Como realizar semantic search nos dados do cluster do Atlas

No Atlas, VáGo para a Clusters página do seu projeto.

1. Se ainda não tiver sido exibido, selecione a organização que contém seu projeto no menu Organizations na barra de navegação.

2. Se ainda não estiver exibido, selecione o projeto desejado no menu Projects na barra de navegação.

3. Se ainda não estiver exibido, clique em Clusters na barra lateral.

   A página Clusters é exibida.

Acesse a página do Atlas Search do seu cluster.

Você pode acessar a página do Atlas Search pela barra lateral, pelo Data Explorer ou pela página de detalhes do cluster.

Defina o índice do Atlas Vector Search.

1. Clique em Create Search Index.

2. Em Atlas Vector Search, selecione JSON Editor e clique em Next.

3. Na seção Database and Collection, localize o banco de dados sample_mflix e selecione a coleção embedded_movies.

4. No campo Index Name, digite vector_index.

5. Substitua a definição padrão pela seguinte definição de índice e clique em Next.

Defina o índice do Atlas Vector Search.

1. Substitua a definição padrão pela seguinte definição de índice.

   Essa definição de índice especifica a indexação dos seguintes campos em um índice do tipo vectorSearch:

   • plot_embedding campo como o tipo de vetor . O campo plot_embedding contém incorporações criadas utilizando o modelo de incorporação text-embedding-ada-002 do OpenAI. A definição de índice especifica 1536 dimensões vetoriais e mede a similaridade usando euclidean.

   • genres campo como o tipo de filtro para pré-filtrar dados por valores de cadeia de caracteres no campo.

   • year campo como o tipo de filtro para pré-filtrar dados por valores numéricos no campo.

   1
   {
   2
     "fields": [
   3
       {
   4
         "type": "vector",
   5
         "path": "plot_embedding",
   6
         "numDimensions": 1536,
   7
         "similarity": "euclidean"
   8
       },
   9
       {
   10
         "type": "filter",
   11
         "path": "genres"
   12
       },
   13
       {
   14
         "type": "filter",
   15
         "path": "year"
   16
       }
   17
     ]
   18
   }

Revise a definição do índice e clique em Create Search Index.

Uma janela modal é exibida para que você saiba que seu índice está construindo.

Clique em Close para fechar a janela modal You're All Set! e aguarde a conclusão da criação do índice.

O índice deve levar cerca de um minuto para ser criado. Enquanto é compilado, a coluna Status mostra Initial Sync. Quando a compilação terminar, a coluna Status mostrará Active.

Conecte-se ao seu cluster no mongosh.

Abra o mongosh em uma janela do terminal e conecte-se ao seu cluster. Para obter instruções detalhadas sobre como conectar, consulte Conectar via mongosh.

Mude para o banco de dados em que deseja criar o índice.

use sample_mflix

switched to db sample_mflix

Execute o método db.collection.createSearchIndex() .

A definição de índice a seguir indexa o campo plot_embedding como o tipo vector e os campos genres e year como o tipo filter em um índice do Atlas Vector Search. O campo plot_embedding contém incorporações criadas utilizando o modelo de incorporações text-embedding-ada-002 da OpenAI. A definição do índice especifica dimensões vetoriais 1536 e mede a similaridade usando a distância euclidean.

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": "euclidean"
11
      },
12
      {
13
        "type": "filter",
14
        "path": "genres"
15
      },
16
      {
17
        "type": "filter",
18
        "path": "year"
19
      }
20
    ]
21
  }
22
);

Adicione o Driver Go ao seu projeto.

Adicione o Driver Go como dependência no seu projeto:

go get go.mongodb.org/mongo-driver/mongo

Defina o índice.

Crie um arquivo denominado vector-index.go. Copie e cole o código a seguir no arquivo e substitua o valor do espaço reservado <connectionString>.

