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How to Perform Semantic Search Against Data in Your Atlas Cluster

This tutorial describes how to perform an ANN search on a vector in the plot_embedding field in the sample_mflix.embedded_movies collection on your Atlas cluster. To demonstrate this, it takes you through the following steps:

  1. Create an Atlas Vector Search index on the numeric field named plot_embedding in the sample_mflix.embedded_movies collection.

  2. Run Atlas Vector Search queries against the plot_embedding field in the sample_mflix.embedded_movies collection.


➤ Use the Select your language drop-down menu to select the client to use to create the Atlas Vector Search index and run queries.


Tip

Work with a runnable version of this tutorial as a Python notebook.

To complete this tutorial, you must have the following:

This section demonstrates how to create an Atlas Vector Search index on the plot_embedding field in the sample_mflix.embedded_movies collection for running vector queries against the field.

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

1
  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.

2

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

  1. In the sidebar, click Atlas Search under the Services heading.

    Note

    If you have no clusters, click Create cluster to create one. To learn more, see Create a Cluster.

  2. From the Select data source dropdown, select your cluster and click Go to Atlas Search.

    The Atlas Search page displays.

  1. Click the Browse Collections button for your cluster.

  2. Expand the database and select the collection.

  3. Click the Search Indexes tab for the collection.

    The Atlas Search page displays.

  1. Click the cluster's name.

  2. Click the Atlas Search tab.

    The Atlas Search page displays.

3
4

Make the following selections on the page and then click Next.

Search Type

Select the Vector Search index type.

Index Name and Data Source

Specify the following information:

  • Index Name: vector_index

  • Database and Collection:

    • sample_mflix database

    • embedded_movies collection

Configuration Method

For a guided experience, select Visual Editor.

To edit the raw index definition, select JSON Editor.
5

In this step, you define an index of the vectorSearch type for the following fields:

  • 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.

Atlas automatically detects fields that contain vector embeddings, as well as their corresponding dimensions. For the sample_mflix.embedded_movies collection, the plot_embedding field displays.

To configure the index, do the following:

  1. Select Dot Product from the Similarity Method dropdown.

  2. Click Advanced, then select Scalar quantization from the dropdown menu.

  3. In the Filter Field section, specify the genres and year fields to filter the data by.

Replace the default definition with the following index definition.

1{
2 "fields": [
3 {
4 "type": "vector",
5 "path": "plot_embedding",
6 "numDimensions": 1536,
7 "similarity": "dotProduct",
8 "quantization": "scalar"
9 },
10 {
11 "type": "filter",
12 "path": "genres"
13 },
14 {
15 "type": "filter",
16 "path": "year"
17 }
18 ]
19}
6
7

Atlas displays a modal window to let you know your index is building.

8
9

The newly created index displays on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.

Note

Larger collections take longer to index. You will receive an email notification when your index is finished building.

1

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

2

Example

use sample_mflix
switched to db sample_mflix
3

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.

1db.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);
1

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

dotnet add package MongoDB.Driver
2

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

IndexService.cs
1namespace query_quick_start;
2
3using MongoDB.Bson;
4using MongoDB.Driver;
5using System;
6using System.Threading;
7
8public 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 { "quantization", "scalar"}
36 },
37 new BsonDocument
38 {
39 {"type", "filter"},
40 {"path", "genres"}
41 },
42 new BsonDocument
43 {
44 {"type", "filter"},
45 {"path", "year"}
46 }
47 }
48 }
49 };
50
51 var model = new CreateSearchIndexModel(name, type, definition);
52
53 searchIndexView.CreateOne(model);
54 Console.WriteLine($"New search index named {name} is building.");
55
56 // Polling for index status
57 Console.WriteLine("Polling to check if the index is ready. This may take up to a minute.");
58 bool queryable = false;
59 while (!queryable)
60 {
61 var indexes = searchIndexView.List();
62 foreach (var index in indexes.ToEnumerable())
63 {
64 if (index["name"] == name)
65 {
66 queryable = index["queryable"].AsBoolean;
67 }
68 }
69 if (!queryable)
70 {
71 Thread.Sleep(5000);
72 }
73 }
74 Console.WriteLine($"{name} is ready for querying.");
75 }
76 catch (Exception e)
77 {
78 Console.WriteLine($"Exception: {e.Message}");
79 }
80 }
81}

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.

3

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

Program.cs
using query_quick_start;
var indexService = new IndexService();
indexService.CreateVectorIndex();
4

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.
1

Add the Go Driver as a dependency in your project:

go get go.mongodb.org/mongo-driver/mongo
2

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

vector-index.go
1package main
2
3import (
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
14func 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 Quantization string `bson:"quantization"`
39 }
40
41 type filterField struct {
42 Type string `bson:"type"`
43 Path string `bson:"path"`
44 }
45
46 type indexDefinition struct {
47 Fields []vectorDefinitionField `bson:"fields"`
48 }
49
50 vectorDefinition := vectorDefinitionField{
51 Type: "vector",
52 Path: "plot_embedding",
53 NumDimensions: 1536,
54 Similarity: "dotProduct",
55 Quantization: "scalar"}
56 genreFilterDefinition := filterField{"filter", "genres"}
57 yearFilterDefinition := filterField{"filter", "year"}
58
59 indexModel := mongo.SearchIndexModel{
60 Definition: bson.D{{"fields", [3]interface{}{
61 vectorDefinition,
62 genreFilterDefinition,
63 yearFilterDefinition}}},
64 Options: opts,
65 }
66
67 // Create the index
68 searchIndexName, err := coll.SearchIndexes().CreateOne(ctx, indexModel)
69 if err != nil {
70 log.Fatalf("failed to create the search index: %v", err)
71 }
72 log.Println("New search index named " + searchIndexName + " is building.")
73
74 // Await the creation of the index.
75 log.Println("Polling to check if the index is ready. This may take up to a minute.")
76 searchIndexes := coll.SearchIndexes()
77 var doc bson.Raw
78 for doc == nil {
79 cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName))
80 if err != nil {
81 fmt.Errorf("failed to list search indexes: %w", err)
82 }
83
84 if !cursor.Next(ctx) {
85 break
86 }
87
88 name := cursor.Current.Lookup("name").StringValue()
89 queryable := cursor.Current.Lookup("queryable").Boolean()
90 if name == searchIndexName && queryable {
91 doc = cursor.Current
92 } else {
93 time.Sleep(5 * time.Second)
94 }
95 }
96
97 log.Println(searchIndexName + " is ready for querying.")
98}

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.

3
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
1
  1. Add the Java driver version 5.2 or higher as a dependency in your project, depending on your package manager:

    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.

2

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

VectorIndex.java
1import com.mongodb.client.MongoClient;
2import com.mongodb.client.MongoClients;
3import com.mongodb.client.MongoCollection;
4import com.mongodb.client.MongoDatabase;
5import com.mongodb.client.model.SearchIndexModel;
6import com.mongodb.client.model.SearchIndexType;
7import org.bson.Document;
8import org.bson.conversions.Bson;
9
10import java.util.Arrays;
11import java.util.Collections;
12import java.util.List;
13
14public class VectorIndex {
15
16 public static void main(String[] args) {
17
18 // Replace the placeholder with your Atlas connection string
19 String uri = "<connectionString>";
20
21 // Connect to your Atlas cluster
22 try (MongoClient mongoClient = MongoClients.create(uri)) {
23
24 // Set the namespace
25 MongoDatabase database = mongoClient.getDatabase("sample_mflix");
26 MongoCollection<Document> collection = database.getCollection("embedded_movies");
27
28 // Define the index details with the filter fields
29 String indexName = "vector_index";
30 Bson definition = new Document(
31 "fields",
32 Arrays.asList(
33 new Document("type", "vector")
34 .append("path", "plot_embedding")
35 .append("numDimensions", 1536)
36 .append("similarity", "dotProduct")
37 .append("quantization", "scalar"),
38 new Document("type", "filter")
39 .append("path", "genres"),
40 new Document("type", "filter")
41 .append("path", "year")));
42
43 // Define the index model
44 SearchIndexModel indexModel = new SearchIndexModel(
45 indexName,
46 definition,
47 SearchIndexType.vectorSearch());
48
49 // Create the index using the defined model
50 List<String> result = collection.createSearchIndexes(Collections.singletonList(indexModel));
51 System.out.println("Successfully created vector index named: " + result.get(0));
52 System.out.println("It may take up to a minute for the index to leave the BUILDING status and become queryable.");
53
54 // Wait for Atlas to build the index
55 System.out.println("Polling to confirm the index has left the BUILDING status.");
56 // No special handling in case of a timeout. Custom handling can be implemented.
57 waitForIndex(collection, indexName);
58 }
59 }
60
61 /**
62 * Polls the collection to check whether the specified index is ready to query.
63 */
64 public static <T> boolean waitForIndex(final MongoCollection<T> collection, final String indexName) {
65 long startTime = System.nanoTime();
66 long timeoutNanos = TimeUnit.SECONDS.toNanos(60);
67 while (System.nanoTime() - startTime < timeoutNanos) {
68 Document indexRecord = StreamSupport.stream(collection.listSearchIndexes().spliterator(), false)
69 .filter(index -> indexName.equals(index.getString("name")))
70 .findAny().orElse(null);
71 if (indexRecord != null) {
72 if ("FAILED".equals(indexRecord.getString("status"))) {
73 throw new RuntimeException("Search index has FAILED status.");
74 }
75 if (indexRecord.getBoolean("queryable")) {
76 System.out.println(indexName + " index is ready to query");
77 return true;
78 }
79 }
80 try {
81 Thread.sleep(100); // busy-wait, avoid in production
82 } catch (InterruptedException e) {
83 Thread.currentThread().interrupt();
84 throw new RuntimeException(e);
85 }
86 }
87 return false;
88 }
89}

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.

