Atlas Vector Search Quick Start

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Objectives

Prerequisites
Index Vector Embeddings
Run a Vector Search Query

This quick start describes how to index vector embeddings in your data on an Atlas cluster and run queries that search vector embeddings for similar data.

A vector is an array of values arranged in one or more dimensions. Vector embeddings that bidirectional encoder models, like OpenAI text-embedding-ada-002, produce can represent words, phrases, and sentences. You can index vector embeddings and search for items related in meaning or context. To learn more, see Atlas Vector Search Overview.

Objectives

In this quick start, you will do the following steps:

Create an index definition for the sample_mflix.embedded_movies collection that indexes the plot_embedding field as the vector type. The plot_embedding field contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. The index definition specifies 1536 vector dimensions and measure similarity using cosine.
Run an Atlas Vector Search query that searches the sample sample_mflix.embedded_movies collection. The query uses the $vectorSearch stage to search the plot_embedding field, which contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. The query searches the plot_embedding field using vector embeddings for the string time travel. It considers up to 150 nearest neighbors, and returns 10 documents in the results.

Prerequisites

To complete this quick start, you must meet the following prerequisites.

Required Cluster Configuration

You must have the following cluster configuration:

An Atlas cluster with MongoDB version 6.0.11, or v7.0.2 or later (including RCs).
The sample data loaded into your Atlas cluster.

Supported Clients

You must have one of the following applications to run queries on your Atlas cluster:

You can also use Atlas Vector Search with local Atlas deployments that you create with the Atlas CLI. To learn more, see Create a Local Atlas Deployment.

Required Access

To create an Atlas Vector Search index, you must have Project Data Access Admin or higher access to the project.

Index Vector Embeddings

Use the following example to index vector embeddings in your data on an Atlas cluster.

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

If it is not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.
If it is not already displayed, select your desired project from the Projects menu in the navigation bar.
If the Clusters page is not already displayed, click Database in the sidebar.

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.

In the sidebar, click Atlas Search under the Services heading.
From the Select data source dropdown, select your cluster and click Go to Atlas Search.

Click the Browse Collections button for your cluster.
Expand the database and select the collection.
Click the Search Indexes tab for the collection.

Click the cluster's name.
Click the Atlas Search tab.

Go to the JSON Editor.

Click Create Search Index.
In the Atlas Vector Search section, select JSON Editor.
Click Next.

Specify the database, collection, and index name.

In the Database and Collection section, find the sample_mflix database, and select the embedded_movies collection.
In the Index Name field, specify vector_index.

Define your Atlas Vector Search index.

The following example index definition for the sample_mflix.embedded_movies collection indexes the plot_embedding field as the vector type. 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 cosine.

1 {
2   "fields": [{
3     "type": "vector",
4     "path": "plot_embedding",
5     "numDimensions": 1536,
6     "similarity": "cosine"
7   }]
8 }

To learn more, see How to Index Fields for Vector Search.

Create the search index.

Review the index definition and click Create Search Index.

A modal window indicates that your index is building.

Wait for the index to finish 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.

Run a Vector Search Query

Use the following example to run a query that searches vector embeddings.

➤ Use the Select your language drop-down menu to select the client to use to run the example queries in this section.

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

If it is not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.
If it is not already displayed, select your desired project from the Projects menu in the navigation bar.
If the Clusters page is not already displayed, click Database in the sidebar.

Go to the Collections page.

Click the Browse Collections button for your cluster.

Go to the Aggregation tab for the collection.

Expand sample_mflix under the list of databases and select embedded_movies from the list of collections in that database.
Click the Aggregation tab for the sample_mflix.embedded_movies collection.

Construct and run your vector search query.

The following example query searches the sample sample_mflix.embedded_movies collection. It uses the $vectorSearch stage to search the plot_embedding field, which contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. The query searches the plot_embedding field using vector embeddings for the string time travel. It considers up to 150 nearest neighbors, and returns 10 documents in the results. The query also specifies a $project stage to do the following:

Exclude the _id field and include only the plot and title fields in the results.
Add a field named score that shows the vector search score for each document in the results.

