43 predicate=None 44 if predicate is None: 45 predicate = lambda index: index.get("queryable") is True 46 47 while True: 48 indices = list(collection.list_search_indexes(result)) 49 if len(indices) and predicate(indices[0]): 50 break 51 time.sleep(5) 52 print(result + " is ready for querying.") 53 54 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,使用 dotProduct
函数测量相似性,并启用自动 scalar
量化以高效处理您的向量。
使用 $vectorSearch
聚合管道阶段运行查询
Overview
本节演示如何使用 $vectorSearch
阶段查询 sample_mflix.embedded_movies
集合中的索引向量数据。这些示例查询还演示了各种查询和聚合管道操作符,我们可以在查询中使用它们来对我们要执行语义搜索的数据进行预过滤。
步骤
AtlasGoClusters在Atlas中,Go项目的 页面。
如果尚未显示,请从导航栏上的 Organizations 菜单中选择包含所需项目的组织。
如果尚未显示,请从导航栏的Projects菜单中选择所需的项目。
如果尚未出现,请单击侧边栏中的 Clusters(集群)。
会显示集群页面。
转到 Collections(快速入门)页面。
单击集群的对应 Browse Collections 按钮。
显示数据浏览器。
针对索引字段运行 Atlas Vector Search 搜索查询。
单击 </> Text(连接)。
将左窗格中的[ ]替换为对要尝试的预过滤操作符的查询。
以下查询使用$vectorSearch
管道阶段来搜索与指定向量嵌入匹配的电影。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。查询还指定了一个$project
阶段来执行以下操作:
在结果中排除
_id
字段,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 $and
聚合管道操作符查找符合以下两个条件的电影文档:
按
genres
字段过滤,以查找不属于drama
、western
或crime
类型但属于action
、adventure
或family
类型的电影。按
year
字段过滤,以查找在1960
和2000
年份(含这两个年份)之间发行的电影。
1 [ 2 { 3 "$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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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"