3

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
1

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

npm install mongodb

Tip

The examples on this page assume your project manages modules as CommonJS modules. If you're using ES modules, instead, you must modify the import syntax.

2

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

vector-index.js
1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas deployment
4const uri = "<connectionString>";
5
6const client = new MongoClient(uri);
7
8async 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 "quantization": "scalar"
25 },
26 {
27 "type": "filter",
28 "path": "genres"
29 },
30 {
31 "type": "filter",
32 "path": "year"
33 }
34 ]
35 }
36 }
37
38 // run the helper method
39 const result = await collection.createSearchIndex(index);
40 console.log(`New search index named ${result} is building.`);
41
42 // wait for the index to be ready to query
43 console.log("Polling to check if the index is ready. This may take up to a minute.")
44 let isQueryable = false;
45 while (!isQueryable) {
46 const cursor = collection.listSearchIndexes();
47 for await (const index of cursor) {
48 if (index.name === result) {
49 if (index.queryable) {
50 console.log(`${result} is ready for querying.`);
51 isQueryable = true;
52 } else {
53 await new Promise(resolve => setTimeout(resolve, 5000));
54 }
55 }
56 }
57 }
58 } finally {
59 await client.close();
60 }
61}
62run().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.

3
node vector-index.js
vector_index
1

Add the PyMongo Driver as a dependency in your project:

pip install pymongo
2

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

vector-index.py
1from pymongo.mongo_client import MongoClient
2from pymongo.operations import SearchIndexModel
3import time
4
5# Connect to your Atlas deployment
6uri = "<connectionString>"
7client = MongoClient(uri)
8
9# Access your database and collection
10database = client["sample_mflix"]
11collection = database["embedded_movies"]
12
13# Create your index model, then create the search index
14search_index_model = SearchIndexModel(
15 definition={
16 "fields": [
17 {
18 "type": "vector",
19 "path": "plot_embedding",
20 "numDimensions": 1536,
21 "similarity": "dotProduct",
22 "quantization": "scalar"
23 },
24 {
25 "type": "filter",
26 "path": "genres"
27 },
28 {
29 "type": "filter",
30 "path": "year"
31 }
32 ]
33 },
34 name="vector_index",
35 type="vectorSearch",
36)
37
38result = collection.create_search_index(model=search_index_model)
39print("New search index named " + result + " is building.")
40
41# Wait for initial sync to complete
42print("Polling to check if the index is ready. This may take up to a minute.")
43predicate=None
44if predicate is None:
45 predicate = lambda index: index.get("queryable") is True
46
47while True:
48 indices = list(collection.list_search_indexes(result))
49 if len(indices) and predicate(indices[0]):
50 break
51 time.sleep(5)
52print(result + " is ready for querying.")
53
54client.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, measures similarity using dotProduct function, and enables automatic scalar quantization for efficient processing of your vectors.

3
python vector-index.py
vector_index

This section demonstrates how to query the indexed vector data in the sample_mflix.embedded_movies collection using the the $vectorSearch stage. These sample queries also demonstrate the various query and aggregation pipeline operators that we can use in the query to pre-filter the data that we perform the semantic search on.

1
  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.

2

Click the Browse Collections button for your cluster.

The Data Explorer displays.

3
  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.

4
  1. Click </> Text.

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

The following queries use the $vectorSearch pipeline stage to search for movies that match the specified vector embeddings. 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.

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]
1year: 1993
2plot: "A botched mid-air heist results in suitcases full of cash being search…"
3genres: Array (3)
4title: "Cliffhanger"
5score: 0.7694521546363831
6
7genres: Array (3)
8title: "It's a Mad, Mad, Mad, Mad World"
9year: 1963
10score: 0.7638954520225525
11plot: "The dying words of a thief spark a madcap cross-country rush to find s…"
12
13score: 0.7538407444953918
14year: 1991
15plot: "A cat burglar is forced to steal Da Vinci works of art for a world dom…"
16genres: Array (3)
17title: "Hudson Hawk"
18
19plot: "In 1997, when the US President crashes into Manhattan, now a giant max…"
20genres: Array (2)
21title: "Escape from New York"
22year: 1981
23score: 0.7487208843231201
24
25title: "The Romanov Stones"
26year: 1993
27score: 0.7467736005783081
28plot: "Patrick and Tony are hired by the wealthy gambler to steal the pricele…"
29genres: Array (2)
30
31plot: "An attempted robbery turns to be an unexpected recruitment when two un…"
32genres: Array (3)
33title: "Crime Busters"
34year: 1977
35score: 0.7437351942062378
36
37plot: "In the aftermath of the Persian Gulf War, 4 soldiers set out to steal …"
38genres: Array (3)
39title: "Three Kings"
40year: 1999
41score: 0.7425670623779297
42
43plot: "A professional thief is hired by the FBI to steal a data tape from a c…"
44genres: Array (3)
45title: "Black Moon Rising"
46year: 1986
47score: 0.7397696375846863
48
49plot: "A group of heavily armed hijackers board a luxury ocean liner in the S…"
50genres: Array (3)
51title: "Deep Rising"
52year: 1998
53score: 0.7392246127128601
54
55year: 1964
56score: 0.7357995510101318
57plot: "A young man comes to the rescue of his girlfriend abducted by thieves …"
58genres: Array (3)
59title: "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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13plot: "A young martial artist embarks on an adventure, encountering other mar…"
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28title: "Kung Fu Killer"
29year: 2014
30
31plot: "A young woman is trained by a martial arts specialist to become a prof…"
32genres: Array (3)
33title: "Naked Killer"
34year: 1992
35score: 0.7693743109703064
36
37plot: "The life of the greatest karate master of a generation."
38genres: Array (3)
39title: "The Real Miyagi"
40year: 2015
41score: 0.768799901008606
42
43plot: "At his new high school, a rebellious teen is lured into an underground…"
44genres: Array (3)
45title: "Never Back Down"
46year: 2008
47score: 0.768179714679718
48
49score: 0.7679706811904907
50plot: "Three young martial arts masters emerge from the back streets of Hong …"
51genres: Array (2)
52title: "Dragon Tiger Gate"
53year: 2006
54
55title: "Shaolin Soccer"
56score: 0.7660527229309082
57year: 2001
58plot: "A young Shaolin follower reunites with his discouraged brothers to for…"
59genres: Array (3)

Note

The Pipeline Output pane displays the results of your query.

5

The Atlas UI 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.

1

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

2
use sample_mflix
switched to db sample_mflix
3

The following queries use the $vectorSearch pipeline stage to search for movies that match the specified vector embeddings. 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.

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.

1db.embedded_movies.aggregate([
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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2 {
3 year: 1993,
4 plot: 'A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.',
5 genres: [ 'Action', 'Adventure', 'Thriller' ],
6 title: 'Cliffhanger',
7 score: 0.7694521546363831
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9 {
10 plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
11 genres: [ 'Action', 'Adventure', 'Comedy' ],
12 title: "It's a Mad, Mad, Mad, Mad World",
13 year: 1963,
14 score: 0.7638954520225525
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16 {
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18 plot: 'A cat burglar is forced to steal Da Vinci works of art for a world domination plot.',
19 genres: [ 'Action', 'Adventure', 'Comedy' ],
20 title: 'Hudson Hawk',
21 score: 0.7538407444953918
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23 {
24 plot: 'In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.',
25 genres: [ 'Action', 'Sci-Fi' ],
26 title: 'Escape from New York',
27 year: 1981,
28 score: 0.7487208843231201
29 },
30 {
31 plot: 'Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.',
32 genres: [ 'Action', 'Thriller' ],
33 title: 'The Romanov Stones',
34 year: 1993,
35 score: 0.7467736005783081
36 },
37 {
38 plot: 'An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.',
39 genres: [ 'Action', 'Comedy', 'Adventure' ],
40 title: 'Crime Busters',
41 year: 1977,
42 score: 0.7437351942062378
43 },
44 {
45 plot: 'In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.',
46 genres: [ 'Action', 'Adventure', 'Comedy' ],
47 title: 'Three Kings',
48 year: 1999,
49 score: 0.7425670623779297
50 },
51 {
52 plot: 'A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...',
53 genres: [ 'Action', 'Sci-Fi', 'Thriller' ],
54 title: 'Black Moon Rising',
55 year: 1986,
56 score: 0.7397696375846863
57 },
58 {
59 plot: 'A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.',
60 genres: [ 'Action', 'Adventure', 'Horror' ],
61 title: 'Deep Rising',
62 year: 1998,
63 score: 0.7392246127128601
64 },
65 {
66 plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.',
67 genres: [ 'Action', 'Adventure', 'Comedy' ],
68 title: 'That Man from Rio',
69 year: 1964,
70 score: 0.7357995510101318
71 }
72]

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.