Click </> Text.
Copy the following sample query.
Replace [ ] in the left pane with the following sample query:

1 [
2   {
3     "$vectorSearch": {
4       "index": "vector_index",
5       "path": "plot_embedding",
6       "queryVector": 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10   },
11   {
12     "$project": {
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14       "plot": 1,
15       "title": 1,
16       "score": { $meta: "vectorSearchScore" }
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1 {
2     "score": 0.7892671227455139,
3     "plot": "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.",
4     "title": "Thrill Seekers"
5 }
6 {
7     "plot": "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.",
8     "title": "About Time",
9     "score": 0.7843604683876038
10 }
11 {
12     "plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.",
13     "title": "The Time Machine",
14     "score": 0.7801066637039185
15 }
16 {
17     "plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
18     "title": "Crusade in Jeans",
19     "score": 0.7789170742034912
20 }
21 {
22     "title": "Timecop",
23     "score": 0.7771612405776978,
24     "plot": "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past."
25 }
26 {
27     "plot": "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...",
28     "title": "A.P.E.X.",
29     "score": 0.7730885744094849
30 }
31 {
32     "plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
33     "title": "Men in Black 3",
34     "score": 0.7712380886077881
35 }
36 {
37     "score": 0.7669923901557922,
38     "plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.",
39     "title": "Tomorrowland"
40 }
41 {
42     "plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.",
43     "title": "Love Story 2050",
44     "score": 0.7649372816085815
45 }
46 {
47     "plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...",
48     "title": "The Portal",
49     "score": 0.7640786170959473
50 }

The Pipeline Output pane displays the results of your query.

To learn more about this pipeline stage, see Run Vector Search Queries.

Expand your query results.

The Data Explorer might not display all the values in the documents it returns. To view all the values for the fields that you searched in the query path, expand the fields in the documents.

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 `sample_mflix` database.

use sample_mflix

switched to db sample_mflix

Construct and run your vector search query.

Exclude the _id field and include only the plot and title fields in the results.
Add a field named score that shows the vector search score for each document in the results.

Copy and paste the following sample query into your terminal
Note
Pasting the queryVector in the sample code into your terminal might take a while depending on your machine.
Verify your query syntax and then run it using mongosh.

1 db.embedded_movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "vector_index",
5       "path": "plot_embedding",
6       "queryVector": 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8       "limit": 10
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10   },
11   {
12     "$project": {
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16       "score": { $meta: "vectorSearchScore" }
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1 [
2   {
3     plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
4     title: 'Thrill Seekers',
5     score: 0.7892671227455139
6   },
7   {
8     plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.',
9     title: 'About Time',
10     score: 0.7843604683876038
11   },
12   {
13     plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.',
14     title: 'The Time Machine',
15     score: 0.7801066637039185
16   },
17   {
18     plot: "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
19     title: 'Crusade in Jeans',
20     score: 0.7789170742034912
21   },
22   {
23     plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.',
24     title: 'Timecop',
25     score: 0.7771612405776978
26   },
27   {
28     plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...',
29     title: 'A.P.E.X.',
30     score: 0.7730885744094849
31   },
32   {
33     plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
34     title: 'Men in Black 3',
35     score: 0.7712380886077881
36   },
37   {
38     plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.',
39     title: 'Tomorrowland',
40     score: 0.7669923901557922
41   },
42   {
43     plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
44     title: 'Love Story 2050',
45     score: 0.7649372816085815
46   },
47   {
48     plot: 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...',
49     title: 'The Portal',
50     score: 0.7640786170959473
51   }
52 ]

To learn more about this pipeline stage, see Run Vector Search Queries.

Set up and initialize the .NET/C# project for the query.

Create a new directory called query-quick-start and initialize your project with the dotnet new command.
```
mkdir query-quick-start
cd query-quick-start
dotnet new console
```
Add the .NET/C# Driver to your project as a dependency.
```
dotnet add package MongoDB.Driver
```

Construct your vector search query.

Exclude the _id field and include only the plot and title fields in the results.
Add a field named score that shows the vector search score for each document in the results.

Edit the Program.cs file.

