Atlas 클러스터의 데이터에 대해 시맨틱 검색을 수행하는 방법

AtlasGo Atlas 에서 프로젝트 의 Clusters 페이지로 고 (Go) 합니다.

1. 아직 표시되지 않은 경우 탐색 표시줄의 Organizations 메뉴에서 원하는 프로젝트가 포함된 조직을 선택합니다.

2. 아직 표시되지 않은 경우 탐색 표시줄의 Projects 메뉴에서 원하는 프로젝트를 선택합니다.

3. 아직 표시되지 않은 경우 사이드바에서 Clusters를 클릭합니다.

   Clusters(클러스터) 페이지가 표시됩니다.

클러스터의 Atlas Search 페이지로 이동합니다.

사이드바, Data Explorer 또는 클러스터 세부 정보 페이지에서 Atlas Search 페이지로 이동할 수 있습니다.

Atlas Vector Search 인덱스를 정의합니다.

1. Create Search Index를 클릭합니다.

2. Atlas Vector Search 아래에서 JSON Editor 을 선택한 다음 Next 를 클릭합니다.

3. Database and Collection 섹션에서 sample_mflix 데이터베이스를 찾고 embedded_movies 컬렉션을 선택합니다.

4. Index Name 필드에 vector_index를 입력합니다.

5. 기본 정의를 다음 인덱스 정의로 바꾼 다음 Next를 클릭합니다.

Atlas Vector Search 인덱스를 정의합니다.

1. 기본 정의를 다음 인덱스 정의로 바꿉니다.

   이 인덱스 정의는 VectorSearch 유형의 인덱스에서 다음 필드를 인덱싱하도록 지정합니다.

   • plot_embedding 필드를 벡터 유형으로 지정합니다. plot_embedding 필드에는 OpenAI의 text-embedding-ada-002 임베딩 모델을 사용하여 생성된 임베딩이 포함되어 있습니다. 인덱스 정의는 1536 벡터 차원을 지정하고 euclidean 를 사용하여 유사성을 측정합니다.

   • genres 필드의 문자열 값을 기준으로 데이터를 사전 필터링하기 위한 필터 유형으로 필드를 지정합니다.

   • year 필드의 숫자 값을 기준으로 데이터를 사전 필터링하기 위한 필터 유형으로 필드를 지정합니다.

   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
   }

인덱스 정의를 검토한 후 Create Search Index를 클릭합니다.

인덱스가 작성 중임을 보여주는 모달 창이 표시됩니다.

Close를 클릭하여 You're All Set! 모달 창을 닫고 인덱스 생성이 완료될 때까지 기다립니다.

인덱스 작성에는 약 1분 정도가 소요됩니다. 작성하는 동안 Status 열은 Initial Sync를 읽습니다. 빌드가 완료되면 Status 열에 Active가 표시됩니다.

mongosh에서 클러스터에 연결합니다.

터미널 창에서 mongosh를 열고 cluster에 연결합니다. 연결에 대한 자세한 지침은 mongosh를 통해 연결을 참조하세요.

인덱스를 생성하려는 데이터베이스로 전환합니다.

use sample_mflix

switched to db sample_mflix

db.collection.createSearchIndex() 메서드를 실행합니다.

다음 인덱스 정의는 Atlas Vector Search 인덱스 에서 plot_embedding 필드를 vector 유형으로, genres 및 year 필드를 filter 유형으로 인덱싱합니다. plot_embedding 필드에는 OpenAI의 text-embedding-ada-002 임베딩 모델을 사용하여 생성된 임베딩이 포함되어 있습니다. 인덱스 정의는 1536 벡터 차원을 지정하고 euclidean 거리를 사용하여 유사성을 측정합니다.

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
);

Go 드라이버를 프로젝트에 추가합니다.

프로젝트에 Go 드라이버를 종속성으로 추가합니다.

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

인덱스를 정의합니다.

vector-index.go라는 파일을 만듭니다. 다음 코드를 복사하여 파일에 붙여넣고 <connectionString> 자리 표시자 값을 바꿉니다.