1db.embedded_movies.aggregate([
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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1[
2 {
3 year: 2008,
4 plot: 'A young Thai boxer learns the skills and inner meaning of martial arts.',
5 genres: [ 'Action' ],
6 title: 'Ong-bak 2',
7 score: 0.7976737022399902
8 },
9 {
10 plot: 'A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.',
11 genres: [ 'Action', 'Drama', 'Family' ],
12 title: 'The Karate Kid',
13 year: 1984,
14 score: 0.7929348349571228
15 },
16 {
17 plot: 'A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.',
18 genres: [ 'Action', 'Adventure', 'Fantasy' ],
19 title: 'Circle of Iron',
20 year: 1978,
21 score: 0.7852014899253845
22 },
23 {
24 plot: 'A lionized account of the life of the martial arts superstar.',
25 genres: [ 'Action', 'Biography', 'Drama' ],
26 title: 'Dragon: The Bruce Lee Story',
27 year: 1993,
28 score: 0.7763040661811829
29 },
30 {
31 plot: 'A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.',
32 genres: [ 'Action', 'Thriller' ],
33 title: 'Kung Fu Killer',
34 year: 2014,
35 score: 0.7731232643127441
36 },
37 {
38 plot: 'A young woman is trained by a martial arts specialist to become a professional assassin.',
39 genres: [ 'Action', 'Crime', 'Romance' ],
40 title: 'Naked Killer',
41 year: 1992,
42 score: 0.7693743109703064
43 },
44 {
45 plot: 'The life of the greatest karate master of a generation.',
46 genres: [ 'Documentary', 'Action', 'History' ],
47 title: 'The Real Miyagi',
48 year: 2015,
49 score: 0.768799901008606
50 },
51 {
52 plot: 'At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.',
53 genres: [ 'Action', 'Drama', 'Sport' ],
54 title: 'Never Back Down',
55 year: 2008,
56 score: 0.768179714679718
57 },
58 {
59 plot: 'Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.',
60 genres: [ 'Action', 'Drama' ],
61 title: 'Dragon Tiger Gate',
62 year: 2006,
63 score: 0.7679706811904907
64 },
65 {
66 year: 2001,
67 plot: 'A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.',
68 genres: [ 'Action', 'Comedy', 'Sport' ],
69 title: 'Shaolin Soccer',
70 score: 0.7660527229309082
71 }
72]
73
1
2

The following queries use the $vectorSearch pipeline stage to search for movies that match the specified vector embeddings. 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.

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.

DataService.cs
1namespace query_quick_start;
2
3using MongoDB.Driver;
4using MongoDB.Bson;
5
6public class DataService
7{
8 private const string ConnectionString = "<connection-string>";
9 private static readonly MongoClient Client = new MongoClient(ConnectionString);
10 private static readonly IMongoDatabase Database = Client.GetDatabase("sample_mflix");
11 private static readonly IMongoCollection<BsonDocument> Collection = Database.GetCollection<BsonDocument>("embedded_movies");
12
13 public List<BsonDocument>? PerformVectorQuery()
14 {
15 double[] queryVector = {
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-0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783,
17 };
18 var filter = Builders<BsonDocument>.Filter.And(
19 Builders<BsonDocument>.Filter.And(
20 Builders<BsonDocument>.Filter.Nin("genres", ["Drama", "Western", "Crime"]),
21 Builders<BsonDocument>.Filter.In("genres", ["Action", "Adventure", "Family"])
22 ),
23 Builders<BsonDocument>.Filter.And(
24 Builders<BsonDocument>.Filter.Gte("year", 1960),
25 Builders<BsonDocument>.Filter.Lte("year", 2000)
26 )
27 );
28
29 var options = new VectorSearchOptions<BsonDocument>()
30 {
31 IndexName = "vector_index",
32 Filter = filter,
33 NumberOfCandidates = 200,
34 };
35
36 var pipeline = new EmptyPipelineDefinition<BsonDocument>()
37 .VectorSearch("plot_embedding", queryVector, 10, options)
38 .Project(Builders<BsonDocument>.Projection
39 .Exclude("_id")
40 .Include("title")
41 .Include("genres")
42 .Include("plot")
43 .Include("year")
44 .Meta("score", "vectorSearchScore")
45 );
46 return Collection.Aggregate<BsonDocument>(pipeline).ToList();
47 }
48}

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.

DataService.cs
1namespace query_quick_start;
2
3using MongoDB.Driver;
4using MongoDB.Bson;
5
6public class DataService
7{
8 private const string ConnectionString = "<connection-string>";
9 private static readonly MongoClient Client = new MongoClient(ConnectionString);
10 private static readonly IMongoDatabase Database = Client.GetDatabase("sample_mflix");
11 private static readonly IMongoCollection<BsonDocument> Collection = Database.GetCollection<BsonDocument>("embedded_movies");
12
13 public List<BsonDocument>? PerformVectorQuery()
14 {
15 double[] queryVector = {
16 -0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 0.014710125, 0.0060869483, 0.016192842, 0.0026484078, 0.002417546, -0.007764761, -0.0053781047, 0.0016843157, 0.009071894, 0.0026158919, -0.016465975, 0.019054228, 0.030720878, 0.028145632, -0.0124340225, -0.011491066, -0.01273967, -0.0076542073, 0.00685432, 0.002936172, 0.03194347, -0.030798918, -0.004961903, 0.012661632, -0.024646938, 0.0085256295, -0.01133499, 0.016648063, -0.017857648, 0.01627088, -0.020823086, -0.002233832, 0.008330535, 0.024894057, 0.008558145, 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0.0025346025, 0.018937172, 0.0085971635, -0.012395004, 0.011608122, 0.00065397297, 0.040085416, 0.008883302, -0.0013339586, -0.0064413697, -0.007296534, 0.0028467537, -0.092917, -0.00801188, 0.016192842, 0.027469303, -0.033166062, 0.017662555, -0.002692304, 0.036079474, -0.0144760115, 0.020445902, -0.00036641184, -0.018442933, -0.008493113, -0.01476215, 0.018703058, 0.001552627, 0.0065876907, -0.017974706, -0.0076542073, 0.022149729, -0.010066876, 0.004932639, -0.0061292187, 0.0016436711, -0.007959855, 0.0031393955, -0.0324117, 0.037093967, 0.007114446, 0.019821601, -0.016518, -0.0034596757, 0.021668496, -0.03332214, 0.012785193, -0.007810283, -0.018833121, -0.0029963262, 0.0210572, -0.019392392, 0.0021135237, 0.015685597, 0.013116853, -0.027495317, 0.011250449, -0.0071924836, -0.019106254, 0.0107562095, 0.014007784, -0.034492705, -0.03394644, -0.024750987, -0.041047882, -0.006685238, 0.0068868357, 0.008694711, 0.019093247, 0.01261611, -0.009748221, -0.0027394518, 0.014918226, -0.0032710843, -0.0044936766, 0.01546449, 0.018208819, 0.004288827, -0.021538433, -0.026897028, 0.023879565, -0.018299863, -0.012245431, 0.015516515, -0.040891804, -0.011816223, -0.02221476, 0.012082852, -0.02417871, -0.009338523, 0.010047366, -0.027079115, -0.016010754, -0.021863589, -0.004305085, -0.027261203, 0.024100672, 0.000711282, 0.001071394, -0.0037263047, -0.007829792, -0.0049456456, -0.021603463, 0.03274986, 0.01866404, -0.032567773, -0.0044318964, 0.017935688, 0.018638028, -0.015282402, -0.009377542, 0.03012259, 0.015178352, -0.004792821, -0.048305396, 0.007075427, 0.020784067, 0.010678172, 0.0019509449, 0.0045684627, 0.02242286, -0.011374009, 0.024477856, -0.0021736778, -0.021291312, -0.009188951, 0.02692304, -0.0038628709, -0.02442583, -0.029394237, 0.019054228, 0.01606278, -0.0035051976, -0.00012284856, 0.01911926, 0.0088768, 0.007036408, 0.0015461239, -0.0030711126, 0.0034629272, -0.015152339, 0.02097916, -0.031163093, 0.0061909985, 0.012694148, -0.021902608, -0.030044552, 0.031553283, 0.0002003783, -0.037119977, 0.0003367412, 0.015347433, 0.003105254, 0.034102518, 0.001911926, -0.008102925, 0.0003306445, 0.011647142, -0.004288827, 0.019665524, -0.011907267, 0.01391674, 0.0014428863, -0.01655702, -0.0020387375, 0.009975832, -0.0009982334, -0.018586002, -0.0137606645, -0.031787395, 0.02481602, 0.0059308726, -0.0011941409, -0.009598649, 0.0126811415, 0.015828667, -0.0058918535, -0.0040807263, -0.00406772, -0.0015233628, -0.01402079, -0.0052577965, 0.0018290109, -0.018182807, -0.014736137, 0.014423986, -0.001820882, -0.013799684, 0.0029085337, 0.009949819, 0.02407466, 0.01992565, -0.01986062, 0.02892601, 0.0029768168, 0.00044018193, -0.012882739, 0.022487892, 0.0061682374, 0.014528036, 0.0021785551, 0.006304804, 0.0010722068, -0.0075696665, -0.0040417076, 0.0097937435, -0.01216089, -0.0044026324, -0.018494958, 0.053898104, -0.0037100469, -0.013929747, 0.0210572, 0.014293923, 0.028613858, -0.005791055, -0.008863793, -0.021668496, 0.0011803217, 0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914,
17 };
18 var filter = Builders<BsonDocument>.Filter.Or(
19 Builders<BsonDocument>.Filter.Ne("genres", "Crime"),
20 Builders<BsonDocument>.Filter.And(
21 Builders<BsonDocument>.Filter.Lte("year", 2015),
22 Builders<BsonDocument>.Filter.Eq("genres", "Action")
23 )
24 );
25
26 var options = new VectorSearchOptions<BsonDocument>()
27 {
28 IndexName = "vector_index",
29 Filter = filter,
30 NumberOfCandidates = 200,
31 };
32
33 var pipeline = new EmptyPipelineDefinition<BsonDocument>()
34 .VectorSearch("plot_embedding", queryVector, 10, options)
35 .Project(Builders<BsonDocument>.Projection
36 .Exclude("_id")
37 .Include("title")
38 .Include("genres")
39 .Include("plot")
40 .Include("year")
41 .Meta("score", "vectorSearchScore")
42 );
43 return Collection.Aggregate<BsonDocument>(pipeline).ToList();
44 }
45}
3

