Atlas Vector Search Quick Start

On this page

Create a Vector Search Index

Run a Vector Search Query
Learning Summary
Next Steps

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

Time required: 15 minutes

Create a Vector Search Index

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

Set up your Atlas cluster.

Create a free Atlas account or sign in to an existing account.
If you don't yet have an Atlas cluster, create a free M0 cluster. To learn more about creating an Atlas cluster, see Create a Cluster.
Note
If you are working with an existing cluster, you must have Project Data Access Admin or higher access to your Atlas project.
If you create a new cluster, you have the necessary permissions by default.
You can create only one M0 free cluster per project.
In the left sidebar, click Atlas Search. Choose your cluster from the Select data source menu and click Go to Atlas Search.
If you haven't yet loaded the sample dataset onto your cluster, click Load a Sample Dataset. In the Load Sample Dataset dialog box, click Load Sample Dataset to confirm.
If you already loaded the sample dataset, check that the sample_mflix database contains the embedded_movies collection. If it doesn't, drop the sample databases and reload the sample dataset.
Loading the sample dataset can take several minutes to complete.

Create a Vector Search index.

When the sample data finishes loading, click Create Search Index.
Under the Atlas Vector Search section, select the JSON Editor and click Next.
In the Database and Collection section, expand the sample_mflix database and select the embedded_movies collection.
Each document in this collection contains information about a movie, including a summary of the movie's plot as a string, which has also been converted to and stored as a vector embedding in the document's plot_embedding field.
In the Index Name field, specify vector_index.

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

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

This index definition:

Indexes the plot_embedding field as the vector type. This field contains vector embeddings that represent the summary of a movie's plot.
Specifies 1536 vector dimensions.
Measures similarity using dotProduct similarity.

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

Install the dependencies.

Install the Atlas CLI and MongoDB Database Tools.
If you use Homebrew, you can run the following commands in your terminal:
```
brew install mongodb-atlas-cli
brew tap mongodb/brew && brew install mongodb-database-tools
```
Install Docker.
- For MacOS or Windows, install Docker Desktop v4.31+.
- For Linux, install Docker Engine v27.0+.

For detailed instructions, see Prerequisites.

Set up your local Atlas deployment.

If you don't have an existing Atlas account, run atlas setup in your terminal or create a new account.
In your terminal, run atlas auth login to authenticate with your Atlas login credentials. To learn more, see Connect from the Atlas CLI.
Run atlas deployments setup and follow the prompts to create a local deployment. When prompted to connect to the deployment, select skip.
For detailed instructions, see Create a Local Atlas Deployment.

Load the sample data.

Run the following command in your terminal to download the sample data:
```
curl  https://atlas-education.s3.amazonaws.com/sampledata.archive -o sampledata.archive
```
Run the following command to load the data into your deployment, replacing <port-number> with the port where you're hosting the deployment:
```
mongorestore --archive=sampledata.archive --port=<port-number>
```

Create a Vector Search index.

Create a file named vector-index.json

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

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

This index definition:

Indexes the plot_embedding field as the vector type. This field contains vector embeddings that represent the summary of a movie's plot.
Specifies 1536 vector dimensions.
Measures similarity using dotProduct similarity.

Save the file, and then run the following command in your terminal, replacing <path-to-file> with the path to the vector-index.json file that you created.
```
atlas deployments search indexes create --file <path-to-file>
```

Run a Vector Search Query

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

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

Click the Collections tab to view the Data Explorer.

Go to the Aggregation tab for the collection.

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

Construct and run your vector search query.

In the aggregation pipeline pane, click the </> Text toggle to enable text mode for pipeline editing.
Copy and paste the following sample query into the text editor.
This query searches for documents that include text in the plot field that is semantically related to the term "time travel".

1 [
2   {
3     "$vectorSearch": {
4       "index": "vector_index",
5       "path": "plot_embedding",
6       "queryVector": 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10   },
11   {
12     "$project": {
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14       "plot": 1,
15       "title": 1,
16       "score": { $meta: "vectorSearchScore" }
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1 {
2     "score": 0.9332506656646729,
3     "plot": "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.",
4     "title": "Thrill Seekers"
5 }
6 {
7     "plot": "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.",
8     "title": "About Time",
9     "score": 0.9312690496444702
10 }
11 {
12     "plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.",
13     "title": "The Time Machine",
14     "score": 0.929530143737793
15 }
16 {
17     "plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
18     "title": "Crusade in Jeans",
19     "score": 0.9290417432785034
20 }
21 {
22     "title": "Timecop",
23     "score": 0.9283161759376526,
24     "plot": "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past."
25 }
26 {
27     "plot": "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...",
28     "title": "A.P.E.X.",
29     "score": 0.9266218543052673
30 }
31 {
32     "plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
33     "title": "Men in Black 3",
34     "score": 0.9258455038070679
35 }
36 {
37     "score": 0.9240515828132629,
38     "plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.",
39     "title": "Tomorrowland"
40 }
41 {
42     "plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.",
43     "title": "Love Story 2050",
44     "score": 0.923175573348999
45 }
46 {
47     "plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...",
48     "title": "The Portal",
49     "score": 0.9228089451789856
50 }

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

The Pipeline Output pane displays the results of your query.

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

Download and install `mongosh`.

Connect to your cluster or local deployment in `mongosh`.

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

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

Switch to the `sample_mflix` database.

use sample_mflix

switched to db sample_mflix

Construct and run your vector search query.

Copy and paste the following sample query into your terminal and then run it using mongosh. mongosh might lag slightly when you paste in the query due to the number of characters in the vector embedding.
This query searches for documents that include text in the plot field that is semantically related to the term "time travel".

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

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Install the MongoDB C Driver.

For detailed installation instructions, refer to the MongoDB C Driver documentation.

Construct your vector search query.

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

Create a file named atlas-vector-search-quick-start.c and copy and paste the following sample query into the file:

1 #include <bson/bson.h>
2 #include <mongoc/mongoc.h>
3 #include <stdio.h>
4 
5 int main(void) {
6   
7     mongoc_client_t *client;
8     mongoc_collection_t *collection;
9     mongoc_cursor_t *cursor;
10     bson_array_builder_t *builder;
11     bson_t *pipeline;
12     bson_t query_vector;
13     bson_error_t error;
14     const bson_t *doc;
15     char *str;
16     
17     int arrSize = 1536; // This collection has a vector array of 1536 dimensions; depending on your vector embedding model, this mary vary
18     double embeddings[] = 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19 
20     // Initialize the MongoDB C Driver
21     mongoc_init();
22 
23     // Connect to your Atlas collection
24     client =  mongoc_client_new("<connection-string>");
25     collection = mongoc_client_get_collection (client, "sample_mflix", "embedded_movies");
26 
27     // Build query vector array
28     builder = bson_array_builder_new();
29     
30     for(int i = 0; i < arrSize; i++){
31         bson_array_builder_append_double(builder, embeddings[i]);
32     }
33 
34     bson_array_builder_build(builder, &query_vector);
35     bson_array_builder_destroy(builder);
36 
37     // Create aggregation pipeline for Vector Search
38     pipeline = BCON_NEW(
39         "pipeline",
40         "[",
41             "{",
42                 "$vectorSearch", "{", 
43                     "index", "vector_index", 
44                     "path", "plot_embedding", 
45                     "queryVector", BCON_ARRAY(&query_vector),
46                     "numCandidates", BCON_INT32(150), 
47                     "limit", BCON_INT32(10), 
48                 "}", 
49             "}",
50             "{",
51                 "$project", "{", 
52                     "title", BCON_BOOL(true),
53                     "plot", BCON_BOOL(true),
54                     "_id", BCON_BOOL(false),
55                     "score", "{",
56                         "$meta", "vectorSearchScore",
57                     "}",
58                 "}",
59             "}",
60         "]"
61     );
62 
63     // Run query
64     cursor = mongoc_collection_aggregate(collection, MONGOC_QUERY_NONE, pipeline, NULL, NULL );
65   
66     // Print results
67     while (mongoc_cursor_next (cursor, &doc)) {
68         str = bson_as_canonical_extended_json (doc, NULL);
69         printf ("%s\n", str);
70         bson_free (str);
71     }
72 
73     // Cleanup
74     bson_destroy(pipeline);
75     mongoc_cursor_destroy(cursor);
76     mongoc_collection_destroy(collection);
77     mongoc_client_destroy(client);
78     mongoc_cleanup();
79 
80     return 0;
81 }

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Configure CMake for your project.

In your project directory, create a file named CMakeLists.txt and copy the following code into the file.

cmake_minimum_required(VERSION 3.30)
project(atlas-vector-search-quick-start)
# Specify the minimum version you require.
find_package (mongoc-1.0 1.27.6 REQUIRED)
add_executable(atlas-vector-search-quick-start
  atlas-vector-search-quick-start.c
)
# The "atlas-vector-search-quick-start.c" sample program is shared among four tests.
target_link_libraries (atlas-vector-search-quick-start PRIVATE mongo::mongoc_shared)

Create a directory in your project folder called build.
```
mkdir build
```
Enter the build directory and run the cmake command to create your Makefile.
```
cd build
cmake ../
```

Run your query.