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
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	// Connect to your Atlas cluster
21
	clientOptions := options.Client().ApplyURI(uri)
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	client, err := mongo.Connect(ctx, clientOptions)
23
	if err != nil {
24
		log.Fatalf("failed to connect to the server: %v", err)
25
	}
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	defer func() { _ = client.Disconnect(ctx) }()
27
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	// Set the namespace
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	coll := client.Database("sample_mflix").Collection("embedded_movies")
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	indexName := "vector_index"
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	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:    "euclidean"}
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.
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	log.Println("Polling to check if the index is ready. This may take up to a minute.")
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	searchIndexes := coll.SearchIndexes()
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	var doc bson.Raw
76
	for doc == nil {
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		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) {
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			break
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		}
85
86
		name := cursor.Current.Lookup("name").StringValue()
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		queryable := cursor.Current.Lookup("queryable").Boolean()
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		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
}

Este índice indexa o campo plot_embedding como o tipo vector e os campos genres e year como o tipo filter em um índice do Atlas Vector Search. O campo plot_embedding contém incorporações criadas utilizando o modelo de incorporações text-embedding-ada-002 da OpenAI. A definição do índice especifica dimensões vetoriais 1536 e mede a similaridade usando a distância euclidean.

Execute o seguinte comando para criar o índice.

go run vector-index.go

2024/10/17 09:38:21 Creating the index.
2024/10/17 09:38:22 Polling to confirm successful index creation.
2024/10/17 09:38:22 NOTE: This may take up to a minute.
2024/10/17 09:38:48 Name of Index Created: vector_index

Adicione o Java Sync Driver do MongoDB ao seu projeto.

1. Adicione o driver Java versão 5.2 ou superior como uma dependência no projeto:

   • Se você estiver usando o Maven, adicione a seguinte dependência à sua lista de dependências do pom.xml:

     <dependency>
        <groupId>org.mongodb</groupId>
        <artifactId>mongodb-driver-sync</artifactId>
        <version>[5.2.0,)</version>
     </dependency>

   • Se você estiver usando o Gradle, adicione a seguinte dependência à sua lista de dependências do build.gradle:

     dependencies {
        implementation 'org.mongodb:mongodb-driver-sync:[5.2.0,)'
     }

2. Adicione os arquivos JAR do driver Java ao seu CLASSPATH.

Para obter instruções de instalação mais detalhadas e compatibilidade de versões, consulte a documentação do MongoDB Java Driver.

Defina o índice.

Crie um arquivo denominado VectorIndex.java. Copie e cole o código a seguir no arquivo e substitua o valor do espaço reservado <connectionString>.

1
import com.mongodb.client.ListSearchIndexesIterable;
2
import com.mongodb.client.MongoClient;
3
import com.mongodb.client.MongoClients;
4
import com.mongodb.client.MongoCollection;
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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
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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
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        // 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", "euclidean"),
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
}

Este índice indexa o campo plot_embedding como o tipo vector e os campos genres e year como o tipo filter em um índice do Atlas Vector Search. O campo plot_embedding contém incorporações criadas utilizando o modelo de incorporações text-embedding-ada-002 da OpenAI. A definição do índice especifica dimensões vetoriais 1536 e mede a similaridade usando a distância euclidean.

Execute o arquivo para criar o índice.

Execute o arquivo no seu IDE ou execute um comando a partir da linha de comando para executar o código.

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

Adicione o Driver Node.js do MongoDB ao seu projeto.

Adicione o Driver Node do MongoDB como dependência no seu projeto:

npm install mongodb

Defina o índice.

Crie um arquivo denominado vector-index.js. Copie e cole o código a seguir no arquivo e substitua o valor do espaço reservado <connectionString>.

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": "euclidean"
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);

Este índice indexa o campo plot_embedding como o tipo vector e os campos genres e year como o tipo filter em um índice do Atlas Vector Search. O campo plot_embedding contém incorporações criadas utilizando o modelo de incorporações text-embedding-ada-002 da OpenAI. A definição do índice especifica dimensões vetoriais 1536 e mede a similaridade usando a distância euclidean.

Execute o seguinte comando para criar o índice.

node vector-index.js

vector_index

Adicione o Driver PyMongo ao seu projeto.