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

4

Update Program.cs to initialize the DataService and call the method to perform the query.

Program.cs
1using query_quick_start;
2
3var dataService = new DataService();
4var results = dataService.PerformVectorQuery();
5if (results != null)
6{
7 foreach (var document in results)
8 {
9 Console.WriteLine(document);
10 }
11}
12else
13{
14 Console.WriteLine("The query returned no results.");
15}
5

Save the file, and then compile and run your project to perform the query.

dotnet run query_quick_start.csproj
1{ "year" : 1993, "plot" : "A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.", "genres" : ["Action", "Adventure", "Thriller"], "title" : "Cliffhanger", "score" : 0.92509341239929199 }
2{ "plot" : "The dying words of a thief spark a madcap cross-country rush to find some treasure.", "genres" : ["Action", "Adventure", "Comedy"], "title" : "It's a Mad, Mad, Mad, Mad World", "year" : 1963, "score" : 0.92273008823394775 }
3{ "year" : 1991, "plot" : "A cat burglar is forced to steal Da Vinci works of art for a world domination plot.", "genres" : ["Action", "Adventure", "Comedy"], "title" : "Hudson Hawk", "score" : 0.9183650016784668 }
4{ "plot" : "In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.", "genres" : ["Action", "Sci-Fi"], "title" : "Escape from New York", "year" : 1981, "score" : 0.91609716415405273 }
5{ "plot" : "Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.", "genres" : ["Action", "Thriller"], "title" : "The Romanov Stones", "year" : 1993, "score" : 0.9152265191078186 }
6{ "plot" : "An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.", "genres" : ["Action", "Comedy", "Adventure"], "title" : "Crime Busters", "year" : 1977, "score" : 0.91385889053344727 }
7{ "plot" : "In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.", "genres" : ["Action", "Adventure", "Comedy"], "title" : "Three Kings", "year" : 1999, "score" : 0.91333001852035522 }
8{ "plot" : "A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...", "genres" : ["Action", "Sci-Fi", "Thriller"], "title" : "Black Moon Rising", "year" : 1986, "score" : 0.91205686330795288 }
9{ "plot" : "A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.", "genres" : ["Action", "Adventure", "Horror"], "title" : "Deep Rising", "year" : 1998, "score" : 0.91180777549743652 }
10{ "plot" : "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "genres" : ["Action", "Adventure", "Comedy"], "title" : "That Man from Rio", "year" : 1964, "score" : 0.91023355722427368 }
dotnet run query_quick_start.csproj
1{ "year" : 2008, "plot" : "A young Thai boxer learns the skills and inner meaning of martial arts.", "genres" : ["Action"], "title" : "Ong-bak 2", "score" : 0.93658864498138428 }
2{ "plot" : "A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.", "genres" : ["Action", "Drama", "Family"], "title" : "The Karate Kid", "year" : 1984, "score" : 0.93471556901931763 }
3{ "plot" : "A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.", "genres" : ["Action", "Adventure", "Fantasy"], "title" : "Circle of Iron", "year" : 1978, "score" : 0.93161046504974365 }
4{ "plot" : "A lionized account of the life of the martial arts superstar.", "genres" : ["Action", "Biography", "Drama"], "title" : "Dragon: The Bruce Lee Story", "year" : 1993, "score" : 0.92796128988265991 }
5{ "plot" : "A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.", "genres" : ["Action", "Thriller"], "title" : "Kung Fu Killer", "year" : 2014, "score" : 0.92663633823394775 }
6{ "plot" : "A young woman is trained by a martial arts specialist to become a professional assassin.", "genres" : ["Action", "Crime", "Romance"], "title" : "Naked Killer", "year" : 1992, "score" : 0.92506062984466553 }
7{ "plot" : "The life of the greatest karate master of a generation.", "genres" : ["Documentary", "Action", "History"], "title" : "The Real Miyagi", "year" : 2015, "score" : 0.92481791973114014 }
8{ "plot" : "At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.", "genres" : ["Action", "Drama", "Sport"], "title" : "Never Back Down", "year" : 2008, "score" : 0.92455542087554932 }
9{ "plot" : "Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.", "genres" : ["Action", "Drama"], "title" : "Dragon Tiger Gate", "year" : 2006, "score" : 0.92446672916412354 }
10{ "year" : 2001, "plot" : "A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.", "genres" : ["Action", "Comedy", "Sport"], "title" : "Shaolin Soccer", "score" : 0.92365169525146484 }
1
2

The following queries use the $vectorSearch pipeline stage to search for movies that match the specified vector embeddings. 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.

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.

