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Atlas Vector Search

Atlas Vector Search Quick Start

On this page

  • Create a Vector Search Index
  • Run a Vector Search Query
  • Learning Summary
  • Next Steps

This quick start describes how to load sample documents that contain vector embeddings into an Atlas cluster or local Atlas deployment, create an Atlas Vector Search index on those embeddings, and then perform semantic search to return documents that are similar to your query.

Time required: 15 minutes

Create a Vector Search Index

In this section, you create an Atlas Vector Search index on sample data that you load into an Atlas cluster or a deployment hosted on your local computer:

1

Set up your Atlas cluster.

  1. Create a free Atlas account or sign in to an existing account.

  2. If you don't yet have an Atlas cluster, create a free M0 cluster. To learn more about creating an Atlas cluster, see Create a Cluster.

    Note

    If you are working with an existing cluster, you must have Project Data Access Admin or higher access to your Atlas project.

    If you create a new cluster, you have the necessary permissions by default.

    You can create only one M0 free cluster per project.

  3. In the left sidebar, click Atlas Search. Choose your cluster from the Select data source menu and click Go to Atlas Search.

  4. Click Load a Sample Dataset. In the Load Sample Dataset dialog box, click Load Sample Dataset to confirm.

    If this cluster already has the sample dataset loaded, check that the sample_mflix database contains the embedded_movies collection. If it doesn't, drop the sample databases and reload the sample dataset.

    Loading the sample dataset can take several minutes to complete.

2

Create a Vector Search index.

  1. When the sample data finishes loading, click Create Search Index.

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

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

    Each document in this collection contains information about a movie, including a summary of the movie's plot as a string, which has also been converted to and stored as a vector embedding in the document's plot_embedding field.

  4. In the Index Name field, specify vector_index.

  5. Copy and paste the following vector search index definition into the JSON Editor.

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

    This index definition:

  6. Click Next.

  7. Click Create Search Index.

    The index should take about one minute to build. When your vector index is finished building, the Status column reads Active.

1

Install the dependencies.

  1. Install the Atlas CLI and MongoDB Database Tools.

    If you use Homebrew, you can run the following commands in your terminal:

    brew install mongodb-atlas-cli
    brew tap mongodb/brew && brew install mongodb-database-tools

  2. Install Docker.

For detailed instructions, see Prerequisites.

2

Set up your local Atlas deployment.

  1. If you don't have an existing Atlas account, run atlas setup in your terminal or create a new account.

  2. In your terminal, run atlas auth login to authenticate with your Atlas login credentials. To learn more, see Connect from the Atlas CLI.

  3. Run atlas deployments setup and follow the prompts to create a local deployment. When prompted to connect to the deployment, select skip.

    For detailed instructions, see Create a Local Atlas Deployment.

3

Load the sample data.

  1. Run the following command in your terminal to download the sample data:

    curl  https://atlas-education.s3.amazonaws.com/sampledata.archive -o sampledata.archive

  2. Run the following command to load the data into your deployment, replacing <port-number> with the port where you're hosting the deployment:

    mongorestore --archive=sampledata.archive --port=<port-number>
4

Create a Vector Search index.

  1. Create a file named vector-index.json

  2. Copy and paste the following index definition into the JSON file.

    {
      "database": "sample_mflix",
      "collectionName": "embedded_movies",
      "type": "vectorSearch",
      "name": "vector_index",
        "fields": [
          {
            "type": "vector",
            "path": "plot_embedding",
            "numDimensions": 1536,
            "similarity": "dotProduct"
          }
        ]
    }

    This index definition:

  3. Save the file, and then run the following command in your terminal, replacing <path-to-file> with the path to the vector-index.json file that you created.

    atlas deployments search indexes create --file <path-to-file>

Run a Vector Search Query

In this section, you run a sample vector search query on your indexed embeddings.

➤ To set the client you use to run the examples in this section, use the Select your language drop-down menu in the right navigation pane of this page.

1

Click the Collections tab to view the Data Explorer.

2

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.

3

Construct and run your vector search query.

  1. In the aggregation pipeline pane, click the </> Text toggle to enable text mode for pipeline editing.

  2. Copy and paste the following sample query into the text editor.

    This query searches for documents that include text in the plot field that is semantically related to the term "time travel".

1[
2  {
3    "$vectorSearch": {
4      "index": "vector_index",
5      "path": "plot_embedding",
6      "queryVector": 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1{
2    "score": 0.9332506656646729,
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": 0.9312690496444702
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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": 0.929530143737793
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": 0.9290417432785034
20}
21{
22    "title": "Timecop",
23    "score": 0.9283161759376526,
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": 0.9266218543052673
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": 0.9258455038070679
35}
36{
37    "score": 0.9240515828132629,
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": 0.923175573348999
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": 0.9228089451789856
50}

This query uses the $vectorSearch stage to:

  • Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.

  • Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

  • Only include the movie plot and title fields in the results.

  • Add a score field to show the relevance of each result to the search term.

The Pipeline Output pane displays the results of your query.

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

1

Download and install mongosh.

2

Connect to your cluster or local deployment in mongosh.

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

In a terminal window, run atlas deployments connect and follow the prompts to connect to your local Atlas deployment via mongosh. For detailed instructions on connecting, see Manage a Local Atlas Deployment.

3

Switch to the sample_mflix database.

use sample_mflix
switched to db sample_mflix
4

Construct and run your vector search query.

  1. Copy and paste the following sample query into your terminal and then run it using mongosh. mongosh might lag slightly when you paste in the query due to the number of characters in the vector embedding.

    This query searches for documents that include text in the plot field that is semantically related to the term "time travel".

1db.embedded_movies.aggregate([
2  {
3    "$vectorSearch": {
4      "index": "vector_index",
5      "path": "plot_embedding",
6      "queryVector": 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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: 0.9332506656646729
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: 0.9312690496444702
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: 0.929530143737793
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: 0.9290417432785034
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: 0.9283161759376526
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: 0.9266218543052673
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: 0.9258455038070679
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: 0.9240515828132629
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: 0.923175573348999
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: 0.9228089451789856
51  }
52]

This query uses the $vectorSearch stage to:

  • Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.

  • Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

  • Only include the movie plot and title fields in the results.

  • Add a score field to show the relevance of each result to the search term.

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

1

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

  1. 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

  2. Add the .NET/C# Driver to your project as a dependency.

    dotnet add package MongoDB.Driver

For more detailed installation instructions, see the MongoDB C# Driver documentation.

2

Construct your vector search query.

This query searches for documents that include text in the plot field that is semantically related to the term "time travel".

  1. Edit the Program.cs file.

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

    1using MongoDB.Bson;
    2using MongoDB.Bson.Serialization.Attributes;
    3using MongoDB.Bson.Serialization.Conventions;
    4using MongoDB.Driver;
    5
    6public class vectorSearchBasicQuery 
    7{
    8  // define connection to your Atlas cluster
    9  private const string MongoConnectionString = "<connection-string>";
    10
    11  public static void Main(string[] args){
    12    var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
    13    ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
    14
    15    // connect to your Atlas cluster
    16    var mongoClient = new MongoClient(MongoConnectionString);
    17
    18    // define namespace
    19    var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
    20    var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
    21
    22    // define vector embeddings to search
    23    var vector = new[] 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    24    var options = new VectorSearchOptions<EmbeddedMovie>() {
    25        IndexName = "vector_index",
    26        NumberOfCandidates = 150
    27    };
    28
    29    // run query
    30    var results = moviesCollection.Aggregate()
    31                .VectorSearch(movie => movie.Embedding, vector, 10, options)
    32                .Project(Builders<EmbeddedMovie>.Projection
    33                  .Include(movie => movie.Title)
    34                  .Include(movie => movie.Plot)
    35                  .Exclude(movie => movie.Id)
    36                  .MetaVectorSearchScore(movie => movie.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]
    48public 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}

This query uses the $vectorSearch stage to:

  • Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.

  • Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

  • Only include the movie plot and title fields in the results.

  • Add a score field to show the relevance of each result to the search term.

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

3

Specify the <connection-string>.

  1. Replace <connection-string> with the connection string for your Atlas cluster or local deployment.

    Your connection string should use the following format:

    mongodb+srv://<username>:<password>@<clusterName>.<hostname>.mongodb.net

    Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.

    Your connection string should use the following format:

    mongodb://localhost:<port-number>/?directConnection=true

  2. Save the file.

4

Run your query.

Compile and run the Program.cs file.

dotnet run query-quick-start.csproj
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" : 0.9332506656646729 }
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" : 0.9312690496444702 }
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" : 0.929530143737793 }
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" : 0.9290417432785034 }
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" : 0.9283161759376526 }
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" : 0.9266218543052673 }
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" : 0.9258455038070679 }
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" : 0.9240515828132629 }
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" : 0.923175573348999 }
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" : 0.9228089451789856 }
1

Add the MongoDB Go Driver to your project.

Run the following commands:

  1. Initialize your Go module:

    mkdir go-vector-quickstart && cd go-vector-quickstart
    go mod init go-vector-quickstart

  2. Add MongoDB Go as a dependency:

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

For more detailed installation instructions, see the MongoDB Go Driver documentation.

2

Construct your vector search query.

This query searches for documents that include text in the plot field that is semantically related to the term "time travel".

