How to Perform Semantic Search Against Data in Your Atlas Cluster

In Atlas, go to the Clusters page for your project.

1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

3. If it's not already displayed, click Clusters in the sidebar.

   The Clusters page displays.

Go to the Atlas Search page for your cluster.

You can go the Atlas Search page from the sidebar, the Data Explorer, or your cluster details page.

Define the Atlas Vector Search index.

1. Click Create Search Index.

2. Under Atlas Vector Search, select JSON Editor and then click Next.

3. In the Database and Collection section, find the sample_mflix database, and select the embedded_movies collection.

4. In the Index Name field, enter vector_index.

5. Replace the default definition with the following index definition and then click Next.

Define the Atlas Vector Search index.

1. Replace the default definition with the following index definition.

   This index definition specifies indexing the following fields in an index of the vectorSearch type:

   • plot_embedding field as the vector type. The plot_embedding field contains embeddings created using OpenAI's text-embedding-ada-002 embedding model. The index definition specifies 1536 vector dimensions and measures similarity using dotProduct.

   • genres field as the filter type for pre-filtering data by string values in the field.

   • year field as the filter type for pre-filtering data by numeric values in the field.

   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
   }

Review the index definition and then click Create Search Index.

A modal window displays to let you know that your index is building.

Click Close to close the You're All Set! modal window and wait for the index to finish building.

The index should take about one minute to build. While it builds, the Status column reads Initial Sync. When it finishes building, the Status column reads Active.

Connect to your cluster in mongosh.

Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect via mongosh.

Switch to the database where you want to create the index.

use sample_mflix

switched to db sample_mflix

Run the db.collection.createSearchIndex() method.

The following index definition indexes the plot_embedding field as the vector type and the genres and year fields as the filter type in an Atlas Vector Search index. The plot_embedding field contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. The index definition specifies 1536 vector dimensions and measures similarity using dotProduct function.

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
);

Add the MongoDB C# Driver to your .NET project.

Add the MongoDB C# Driver as a dependency in your project:

dotnet add package MongoDB.Driver

Define the index.

Create a file named IndexService.cs. Copy and paste the following code into the file, and replace the <connection-string> placeholder value.

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
                        },
36
                        new BsonDocument
37
                        {
38
                            {"type", "filter"},
39
                            {"path", "genres"}
40
                        },
41
                        new BsonDocument
42
                        {
43
                            {"type", "filter"},
44
                            {"path", "year"}
45
                        }
46
                    }
47
                }
48
            };
49
50
            var model = new CreateSearchIndexModel(name, type, definition);
51
52
            searchIndexView.CreateOne(model);
53
            Console.WriteLine($"New search index named {name} is building.");
54
55
            // Polling for index status
56
            Console.WriteLine("Polling to check if the index is ready. This may take up to a minute.");
57
            bool queryable = false;
58
            while (!queryable)
59
            {
60
                var indexes = searchIndexView.List();
61
                foreach (var index in indexes.ToEnumerable())
62
                {
63
                    if (index["name"] == name)
64
                    {
65
                        queryable = index["queryable"].AsBoolean;
66
                    }
67
                }
68
                if (!queryable)
69
                {
70
                    Thread.Sleep(5000);
71
                }
72
            }
73
            Console.WriteLine($"{name} is ready for querying.");
74
        }
75
        catch (Exception e)
76
        {
77
            Console.WriteLine($"Exception: {e.Message}");
78
        }
79
    }
80
}

This index definition indexes the plot_embedding field as the vector type and the genres and year fields as the filter type in an Atlas Vector Search index. The plot_embedding field contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. The index definition specifies 1536 vector dimensions and measures similarity using dotProduct function.

Update Program.cs.

Update Program.cs to initialize the IndexService and call the method to create the index.

using query_quick_start;
var indexService = new IndexService();
indexService.CreateVectorIndex();

Save and run the project.

Save the file, and then compile and run your project to create the index.

dotnet run query_quick_start.csproj

New search index named vector_index is building.
Polling to check if the index is ready. This may take up to a minute.
vector_index is ready for querying.

Add the Go Driver to your project.

Add the Go Driver as a dependency in your project:

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

Define the index.

Create a file named vector-index.go. Copy and paste the following code into the file, and replace the <connectionString> placeholder value.

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
}

This index definition indexes the plot_embedding field as the vector type and the genres and year fields as the filter type in an Atlas Vector Search index. The plot_embedding field contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. The index definition specifies 1536 vector dimensions and measures similarity using dotProduct function.

Run the following command to create the index.

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

Add the MongoDB Java Sync Driver to your project.