From your build directory, compile and run the atlas-vector-search-quick-start.c file.

cmake --build .
./atlas-vector-search-quick-start

1 { "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : { "$numberDouble" : "0.78926712274551391602" } }
2 { "plot" : "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title" : "About Time", "score" : { "$numberDouble" : "0.78436046838760375977" } }
3 { "plot" : "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title" : "The Time Machine", "score" : { "$numberDouble" : "0.78010666370391845703" } }
4 { "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : { "$numberDouble" : "0.77891707420349121094" } }
5 { "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : { "$numberDouble" : "0.77716124057769775391" } }
6 { "plot" : "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title" : "A.P.E.X.", "score" : { "$numberDouble" : "0.77308857440948486328" } }
7 { "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : { "$numberDouble" : "0.77123808860778808594" } }
8 { "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title" : "Tomorrowland", "score" : { "$numberDouble" : "0.76699239015579223633" } }
9 { "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : { "$numberDouble" : "0.76493728160858154297" } }
10 { "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal", "score" : { "$numberDouble" : "0.76407861709594726562" } }

Add the MongoDB C++ Driver to your project.

Install the C++ Driver. For detailed installation instructions, refer to the MongoDB C++ Driver documentation.

Create a new directory called query-quick-start.

mkdir query-quick-start

Enter the directory, and create a CMakeLists.txt file.

cd query-quick-start
touch CMakeLists.txt

Copy and paste the following lines into the CMakeLists.txt file:

cmake_minimum_required(VERSION 3.30)
project(query_quick_start)
set(CMAKE_CXX_STANDARD 17)
find_package(mongocxx REQUIRED)
find_package(bsoncxx REQUIRED)
add_executable(query_quick_start
  atlas_vector_search_quick_start.cpp
)
target_link_libraries(query_quick_start PRIVATE mongo::mongocxx_shared)
target_link_libraries(query_quick_start PRIVATE mongo::bsoncxx_shared)

Construct your vector search query.

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

Create a file named atlas_vector_search_quick_start.cpp .

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

1 #include <bsoncxx/builder/basic/document.hpp>
2 #include <bsoncxx/builder/list.hpp>
3 #include <bsoncxx/json.hpp>
4 #include <mongocxx/client.hpp>
5 #include <mongocxx/instance.hpp>
6 #include <mongocxx/uri.hpp>
7 
8 using bsoncxx::builder::basic::kvp;
9 using bsoncxx::builder::basic::make_document;
10 
11 int main() {
12   try {
13     mongocxx::instance inst{};
14 
15     // Replace the placeholder with your Atlas connection string
16     const auto uri = mongocxx::uri{"<connection-string>"};
17 
18     // Connect to your Atlas cluster
19     auto client = mongocxx::client{uri};
20     auto db = client["sample_mflix"];
21     auto collection = db["embedded_movies"];
22 
23     // Define vector embeddings to search
24     auto vectors = bsoncxx::builder::list{
25       -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 0.01794076, 0.011349596, -0.0052715978, 0.014755142, -0.019753495, -0.011156326, 0.011202978, 0.022126047, 0.00846388, 0.030549942, -0.0041386373, 0.018847128, -0.00033655585, 0.024925126, -0.003555496, -0.019300312, 0.010749794, 0.0075308536, -0.018287312, -0.016567878, -0.012869096, -0.015528221, 0.0078107617, -0.011156326, 0.013522214, -0.020646535, -0.01211601, 0.055928253, 0.011596181, -0.017247654, 0.0005939711, -0.026977783, -0.003942035, -0.009583511, -0.0055248477, -0.028737204, 0.023179034, 0.003995351, 0.0219661, -0.008470545, 0.023392297, 0.010469886, -0.015874773, 0.007890735, -0.009690142, -0.00024970944, 0.012775794, 0.0114762215, 0.013422247, 0.010429899, -0.03686786, -0.006717788, -0.027484283, 0.011556195, -0.036068123, -0.013915418, -0.0016327957, 0.0151016945, -0.020473259, 0.004671795, -0.012555866, 0.0209531, 0.01982014, 0.024485271, 0.0105431955, -0.005178295, 0.033162415, -0.013795458, 0.007150979, 0.010243294, 0.005644808, 0.017260984, -0.0045618312, 0.0024725192, 0.004305249, -0.008197301, 0.0014203656, 0.0018460588, 0.005015015, -0.011142998, 0.01439526, 0.022965772, 0.02552493, 0.007757446, -0.0019726837, 0.009503538, -0.032042783, 0.008403899, -0.04609149, 0.013808787, 0.011749465, 0.036388017, 0.016314628, 0.021939443, -0.0250051, -0.017354285, -0.012962398, 0.00006107364, 0.019113706, 0.03081652, -0.018114036, -0.0084572155, 0.009643491, -0.0034721901, 0.0072642746, -0.0090636825, 0.01642126, 0.013428912, 0.027724205, 0.0071243206, -0.6858542, -0.031029783, -0.014595194, -0.011449563, 0.017514233, 0.01743426, 0.009950057, 0.0029706885, -0.015714826, -0.001806072, 0.011856096, 0.026444625, -0.0010663156, -0.006474535, 0.0016161345, -0.020313311, 0.0148351155, -0.0018393943, 0.0057347785, 0.018300641, -0.018647194, 0.03345565, -0.008070676, 0.0071443142, 0.014301958, 0.0044818576, 0.003838736, -0.007350913, -0.024525259, -0.001142124, -0.018620536, 0.017247654, 0.007037683, 0.010236629, 0.06046009, 0.0138887605, -0.012122675, 0.037694257, 0.0055081863, 0.042492677, 0.00021784494, -0.011656162, 0.010276617, 0.022325981, 0.005984696, -0.009496873, 0.013382261, -0.0010563189, 0.0026507939, -0.041639622, 0.008637156, 0.026471283, -0.008403899, 0.024858482, -0.00066686375, -0.0016252982, 0.027590916, 0.0051449724, 0.0058647357, -0.008743787, -0.014968405, 0.027724205, -0.011596181, 0.0047650975, -0.015381602, 0.0043718936, 0.002159289, 0.035908177, -0.008243952, -0.030443309, 0.027564257, 0.042625964, -0.0033688906, 0.01843393, 0.019087048, 0.024578573, 0.03268257, -0.015608194, -0.014128681, -0.0033538956, -0.0028757197, -0.004121976, -0.032389335, 0.0034322033, 0.058807302, 0.010943064, -0.030523283, 0.008903735, 0.017500903, 0.00871713, -0.0029406983, 0.013995391, -0.03132302, -0.019660193, -0.00770413, -0.0038853872, 0.0015894766, -0.0015294964, -0.006251275, -0.021099718, -0.010256623, -0.008863748, 0.028550599, 0.02020668, -0.0012962399, -0.003415542, -0.0022509254, 0.0119360695, 0.027590916, -0.046971202, -0.0015194997, -0.022405956, 0.0016677842, -0.00018535563, -0.015421589, -0.031802863, 0.03814744, 0.0065411795, 0.016567878, -0.015621523, 0.022899127, -0.011076353, 0.02841731, -0.002679118, -0.002342562, 0.015341615, 0.01804739, -0.020566562, -0.012989056, -0.002990682, 0.01643459, 0.00042527664, 0.008243952, -0.013715484, -0.004835075, -0.009803439, 0.03129636, -0.021432944, 0.0012087687, -0.015741484, -0.0052016205, 0.00080890034, -0.01755422, 0.004811749, -0.017967418, -0.026684547, -0.014128681, 0.0041386373, -0.013742141, -0.010056688, -0.013268964, -0.0110630235, -0.028337335, 0.015981404, -0.00997005, -0.02424535, -0.013968734, -0.028310679, -0.027750863, -0.020699851, 0.02235264, 0.001057985, 0.00081639783, -0.0099367285, 0.013522214, -0.012016043, -0.00086471526, 0.013568865, 0.0019376953, -0.019020405, 0.017460918, -0.023045745, 0.008503866, 0.0064678704, -0.011509543, 0.018727167, -0.003372223, -0.0028690554, -0.0027024434, -0.011902748, -0.012182655, -0.015714826, -0.0098634185, 0.00593138, 0.018753825, 0.0010146659, 0.013029044, 0.0003521757, -0.017620865, 0.04102649, 0.00552818, 0.024485271, -0.009630162, -0.015608194, 0.0006718621, -0.0008418062, 0.012395918, 0.0057980907, 0.016221326, 0.010616505, 0.004838407, -0.012402583, 0.019900113, -0.0034521967, 0.000247002, -0.03153628, 0.0011038032, -0.020819811, 0.016234655, -0.00330058, -0.0032289368, 0.00078973995, -0.021952773, -0.022459272, 0.03118973, 0.03673457, -0.021472929, 0.0072109587, -0.015075036, 0.004855068, -0.0008151483, 0.0069643734, 0.010023367, -0.010276617, -0.023019087, 0.0068244194, -0.0012520878, -0.0015086699, 0.022046074, -0.034148756, -0.0022192693, 0.002427534, -0.0027124402, 0.0060346797, 0.015461575, 0.0137554705, 0.009230294, -0.009583511, 0.032629255, 0.015994733, -0.019167023, -0.009203636, 0.03393549, -0.017274313, -0.012042701, -0.0009930064, 0.026777849, -0.013582194, -0.0027590916, -0.017594207, -0.026804507, -0.0014236979, -0.022032745, 0.0091236625, -0.0042419364, -0.00858384, -0.0033905501, -0.020739838, 0.016821127, 0.022539245, 0.015381602, 0.015141681, 0.028817179, -0.019726837, -0.0051283115, -0.011489551, -0.013208984, -0.0047017853, -0.0072309524, 0.01767418, 0.0025658219, -0.010323267, 0.012609182, -0.028097415, 0.026871152, -0.010276617, 0.021912785, 0.0022542577, 0.005124979, -0.0019710176, 0.004518512, -0.040360045, 0.010969722, -0.0031539614, -0.020366628, -0.025778178, -0.0110030435, -0.016221326, 0.0036587953, 0.016207997, 0.003007343, -0.0032555948, 0.0044052163, -0.022046074, -0.0008822095, -0.009363583, 0.028230704, -0.024538586, 0.0029840174, 0.0016044717, -0.014181997, 0.031349678, -0.014381931, -0.027750863, 0.02613806, 0.0004136138, -0.005748107, -0.01868718, -0.0010138329, 0.0054348772, 0.010703143, -0.003682121, 0.0030856507, -0.004275259, -0.010403241, 0.021113047, -0.022685863, -0.023032416, 0.031429652, 0.001792743, -0.005644808, -0.011842767, -0.04078657, -0.0026874484, 0.06915057, -0.00056939584, -0.013995391, 0.010703143, -0.013728813, -0.022939114, -0.015261642, -0.022485929, 0.016807798, 0.007964044, 0.0144219175, 0.016821127, 0.0076241563, 0.005461535, -0.013248971, 0.015301628, 0.0085171955, -0.004318578, 0.011136333, -0.0059047225, -0.010249958, -0.018207338, 0.024645219, 0.021752838, 0.0007614159, -0.013648839, 0.01111634, -0.010503208, -0.0038487327, -0.008203966, -0.00397869, 0.0029740208, 0.008530525, 0.005261601, 0.01642126, -0.0038753906, -0.013222313, 0.026537929, 0.024671877, -0.043505676, 0.014195326, 0.024778508, 0.0056914594, -0.025951454, 0.017620865, -0.0021359634, 0.008643821, 0.021299653, 0.0041686273, -0.009017031, 0.04044002, 0.024378639, -0.027777521, -0.014208655, 0.0028623908, 0.042119466, 0.005801423, -0.028124074, -0.03129636, 0.022139376, -0.022179363, -0.04067994, 0.013688826, 0.013328944, 0.0046184794, -0.02828402, -0.0063412455, -0.0046184794, -0.011756129, -0.010383247, -0.0018543894, -0.0018593877, -0.00052024535, 0.004815081, 0.014781799, 0.018007403, 0.01306903, -0.020433271, 0.009043689, 0.033189073, -0.006844413, -0.019766824, -0.018767154, 0.00533491, -0.0024575242, 0.018727167, 0.0058080875, -0.013835444, 0.0040719924, 0.004881726, 0.012029372, 0.005664801, 0.03193615, 0.0058047553, 0.002695779, 0.009290274, 0.02361889, 0.017834127, 0.0049017193, -0.0036388019, 0.010776452, -0.019793482, 0.0067777685, -0.014208655, -0.024911797, 0.002385881, 0.0034988478, 0.020899786, -0.0025858153, -0.011849431, 0.033189073, -0.021312982, 0.024965113, -0.014635181, 0.014048708, -0.0035921505, -0.003347231, 0.030869836, -0.0017161017, -0.0061346465, 0.009203636, -0.025165047, 0.0068510775, 0.021499587, 0.013782129, -0.0024475274, -0.0051149824, -0.024445284, 0.006167969, 0.0068844, -0.00076183246, 0.030150073, -0.0055948244, -0.011162991, -0.02057989, -0.009703471, -0.020646535, 0.008004031, 0.0066378145, -0.019900113, -0.012169327, -0.01439526, 0.0044252095, -0.004018677, 0.014621852, -0.025085073, -0.013715484, -0.017980747, 0.0071043274, 0.011456228, -0.01010334, -0.0035321703, -0.03801415, -0.012036037, -0.0028990454, -0.05419549, -0.024058744, -0.024272008, 0.015221654, 0.027964126, 0.03182952, -0.015354944, 0.004855068, 0.011522872, 0.004771762, 0.0027874154, 0.023405626, 0.0004242353, -0.03132302, 0.007057676, 0.008763781, -0.0027057757, 0.023005757, -0.0071176565, -0.005238275, 0.029110415, -0.010989714, 0.013728813, -0.009630162, -0.029137073, -0.0049317093, -0.0008630492, -0.015248313, 0.0043219104, -0.0055681667, -0.013175662, 0.029723546, 0.025098402, 0.012849103, -0.0009996708, 0.03118973, -0.0021709518, 0.0260181, -0.020526575, 0.028097415, -0.016141351, 0.010509873, -0.022965772, 0.002865723, 0.0020493253, 0.0020509914, -0.0041419696, -0.00039695262, 0.017287642, 0.0038987163, 0.014795128, -0.014661839, -0.008950386, 0.004431874, 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26     };
27 
28     // Define the pipeline with vectorSearch query options
29     mongocxx::pipeline stages;
30 
31     stages
32         .append_stage(make_document(
33             kvp("$vectorSearch",
34                 make_document(kvp("index", "vector_index"),
35                               kvp("path", "plot_embedding"),
36                               kvp("queryVector", vectors),
37                               kvp("numCandidates", 150), kvp("limit", 10)))))
38         .project(make_document(
39             kvp("_id", 0), kvp("plot", 1), kvp("title", 1),
40             kvp("score", make_document(kvp("$meta", "vectorSearchScore")))));
41 
42     auto cursor = collection.aggregate(stages);
43 
44     for (auto&& doc : cursor) {
45       std::cout << bsoncxx::to_json(doc) << std::endl;
46     }
47   } catch (const std::exception& e) {
48     std::cout << "Exception: " << e.what() << std::endl;
49   }
50   return 0;
51 }