1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7
8 "go.mongodb.org/mongo-driver/bson"
9 "go.mongodb.org/mongo-driver/mongo"
10 "go.mongodb.org/mongo-driver/mongo/options"
11)
12
13func main() {
14 ctx := context.Background()
15
16 // Replace the placeholder with your Atlas connection string
17 const uri = "<connection-string>"
18
19 // Connect to your Atlas cluster
20 clientOptions := options.Client().ApplyURI(uri)
21 client, err := mongo.Connect(ctx, clientOptions)
22 if err != nil {
23 log.Fatalf("failed to connect to the server: %v", err)
24 }
25 defer func() { _ = client.Disconnect(ctx) }()
26
27 // Set the namespace
28 coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30 queryVector := [1536]float64{
31 -0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 0.010111088, 0.0074623227, 0.009555174, 0.008338705, -0.002240005, -0.0010603234, -0.004973792, 0.003391073, 0.021543289, 0.013341927, 0.0005980159, 0.010693162, 0.005336771, 0.016062634, 0.005768421, 0.005186347, 0.039790336, 0.0021942237, -0.0026275094, 0.010431555, 0.0042151334, -0.0050359233, 0.025768232, -0.021451725, 0.01833861, -0.01836477, -0.013433489, 0.030006256, -0.014793842, 0.017475309, 0.0020585153, -0.012975678, -0.017266022, -0.01593183, -0.014257549, 0.0010676811, -0.007887433, -0.0045911926, 0.00012303676, -0.0014976967, 0.03552615, 0.0065630507, -0.037435878, 0.011929252, -0.00939167, 0.016768971, 0.01223664, 0.007789331, -0.037200432, 0.013145722, 0.00896002, 0.021857215, 0.010333453, 0.021582529, -0.007089534, -0.007154935, -0.02485261, 0.0040254686, -0.00088864425, 0.023466095, -0.020719228, -0.006690584, -0.021006994, -0.018286288, 0.025545865, -0.0096598165, 0.008803056, -0.023021365, -0.040078104, 0.015408617, 0.017043658, -0.011242535, 0.0063537657, -0.026618453, 0.0071614753, 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-0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783,
32 }
33
34 genresFilterCondition := bson.D{
35 {"$and", bson.A{
36 bson.D{{"genres", bson.D{{"$nin", bson.A{"Drama", "Western", "Crime"}}}}},
37 bson.D{{"genres", bson.D{{"$in", bson.A{"Action", "Adventure", "Family"}}}}},
38 }}}
39
40 yearFilterCondition := bson.D{
41 {"$and", bson.A{
42 bson.D{{"year", bson.D{{"$gte", 1960}}}},
43 bson.D{{"year", bson.D{{"$lte", 2000}}}},
44 }},
45 }
46
47 filter := bson.D{{"$and", bson.A{
48 genresFilterCondition, yearFilterCondition,
49 }},
50 }
51
52 vectorSearchStage := bson.D{
53 {"$vectorSearch", bson.D{
54 {"index", "vector_index"},
55 {"path", "plot_embedding"},
56 {"filter", filter},
57 {"queryVector", queryVector},
58 {"numCandidates", 200},
59 {"limit", 10},
60 }},
61 }
62
63 projectStage := bson.D{
64 {"$project", bson.D{
65 {"_id", 0},
66 {"plot", 1},
67 {"title", 1},
68 {"genres", 1},
69 {"year", 1},
70 {"score", bson.D{{"$meta", "vectorSearchScore"}}},
71 }}}
72
73 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
74 if err != nil {
75 log.Fatalf("failed to retrieve data from the server: %v", err)
76 }
77 // display the results
78 type ProjectedMovieResult struct {
79 Title string `bson:"title"`
80 Plot string `bson:"plot"`
81 Genres []string `bson:"genres"`
82 Year int32 `bson:"year"`
83 Score float64 `bson:"score"`
84 }
85
86 var results []ProjectedMovieResult
87 if err = cursor.All(ctx, &results); err != nil {
88 log.Fatalf("failed to unmarshal results to ProjectedMovieResult objects: %v", err)
89 }
90 for _, result := range results {
91 fmt.Printf("Title: %v \nPlot: %v \nGenres: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Genres, result.Year, result.Score)
92 }
93}

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.

1package main
2
3import (
4 "context"
5 "fmt"
6 "log"
7
8 "go.mongodb.org/mongo-driver/bson"
9 "go.mongodb.org/mongo-driver/mongo"
10 "go.mongodb.org/mongo-driver/mongo/options"
11)
12
13func main() {
14 ctx := context.Background()
15
16 // Replace the placeholder with your Atlas connection string
17 const uri = "<connection-string>"
18
19 // Connect to your Atlas cluster
20 clientOptions := options.Client().ApplyURI(uri)
21 client, err := mongo.Connect(ctx, clientOptions)
22 if err != nil {
23 log.Fatalf("failed to connect to the server: %v", err)
24 }
25 defer func() { _ = client.Disconnect(ctx) }()
26
27 // Set the namespace
28 coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30 queryVector := [1536]float64{
31 -0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 0.014710125, 0.0060869483, 0.016192842, 0.0026484078, 0.002417546, -0.007764761, -0.0053781047, 0.0016843157, 0.009071894, 0.0026158919, -0.016465975, 0.019054228, 0.030720878, 0.028145632, -0.0124340225, -0.011491066, -0.01273967, -0.0076542073, 0.00685432, 0.002936172, 0.03194347, -0.030798918, -0.004961903, 0.012661632, -0.024646938, 0.0085256295, -0.01133499, 0.016648063, -0.017857648, 0.01627088, -0.020823086, -0.002233832, 0.008330535, 0.024894057, 0.008558145, 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0.0025573636, 0.010463567, -0.028691897, -0.016752115, 0.008974346, 0.024373805, -0.003908393, -0.026142662, 0.015906705, -0.012440525, 0.006808798, 0.033998467, -0.006808798, 0.011764198, 0.0033913925, 0.003986431, 0.0067632757, 0.012154386, -0.025609404, 0.008616674, 0.0023037407, 0.028093606, -0.02762538, 0.0077127353, -0.015828667, 0.014033797, 0.009039378, 0.0032905939, 0.011829229, -0.020133752, -0.0066137034, 0.008902812, -0.020667009, 0.008655692, 0.020693023, 0.022266785, -0.01216089, 0.03620954, -0.0069973893, 0.0014347574, -0.0002489487, 0.020536946, 0.0008933702, -0.021135237, -0.0017704825, -0.007875314, -0.0022078194, 0.005384608, -0.030200627, -0.010821241, -0.0011136644, -0.013383482, 0.01986062, 0.007647704, 0.010353014, -0.0065454203, 0.0041490095, 0.005170004, -0.0070949364, -0.04031953, -0.0148401875, -0.0075826724, -0.020510934, 0.0012607982, 0.006425112, -0.017441448, 0.011165908, -0.0019606997, -0.017532492, -0.017649548, 0.019548468, -0.023593428, -0.027989557, 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0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914,
32 }
33
34 genresFilterCondition := bson.D{{"genres", bson.D{{"$ne", "Crime"}}}}
35
36 yearAndGenresFilterCondition := bson.D{
37 {"$and", bson.A{
38 bson.D{{"year", bson.D{{"$lte", 2015}}}},
39 bson.D{{"genres", bson.D{{"$eq", "Action"}}}},
40 }},
41 }
42
43 filter := bson.D{{"$or", bson.A{
44 genresFilterCondition, yearAndGenresFilterCondition,
45 }},
46 }
47
48 vectorSearchStage := bson.D{
49 {"$vectorSearch", bson.D{
50 {"index", "vector_index"},
51 {"path", "plot_embedding"},
52 {"filter", filter},
53 {"queryVector", queryVector},
54 {"numCandidates", 200},
55 {"limit", 10},
56 }},
57 }
58
59 projectStage := bson.D{
60 {"$project", bson.D{
61 {"_id", 0},
62 {"plot", 1},
63 {"title", 1},
64 {"genres", 1},
65 {"year", 1},
66 {"score", bson.D{{"$meta", "vectorSearchScore"}}},
67 }}}
68
69 cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
70 if err != nil {
71 log.Fatalf("failed to retrieve data from the server: %v", err)
72 }
73 // display the results
74 type ProjectedMovieResult struct {
75 Title string `bson:"title"`
76 Plot string `bson:"plot"`
77 Genres []string `bson:"genres"`
78 Year int32 `bson:"year"`
79 Score float64 `bson:"score"`
80 }
81
82 var results []ProjectedMovieResult
83 if err = cursor.All(ctx, &results); err != nil {
84 log.Fatalf("failed to unmarshal results to ProjectedMovieResult objects: %v", err)
85 }
86 for _, result := range results {
87 fmt.Printf("Title: %v \nPlot: %v \nGenres: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Genres, result.Year, result.Score)
88 }
89}
3

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

4
go run AtlasVectorSearchTutorial.go
1Title: Cliffhanger
2Plot: A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.
3Genres: [Action Adventure Thriller]
4Year: 1993
5Score: 0.7694521546363831
6
7Title: It's a Mad, Mad, Mad, Mad World
8Plot: The dying words of a thief spark a madcap cross-country rush to find some treasure.
9Genres: [Action Adventure Comedy]
10Year: 1963
11Score: 0.7638954520225525
12
13Title: Hudson Hawk
14Plot: A cat burglar is forced to steal Da Vinci works of art for a world domination plot.
15Genres: [Action Adventure Comedy]
16Year: 1991
17Score: 0.7538407444953918
18
19Title: Escape from New York
20Plot: In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.
21Genres: [Action Sci-Fi]
22Year: 1981
23Score: 0.7487208843231201
24
25Title: The Romanov Stones
26Plot: Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.
27Genres: [Action Thriller]
28Year: 1993
29Score: 0.7467736005783081
30
31Title: Crime Busters
32Plot: An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.
33Genres: [Action Comedy Adventure]
34Year: 1977
35Score: 0.7437351942062378
36
37Title: Three Kings
38Plot: In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.
39Genres: [Action Adventure Comedy]
40Year: 1999
41Score: 0.7425670623779297
42
43Title: Black Moon Rising
44Plot: A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...
45Genres: [Action Sci-Fi Thriller]
46Year: 1986
47Score: 0.7397696375846863
48
49Title: Deep Rising
50Plot: A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.
51Genres: [Action Adventure Horror]
52Year: 1998
53Score: 0.7392246127128601
54
55Title: That Man from Rio
56Plot: A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.
57Genres: [Action Adventure Comedy]
58Year: 1964
59Score: 0.7357995510101318
go run AtlasVectorSearchTutorial.go
1Title: Ong-bak 2
2Plot: A young Thai boxer learns the skills and inner meaning of martial arts.
3Genres: [Action]
4Year: 2008
5Score: 0.7976737022399902
6
7Title: The Karate Kid
8Plot: A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.
9Genres: [Action Drama Family]
10Year: 1984
11Score: 0.7929348349571228
12
13Title: Circle of Iron
14Plot: A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.
15Genres: [Action Adventure Fantasy]
16Year: 1978
17Score: 0.7852014899253845
18
19Title: Dragon: The Bruce Lee Story
20Plot: A lionized account of the life of the martial arts superstar.
21Genres: [Action Biography Drama]
22Year: 1993
23Score: 0.7763040661811829
24
25Title: Kung Fu Killer
26Plot: A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.
27Genres: [Action Thriller]
28Year: 2014
29Score: 0.7731232643127441
30
31Title: Naked Killer
32Plot: A young woman is trained by a martial arts specialist to become a professional assassin.
33Genres: [Action Crime Romance]
34Year: 1992
35Score: 0.7693743109703064
36
37Title: The Real Miyagi
38Plot: The life of the greatest karate master of a generation.
39Genres: [Documentary Action History]
40Year: 2015
41Score: 0.768799901008606
42
43Title: Never Back Down
44Plot: At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.
45Genres: [Action Drama Sport]
46Year: 2008
47Score: 0.768179714679718
48
49Title: Dragon Tiger Gate
50Plot: Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.
51Genres: [Action Drama]
52Year: 2006
53Score: 0.7679706811904907
54
55Title: Shaolin Soccer
56Plot: A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.
57Genres: [Action Comedy Sport]
58Year: 2001
59Score: 0.7660527229309082
1
2