Copy and paste the following sample query into the Program.cs file:

1 using MongoDB.Bson;
2 using MongoDB.Bson.Serialization.Attributes;
3 using MongoDB.Bson.Serialization.Conventions;
4 using MongoDB.Driver;
5 using MongoDB.Driver.Search;
6 
7 public class vectorSearchBasicQuery 
8 {
9   // define connection to your Atlas cluster
10   private const string MongoConnectionString = "<connection-string>";
11 
12   public static void Main(string[] args){
13     var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
14     ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
15 
16     // connect to your Atlas cluster
17     var mongoClient = new MongoClient(MongoConnectionString);
18 
19     // define namespace
20     var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
21     var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
22 
23     // define vector embeddings to search
24     var vector = new[] 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25     var options = new VectorSearchOptions<EmbeddedMovie>() {
26         IndexName = "vector_index",
27         NumberOfCandidates = 150
28     };
29 
30     // run query
31     var results = moviesCollection.Aggregate()
32                 .VectorSearch(m => m.Embedding, vector, 10, options)
33                 .Project(Builders<EmbeddedMovie>.Projection
34                   .Include(m => m.Title)
35                   .Include(m => m.Plot)
36                   .MetaVectorSearchScore(m => m.Score))
37                 .ToList();
38 
39     // print results
40     foreach (var movie in results)
41       {
42         Console.WriteLine(movie.ToJson());
43       }
44   }
45 }
46 
47 [BsonIgnoreExtraElements]
48 public class EmbeddedMovie
49 {
50     [BsonIgnoreIfDefault]
51     public string Title { get; set; }
52     public string Plot { get; set; }
53     [BsonElement("plot_embedding")]
54     public double[] Embedding { get; set; }
55     public double Score { get; set; }
56 }

To learn more about this pipeline stage, see Run Vector Search Queries.

Specify the `<connection-string>`.

Replace the <connection-string> with your Atlas connection string.
Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.
Save the file.

Run your query.

Compile and run the Program.cs file.

dotnet run query-quick-start.csproj

1 { "_id" : ObjectId("573a13a0f29313caabd04a4f"), "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : 0.78926712274551392 }
2 { "_id" : ObjectId("573a13d8f29313caabda6557"), "plot" : "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title" : "About Time", "score" : 0.78436046838760376 }
3 { "_id" : ObjectId("573a13a5f29313caabd13b4b"), "plot" : "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title" : "The Time Machine", "score" : 0.78010666370391846 }
4 { "_id" : ObjectId("573a13aef29313caabd2e2d7"), "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : 0.77891707420349121 }
5 { "_id" : ObjectId("573a1399f29313caabceec0e"), "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : 0.77716124057769775 }
6 { "_id" : ObjectId("573a1399f29313caabcee36f"), "plot" : "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title" : "A.P.E.X.", "score" : 0.77308857440948486 }
7 { "_id" : ObjectId("573a13c6f29313caabd715d3"), "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : 0.77123808860778809 }
8 { "_id" : ObjectId("573a13d4f29313caabd98c13"), "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title" : "Tomorrowland", "score" : 0.76699239015579224 }
9 { "_id" : ObjectId("573a13b6f29313caabd477fa"), "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : 0.76493728160858154 }
10 { "_id" : ObjectId("573a13e5f29313caabdc40c9"), "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal", "score" : 0.76407861709594727 }

Ensure that your `CLASSPATH` contains the following libraries.

`junit`	4.11 or higher version
`mongodb-driver-sync`	4.3.0 or higher version
`slf4j-log4j12`	1.7.30 or higher version

Construct your vector search query.

Exclude the _id field and include only the plot and title fields in the results.
Add a field named score that shows the vector search score for each document in the results.

Create a file named BasicQuery.java.

Copy and paste the following sample query into the BasicQuery.java file:

1 import static com.mongodb.client.model.Aggregates.*;
2 import static com.mongodb.client.model.Projections.*;
3 import static com.mongodb.client.model.search.SearchPath.fieldPath;
4 import static java.util.Arrays.asList;
5 import com.mongodb.client.MongoClient;
6 import com.mongodb.client.MongoClients;
7 import com.mongodb.client.MongoCollection;
8 import com.mongodb.client.MongoDatabase;
9 import java.util.List;
10 import com.mongodb.client.model.search.FieldSearchPath;
11 import org.bson.Document;
12 import org.bson.conversions.Bson;
13 
14 public class BasicQuery {
15   public static void main( String[] args ) {
16     // specify connection
17     String uri = "<connection-string>";
18 
19     // establish connection and set namespace
20     try (MongoClient mongoClient = MongoClients.create(uri)) {
21       MongoDatabase database = mongoClient.getDatabase("sample_mflix");
22       MongoCollection<Document> collection = database.getCollection("embedded_movies");
23       
24   	  // define $vectorSearch query options 
25 	    List<Double> queryVector = (asList(-0.0016261312d, -0.028070757d, -0.011342932d, -0.012775794d, -0.0027440966d, 0.008683807d, -0.02575152d, -0.02020668d, -0.010283281d, -0.0041719596d, 0.021392956d, 0.028657231d, -0.006634482d, 0.007490867d, 0.018593878d, 0.0038187427d, 0.029590257d, -0.01451522d, 0.016061379d, 0.00008528442d, -0.008943722d, 0.01627464d, 0.024311995d, -0.025911469d, 0.00022596726d, -0.008863748d, 0.008823762d, -0.034921836d, 0.007910728d, -0.01515501d, 0.035801545d, -0.0035688248d, -0.020299982d, -0.03145631d, -0.032256044d, -0.028763862d, -0.0071576433d, -0.012769129d, 0.012322609d, -0.006621153d, 0.010583182d, 0.024085402d, -0.001623632d, 0.007864078d, -0.021406285d, 0.002554159d, 0.012229307d, -0.011762793d, 0.0051682983d, 0.0048484034d, 0.018087378d, 0.024325324d, -0.037694257d, -0.026537929d, -0.008803768d, -0.017767483d, -0.012642504d, -0.0062712682d, 0.0009771782d, -0.010409906d, 0.017754154d, -0.004671795d, -0.030469967d, 0.008477209d, -0.005218282d, -0.0058480743d, -0.020153364d, -0.0032805866d, 0.004248601d, 0.0051449724d, 0.006791097d, 0.007650814d, 0.003458861d, -0.0031223053d, -0.01932697d, -0.033615597d, 0.00745088d, 0.006321252d, -0.0038154104d, 0.014555207d, 0.027697546d, -0.02828402d, 0.0066711367d, 0.0077107945d, 0.01794076d, 0.011349596d, -0.0052715978d, 0.014755142d, -0.019753495d, -0.011156326d, 0.011202978d, 0.022126047d, 0.00846388d, 0.030549942d, -0.0041386373d, 0.018847128d, -0.00033655585d, 0.024925126d, -0.003555496d, -0.019300312d, 0.010749794d, 0.0075308536d, -0.018287312d, -0.016567878d, -0.012869096d, -0.015528221d, 0.0078107617d, -0.011156326d, 0.013522214d, -0.020646535d, -0.01211601d, 0.055928253d, 0.011596181d, -0.017247654d, 0.0005939711d, -0.026977783d, -0.003942035d, -0.009583511d, -0.0055248477d, -0.028737204d, 0.023179034d, 0.003995351d, 0.0219661d, -0.008470545d, 0.023392297d, 0.010469886d, -0.015874773d, 0.007890735d, -0.009690142d, -0.00024970944d, 0.012775794d, 0.0114762215d, 0.013422247d, 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26 	    String indexName = "vector_index";
27 	    FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
28 	    int numCandidates = 150;
29       int limit = 10;
30       
31       // define pipeline 
32       List<Bson> pipeline = asList(
33           vectorSearch(
34            fieldSearchPath,
35            queryVector,
36            indexName,
37            numCandidates,
38            limit),
39           project(
40            fields(exclude("_id"), include("title"), include("plot"), 
41            metaVectorSearchScore("score"))));
42       
43 	    // run query and print results
44       collection.aggregate(pipeline)
45         .forEach(doc -> System.out.println(doc.toJson()));	
46     }
47   }
48 }

Note

To run the sample code in your Maven environment, add the following code above the import statements in your file.

package com.mongodb.drivers;

To learn more about this pipeline stage, see Run Vector Search Queries.

Specify the `<connection-string>`.

Replace the <connection-string> with your Atlas connection string.
Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.
Save the file.

Run your query.

Compile and run the BasicQuery.java file:

javac BasicQuery.java
java BasicQuery

1 {"plot": "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title": "Thrill Seekers", "score": 0.7892671227455139}
2 {"plot": "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title": "About Time", "score": 0.7843604683876038}
3 {"plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title": "The Time Machine", "score": 0.7801066637039185}
4 {"plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title": "Crusade in Jeans", "vectorSearchScore": 0.7789170742034912}
5 {"plot": "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title": "Timecop", "score": 0.7771612405776978}
6 {"plot": "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title": "A.P.E.X.", "vectorSearchScore": 0.7730885744094849}
7 {"plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title": "Men in Black 3", "score": 0.7712380886077881}
8 {"plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title": "Tomorrowland", "score": 0.7669923901557922}
9 {"plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title": "Love Story 2050", "vectorSearchScore": 0.7649372816085815}
10 {"plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title": "The Portal", "score": 0.7640786170959473}

Construct your vector search query.