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Compile and run your query.

Create and enter the /build directory:
```
mkdir build && cd build
```
Prepare the project.
```
cmake ../
```
Build the app.
```
cmake --build .
```

Execute the app to run the query.

./query_quick_start

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

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

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

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

Construct your vector search query.

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

Edit the Program.cs file.

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

1 using MongoDB.Bson;
2 using MongoDB.Bson.Serialization.Attributes;
3 using MongoDB.Bson.Serialization.Conventions;
4 using MongoDB.Driver;
5 
6 public class vectorSearchBasicQuery 
7 {
8   // define connection to your Atlas cluster
9   private const string MongoConnectionString = "<connection-string>";
10 
11   public static void Main(string[] args){
12     var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
13     ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
14 
15     // connect to your Atlas cluster
16     var mongoClient = new MongoClient(MongoConnectionString);
17 
18     // define namespace
19     var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
20     var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
21 
22     // define vector embeddings to search
23     var vector = new[] 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24     var options = new VectorSearchOptions<EmbeddedMovie>() {
25         IndexName = "vector_index",
26         NumberOfCandidates = 150
27     };
28 
29     // run query
30     var results = moviesCollection.Aggregate()
31                 .VectorSearch(movie => movie.Embedding, vector, 10, options)
32                 .Project(Builders<EmbeddedMovie>.Projection
33                   .Include(movie => movie.Title)
34                   .Include(movie => movie.Plot)
35                   .Exclude(movie => movie.Id)
36                   .MetaVectorSearchScore(movie => movie.Score))
37                 .ToList();
38 
39     // print results
40     foreach (var movie in results)
41       {
42         Console.WriteLine(movie.ToJson());
43       }
44   }
45 }
46 
47 [BsonIgnoreExtraElements]
48 public class EmbeddedMovie
49 {
50     [BsonIgnoreIfDefault]
51     public ObjectId Id { get; set; }
52     public string Title { get; set; }
53     public string Plot { get; set; }
54     [BsonElement("plot_embedding")]
55     public double[] Embedding { get; set; }
56     public double Score { get; set; }
57 }

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

Compile and run the Program.cs file.

dotnet run query-quick-start.csproj

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

Add the MongoDB Go Driver to your project.

Run the following commands:

Initialize your Go module:
```
mkdir go-vector-quickstart && cd go-vector-quickstart
go mod init go-vector-quickstart
```
Add MongoDB Go as a dependency:
```
go get go.mongodb.org/mongo-driver/mongo
```

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

Construct your vector search query.

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

Create a file named atlas_vector_search_quick_start.go .