The following queries use the $vectorSearch pipeline stage to search for movies that match the specified vector embeddings. 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.

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.

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;
5
6import com.mongodb.client.MongoClient;
7import com.mongodb.client.MongoClients;
8import com.mongodb.client.MongoCollection;
9import com.mongodb.client.MongoDatabase;
10import com.mongodb.client.model.*;
11import com.mongodb.client.model.search.VectorSearchOptions;
12import java.util.Arrays;
13import java.util.List;
14import com.mongodb.client.model.search.FieldSearchPath;
15import org.bson.Document;
16import org.bson.conversions.Bson;
17
18public class AtlasVectorSearchTutorial {
19 public static void main( String[] args ) {
20 // specify connection
21 String uri = "<connection-string>";
22
23 // establish connection and set namespace
24 try (MongoClient mongoClient = MongoClients.create(uri)) {
25 MongoDatabase database = mongoClient.getDatabase("sample_mflix");
26 MongoCollection<Document> collection = database.getCollection("embedded_movies");
27
28 // define $vectorSearch query options
29 List<Double> queryVector = (asList(-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 0.010111088, 0.0074623227, 0.009555174, 0.008338705, -0.002240005, -0.0010603234, -0.004973792, 0.003391073, 0.021543289, 0.013341927, 0.0005980159, 0.010693162, 0.005336771, 0.016062634, 0.005768421, 0.005186347, 0.039790336, 0.0021942237, -0.0026275094, 0.010431555, 0.0042151334, -0.0050359233, 0.025768232, -0.021451725, 0.01833861, -0.01836477, -0.013433489, 0.030006256, -0.014793842, 0.017475309, 0.0020585153, -0.012975678, -0.017266022, -0.01593183, -0.014257549, 0.0010676811, -0.007887433, -0.0045911926, 0.00012303676, -0.0014976967, 0.03552615, 0.0065630507, -0.037435878, 0.011929252, -0.00939167, 0.016768971, 0.01223664, 0.007789331, -0.037200432, 0.013145722, 0.00896002, 0.021857215, 0.010333453, 0.021582529, -0.007089534, -0.007154935, -0.02485261, 0.0040254686, -0.00088864425, 0.023466095, -0.020719228, -0.006690584, -0.021006994, -0.018286288, 0.025545865, -0.0096598165, 0.008803056, -0.023021365, -0.040078104, 0.015408617, 0.017043658, -0.011242535, 0.0063537657, -0.026618453, 0.0071614753, -0.014623798, 0.00067322777, -0.00083427917, -0.028070368, 0.03714811, -0.004529061, 0.0054087127, 0.0028727653, 0.008384486, 0.010026066, -0.006190262, -0.0002493436, 0.0029953935, -0.026226042, -0.018417092, 0.009941043, 0.0036494094, -0.00982332, 0.013551212, 0.02574207, -0.0022645304, -0.0006004685, 0.012805633, -0.024303235, 0.008194821, -0.014179068, -0.02977081, 0.003095131, -0.0015941641, 0.029953934, 0.0052680993, 0.025388902, -0.031392768, -0.021386323, 0.014898485, 0.022419669, 0.00897964, 0.013243824, 0.006854088, 0.0066415328, -0.003839074, -0.01877026, 0.021216279, -0.015055449, -0.0015508354, 0.013211124, -0.008783435, 0.0052157775, -0.68938524, -0.01221702, -0.04125533, -0.016232677, 0.020039052, -0.0026422248, -0.0037050007, 0.0064682183, -0.0047579664, 0.0032749851, -0.0035382267, 0.031942144, -0.00035643874, -0.011628405, -0.043086577, -0.0196074, -0.0066088317, -0.014872325, 0.028331975, 0.010294212, -0.013930541, 0.031994462, -0.018626377, 0.017462227, 0.026343765, -0.010274592, 0.0046827546, -0.029430721, -0.011746128, 0.0024362097, 0.0023054064, 0.0027730279, -0.002406779, 0.003917556, 0.059436977, 0.008665713, -0.0018901062, 0.06037876, 0.017880797, 0.05185039, 0.0067102043, -0.020300657, 0.005604917, 0.018704858, 0.012073136, 0.0144145135, 0.012413224, -0.0074819434, 0.015801027, -0.0061412104, 0.008613391, -0.0039077457, -0.0036232488, 0.008469507, 0.014087505, 0.0124066835, 0.019267311, -0.002573553, 0.005055544, -0.009417831, -0.009103903, 0.011150973, -0.012046975, 0.0058567133, -0.0053727417, 0.018260127, -0.005588567, 0.015591742, 0.007495024, -0.02567667, 0.024211673, 0.021386323, -0.012890656, -0.016114954, 0.009515933, 0.009679437, 0.025532786, -0.0076454473, -0.02575515, 0.008319084, -0.0068410076, -0.017082898, -0.026173722, -0.0049901423, 0.01918883, -0.008646091, -0.031759016, 0.014820003, 0.011850771, 0.01836477, 0.012700991, -0.0011437106, 0.005058814, 0.0151993325, 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-0.024290156, 0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783));
30 String indexName = "vector_index";
31 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
32 int numCandidates = 200;
33 int limit = 10;
34 List<String> genresNotInCriteria = Arrays.asList("Drama", "Western", "Crime");
35 List<String> genresInCriteria = Arrays.asList("Action", "Adventure", "Family");
36 Bson criteria = Filters.and(
37 Filters.nin("genres", genresNotInCriteria),
38 Filters.in("genres", genresInCriteria),
39 Filters.gte("year", 1960),
40 Filters.lte("year", 2000));
41 VectorSearchOptions options = VectorSearchOptions
42 .approximateVectorSearchOptions(numCandidates)
43 .filter(criteria);
44
45 // define pipeline
46 List<Bson> pipeline = asList(
47 vectorSearch(
48 fieldSearchPath,
49 queryVector,
50 indexName,
51 limit,
52 options),
53 project(
54 fields(exclude("_id"), include("title"), include("plot"), include("year"),
55 include("genres"), metaVectorSearchScore("score"))));
56
57 // run query and print results
58 collection.aggregate(pipeline)
59 .forEach(doc -> System.out.println(doc.toJson()));
60 }
61 }
62}

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.