1. Add the Java driver version 5.2 or higher as a dependency in your project, depending on your package manager:

   Maven

   If you are using Maven, add the following dependencies to the dependencies array in your project's pom.xml file:

   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>

2. Run your package manager to install the dependencies to your project.

For more detailed installation instructions and version compatibility, see the MongoDB Java Driver documentation.

Define the index.

Create a file named VectorIndex.java. Copy and paste the following code into the file, and replace the <connectionString> placeholder value.

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
}

This index definition indexes the plot_embedding field as the vector type and the genres and year fields as the filter type in an Atlas Vector Search index. The plot_embedding field contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. The index definition specifies 1536 vector dimensions and measures similarity using dotProduct function.

Run the file to create the index.

Run the file in your IDE, or execute a command from the command line to run the code.

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

Add the MongoDB Node.js Driver to your project.

Add the MongoDB Node Driver as a dependency in your project:

npm install mongodb

Define the index.

Create a file named vector-index.js. Copy and paste the following code into the file, and replace the <connectionString> placeholder value.

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);

This index definition indexes the plot_embedding field as the vector type and the genres and year fields as the filter type in an Atlas Vector Search index. The plot_embedding field contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. The index definition specifies 1536 vector dimensions and measures similarity using dotProduct function.

Run the following command to create the index.

node vector-index.js

vector_index

Add the PyMongo Driver to your project.

Add the PyMongo Driver as a dependency in your project:

pip install pymongo

Define the index.

Create a file named vector-index.py. Copy and paste the following code into the file, and replace the <connectionString> placeholder value.

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
40
# Wait for initial sync to complete
41
print("Polling to check if the index is ready. This may take up to a minute.")
42
predicate=None
43
if predicate is None:
44
  predicate = lambda index: index.get("queryable") is True
45
46
while True:
47
  indices = list(collection.list_search_indexes(result))
48
  if len(indices) and predicate(indices[0]):
49
    break
50
  time.sleep(5)
51
print(result + " is ready for querying.")
52
53
client.close()

This index definition indexes the plot_embedding field as the vector type and the genres and year fields as the filter type in an Atlas Vector Search index. The plot_embedding field contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. The index definition specifies 1536 vector dimensions and measures similarity using dotProduct function.

Run the following command to create the index.

python vector-index.py

vector_index

In Atlas, go to the Clusters page for your project.

1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

3. If it's not already displayed, click Clusters in the sidebar.

   The Clusters page displays.

Go to the Collections page.

Click the Browse Collections button for your cluster.

The Data Explorer displays.

Go to the Aggregation tab for the collection.

1. Expand sample_mflix under the list of databases and select embedded_movies from the list of collections in that database.

2. Click the Aggregation tab for the sample_mflix.embedded_movies collection.

Run the Atlas Vector Search queries against the indexed field.

The following queries use the $vectorSearch pipeline stage to search for movies that match the specified vector embeddings. Note that the queries use ada-002-text embedding, which is the same as the vector embedding in the plot_embedding field. The queries specify a search for up to 100 nearest neighbors and limit the results to 10 documents only. The queries also specify a $project stage to perform the following actions:

• Exclude the _id field and include only the title, genres, plot, and year fields in the results.

• Add a field named score that shows the vector search score for each document in the results.

1. Click </> Text.

2. Replace [ ] in the left pane with the query for the pre-filter operator that you want to try.

   Pre-Filter by AND Operator

   Pre-Filter by OR or AND Operator

   The following query searches the plot_embedding field using the vector embeddings for the string historical heist. The query uses ada-002-text embedding, which is the same as the vector embedding in the plot_embedding field. It specifies multiple comparison query operators per field to pre-filter the documents against which to perform the semantic search.

   The filter uses the $and aggregation pipeline operator to find movie documents that match both the following criteria:

   • Filter by the genres field to find movies that aren't in the drama, western, or crime genre, but in the action, adventure, or family genre.

   • Filter by the year field to find movies that were released between the years 1960 and 2000, both inclusive.

   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
   ]

   HIDE OUTPUT

   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"

   The following query searches the plot_embedding field using the vector embeddings for the string martial arts. The query uses ada-002-text embedding, which is the same as the vector embedding in the plot_embedding field. It specifies aggregation pipeline and comparison query operators to demonstrate a combined use of the operators to filter the data.

   The filter uses the $or aggregation pipeline operator to find movie documents that match either one of the following criteria:

   • Filter by the genres field to find movies that aren't in the crime genre.

   • Filter by the year field to find movies that were released in or before the year 2015 and by the genres field to find movies in the action genre.

   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
   ]

   HIDE OUTPUT

   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)