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

1 package main
2 
3 import (
4 	"context"
5 	"encoding/json"
6 	"fmt"
7 	"log"
8 
9 	"go.mongodb.org/mongo-driver/bson"
10 	"go.mongodb.org/mongo-driver/mongo"
11 	"go.mongodb.org/mongo-driver/mongo/options"
12 )
13 
14 func main() {
15 	ctx := context.Background()
16 
17 	// Replace the placeholder with your Atlas connection string
18 	const uri = "<connection-string>"
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 
31 	// Define the pipeline with $vectorSearch query options
32 	queryVectorValues := []float64{
33 		-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 0.01794076, 0.011349596, -0.0052715978, 0.014755142, -0.019753495, -0.011156326, 0.011202978, 0.022126047, 0.00846388, 0.030549942, -0.0041386373, 0.018847128, -0.00033655585, 0.024925126, -0.003555496, -0.019300312, 0.010749794, 0.0075308536, -0.018287312, -0.016567878, -0.012869096, -0.015528221, 0.0078107617, -0.011156326, 0.013522214, -0.020646535, -0.01211601, 0.055928253, 0.011596181, -0.017247654, 0.0005939711, -0.026977783, -0.003942035, -0.009583511, -0.0055248477, -0.028737204, 0.023179034, 0.003995351, 0.0219661, -0.008470545, 0.023392297, 0.010469886, -0.015874773, 0.007890735, -0.009690142, -0.00024970944, 0.012775794, 0.0114762215, 0.013422247, 0.010429899, -0.03686786, -0.006717788, -0.027484283, 0.011556195, -0.036068123, -0.013915418, -0.0016327957, 0.0151016945, -0.020473259, 0.004671795, -0.012555866, 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0.0044818576, 0.003838736, -0.007350913, -0.024525259, -0.001142124, -0.018620536, 0.017247654, 0.007037683, 0.010236629, 0.06046009, 0.0138887605, -0.012122675, 0.037694257, 0.0055081863, 0.042492677, 0.00021784494, -0.011656162, 0.010276617, 0.022325981, 0.005984696, -0.009496873, 0.013382261, -0.0010563189, 0.0026507939, -0.041639622, 0.008637156, 0.026471283, -0.008403899, 0.024858482, -0.00066686375, -0.0016252982, 0.027590916, 0.0051449724, 0.0058647357, -0.008743787, -0.014968405, 0.027724205, -0.011596181, 0.0047650975, -0.015381602, 0.0043718936, 0.002159289, 0.035908177, -0.008243952, -0.030443309, 0.027564257, 0.042625964, -0.0033688906, 0.01843393, 0.019087048, 0.024578573, 0.03268257, -0.015608194, -0.014128681, -0.0033538956, -0.0028757197, -0.004121976, -0.032389335, 0.0034322033, 0.058807302, 0.010943064, -0.030523283, 0.008903735, 0.017500903, 0.00871713, -0.0029406983, 0.013995391, -0.03132302, -0.019660193, -0.00770413, -0.0038853872, 0.0015894766, -0.0015294964, -0.006251275, -0.021099718, -0.010256623, -0.008863748, 0.028550599, 0.02020668, -0.0012962399, -0.003415542, -0.0022509254, 0.0119360695, 0.027590916, -0.046971202, -0.0015194997, -0.022405956, 0.0016677842, -0.00018535563, -0.015421589, -0.031802863, 0.03814744, 0.0065411795, 0.016567878, -0.015621523, 0.022899127, -0.011076353, 0.02841731, -0.002679118, -0.002342562, 0.015341615, 0.01804739, -0.020566562, -0.012989056, -0.002990682, 0.01643459, 0.00042527664, 0.008243952, -0.013715484, -0.004835075, -0.009803439, 0.03129636, -0.021432944, 0.0012087687, -0.015741484, -0.0052016205, 0.00080890034, -0.01755422, 0.004811749, -0.017967418, -0.026684547, -0.014128681, 0.0041386373, -0.013742141, -0.010056688, -0.013268964, -0.0110630235, -0.028337335, 0.015981404, -0.00997005, -0.02424535, -0.013968734, -0.028310679, -0.027750863, -0.020699851, 0.02235264, 0.001057985, 0.00081639783, -0.0099367285, 0.013522214, -0.012016043, -0.00086471526, 0.013568865, 0.0019376953, -0.019020405, 0.017460918, -0.023045745, 0.008503866, 0.0064678704, -0.011509543, 0.018727167, -0.003372223, -0.0028690554, -0.0027024434, -0.011902748, -0.012182655, -0.015714826, -0.0098634185, 0.00593138, 0.018753825, 0.0010146659, 0.013029044, 0.0003521757, -0.017620865, 0.04102649, 0.00552818, 0.024485271, -0.009630162, -0.015608194, 0.0006718621, -0.0008418062, 0.012395918, 0.0057980907, 0.016221326, 0.010616505, 0.004838407, -0.012402583, 0.019900113, -0.0034521967, 0.000247002, -0.03153628, 0.0011038032, -0.020819811, 0.016234655, -0.00330058, -0.0032289368, 0.00078973995, -0.021952773, -0.022459272, 0.03118973, 0.03673457, -0.021472929, 0.0072109587, -0.015075036, 0.004855068, -0.0008151483, 0.0069643734, 0.010023367, -0.010276617, -0.023019087, 0.0068244194, -0.0012520878, -0.0015086699, 0.022046074, -0.034148756, -0.0022192693, 0.002427534, -0.0027124402, 0.0060346797, 0.015461575, 0.0137554705, 0.009230294, -0.009583511, 0.032629255, 0.015994733, -0.019167023, -0.009203636, 0.03393549, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136,
34 	}
35 
36 	pipeline := mongo.Pipeline{
37 		bson.D{
38 			{"$vectorSearch", bson.D{
39 				{"queryVector", queryVectorValues},
40 				{"index", "vector_index"},
41 				{"path", "plot_embedding"},
42 				{"numCandidates", 150},
43 				{"limit", 10},
44 			}},
45 		},
46 		bson.D{
47 			{"$project", bson.D{
48 				{"_id", 0},
49 				{"plot", 1},
50 				{"title", 1},
51 				{"score", bson.D{
52 					{"$meta", "vectorSearchScore"},
53 				}},
54 			}},
55 		},
56 	}
57 
58 	// Run the pipeline
59 	cursor, err := coll.Aggregate(ctx, pipeline)
60 	if err != nil {
61 		log.Fatalf("Failed to run aggregation: %v", err)
62 	}
63 	defer cursor.Close(ctx)
64 
65 	// Print results
66 	for cursor.Next(ctx) {
67 		var doc bson.M
68 		if err := cursor.Decode(&doc); err != nil {
69 			log.Fatalf("Failed to decode document: %v", err)
70 		}
71 		jsonDoc, err := json.Marshal(doc)
72 		if err != nil {
73 			log.Fatalf("Failed to marshal document to JSON: %v", err)
74 		}
75 		fmt.Println(string(jsonDoc))
76 	}
77 
78 	if err := cursor.Err(); err != nil {
79 		log.Fatalf("Cursor error: %v", err)
80 	}
81 }

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

Run the following command to query your collection:

go run atlas_vector_search_quick_start.go

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

Add the MongoDB Java Sync Driver to your project.

Add the Java driver version 5.2 or higher as a dependency in your project:
- If you are using Maven, add the following dependency to your pom.xml dependencies list:
  <dependency> <groupId>org.mongodb</groupId> <artifactId>mongodb-driver-sync</artifactId> <version>5.2.0</version> </dependency>
- If you are using Gradle, add the following dependency to your build.gradle dependencies list:
  dependencies { implementation 'org.mongodb:mongodb-driver-sync:5.2.0' }
Add the Java driver JAR files to your CLASSPATH.

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

Construct your vector search query.

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

Create a file named BasicQuery.java.

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

1 import com.mongodb.client.MongoClient;
2 import com.mongodb.client.MongoClients;
3 import com.mongodb.client.MongoCollection;
4 import com.mongodb.client.MongoDatabase;
5 import com.mongodb.client.model.search.FieldSearchPath;
6 import org.bson.Document;
7 import org.bson.conversions.Bson;
8 
9 import java.util.List;
10 
11 import static com.mongodb.client.model.Aggregates.project;
12 import static com.mongodb.client.model.Aggregates.vectorSearch;
13 import static com.mongodb.client.model.Projections.fields;
14 import static com.mongodb.client.model.Projections.include;
15 import static com.mongodb.client.model.Projections.exclude;
16 import static com.mongodb.client.model.Projections.metaVectorSearchScore;
17 import static com.mongodb.client.model.search.SearchPath.fieldPath;
18 import static com.mongodb.client.model.search.VectorSearchOptions.approximateVectorSearchOptions;
19 import static java.util.Arrays.asList;
20 
21 public class BasicQuery {
22   public static void main( String[] args ) {
23     // specify connection
24     String uri = "<connection-string>";
25 
26     // establish connection and set namespace
27     try (MongoClient mongoClient = MongoClients.create(uri)) {
28       MongoDatabase database = mongoClient.getDatabase("sample_mflix");
29       MongoCollection<Document> collection = database.getCollection("embedded_movies");
30       
31 
32       // define $vectorSearch query options
33       List<Double> queryVector = (asList(-0.0016261312d, -0.028070757d, -0.011342932d, -0.012775794d, -0.0027440966d, 0.008683807d, -0.02575152d, -0.02020668d, -0.010283281d, -0.0041719596d, 0.021392956d, 0.028657231d, -0.006634482d, 0.007490867d, 0.018593878d, 0.0038187427d, 0.029590257d, -0.01451522d, 0.016061379d, 0.00008528442d, -0.008943722d, 0.01627464d, 0.024311995d, -0.025911469d, 0.00022596726d, -0.008863748d, 0.008823762d, -0.034921836d, 0.007910728d, -0.01515501d, 0.035801545d, -0.0035688248d, -0.020299982d, -0.03145631d, -0.032256044d, -0.028763862d, -0.0071576433d, -0.012769129d, 0.012322609d, -0.006621153d, 0.010583182d, 0.024085402d, -0.001623632d, 0.007864078d, -0.021406285d, 0.002554159d, 0.012229307d, -0.011762793d, 0.0051682983d, 0.0048484034d, 0.018087378d, 0.024325324d, -0.037694257d, -0.026537929d, -0.008803768d, -0.017767483d, -0.012642504d, -0.0062712682d, 0.0009771782d, -0.010409906d, 0.017754154d, -0.004671795d, -0.030469967d, 0.008477209d, -0.005218282d, 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34       String indexName = "vector_index";
35       FieldSearchPath fieldSearchPath = fieldPath("plot_embedding");
36       int limit = 10;
37       int numCandidates = 150;
38       
39       // define pipeline 
40       List<Bson> pipeline = asList(
41         vectorSearch(
42           fieldSearchPath,
43           queryVector,
44           indexName,
45           limit,
46           approximateVectorSearchOptions(numCandidates)),
47         project(
48           fields(exclude("_id"), include("title"), include("plot"),
49           metaVectorSearchScore("score"))));
50       
51       // run query and print results
52       collection.aggregate(pipeline)
53         .forEach(doc -> System.out.println(doc.toJson()));	
54     }
55   }
56 }

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

Compile and run the BasicQuery.java file:

javac BasicQuery.java
java BasicQuery

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

Install the MongoDB Kotlin Coroutine Driver.