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:    "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.
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
}

인덱스 정의는 Atlas Vector Search 인덱스에서 plot_embedding 필드를 vector 유형으로, genres 및 year 필드를 filter 유형으로 인덱싱합니다. plot_embedding 필드에는 OpenAI의 text-embedding-ada-002 임베딩 모델을 사용하여 생성된 임베딩이 포함되어 있습니다. 인덱스 정의는 1536 벡터 차원을 지정하고 euclidean 거리를 사용하여 유사성을 측정합니다.

다음 명령을 실행하여 인덱스를 생성합니다.

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

프로젝트에 MongoDB Java Sync Driver를 추가합니다.

1. 프로젝트에 Java 드라이버 버전 5.2 이상을 종속성으로 추가합니다.

   • Maven을 사용하는 경우 pom.xml 종속성 목록에 다음 종속성을 추가합니다.

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

   • Gradle을 사용하는 경우 build.gradle 종속성 목록에 다음 종속성을 추가합니다.

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

2. CLASSPATH에 Java 드라이버 JAR 파일을 추가합니다.

자세한 설치 지침 및 버전 호환성은 MongoDB Java 드라이버 문서를 참조하세요.

인덱스를 정의합니다.

VectorIndex.java라는 파일을 만듭니다. 다음 코드를 복사하여 파일에 붙여넣고 <connectionString> 자리 표시자 값을 바꿉니다.

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", "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
}

인덱스 정의는 Atlas Vector Search 인덱스에서 plot_embedding 필드를 vector 유형으로, genres 및 year 필드를 filter 유형으로 인덱싱합니다. plot_embedding 필드에는 OpenAI의 text-embedding-ada-002 임베딩 모델을 사용하여 생성된 임베딩이 포함되어 있습니다. 인덱스 정의는 1536 벡터 차원을 지정하고 euclidean 거리를 사용하여 유사성을 측정합니다.

파일을 실행하여 인덱스를 생성합니다.

IDE에서 파일을 실행하거나 명령줄에서 명령을 실행하여 코드를 실행합니다.

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

MongoDB Node.js 드라이버를 프로젝트에 추가합니다.

프로젝트에 MongoDB 노드 드라이버를 종속성으로 추가합니다.

npm install mongodb

인덱스를 정의합니다.

vector-index.js라는 파일을 만듭니다. 다음 코드를 복사하여 파일에 붙여넣고 <connectionString> 자리 표시자 값을 바꿉니다.

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);

인덱스 정의는 Atlas Vector Search 인덱스에서 plot_embedding 필드를 vector 유형으로, genres 및 year 필드를 filter 유형으로 인덱싱합니다. plot_embedding 필드에는 OpenAI의 text-embedding-ada-002 임베딩 모델을 사용하여 생성된 임베딩이 포함되어 있습니다. 인덱스 정의는 1536 벡터 차원을 지정하고 euclidean 거리를 사용하여 유사성을 측정합니다.

다음 명령을 실행하여 인덱스를 생성합니다.

node vector-index.js

vector_index

프로젝트에 PyMongo 드라이버를 추가합니다.

프로젝트에 PyMongo 드라이버를 종속성으로 추가합니다.

pip install pymongo

인덱스를 정의합니다.

vector-index.py라는 파일을 만듭니다. 다음 코드를 복사하여 파일에 붙여넣고 <connectionString> 자리 표시자 값을 바꿉니다.

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()

인덱스 정의는 Atlas Vector Search 인덱스에서 plot_embedding 필드를 vector 유형으로, genres 및 year 필드를 filter 유형으로 인덱싱합니다. plot_embedding 필드에는 OpenAI의 text-embedding-ada-002 임베딩 모델을 사용하여 생성된 임베딩이 포함되어 있습니다. 인덱스 정의는 1536 벡터 차원을 지정하고 euclidean 거리를 사용하여 유사성을 측정합니다.