Adicione o Driver PyMongo como dependência no seu projeto:

pip install pymongo

Defina o índice.

Crie um arquivo denominado vector-index.py. Copie e cole o código a seguir no arquivo e substitua o valor do espaço reservado <connectionString>.

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": "euclidean"
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()

Este índice indexa o campo plot_embedding como o tipo vector e os campos genres e year como o tipo filter em um índice do Atlas Vector Search. O campo plot_embedding contém incorporações criadas utilizando o modelo de incorporações text-embedding-ada-002 da OpenAI. A definição do índice especifica dimensões vetoriais 1536 e mede a similaridade usando a distância euclidean.

Execute o seguinte comando para criar o índice.

python vector-index.py

vector_index

No Atlas, váGo para a Clusters página do seu projeto.

1. Se ainda não tiver sido exibido, selecione a organização que contém seu projeto no menu Organizations na barra de navegação.

2. Se ainda não estiver exibido, selecione o projeto desejado no menu Projects na barra de navegação.

3. Se ainda não estiver exibido, clique em Clusters na barra lateral.

   A página Clusters é exibida.

Acesse a página Collections.

Clique no botão Browse Collections para o seu cluster.

O Data Explorer é exibido.

Acesse a aba Aggregation da coleção.

1. Expanda sample_mflix na lista de bancos de dados e selecione embedded_movies na lista de coleções nesse banco de dados.

2. Clique Aggregation na guia sample_mflix.embedded_movies collection.

Execute a query de pesquisa do Atlas Vector Search no campo indexado.

As queries a seguir usam o estágio do pipeline $vectorSearch para pesquisar filmes que correspondam às incorporações de vetor especificadas. Note que as queries utilizam a incorporação ada-002-text, que é o mesmo que o vetor incorporação no campo plot_embedding. As queries especificam uma pesquisa para até 100 vizinhos mais próximos e limitam os resultados somente para 10 documentos. As consultas também especificam um estágio $project para executar as seguintes ações:

• Exclua o campo _id e inclua somente os campos title, genres, plot e year nos resultados.

• Adicionar um campo chamado score que exibe a pontuação da pesquisa vetorial para cada documento nos resultados.

1. Clique em </> Text.

2. Substitua [ ] no painel esquerdo pela query do operador pré-filtro que você deseja testar.

   Pré-filtro por E Operador

   Pré-filtro por operador OR ou AND

   A query a seguir pesquisa o campo plot_embedding usando as incorporações vetoriais para o heist histórico de strings. A query utiliza a incorporação ada-002-text, que é o mesmo que a incorporação do vetor no campo plot_embedding. Ela especifica vários operadores de query de comparação por campo para pré-filtrar os documentos nos quais executar a pesquisar semântica.

   O filtro usa o operador de pipeline de agregação $and para localizar documentos de filme que correspondam aos seguintes critérios:

   • Filtre pelo campo genres para encontrar filmes que não estão no gênero drama, western ou crime, mas sim no gênero action, adventure ou family.

   • Filtre pelo campo year para encontrar filmes que foram lançados entre os anos 1960 e 2000.

   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
   ]

   OCULTAR SAÍDA

   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"

   A query a seguir pesquisa o campo plot_embedding usando os embeddings vetoriais para a string martial arts. A query utiliza a incorporação ada-002-text, que é o mesmo que a incorporação do vetor no campo plot_embedding. Ela especifica operadores de pipeline de agregação e de query de comparação para demonstrar um uso combinado dos operadores para filtrar os dados.

   O filtro usa o operador de pipeline de agregação $or para localizar documentos de filme que correspondam a um dos seguintes critérios:

   • Filtre pelo campo genres para encontrar filmes que não são do gênero crime .

   • Filtre pelo campo year para encontrar filmes lançados no ano 2015 ou antes e pelo campo genres para encontrar filmes do gênero action .

   1
   [
   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,
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   22
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   30
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   32
   ]

   OCULTAR SAÍDA

   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)