Add the Kotlin Coroutine driver version 5.2 or higher as a dependency in your project:

If you are using Gradle, add the following dependency to your
build.gradle dependencies list:
dependencies { implementation 'mongodb-driver-kotlin-coroutine:5.2.0' }
If you are using Maven, add the following dependency to your
pom.xml dependencies list:
<dependency> <groupId>org.mongodb</groupId> <artifactId>mongodb-driver-kotlin-coroutine</artifactId> <version>5.2.0</version> </dependency>

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

Construct your vector search query.

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

Create a file named atlasVectorSearchQuery.kt.

Copy and paste the following sample query into the atlasVectorSearchQuery.kt file:

1 import com.mongodb.client.model.Aggregates
2 import com.mongodb.client.model.Projections
3 import com.mongodb.client.model.search.SearchPath.fieldPath
4 import com.mongodb.client.model.search.VectorSearchOptions
5 import com.mongodb.kotlin.client.coroutine.MongoClient
6 import kotlinx.coroutines.flow.count
7 import org.bson.Document
8 
9 data class Movie(val title: String, val plot: String, val score: Double?)
10 
11 suspend fun main() {
12 
13     // Replace the placeholder with your Atlas connection string
14     val uri = "<connection-string>"
15     val client = MongoClient.create(uri)
16 
17     try {
18         // Connect to your Atlas cluster
19         val database = client.getDatabase("sample_mflix")
20         val collection = database.getCollection<Movie>("embedded_movies")
21 
22         // Define the pipeline with $vectorSearch query options
23         val queryVector = listOf(
24             -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 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-0.0051616337, 0.03876057, -0.0033455652, 0.00075850025, -0.006951045, -0.0062112883, 0.018140694, -0.006351242, -0.008263946, 0.018154023, -0.012189319, 0.0075508473, -0.044358727, -0.0040153447, 0.0093302615, -0.010636497, 0.032789204, -0.005264933, -0.014235313, -0.018393943, 0.007297597, -0.016114693, 0.015021721, 0.020033404, 0.0137688, 0.0011046362, 0.010616505, -0.0039453674, 0.012109346, 0.021099718, -0.0072842683, -0.019153694, -0.003768759, 0.039320387, -0.006747778, -0.0016852784, 0.018154023, 0.0010963057, -0.015035049, -0.021033075, -0.04345236, 0.017287642, 0.016341286, -0.008610498, 0.00236922, 0.009290274, 0.028950468, -0.014475234, -0.0035654926, 0.015434918, -0.03372223, 0.004501851, -0.012929076, -0.008483873, -0.0044685286, -0.0102233, 0.01615468, 0.0022792495, 0.010876419, -0.0059647025, 0.01895376, -0.0069976957, -0.0042952523, 0.017207667, -0.00036133936, 0.0085905045, 0.008084005, 0.03129636, -0.016994404, -0.014915089, 0.020100048, -0.012009379, -0.006684466, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136,
25             )
26         val path = fieldPath("plot_embedding")
27         val index = "vector_index"
28         val limit = 10L
29         val numCandidates = 150L
30 
31         val pipeline = listOf(
32             Aggregates.vectorSearch(
33                 path,
34                 queryVector,
35                 index,
36                 limit,
37                 VectorSearchOptions.approximateVectorSearchOptions(numCandidates)
38             ),
39             Aggregates.project(
40                 Projections.fields(
41                     Projections.excludeId(),
42                     Projections.include(Movie::plot.name),
43                     Projections.include(Movie::title.name),
44                     Projections.metaVectorSearchScore(Movie::score.name)
45                 )
46             )
47         )
48 
49         // Run the pipeline
50         val resultsFlow = collection.aggregate<Document>(pipeline)
51 
52         // Print results
53         val resultCount = resultsFlow.count()
54         if (resultCount > 0) {
55             resultsFlow.collect { result ->
56                 println(result.toJson())
57             }
58         } else {
59             println("No results found.")
60         }
61     } catch (e: Exception) {
62         println("Error occurred: ${e.message}")
63     } finally {
64         client.close()
65     }
66 }

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

Run the atlasVectorSearchQuery.kt file in your IDE. The output should resemble the following:

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

Install the MongoDB Kotlin Sync Driver.

Add the Kotlin Sync driver version 5.2 or higher as a dependency in your project:

If you are using Gradle, add the following dependency to your
build.gradle dependencies list:
dependencies { implementation 'mongodb-driver-kotlin-sync:5.2.0' }
If you are using Maven, add the following dependency to your
pom.xml dependencies list:
<dependency> <groupId>org.mongodb</groupId> <artifactId>mongodb-driver-kotlin-sync</artifactId> <version>5.2.0</version> </dependency>

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

Construct your vector search query.

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

Create a file named atlasVectorSearchQuery.kt.

Copy and paste the following sample query into the atlasVectorSearchQuery.kt file:

1 import com.mongodb.client.model.Aggregates
2 import com.mongodb.client.model.Projections
3 import com.mongodb.client.model.search.SearchPath.fieldPath
4 import com.mongodb.client.model.search.VectorSearchOptions
5 import com.mongodb.kotlin.client.MongoClient
6 import org.bson.Document
7 
8 data class Movie(val title: String, val plot: String, val score: Double?)
9 
10 fun main() {
11 
12     // Replace the placeholder with your Atlas connection string
13     val uri = "<connection-string>"
14     val client = MongoClient.create(uri)
15 
16     try {
17         // Connect to your Atlas cluster
18         val database = client.getDatabase("sample_mflix")
19         val collection = database.getCollection<Movie>("embedded_movies")
20 
21         // Define the pipeline with $vectorSearch query options
22         val queryVector = listOf(
23             -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136,
24             )
25         val path = fieldPath("plot_embedding")
26         val index = "vector_index"
27         val limit = 10L
28         val numCandidates = 150L
29 
30         val pipeline = listOf(
31             Aggregates.vectorSearch(
32                 path,
33                 queryVector,
34                 index,
35                 limit,
36                 VectorSearchOptions.approximateVectorSearchOptions(numCandidates)
37             ),
38             Aggregates.project(
39                 Projections.fields(
40                     Projections.excludeId(),
41                     Projections.include(Movie::plot.name),
42                     Projections.include(Movie::title.name),
43                     Projections.metaVectorSearchScore(Movie::score.name)
44                 )
45             )
46         )
47 
48         // Run the pipeline
49         val results = collection.aggregate<Document>(pipeline).toList()
50 
51         // Print results
52         if (results.isNotEmpty()) {
53             results.forEach { result ->
54                 println(result.toJson()).toString()
55             }
56         } else {
57             println("No results found.")
58         }
59     } catch (e: Exception) {
60         println("Error occurred: ${e.message}")
61     } finally {
62         client.close()
63     }
64 }

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

Run the atlasVectorSearchQuery.kt file in your IDE. The output should resemble the following:

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

Install the MongoDB Node.js Driver.

Run one of the following commands:

npm install mongodb

yarn add mongodb

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

Construct your vector search query.