  1. Create a file named atlas_vector_search_quick_start.go .

  2. Copy and paste the following sample query into the atlas_vector_search_quick_start.go file:

    1package main
    2
    3import (
    4  "context"
    5  "encoding/json"
    6  "fmt"
    7  "log"
    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
    14func main() {
    15
    16  ctx := context.TODO()
    17
    18  // Replace the placeholder with your Atlas connection string
    19  const uri = "<connection-string>"
    20
    21  // Connect to your Atlas cluster
    22  clientOptions := options.Client().ApplyURI(uri)
    23  client, err := mongo.Connect(ctx, clientOptions)
    24  if err != nil {
    25    log.Fatal(err)
    26  }
    27  defer client.Disconnect(ctx)
    28
    29  // Set the namespace
    30  coll := client.Database("sample_mflix").Collection("embedded_movies")
    31
    32  // Define the pipeline with $vectorSearch query options
    33  queryVectorValues := []float64{
    34    -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136,
    35  }
    36
    37  pipeline := mongo.Pipeline{
    38    bson.D{
    39      {"$vectorSearch", bson.D{
    40        {"queryVector", queryVectorValues},
    41        {"index", "vector_index"},
    42        {"path", "plot_embedding"},
    43        {"numCandidates", 150},
    44        {"limit", 10},
    45      }},
    46    },
    47    bson.D{
    48      {"$project", bson.D{
    49        {"_id", 0},
    50        {"plot", 1},
    51        {"title", 1},
    52        {"score", bson.D{
    53          {"$meta", "vectorSearchScore"},
    54        }},
    55      }},
    56    },
    57  }
    58
    59  // Run the pipeline
    60  cursor, err := coll.Aggregate(ctx, pipeline)
    61  if err != nil {
    62    log.Fatalf("Failed to run aggregation: %v", err)
    63  }
    64  defer cursor.Close(ctx)
    65
    66  // Print results
    67  for cursor.Next(ctx) {
    68    var doc bson.M
    69    if err := cursor.Decode(&doc); err != nil {
    70      log.Fatalf("Failed to decode document: %v", err)
    71    }
    72    jsonDoc, err := json.Marshal(doc)
    73    if err != nil {
    74      log.Fatalf("Failed to marshal document to JSON: %v", err)
    75    }
    76    fmt.Println(string(jsonDoc))
    77  }
    78
    79  if err := cursor.Err(); err != nil {
    80    log.Fatalf("Cursor error: %v", err)
    81  }
    82}

This query uses the $vectorSearch stage to:

  • Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.

  • Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

  • Only include the movie plot and title fields in the results.

  • Add a score field to show the relevance of each result to the search term.

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

3

Specify the <connection-string>.

  1. Replace <connection-string> with the connection string for your Atlas cluster or local deployment.

    Your connection string should use the following format:

    mongodb+srv://<username>:<password>@<clusterName>.<hostname>.mongodb.net

    Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.

    Your connection string should use the following format:

    mongodb://localhost:<port-number>/?directConnection=true

  2. Save the file.

4

Run your query.

Run the following command to query your collection:

go run atlas_vector_search_quick_start.go
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":0.9332506656646729}'
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":0.9312690496444702}'
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":0.929530143737793}'
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":0.9290417432785034}`
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":0.9283161759376526}'
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":0.9266218543052673}'
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":0.9258455038070679}`
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":0.9240515828132629}'
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":0.923175573348999}'
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":0.9228089451789856}'
1

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

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

2

Construct your vector search query.

This query searches for documents that include text in the plot field that is semantically related to the term "time travel".