Exclude the _id field and include only the plot and title fields in the results.
Add a field named score that shows the vector search score for each document in the results.

Create a file named atlas-vector-search-quick-start.js.

Copy and paste the following sample query into the atlas-vector-search-quick-start.js file:

1 const { MongoClient } = require("mongodb");
2 
3 // connect to your Atlas cluster
4 const uri = "<connection-string>";
5     
6 const client = new MongoClient(uri);
7 
8 async function run() {
9     try {
10         await client.connect();
11 
12         // set namespace
13         const database = client.db("sample_mflix");
14         const coll = database.collection("embedded_movies");
15 
16         // define pipeline
17         const agg = [
18             {
19               '$vectorSearch': {
20                 'index': 'vector_index', 
21                 'path': 'plot_embedding', 
22                 'queryVector': [
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24                 ], 
25                 'numCandidates': 150, 
26                 'limit': 10
27               }
28             }, {
29               '$project': {
30                 '_id': 0, 
31                 'plot': 1, 
32                 'title': 1, 
33                 'score': {
34                   '$meta': 'vectorSearchScore'
35                 }
36               }
37             }
38           ];
39         // run pipeline
40         const result = coll.aggregate(agg);
41 
42         // print results
43         await result.forEach((doc) => console.dir(JSON.stringify(doc)));
44     } finally {
45         await client.close();
46     }
47 }
48 run().catch(console.dir);

To learn more about this pipeline stage, see Run Vector Search Queries.

Specify the `<connection-string>`.

Replace the <connection-string> in the query with your Atlas connection string.
Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.
Save the file.

Run your query.

Run the following command to query your collection:

node atlas-vector-search-quick-start.js

1 '{"plot":"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.","title":"Thrill Seekers","score":0.7892671227455139}'
2 '{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.7843604683876038}'
3 '{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.7801066637039185}'
4 `{"plot":"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.7789170742034912}`
5 '{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.7771612405776978}'
6 '{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.7730885744094849}'
7 `{"plot":"Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.7712380886077881}`
8 '{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.7669923901557922}'
9 '{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.7649372816085815}'
10 '{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.7640786170959473}'

Construct your vector search query.

Exclude the _id field and include only the plot and title fields in the results.
Add a field named score that shows the vector search score for each document in the results.

Create a file named atlas-vector-search-quick-start.py.

Copy and paste the following sample query into the atlas-vector-search-quick-start.py file:

1 import pymongo
2 import datetime
3 
4 # connect to your Atlas cluster
5 client = pymongo.MongoClient("<connection-string>")
6 
7 # define pipeline
8 pipeline = [
9   {
10     '$vectorSearch': {
11       'index': 'vector_index', 
12       'path': 'plot_embedding', 
13       'queryVector': [-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136], 
14       'numCandidates': 150, 
15       'limit': 10
16     }
17   }, {
18     '$project': {
19       '_id': 0, 
20       'plot': 1, 
21       'title': 1, 
22       'score': {
23         '$meta': 'vectorSearchScore'
24       }
25     }
26   }
27 ]
28 
29 # run pipeline
30 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
31 
32 # print results
33 for i in result:
34     print(i)
35

To learn more about this pipeline stage, see Run Vector Search Queries.

Specify the `<connection-string>`.

Replace the <connection-string> in the query with your Atlas connection string.
Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.
Save the file.

Run your query.

Run the following command to query your collection:

python atlas-vector-search-quick-start.py

1 {'plot': 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', 'title': 'Thrill Seekers', 'score': 0.7892671227455139}
2 {'plot': 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.', 'title': 'About Time', 'score': 0.7843604683876038}
3 {'plot': 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', 'title': 'The Time Machine', 'score': 0.7801066637039185}
4 {'plot': "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", 'title': 'Crusade in Jeans', 'score': 0.7789170742034912}
5 {'plot': 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', 'title': 'Timecop', 'score': 0.7771612405776978}
6 {'plot': 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', 'title': 'A.P.E.X.', 'score': 0.7730885744094849}
7 {'plot': "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 'title': 'Men in Black 3', 'score': 0.7712380886077881}
8 {'plot': 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', 'title': 'Tomorrowland', 'score': 0.7669923901557922}
9 {'plot': 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 'title': 'Love Story 2050', 'score': 0.7649372816085815}
10 {'plot': 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 'title': 'The Portal', 'score': 0.7640786170959473}