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

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

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

1 const { MongoClient } = require("mongodb");
2 
3 // connect to your Atlas cluster
4 const uri = "<connection-string>";
5 
6 const client = new MongoClient(uri);
7 
8 async function run() {
9     try {
10         await client.connect();
11 
12         // set namespace
13         const database = client.db("sample_mflix");
14         const coll = database.collection("embedded_movies");
15 
16         // define pipeline
17         const agg = [
18             {
19               '$vectorSearch': {
20                 'index': 'vector_index',
21                 'path': 'plot_embedding',
22                 'queryVector': [
23                   -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 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-0.0051616337, 0.03876057, -0.0033455652, 0.00075850025, -0.006951045, -0.0062112883, 0.018140694, -0.006351242, -0.008263946, 0.018154023, -0.012189319, 0.0075508473, -0.044358727, -0.0040153447, 0.0093302615, -0.010636497, 0.032789204, -0.005264933, -0.014235313, -0.018393943, 0.007297597, -0.016114693, 0.015021721, 0.020033404, 0.0137688, 0.0011046362, 0.010616505, -0.0039453674, 0.012109346, 0.021099718, -0.0072842683, -0.019153694, -0.003768759, 0.039320387, -0.006747778, -0.0016852784, 0.018154023, 0.0010963057, -0.015035049, -0.021033075, -0.04345236, 0.017287642, 0.016341286, -0.008610498, 0.00236922, 0.009290274, 0.028950468, -0.014475234, -0.0035654926, 0.015434918, -0.03372223, 0.004501851, -0.012929076, -0.008483873, -0.0044685286, -0.0102233, 0.01615468, 0.0022792495, 0.010876419, -0.0059647025, 0.01895376, -0.0069976957, -0.0042952523, 0.017207667, -0.00036133936, 0.0085905045, 0.008084005, 0.03129636, -0.016994404, -0.014915089, 0.020100048, -0.012009379, -0.006684466, 0.01306903, 0.00015765642, -0.00530492, 0.0005277429, 0.015421589, 0.015528221, 0.032202728, -0.003485519, -0.0014286962, 0.033908837, 0.001367883, 0.010509873, 0.025271678, -0.020993087, 0.019846799, 0.006897729, -0.010216636, -0.00725761, 0.01818068, -0.028443968, -0.011242964, -0.014435247, -0.013688826, 0.006101324, -0.0022509254, 0.013848773, -0.0019077052, 0.017181009, 0.03422873, 0.005324913, -0.0035188415, 0.014128681, -0.004898387, 0.005038341, 0.0012320944, -0.005561502, -0.017847456, 0.0008538855, -0.0047884234, 0.011849431, 0.015421589, -0.013942076, 0.0029790192, -0.013702155, 0.0001199605, -0.024431955, 0.019926772, 0.022179363, -0.016487904, -0.03964028, 0.0050849924, 0.017487574, 0.022792496, 0.0012504216, 0.004048667, -0.00997005, 0.0076041627, -0.014328616, -0.020259995, 0.0005598157, -0.010469886, 0.0016852784, 0.01716768, -0.008990373, -0.001987679, 0.026417969, 0.023792166, 0.0046917885, -0.0071909656, -0.00032051947, -0.023259008, -0.009170313, 0.02071318, -0.03156294, -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136
24                 ],
25                 'numCandidates': 150,
26                 'limit': 10
27               }
28             }, {
29               '$project': {
30                 '_id': 0,
31                 'plot': 1,
32                 'title': 1,
33                 'score': {
34                   '$meta': 'vectorSearchScore'
35                 }
36               }
37             }
38           ];
39         // run pipeline
40         const result = coll.aggregate(agg);
41 
42         // print results
43         await result.forEach((doc) => console.dir(JSON.stringify(doc)));
44     } finally {
45         await client.close();
46     }
47 }
48 run().catch(console.dir);

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

Run the following command to query your collection:

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

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

Install the MongoDB PHP Driver.

For detailed installation instructions, see the MongoDB PHP Library documentation.

Construct your vector search query.

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

Create a file named atlas_vector_search_quick_start.php.

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

1 <?php
2 
3 require 'vendor/autoload.php';
4 
5 // Replace the placeholder with your Atlas connection string
6 $uri = '<connection-string>';
7 
8 try {
9 
10     // Connect to your Atlas cluster
11     $client = new MongoDB\Client($uri);
12 
13     // Set the namespace
14     $database = $client->selectDatabase('sample_mflix');
15     $collection = $database->selectCollection('embedded_movies');
16 
17     // Define the pipeline with $vectorSearch query options
18     $pipeline = [
19         [
20             '$vectorSearch' => [
21                 'queryVector' => [
22                     -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136
23                 ],
24                 'index' => 'vector_index',
25                 'path' => 'plot_embedding',
26                 'numCandidates' => 150,
27                 'limit' => 10
28             ]
29         ],
30         [
31             '$project' => [
32                 '_id' => 0,
33                 'plot' => 1,
34                 'title' => 1,
35                 'score' => [
36                     '$meta' => 'vectorSearchScore'
37                 ]
38             ]
39         ]
40     ];
41 
42     // Run the pipeline
43     $result = $collection->aggregate($pipeline);
44 
45     // Print results
46     foreach ($result as $document) {
47         echo json_encode($document) . "\n";
48     }
49 
50 } catch (MongoDB\Driver\Exception\Exception $e) {
51     echo "Error: " . $e->getMessage() . "\n";
52 }

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

Run the following command to query your collection:

php atlas_vector_search_quick_start.php

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

Install `pymongo`, the Python driver for MongoDB.

Run the following command:

pip install pymongo

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

Construct your vector search query.

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

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

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

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

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

Run the following command to query your collection:

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

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

Install the MongoDB Ruby Driver.

If you don't already have a Gemfile for your project, run the following command to generate one:
```
bundle init
```
Add the mongo gem to your Gemfile:
Gemfile
```
gem "mongo"
```
Run the following command to install the dependency:
```
bundle install
```

This installs the latest version of the Ruby driver. For alternate installation instructions and version compatibility, see the MongoDB Ruby Driver documentation.

Construct your vector search query.

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

Create a file named atlas_vector_search_quick_start.rb .