  1. Create a file named BasicQuery.java .

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

    1import static com.mongodb.client.model.Aggregates.*;
    2import static com.mongodb.client.model.Projections.*;
    3import static com.mongodb.client.model.search.SearchPath.fieldPath;
    4import static java.util.Arrays.asList;
    5import com.mongodb.client.MongoClient;
    6import com.mongodb.client.MongoClients;
    7import com.mongodb.client.MongoCollection;
    8import com.mongodb.client.MongoDatabase;
    9import java.util.List;
    10import com.mongodb.client.model.search.FieldSearchPath;
    11import org.bson.Document;
    12import org.bson.conversions.Bson;
    13
    14public 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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-0.015714826d, -0.001806072d, 0.011856096d, 0.026444625d, -0.0010663156d, -0.006474535d, 0.0016161345d, -0.020313311d, 0.0148351155d, -0.0018393943d, 0.0057347785d, 0.018300641d, -0.018647194d, 0.03345565d, -0.008070676d, 0.0071443142d, 0.014301958d, 0.0044818576d, 0.003838736d, -0.007350913d, -0.024525259d, -0.001142124d, -0.018620536d, 0.017247654d, 0.007037683d, 0.010236629d, 0.06046009d, 0.0138887605d, -0.012122675d, 0.037694257d, 0.0055081863d, 0.042492677d, 0.00021784494d, -0.011656162d, 0.010276617d, 0.022325981d, 0.005984696d, -0.009496873d, 0.013382261d, -0.0010563189d, 0.0026507939d, -0.041639622d, 0.008637156d, 0.026471283d, -0.008403899d, 0.024858482d, -0.00066686375d, -0.0016252982d, 0.027590916d, 0.0051449724d, 0.0058647357d, -0.008743787d, -0.014968405d, 0.027724205d, -0.011596181d, 0.0047650975d, -0.015381602d, 0.0043718936d, 0.002159289d, 0.035908177d, -0.008243952d, -0.030443309d, 0.027564257d, 0.042625964d, -0.0033688906d, 0.01843393d, 0.019087048d, 0.024578573d, 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0.004811749d, -0.017967418d, -0.026684547d, -0.014128681d, 0.0041386373d, -0.013742141d, -0.010056688d, -0.013268964d, -0.0110630235d, -0.028337335d, 0.015981404d, -0.00997005d, -0.02424535d, -0.013968734d, -0.028310679d, -0.027750863d, -0.020699851d, 0.02235264d, 0.001057985d, 0.00081639783d, -0.0099367285d, 0.013522214d, -0.012016043d, -0.00086471526d, 0.013568865d, 0.0019376953d, -0.019020405d, 0.017460918d, -0.023045745d, 0.008503866d, 0.0064678704d, -0.011509543d, 0.018727167d, -0.003372223d, -0.0028690554d, -0.0027024434d, -0.011902748d, -0.012182655d, -0.015714826d, -0.0098634185d, 0.00593138d, 0.018753825d, 0.0010146659d, 0.013029044d, 0.0003521757d, -0.017620865d, 0.04102649d, 0.00552818d, 0.024485271d, -0.009630162d, -0.015608194d, 0.0006718621d, -0.0008418062d, 0.012395918d, 0.0057980907d, 0.016221326d, 0.010616505d, 0.004838407d, -0.012402583d, 0.019900113d, -0.0034521967d, 0.000247002d, -0.03153628d, 0.0011038032d, -0.020819811d, 0.016234655d, -0.00330058d, -0.0032289368d, 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-0.018207338d, 0.024645219d, 0.021752838d, 0.0007614159d, -0.013648839d, 0.01111634d, -0.010503208d, -0.0038487327d, -0.008203966d, -0.00397869d, 0.0029740208d, 0.008530525d, 0.005261601d, 0.01642126d, -0.0038753906d, -0.013222313d, 0.026537929d, 0.024671877d, -0.043505676d, 0.014195326d, 0.024778508d, 0.0056914594d, -0.025951454d, 0.017620865d, -0.0021359634d, 0.008643821d, 0.021299653d, 0.0041686273d, -0.009017031d, 0.04044002d, 0.024378639d, -0.027777521d, -0.014208655d, 0.0028623908d, 0.042119466d, 0.005801423d, -0.028124074d, -0.03129636d, 0.022139376d, -0.022179363d, -0.04067994d, 0.013688826d, 0.013328944d, 0.0046184794d, -0.02828402d, -0.0063412455d, -0.0046184794d, -0.011756129d, -0.010383247d, -0.0018543894d, -0.0018593877d, -0.00052024535d, 0.004815081d, 0.014781799d, 0.018007403d, 0.01306903d, -0.020433271d, 0.009043689d, 0.033189073d, -0.006844413d, -0.019766824d, -0.018767154d, 0.00533491d, -0.0024575242d, 0.018727167d, 0.0058080875d, -0.013835444d, 0.0040719924d, 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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;

This query uses the $vectorSearch stage to:

  • Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.

  • Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

  • Only include the movie plot and title fields in the results.

  • Add a score field to show the relevance of each result to the search term.

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

3

Specify the <connection-string>.

  1. Replace <connection-string> with the connection string for your Atlas cluster or local deployment.

    Your connection string should use the following format:

    mongodb+srv://<username>:<password>@<clusterName>.<hostname>.mongodb.net

    Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.

    Your connection string should use the following format:

    mongodb://localhost:<port-number>/?directConnection=true

  2. Save the file.

4

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": 0.9332506656646729}
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": 0.9312690496444702}
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": 0.929530143737793}
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": 0.9290417432785034}
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": 0.9283161759376526}
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": 0.9266218543052673}
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": 0.9258455038070679}
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": 0.9240515828132629}
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": 0.9228089451789856}
1

Install the MongoDB Node.js Driver.

  1. Run one of the following commands:

npm install mongodb
yarn add mongodb

For more detailed installation instructions, see the MongoDB Node.js Driver documentation.

2

Construct your vector search query.

This query searches for documents that include text in the plot field that is semantically related to the term "time travel".

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

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

    1const { MongoClient } = require("mongodb");
    2
    3// connect to your Atlas cluster
    4const uri = "<connection-string>";
    5
    6const client = new MongoClient(uri);
    7
    8async 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': [
    23                  -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, -