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;
5
6import com.mongodb.client.MongoClient;
7import com.mongodb.client.MongoClients;
8import com.mongodb.client.MongoCollection;
9import com.mongodb.client.MongoDatabase;
10import com.mongodb.client.model.*;
11import com.mongodb.client.model.search.VectorSearchOptions;
12import java.util.List;
13import com.mongodb.client.model.search.FieldSearchPath;
14import org.bson.Document;
15import org.bson.conversions.Bson;
16
17public class AtlasVectorSearchTutorial {
18 public static void main( String[] args ) {
19 // specify connection
20 String uri = "<connection-string>";
21
22 // establish connection and set namespace
23 try (MongoClient mongoClient = MongoClients.create(uri)) {
24 MongoDatabase database = mongoClient.getDatabase("sample_mflix");
25 MongoCollection<Document> collection = database.getCollection("embedded_movies");
26
27 // define $vectorSearch query options
28 List<Double> queryVector = 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29 String indexName = "vector_index";
30 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
31 int numCandidates = 200;
32 int limit = 10;
33 Bson criteria = Filters.or(
34 Filters.lte("year", 2015),
35 Filters.and(
36 Filters.lte("year", 2015),
37 Filters.eq("genres", "Action")));
38 VectorSearchOptions options = VectorSearchOptions
39 .approximateVectorSearchOptions(numCandidates)
40 .filter(criteria);
41
42 // define pipeline
43 List<Bson> pipeline = asList(
44 vectorSearch(
45 fieldSearchPath,
46 queryVector,
47 indexName,
48 limit,
49 options),
50 project(
51 fields(exclude("_id"), include("title"), include("plot"), include("year"),
52 include("genres"), metaVectorSearchScore("score"))));
53
54 // run query and print results
55 collection.aggregate(pipeline)
56 .forEach(doc -> System.out.println(doc.toJson()));
57 }
58 }
59}
3

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

4
javac AtlasVectorSearchTutorial.java
java AtlasVectorSearchTutorial
1{
2 "year": 1993,
3 "plot": "A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.",
4 "genres": ["Action", "Adventure", "Thriller"],
5 "title": "Cliffhanger",
6 "score": 0.7694521546363831
7}
8{
9 "plot": "The dying words of a thief spark a madcap cross-country rush to find some treasure.",
10 "genres": ["Action", "Adventure", "Comedy"],
11 "title": "It's a Mad, Mad, Mad, Mad World",
12 "year": 1963,
13 "score": 0.7638954520225525
14}
15{
16 "year": 1991,
17 "plot": "A cat burglar is forced to steal Da Vinci works of art for a world domination plot.",
18 "genres": ["Action", "Adventure", "Comedy"],
19 "title": "Hudson Hawk",
20 "score": 0.7538407444953918
21}
22{
23 "plot": "In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.",
24 "genres": ["Action", "Sci-Fi"],
25 "title": "Escape from New York",
26 "year": 1981,
27 "score": 0.7487208843231201
28}
29{
30 "plot": "Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.",
31 "genres": ["Action", "Thriller"],
32 "title": "The Romanov Stones",
33 "year": 1993,
34 "score": 0.7467736005783081
35}
36{
37 "plot": "An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.",
38 "genres": ["Action", "Comedy", "Adventure"],
39 "title": "Crime Busters",
40 "year": 1977,
41 "score": 0.7437351942062378
42}
43{
44 "plot": "In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.",
45 "genres": ["Action", "Adventure", "Comedy"],
46 "title": "Three Kings",
47 "year": 1999,
48 "score": 0.7425670623779297
49}
50{
51 "plot": "A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...",
52 "genres": ["Action", "Sci-Fi", "Thriller"],
53 "title": "Black Moon Rising",
54 "year": 1986,
55 "score": 0.7397696375846863
56}
57{
58 "plot": "A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.",
59 "genres": ["Action", "Adventure", "Horror"],
60 "title": "Deep Rising",
61 "year": 1998,
62 "score": 0.7392246127128601
63}
64{
65 "plot": "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.",
66 "genres": ["Action", "Adventure", "Comedy"],
67 "title": "That Man from Rio",
68 "year": 1964,
69 "score": 0.7357995510101318
70}
javac AtlasVectorSearchTutorial.java
java AtlasVectorSearchTutorial
1{
2 "year": 2008,
3 "plot": "A young Thai boxer learns the skills and inner meaning of martial arts.",
4 "genres": ["Action"],
5 "title": "Ong-bak 2",
6 "score": 0.7976737022399902
7}
8{
9 "plot": "A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.",
10 "genres": ["Action", "Drama", "Family"],
11 "title": "The Karate Kid",
12 "year": 1984,
13 "score": 0.7929348349571228
14}
15{
16 "plot": "A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.",
17 "genres": ["Action", "Adventure", "Fantasy"],
18 "title": "Circle of Iron",
19 "year": 1978,
20 "score": 0.7852014899253845
21}
22{
23 "plot": "A lionized account of the life of the martial arts superstar.",
24 "genres": ["Action", "Biography", "Drama"],
25 "title": "Dragon: The Bruce Lee Story",
26 "year": 1993,
27 "score": 0.7763040661811829
28}
29{
30 "plot": "A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.",
31 "genres": ["Action", "Thriller"],
32 "title": "Kung Fu Killer",
33 "year": 2014,
34 "score": 0.7731232643127441
35}
36{
37 "plot": "A young woman is trained by a martial arts specialist to become a professional assassin.",
38 "genres": ["Action", "Crime", "Romance"],
39 "title": "Naked Killer",
40 "year": 1992,
41 "score": 0.7693743109703064
42}
43{
44 "plot": "The life of the greatest karate master of a generation.",
45 "genres": ["Documentary", "Action", "History"],
46 "title": "The Real Miyagi",
47 "year": 2015,
48 "score": 0.768799901008606
49}
50{
51 "plot": "At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.",
52 "genres": ["Action", "Drama", "Sport"],
53 "title": "Never Back Down",
54 "year": 2008,
55 "score": 0.768179714679718
56}
57{
58 "plot": "Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.",
59 "genres": ["Action", "Drama"],
60 "title": "Dragon Tiger Gate",
61 "year": 2006,
62 "score": 0.7679706811904907
63}
64{
65 "year": 2001,
66 "plot": "A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.",
67 "genres": ["Action", "Comedy", "Sport"],
68 "title": "Shaolin Soccer",
69 "score": 0.7660527229309082
70}
1
2

The following queries use the $vectorSearch pipeline stage to search for movies that match the specified vector embeddings. 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.

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.

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 '$vectorSearch': {
20 'index': 'vector_index',
21 'path': 'plot_embedding',
22 'filter': {
23 '$and': [
24 {
25 'genres': {
26 '$nin': [
27 'Drama', 'Western', 'Crime'
28 ],
29 '$in': [
30 'Action', 'Adventure', 'Family'
31 ]
32 }
33 }, {
34 'year': {
35 '$gte': 1960,
36 '$lte': 2000
37 }
38 }
39 ]
40 },
41 'queryVector': [-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 0.017043658, 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42 'numCandidates': 200,
43 'limit': 10
44 }
45 }, {
46 '$project': {
47 '_id': 0,
48 'title': 1,
49 'genres': 1,
50 'plot': 1,
51 'year': 1,
52 'score': {
53 '$meta': 'vectorSearchScore'
54 }
55 }
56 }
57 ];
58
59 // run pipeline
60 const result = coll.aggregate(agg);
61
62 // print results
63 await result.forEach((doc) => console.dir(JSON.stringify(doc)));
64 } finally {
65 await client.close();
66 }
67}
68run().catch(console.dir);

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.

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 '$vectorSearch': {
20 'index': 'vector_index',
21 'path': 'plot_embedding',
22 'filter': {
23 '$or': [
24 {
25 'genres': {
26 '$ne': 'Crime'
27 }
28 }, {
29 '$and': [
30 {
31 'year': {
32 '$lte': 2015
33 }
34 }, {
35 'genres': {
36 '$eq': 'Action'
37 }
38 }
39 ]
40 }
41 ]
42 },
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0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914],
44 'numCandidates': 200,
45 'limit': 10
46 }
47 }, {
48 '$project': {
49 '_id': 0,
50 'title': 1,
51 'genres': 1,
52 'plot': 1,
53 'released': 1,
54 'score': {
55 '$meta': 'vectorSearchScore'
56 }
57 }
58 }
59 ];
60
61 // run pipeline
62 const result = coll.aggregate(agg);
63
64 // print results
65 await result.forEach((doc) => console.dir(JSON.stringify(doc)));
66 } finally {
67 await client.close();
68 }
69}
70run().catch(console.dir);
3

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

4

Run the following command to query your collection:

node atlas-vector-search-tutorial.js
'{
"year":1993,
"plot":"A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.",
"genres":["Action","Adventure","Thriller"],
"title":"Cliffhanger",
"score":0.7694521546363831
}'
`{
"plot":"The dying words of a thief spark a madcap cross-country rush to find some treasure.",
"genres":["Action","Adventure","Comedy"],
"title":"It's a Mad, Mad, Mad, Mad World",
"year":1963,
"score":0.7638954520225525
}`
'{
"year":1991,
"plot":"A cat burglar is forced to steal Da Vinci works of art for a world domination plot.",
"genres":["Action","Adventure","Comedy"],
"title":"Hudson Hawk",
"score":0.7538407444953918
}'
'{
"plot":"In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.",
"genres":["Action","Sci-Fi"],
"title":"Escape from New York",
"year":1981,
"score":0.7487208843231201
}'
'{
"plot":"Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.",
"genres":["Action","Thriller"],
"title":"The Romanov Stones",
"year":1993,
"score":0.7467736005783081
}'
'{
"plot":"An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.",
"genres":["Action","Comedy","Adventure"],
"title":"Crime Busters",
"year":1977,
"score":0.7437351942062378
}'
'{
"plot":"In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.",
"genres":["Action","Adventure","Comedy"],
"title":"Three Kings",
"year":1999,
"score":0.7425670623779297
}'
'{
"plot":"A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...",
"genres":["Action","Sci-Fi","Thriller"],
"title":"Black Moon Rising",
"year":1986,
"score":0.7397696375846863
}'
'{
"plot":"A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.","genres":["Action","Adventure","Horror"],"title":"Deep Rising","year":1998,"score":0.7392246127128601
}'
'{
"plot":"A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.",
"genres":["Action","Adventure","Comedy"],
"title":"That Man from Rio",
"year":1964,
"score":0.7357995510101318
}'
node atlas-search-tutorial.js
'{
"year":2008,
"plot":"A young Thai boxer learns the skills and inner meaning of martial arts.",
"genres":["Action"],
"title":"Ong-bak 2",
"score":0.7976737022399902
}'
'{
"plot":"A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.",
"genres":["Action","Drama","Family"],
"title":"The Karate Kid",
"year":1984,
"score":0.7929348349571228
}'
'{
"plot":"A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.","
genres":["Action","Adventure","Fantasy"],
"title":"Circle of Iron",
"year":1978,
"score":0.7852014899253845
}'
'{
"plot":"A lionized account of the life of the martial arts superstar.",
"genres":["Action","Biography","Drama"],
"title":"Dragon: The Bruce Lee Story",
"year":1993,
"score":0.7763040661811829
}'
'{
"plot":"A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.",
"genres":["Action","Thriller"],
"title":"Kung Fu Killer",
"year":2014,
"score":0.7731232643127441
}'
'{
"plot":"A young woman is trained by a martial arts specialist to become a professional assassin.",
"genres":["Action","Crime","Romance"],
"title":"Naked Killer",
"year":1992,
"score":0.7693743109703064
}'
'{
"plot":"The life of the greatest karate master of a generation.",
"genres":["Documentary","Action","History"],
"title":"The Real Miyagi",
"year":2015,
"score":0.768799901008606
}'
'{
"plot":"At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.",
"genres":["Action","Drama","Sport"],
"title":"Never Back Down",
"year":2008,
"score":0.768179714679718
}'
'{
"plot":"Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.",
"genres":["Action","Drama"],
"title":"Dragon Tiger Gate",
"year":2006,
"score":0.7679706811904907
}'
'{
"year":2001,
"plot":"A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.",
"genres":["Action","Comedy","Sport"],
"title":"Shaolin Soccer",
"score":0.7660527229309082
}'
1
2

The following queries use the $vectorSearch pipeline stage to search for movies that match the specified vector embeddings. 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.

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.

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8 {
9 '$vectorSearch': {
10 'index': 'vector_index',
11 'path': 'plot_embedding',
12 'filter': {
13 '$and': [
14 {
15 'genres': {
16 '$nin': [
17 'Drama', 'Western', 'Crime'
18 ],
19 '$in': [
20 'Action', 'Adventure', 'Family'
21 ]
22 }
23 }, {
24 'year': {
25 '$gte': 1960,
26 '$lte': 2000
27 }
28 }
29 ]
30 },
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32 'numCandidates': 200,
33 'limit': 10
34 }
35 }, {
36 '$project': {
37 '_id': 0,
38 'title': 1,
39 'genres': 1,
40 'plot': 1,
41 'year': 1,
42 'score': {
43 '$meta': 'vectorSearchScore'
44 }
45 }
46 }
47]
48
49# run pipeline
50result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
51
52# print results
53for i in result:
54 print(i)

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.

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8 {
9 '$vectorSearch': {
10 'index': 'vector_index',
11 'path': 'plot_embedding',
12 'filter': {
13 '$or': [
14 {
15 'genres': {
16 '$ne': 'Crime'
17 }
18 }, {
19 '$and': [
20 {
21 'year': {
22 '$lte': 2015
23 }
24 }, {
25 'genres': {
26 '$eq': 'Action'
27 }
28 }
29 ]
30 }
31 ]
32 },
33 'queryVector': [-0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 0.014710125, 0.0060869483, 0.016192842, 0.0026484078, 0.002417546, -0.007764761, -0.0053781047, 0.0016843157, 0.009071894, 0.0026158919, -0.016465975, 0.019054228, 0.030720878, 0.028145632, -0.0124340225, -0.011491066, -0.01273967, -0.0076542073, 0.00685432, 0.002936172, 0.03194347, -0.030798918, -0.004961903, 0.012661632, -0.024646938, 0.0085256295, -0.01133499, 0.016648063, -0.017857648, 0.01627088, -0.020823086, -0.002233832, 0.008330535, 0.024894057, 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0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914],
34 'numCandidates': 200,
35 'limit': 10
36 }
37 }, {
38 '$project': {
39 '_id': 0,
40 'title': 1,
41 'genres': 1,
42 'plot': 1,
43 'year': 1,
44 'score': {
45 '$meta': 'vectorSearchScore'
46 }
47 }
48 }
49]
50
51# run pipeline
52result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
53
54# print results
55for i in result:
56 print(i)
3

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

4
python atlas-vector-search-tutorial.py
{
'year': 1993,
'plot': 'A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.',
'genres': ['Action', 'Adventure', 'Thriller'],
'title': 'Cliffhanger',
'score': 0.7694521546363831
}
{
'plot': 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
'genres': ['Action', 'Adventure', 'Comedy'],
'title': "It's a Mad, Mad, Mad, Mad World",
'year': 1963,
'score': 0.7638954520225525
}
{
'year': 1991,
'plot': 'A cat burglar is forced to steal Da Vinci works of art for a world domination plot.',
'genres': ['Action', 'Adventure', 'Comedy'],
'title': 'Hudson Hawk',
'score': 0.7538407444953918
}
{
'plot': 'In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.',
'genres': ['Action', 'Sci-Fi'],
'title': 'Escape from New York',
'year': 1981,
'score': 0.7487208843231201
}
{
'plot': 'Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.',
'genres': ['Action', 'Thriller'],
'title': 'The Romanov Stones',
'year': 1993,
'score': 0.7467736005783081
}
{
'plot': 'An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.',
'genres': ['Action', 'Comedy', 'Adventure'],
'title': 'Crime Busters',
'year': 1977,
'score': 0.7437351942062378
}
{
'plot': 'In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.',
'genres': ['Action', 'Adventure', 'Comedy'],
'title': 'Three Kings',
'year': 1999,
'score': 0.7425670623779297
}
{
'plot': 'A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...', 'genres': ['Action', 'Sci-Fi', 'Thriller'], 'title': 'Black Moon Rising', 'year': 1986, 'score': 0.7397696375846863
}
{
'plot': 'A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.',
'genres': ['Action', 'Adventure', 'Horror'],
'title': 'Deep Rising',
'year': 1998,
'score': 0.7392246127128601
}
{
'plot': 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.',
'genres': ['Action', 'Adventure', 'Comedy'],
'title': 'That Man from Rio',
'year': 1964,
'score': 0.7357995510101318
}
python atlas-vector-search-tutorial.py
{
'year': 2008,
'plot': 'A young Thai boxer learns the skills and inner meaning of martial arts.',
'genres': ['Action'],
'title': 'Ong-bak 2',
'score': 0.7976737022399902
}
{
'plot': 'A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.',
'genres': ['Action', 'Drama', 'Family'],
'title': 'The Karate Kid',
'year': 1984,
'score': 0.7929348349571228
}
{
'plot': 'A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.',
'genres': ['Action', 'Adventure', 'Fantasy'],
'title': 'Circle of Iron',
'year': 1978,
'score': 0.7852014899253845
}
{
'plot': 'A lionized account of the life of the martial arts superstar.',
'genres': ['Action', 'Biography', 'Drama'],
'title': 'Dragon: The Bruce Lee Story',
'year': 1993,
'score': 0.7763040661811829
}
{
'plot': 'A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.',
'genres': ['Action', 'Thriller'],
'title': 'Kung Fu Killer',
'year': 2014,
'score': 0.7731232643127441
}
{
'plot': 'A young woman is trained by a martial arts specialist to become a professional assassin.',
'genres': ['Action', 'Crime', 'Romance'], '
title': 'Naked Killer',
'year': 1992,
'score': 0.7693743109703064
}
{
'plot': 'The life of the greatest karate master of a generation.',
'genres': ['Documentary', 'Action', 'History'],
'title': 'The Real Miyagi',
'year': 2015,
'score': 0.768799901008606
}
{
'plot': 'At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.',
'genres': ['Action', 'Drama', 'Sport'],
'title': 'Never Back Down',
'year': 2008,
'score': 0.768179714679718
}
{
'plot': 'Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.',
'genres': ['Action', 'Drama'],
'title': 'Dragon Tiger Gate',
'year': 2006,
'score': 0.7679706811904907
}
{
'year': 2001,
'plot': 'A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.',
'genres': ['Action', 'Comedy', 'Sport'],
'title': 'Shaolin Soccer', '
score': 0.7660527229309082
}