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

1 require 'mongo'
2 require 'json'
3 
4 # Replace the placeholder with your Atlas connection string
5 uri = "<connection-string>"
6 
7 begin
8   # Connect to the MongoDB client
9   client = Mongo::Client.new(uri, database: 'sample_mflix')
10 
11   # Set the namespace
12   db = client.database
13   collection = db[:embedded_movies]
14 
15   # Define the pipeline with $vectorSearch query options
16   pipeline = [
17     {
18       '$vectorSearch' => {
19         'queryVector' => [ -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 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-0.03129636, -0.0034038792, -0.006321252, -0.0016161345, 0.033642255, -0.000056075285, -0.005005019, 0.004571828, -0.0024075406, -0.00010215386, 0.0098634185, 0.1980148, -0.003825407, -0.025191706, 0.035161756, 0.005358236, 0.025111731, 0.023485601, 0.0023342315, -0.011882754, 0.018287312, -0.0068910643, 0.003912045, 0.009243623, -0.001355387, -0.028603915, -0.012802451, -0.030150073, -0.014795128, -0.028630573, -0.0013487226, 0.002667455, 0.00985009, -0.0033972147, -0.021486258, 0.009503538, -0.017847456, 0.013062365, -0.014341944, 0.005078328, 0.025165047, -0.015594865, -0.025924796, -0.0018177348, 0.010996379, -0.02993681, 0.007324255, 0.014475234, -0.028577257, 0.005494857, 0.00011725306, -0.013315615, 0.015941417, 0.009376912, 0.0025158382, 0.008743787, 0.023832154, -0.008084005, -0.014195326, -0.008823762, 0.0033455652, -0.032362677, -0.021552904, -0.0056081535, 0.023298996, -0.025444955, 0.0097301295, 0.009736794, 0.015274971, -0.0012937407, -0.018087378, -0.0039387033, 0.008637156, -0.011189649, -0.00023846315, -0.011582852, 0.0066411467, -0.018220667, 0.0060846633, 0.0376676, -0.002709108, 0.0072776037, 0.0034188742, -0.010249958, -0.0007747449, -0.00795738, -0.022192692, 0.03910712, 0.032122757, 0.023898797, 0.0076241563, -0.007397564, -0.003655463, 0.011442899, -0.014115352, -0.00505167, -0.031163072, 0.030336678, -0.006857742, -0.022259338, 0.004048667, 0.02072651, 0.0030156737, -0.0042119464, 0.00041861215, -0.005731446, 0.011103011, 0.013822115, 0.021512916, 0.009216965, -0.006537847, -0.027057758, -0.04054665, 0.010403241, -0.0056281467, -0.005701456, -0.002709108, -0.00745088, -0.0024841821, 0.009356919, -0.022659205, 0.004061996, -0.013175662, 0.017074378, -0.006141311, -0.014541878, 0.02993681, -0.00028448965, -0.025271678, 0.011689484, -0.014528549, 0.004398552, -0.017274313, 0.0045751603, 0.012455898, 0.004121976, -0.025458284, -0.006744446, 0.011822774, -0.015035049, -0.03257594, 0.014675168, -0.0039187097, 0.019726837, -0.0047251107, 0.0022825818, 0.011829439, 0.005391558, -0.016781142, -0.0058747325, 0.010309938, -0.013049036, 0.01186276, -0.0011246296, 0.0062112883, 0.0028190718, -0.021739509, 0.009883412, -0.0073175905, -0.012715813, -0.017181009, -0.016607866, -0.042492677, -0.0014478565, -0.01794076, 0.012302616, -0.015194997, -0.04433207, -0.020606548, 0.009696807, 0.010303274, -0.01694109, -0.004018677, 0.019353628, -0.001991011, 0.000058938927, 0.010536531, -0.17274313, 0.010143327, 0.014235313, -0.024152048, 0.025684876, -0.0012504216, 0.036601283, -0.003698782, 0.0007310093, 0.004165295, -0.0029157067, 0.017101036, -0.046891227, -0.017460918, 0.022965772, 0.020233337, -0.024072073, 0.017220996, 0.009370248, 0.0010363255, 0.0194336, -0.019606877, 0.01818068, -0.020819811, 0.007410893, 0.0019326969, 0.017887443, 0.006651143, 0.00067394477, -0.011889419, -0.025058415, -0.008543854, 0.021579562, 0.0047484366, 0.014062037, 0.0075508473, -0.009510202, -0.009143656, 0.0046817916, 0.013982063, -0.0027990784, 0.011782787, 0.014541878, -0.015701497, -0.029350337, 0.021979429, 0.01332228, -0.026244693, -0.0123492675, -0.003895384, 0.0071576433, -0.035454992, -0.00046984528, 0.0033522295, 0.039347045, 0.0005119148, 0.00476843, -0.012995721, 0.0024042083, -0.006931051, -0.014461905, -0.0127558, 0.0034555288, -0.0074842023, -0.030256703, -0.007057676, -0.00807734, 0.007804097, -0.006957709, 0.017181009, -0.034575284, -0.008603834, -0.005008351, -0.015834786, 0.02943031, 0.016861115, -0.0050849924, 0.014235313, 0.0051449724, 0.0025924798, -0.0025741523, 0.04289254, -0.002104307, 0.012969063, -0.008310596, 0.00423194, 0.0074975314, 0.0018810473, -0.014248641, -0.024725191, 0.0151016945, -0.017527562, 0.0018727167, 0.0002830318, 0.015168339, 0.0144219175, -0.004048667, -0.004358565, 0.011836103, -0.010343261, -0.005911387, 0.0022825818, 0.0073175905, 0.00403867, 0.013188991, 0.03334902, 0.006111321, 0.008597169, 0.030123414, -0.015474904, 0.0017877447, -0.024551915, 0.013155668, 0.023525586, -0.0255116, 0.017220996, 0.004358565, -0.00934359, 0.0099967085, 0.011162991, 0.03092315, -0.021046404, -0.015514892, 0.0011946067, -0.01816735, 0.010876419, -0.10124666, -0.03550831, 0.0056348112, 0.013942076, 0.005951374, 0.020419942, -0.006857742, -0.020873128, -0.021259667, 0.0137554705, 0.0057880944, -0.029163731, -0.018767154, -0.021392956, 0.030896494, -0.005494857, -0.0027307675, -0.006801094, -0.014821786, 0.021392956, -0.0018110704, -0.0018843795, -0.012362596, -0.0072176233, -0.017194338, -0.018713837, -0.024272008, 0.03801415, 0.00015880188, 0.0044951867, -0.028630573, -0.0014070367, -0.00916365, -0.026537929, -0.009576847, -0.013995391, -0.0077107945, 0.0050016865, 0.00578143, -0.04467862, 0.008363913, 0.010136662, -0.0006268769, -0.006591163, 0.015341615, -0.027377652, -0.00093136, 0.029243704, -0.020886457, -0.01041657, -0.02424535, 0.005291591, -0.02980352, -0.009190307, 0.019460259, -0.0041286405, 0.004801752, 0.0011787785, -0.001257086, -0.011216307, -0.013395589, 0.00088137644, -0.0051616337, 0.03876057, -0.0033455652, 0.00075850025, -0.006951045, -0.0062112883, 0.018140694, -0.006351242, -0.008263946, 0.018154023, -0.012189319, 0.0075508473, -0.044358727, -0.0040153447, 0.0093302615, -0.010636497, 0.032789204, -0.005264933, -0.014235313, -0.018393943, 0.007297597, -0.016114693, 0.015021721, 0.020033404, 0.0137688, 0.0011046362, 0.010616505, -0.0039453674, 0.012109346, 0.021099718, -0.0072842683, -0.019153694, -0.003768759, 0.039320387, -0.006747778, -0.0016852784, 0.018154023, 0.0010963057, -0.015035049, -0.021033075, -0.04345236, 0.017287642, 0.016341286, -0.008610498, 0.00236922, 0.009290274, 0.028950468, -0.014475234, -0.0035654926, 0.015434918, -0.03372223, 0.004501851, -0.012929076, -0.008483873, -0.0044685286, -0.0102233, 0.01615468, 0.0022792495, 0.010876419, -0.0059647025, 0.01895376, -0.0069976957, -0.0042952523, 0.017207667, -0.00036133936, 0.0085905045, 0.008084005, 0.03129636, -0.016994404, -0.014915089, 0.020100048, -0.012009379, -0.006684466, 0.01306903, 0.00015765642, -0.00530492, 0.0005277429, 0.015421589, 0.015528221, 0.032202728, -0.003485519, -0.0014286962, 0.033908837, 0.001367883, 0.010509873, 0.025271678, -0.020993087, 0.019846799, 0.006897729, -0.010216636, -0.00725761, 0.01818068, -0.028443968, -0.011242964, -0.014435247, -0.013688826, 0.006101324, -0.0022509254, 0.013848773, -0.0019077052, 0.017181009, 0.03422873, 0.005324913, -0.0035188415, 0.014128681, -0.004898387, 0.005038341, 0.0012320944, -0.005561502, -0.017847456, 0.0008538855, -0.0047884234, 0.011849431, 0.015421589, -0.013942076, 0.0029790192, -0.013702155, 0.0001199605, -0.024431955, 0.019926772, 0.022179363, -0.016487904, -0.03964028, 0.0050849924, 0.017487574, 0.022792496, 0.0012504216, 0.004048667, -0.00997005, 0.0076041627, -0.014328616, -0.020259995, 0.0005598157, -0.010469886, 0.0016852784, 0.01716768, -0.008990373, -0.001987679, 0.026417969, 0.023792166, 0.0046917885, -0.0071909656, -0.00032051947, -0.023259008, -0.009170313, 0.02071318, -0.03156294, -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136 ],
20         'index' => 'vector_index',
21         'path' => 'plot_embedding',
22         'numCandidates' => 150,
23         'limit' => 10
24       }
25     },
26     {
27       '$project' => {
28         '_id' => 0,
29         'plot' => 1,
30         'title' => 1,
31         'score' => { '$meta' => 'vectorSearchScore' }
32       }
33     }
34   ]
35 
36   # Run the pipeline
37   results = collection.aggregate(pipeline)
38 
39   # Print results
40   results.each do |document|
41     puts JSON(document)
42   end
43 
44 rescue Mongo::Error::NoServerAvailable => e
45   puts "Failed to connect to MongoDB: #{e.message}"
46 
47 rescue Mongo::Error::OperationFailure => e
48   puts "Failed to run aggregation: #{e.message}"
49 
50 rescue Mongo::Error => e
51   puts "A MongoDB error occurred: #{e.message}"
52 
53 rescue StandardError => e
54   puts "A general error occurred: #{e.message}"
55 
56 ensure
57   client&.close
58   puts "MongoDB client closed."
59 end

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

Run the following command to query your collection:

bundle exec ruby atlas_vector_search_quick_start.rb

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

Install the Rust driver for MongoDB.

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

Construct your vector search query.

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

In the main.rs file for your project, paste the following code:

#![recursion_limit = "2560"]
use mongodb::{ 
    bson::{Document, doc},
    Client
};
use futures::TryStreamExt;
#[tokio::main]
async fn main() -> mongodb::error::Result<()> {
    // Replace the placeholder with your Atlas connection string
    let client = Client::with_uri_str("<connection-string>").await?;
    
    let pipeline  = vec! [
        doc! {
            "$vectorSearch": doc! {
            "queryVector": [ -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 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            ],
            "path": "plot_embedding",
            "numCandidates": 150,
            "index": "vector_index",
            "limit": 10
        }
        },
        doc! {
            "$project": doc! {
                "_id": 0,
                "plot": 1,
                "title": 1,
                "score": doc! { "$meta": "vectorSearchScore"
                }
            }
        }
    ];
    let coll = client.database("sample_mflix").collection::<Document>("embedded_movies");
    let mut results = coll.aggregate(pipeline).await?;
    while let Some(result) = results.try_next().await? {
        println!("{}", result);
    }
    Ok(())
}

#![recursion_limit = "2560"]
use mongodb::{ 
    bson::{Document, doc},
    sync::Client
};
fn main() -> mongodb::error::Result<()> {
    // Replace the placeholder with your Atlas connection string
    let client = Client::with_uri_str("<connection-string>")?;
    let pipeline  = vec! [
        doc! {
            "$vectorSearch": doc! {
            "queryVector": [ -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 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-0.006684466, 0.01306903, 0.00015765642, -0.00530492, 0.0005277429, 0.015421589, 0.015528221, 0.032202728, -0.003485519, -0.0014286962, 0.033908837, 0.001367883, 0.010509873, 0.025271678, -0.020993087, 0.019846799, 0.006897729, -0.010216636, -0.00725761, 0.01818068, -0.028443968, -0.011242964, -0.014435247, -0.013688826, 0.006101324, -0.0022509254, 0.013848773, -0.0019077052, 0.017181009, 0.03422873, 0.005324913, -0.0035188415, 0.014128681, -0.004898387, 0.005038341, 0.0012320944, -0.005561502, -0.017847456, 0.0008538855, -0.0047884234, 0.011849431, 0.015421589, -0.013942076, 0.0029790192, -0.013702155, 0.0001199605, -0.024431955, 0.019926772, 0.022179363, -0.016487904, -0.03964028, 0.0050849924, 0.017487574, 0.022792496, 0.0012504216, 0.004048667, -0.00997005, 0.0076041627, -0.014328616, -0.020259995, 0.0005598157, -0.010469886, 0.0016852784, 0.01716768, -0.008990373, -0.001987679, 0.026417969, 0.023792166, 0.0046917885, -0.0071909656, -0.00032051947, -0.023259008, -0.009170313, 0.02071318, -0.03156294, -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136
            ],
            "path": "plot_embedding",
            "numCandidates": 150,
            "index": "vector_index",
            "limit": 10
        }
        },
        doc! {
            "$project": doc! {
                "_id": 0,
                "plot": 1,
                "title": 1,
                "score": doc! { "$meta": "vectorSearchScore"
                }
            }
        }
    ];
    let coll = client.database("sample_mflix").collection::<Document>("embedded_movies");
    let mut cursor = coll.aggregate(pipeline).run()?;
    while cursor.advance()? {
        println!("{}", cursor.deserialize_current()?);
    }
    Ok(())
}

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

Run the following command to query your collection:

cargo run

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

Install the MongoDB Scala Driver.