다음 명령을 실행하여 인덱스를 생성합니다.

python vector-index.py

vector_index

AtlasGo Atlas 에서 프로젝트 의 Clusters 페이지로 고 (Go) 합니다.

1. 아직 표시되지 않은 경우 탐색 표시줄의 Organizations 메뉴에서 원하는 프로젝트가 포함된 조직을 선택합니다.

2. 아직 표시되지 않은 경우 탐색 표시줄의 Projects 메뉴에서 원하는 프로젝트를 선택합니다.

3. 아직 표시되지 않은 경우 사이드바에서 Clusters를 클릭합니다.

   Clusters(클러스터) 페이지가 표시됩니다.

Collections 페이지로 이동합니다.

cluster의 Browse Collections 버튼을 클릭합니다.

데이터 탐색기 가 표시됩니다.

Aggregation 컬렉션의 탭으로 이동합니다.

1. 데이터베이스 목록 아래에서 sample_mflix 를 확장하고 해당 데이터베이스의 컬렉션 목록에서 embedded_movies 를 선택합니다.

2. sample_mflix.embedded_movies collection의 Aggregation 탭을 클릭합니다.

인덱싱된 필드에 대해 Atlas Vector Search 쿼리를 실행합니다.

다음 쿼리는 $vectorSearch 파이프라인 단계를 사용하여 지정된 벡터 임베딩과 일치하는 영화를 검색합니다. 이 쿼리에서는 plot_embedding 필드의 벡터 임베딩과 동일한 ada-002-text 임베딩을 사용합니다. 쿼리는 최대 100개의 최근접 이웃에 대한 검색을 지정하고 결과를 10개의 문서로만 제한합니다. 또한 다음 작업을 수행하기 위해 $project 단계를 지정합니다.

• _id 필드를 제외하고 title, genres, plot, year 필드만 결과에 포함합니다.

• 결과의 각 문서에 대한 벡터 검색 점수를 보여주는 score 필드를 추가합니다.

1. </> Text를 클릭합니다.

2. 왼쪽 창의 [ ] 을(를) 시도하려는 사전 필터 연산자에 대한 쿼리로 바꿉니다.

   AND 연산자로 사전 필터링

   OR 또는 AND 연산자로 사전 필터링

   다음 쿼리는 문자열 historical heist에 대한 벡터 임베딩을 사용하여 plot_embedding 필드를 검색합니다. 이 쿼리는 plot_embedding 필드의 벡터 임베딩과 동일한 ada-002-text 임베딩을 사용합니다. 시맨틱 검색을 수행할 문서를 사전 필터링하기 위해 필드당 여러 비교 쿼리 연산자를 지정합니다.

   이 필터는 $and 집계 파이프라인 연산자를 사용하여 다음 기준과 모두 일치하는 동영상 문서를 찾습니다.

   • genres 필드를 사용하여 drama, western, crime가 아닌 action, adventure, family에 속하는 영화를 필터링하세요.

   • year 필드로 필터링하여 1960년부터 2000년까지 개봉한 영화를 찾습니다.

   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
   ]

   출력 숨기기

   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"

   다음 쿼리는 벡터 임베딩을 사용하여 문자열 무술에 대한 plot_embedding 필드를 검색합니다. 이 쿼리는 plot_embedding 필드의 벡터 임베딩과 동일한 ada-002-text 임베딩을 사용합니다. 집계 파이프라인 연산자와 비교 쿼리 연산자를 지정하여 데이터를 필터링하기 위해 연산자를 함께 사용하는 방법을 보여줍니다.

   필터는 $or 집계 파이프라인 연산자를 사용하여 다음 기준 중 하나와 일치하는 영화 문서를 찾습니다.

   • genres 필드로 필터링하여 crime 장르에 속하지 않는 영화를 찾습니다.

   • year 필드로 필터링하여 2015년 또는 그 이전에 개봉된 영화를 찾고 그리고 genres 필드로 필터링하여 action 장르의 영화를 찾습니다.

   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,
   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
   ]

   출력 숨기기

   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)