Back

Atlas Vector Search

How to Create Vector Embeddings

1	{
2	"fields": [{
3	"type": "vector",
4	"path": "plot_embedding",
5	"numDimensions": 1536,
6	"similarity": "cosine"
7	}]
8	}

1	[
2	{
3	"$vectorSearch": {
4	"index": "vector_index",
5	"path": "plot_embedding",
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1	{
2	"score": 0.7892671227455139,
3	"plot": "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.",
4	"title": "Thrill Seekers"
5	}
6	{
7	"plot": "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.",
8	"title": "About Time",
9	"score": 0.7843604683876038
10	}
11	{
12	"plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.",
13	"title": "The Time Machine",
14	"score": 0.7801066637039185
15	}
16	{
17	"plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
18	"title": "Crusade in Jeans",
19	"score": 0.7789170742034912
20	}
21	{
22	"title": "Timecop",
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24	"plot": "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past."
25	}
26	{
27	"plot": "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...",
28	"title": "A.P.E.X.",
29	"score": 0.7730885744094849
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31	{
32	"plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
33	"title": "Men in Black 3",
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38	"plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.",
39	"title": "Tomorrowland"
40	}
41	{
42	"plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.",
43	"title": "Love Story 2050",
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46	{
47	"plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...",
48	"title": "The Portal",
49	"score": 0.7640786170959473
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12	"$project": {
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1	[
2	{
3	plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
4	title: 'Thrill Seekers',
5	score: 0.7892671227455139
6	},
7	{
8	plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.',
9	title: 'About Time',
10	score: 0.7843604683876038
11	},
12	{
13	plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.',
14	title: 'The Time Machine',
15	score: 0.7801066637039185
16	},
17	{
18	plot: "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
19	title: 'Crusade in Jeans',
20	score: 0.7789170742034912
21	},
22	{
23	plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.',
24	title: 'Timecop',
25	score: 0.7771612405776978
26	},
27	{
28	plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...',
29	title: 'A.P.E.X.',
30	score: 0.7730885744094849
31	},
32	{
33	plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
34	title: 'Men in Black 3',
35	score: 0.7712380886077881
36	},
37	{
38	plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.',
39	title: 'Tomorrowland',
40	score: 0.7669923901557922
41	},
42	{
43	plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
44	title: 'Love Story 2050',
45	score: 0.7649372816085815
46	},
47	{
48	plot: 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...',
49	title: 'The Portal',
50	score: 0.7640786170959473
51	}
52	]

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3	{"plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title": "The Time Machine", "score": 0.7801066637039185}
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7	{"plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title": "Men in Black 3", "score": 0.7712380886077881}
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3	{'plot': 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', 'title': 'The Time Machine', 'score': 0.7801066637039185}
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7	{'plot': "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 'title': 'Men in Black 3', 'score': 0.7712380886077881}
8	{'plot': 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', 'title': 'Tomorrowland', 'score': 0.7669923901557922}
9	{'plot': 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 'title': 'Love Story 2050', 'score': 0.7649372816085815}
10	{'plot': 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 'title': 'The Portal', 'score': 0.7640786170959473}

Objectives

Prerequisites

Required Cluster Configuration

Supported Clients

Required Access

Index Vector Embeddings

In Atlas, go to the .css-h15tq0{font-style:normal;font-weight:700;}Clusters page for your project.

Go to the Atlas Search page for your cluster.

Go to the JSON Editor.

Specify the database, collection, and index name.

Define your Atlas Vector Search index.

Create the search index.

Wait for the index to finish building.

Run a Vector Search Query

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

Go to the Collections page.

Go to the Aggregation tab for the collection.

Construct and run your vector search query.

Expand your query results.

Connect to your cluster in mongosh.

Switch to the sample_mflix database.

Construct and run your vector search query.

Note

Set up and initialize the .NET/C# project for the query.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Ensure that your CLASSPATH contains the following libraries.

Construct your vector search query.

Note

Specify the <connection-string>.

Run your query.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

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

Connect to your cluster in `mongosh`.

Switch to the `sample_mflix` database.

Specify the `<connection-string>`.

Ensure that your `CLASSPATH` contains the following libraries.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Specify the `<connection-string>`.