For installation instructions based on your environment and the version of Scala you are using, refer to the MongoDB Scala Driver documentation.

Construct your vector search query.

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

Create a new Scala project with the tools you normally use. For this quick start, we create a project named quick-start, by using sbt.
```
sbt new scala/hello-world.g8
```
When prompted, name the application quick-start.

Open the Main.scala file and replace the contents with the following vector search query:

import org.mongodb.scala._
import org.mongodb.scala.model._
import org.mongodb.scala.model.Aggregates._
import org.mongodb.scala.model.Projections._
import org.mongodb.scala.model.search.SearchPath.fieldPath
object Main extends App {
    val mongoClient: MongoClient = MongoClient("<connection-string>")
    val database: MongoDatabase = mongoClient.getDatabase("sample_mflix")
    val collection: MongoCollection[Document] = database.getCollection("embedded_movies")
    collection.aggregate(Seq(
        Aggregates.vectorSearch(fieldPath("plot_embedding"),
            List(-0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 0.01794076, 0.011349596, -0.0052715978, 0.014755142, -0.019753495, -0.011156326, 0.011202978, 0.022126047, 0.00846388, 0.030549942, -0.0041386373, 0.018847128, -0.00033655585, 0.024925126, -0.003555496, -0.019300312, 0.010749794, 0.0075308536, -0.018287312, -0.016567878, -0.012869096, -0.015528221, 0.0078107617, -0.011156326, 0.013522214, -0.020646535, -0.01211601, 0.055928253, 0.011596181, -0.017247654, 0.0005939711, -0.026977783, -0.003942035, -0.009583511, -0.0055248477, -0.028737204, 0.023179034, 0.003995351, 0.0219661, -0.008470545, 0.023392297, 0.010469886, -0.015874773, 0.007890735, -0.009690142, -0.00024970944, 0.012775794, 0.0114762215, 0.013422247, 0.010429899, -0.03686786, -0.006717788, -0.027484283, 0.011556195, -0.036068123, -0.013915418, -0.0016327957, 0.0151016945, -0.020473259, 0.004671795, -0.012555866, 0.0209531, 0.01982014, 0.024485271, 0.0105431955, -0.005178295, 0.033162415, -0.013795458, 0.007150979, 0.010243294, 0.005644808, 0.017260984, -0.0045618312, 0.0024725192, 0.004305249, -0.008197301, 0.0014203656, 0.0018460588, 0.005015015, -0.011142998, 0.01439526, 0.022965772, 0.02552493, 0.007757446, -0.0019726837, 0.009503538, -0.032042783, 0.008403899, -0.04609149, 0.013808787, 0.011749465, 0.036388017, 0.016314628, 0.021939443, -0.0250051, -0.017354285, -0.012962398, 0.00006107364, 0.019113706, 0.03081652, -0.018114036, -0.0084572155, 0.009643491, -0.0034721901, 0.0072642746, -0.0090636825, 0.01642126, 0.013428912, 0.027724205, 0.0071243206, -0.6858542, -0.031029783, -0.014595194, -0.011449563, 0.017514233, 0.01743426, 0.009950057, 0.0029706885, -0.015714826, -0.001806072, 0.011856096, 0.026444625, -0.0010663156, -0.006474535, 0.0016161345, -0.020313311, 0.0148351155, -0.0018393943, 0.0057347785, 0.018300641, -0.018647194, 0.03345565, -0.008070676, 0.0071443142, 0.014301958, 0.0044818576, 0.003838736, -0.007350913, -0.024525259, -0.001142124, -0.018620536, 0.017247654, 0.007037683, 0.010236629, 0.06046009, 0.0138887605, -0.012122675, 0.037694257, 0.0055081863, 0.042492677, 0.00021784494, -0.011656162, 0.010276617, 0.022325981, 0.005984696, -0.009496873, 0.013382261, -0.0010563189, 0.0026507939, -0.041639622, 0.008637156, 0.026471283, -0.008403899, 0.024858482, -0.00066686375, -0.0016252982, 0.027590916, 0.0051449724, 0.0058647357, -0.008743787, -0.014968405, 0.027724205, -0.011596181, 0.0047650975, -0.015381602, 0.0043718936, 0.002159289, 0.035908177, -0.008243952, -0.030443309, 0.027564257, 0.042625964, -0.0033688906, 0.01843393, 0.019087048, 0.024578573, 0.03268257, -0.015608194, -0.014128681, -0.0033538956, -0.0028757197, -0.004121976, -0.032389335, 0.0034322033, 0.058807302, 0.010943064, -0.030523283, 0.008903735, 0.017500903, 0.00871713, -0.0029406983, 0.013995391, -0.03132302, -0.019660193, -0.00770413, -0.0038853872, 0.0015894766, -0.0015294964, -0.006251275, -0.021099718, -0.010256623, -0.008863748, 0.028550599, 0.02020668, -0.0012962399, -0.003415542, -0.0022509254, 0.0119360695, 0.027590916, -0.046971202, -0.0015194997, -0.022405956, 0.0016677842, -0.00018535563, -0.015421589, -0.031802863, 0.03814744, 0.0065411795, 0.016567878, -0.015621523, 0.022899127, -0.011076353, 0.02841731, -0.002679118, -0.002342562, 0.015341615, 0.01804739, -0.020566562, -0.012989056, -0.002990682, 0.01643459, 0.00042527664, 0.008243952, -0.013715484, -0.004835075, -0.009803439, 0.03129636, -0.021432944, 0.0012087687, -0.015741484, -0.0052016205, 0.00080890034, -0.01755422, 0.004811749, -0.017967418, -0.026684547, -0.014128681, 0.0041386373, -0.013742141, -0.010056688, -0.013268964, -0.0110630235, -0.028337335, 0.015981404, -0.00997005, -0.02424535, -0.013968734, -0.028310679, -0.027750863, -0.020699851, 0.02235264, 0.001057985, 0.00081639783, -0.0099367285, 0.013522214, -0.012016043, -0.00086471526, 0.013568865, 0.0019376953, -0.019020405, 0.017460918, -0.023045745, 0.008503866, 0.0064678704, -0.011509543, 0.018727167, -0.003372223, -0.0028690554, -0.0027024434, -0.011902748, -0.012182655, -0.015714826, -0.0098634185, 0.00593138, 0.018753825, 0.0010146659, 0.013029044, 0.0003521757, -0.017620865, 0.04102649, 0.00552818, 0.024485271, -0.009630162, -0.015608194, 0.0006718621, -0.0008418062, 0.012395918, 0.0057980907, 0.016221326, 0.010616505, 0.004838407, -0.012402583, 0.019900113, -0.0034521967, 0.000247002, -0.03153628, 0.0011038032, -0.020819811, 0.016234655, -0.00330058, -0.0032289368, 0.00078973995, -0.021952773, -0.022459272, 0.03118973, 0.03673457, -0.021472929, 0.0072109587, -0.015075036, 0.004855068, -0.0008151483, 0.0069643734, 0.010023367, -0.010276617, -0.023019087, 0.0068244194, -0.0012520878, -0.0015086699, 0.022046074, -0.034148756, -0.0022192693, 0.002427534, -0.0027124402, 0.0060346797, 0.015461575, 0.0137554705, 0.009230294, -0.009583511, 0.032629255, 0.015994733, -0.019167023, -0.009203636, 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0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136),
        "vector_index",
        150,
        10
        ),
        Aggregates.project(
        Projections.fields(
            Projections.excludeId(),
            Projections.include("plot"),
            Projections.include("title"),
            Projections.meta(
            "score", "vectorSearchScore"
            )
        )
    )
  )).foreach { doc => println(doc.toBsonDocument()) }
}

This query uses the $vectorSearch stage to:

Compare vector embeddings of the search term against vector embeddings of movie plots in the plot_embedding field of the sample_mflix.embedded_moves collection.
Consider up to the 150 most similar movie plots and return the top 10 results.

It uses the $project stage to:

Only include the movie plot and title fields in the results.
Add a score field to show the relevance of each result to the search term.

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

Specify the `<connection-string>`.

Replace <connection-string> with the connection string for your Atlas cluster or local deployment.
Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net
Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true
Save the file.

Run your query.

There are many tools and environments in which Scala runs. In this example, we run the new Scala project by starting the sbt server with the sbt command, then typing ~run.

sbt:quick-start> ~run

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

Learning Summary

This quick start focused on retrieving documents from your Atlas cluster that contain text that is semantically related to a provided query. However, you can create a vector search index on embeddings that represent any type of data that you might write to an Atlas cluster, such as images or videos.

Sample Data

This quick start uses the sample_mflix.embedded_movies collection which contains details about movies. In each document in the collection, the plot_embedding field contains a vector embedding that represents the string in the plot field. For more information on the schema of the documents in the collection, see Sample Mflix Dataset.

By storing your source data and its corresponding vector embeddings in the same document, you can leverage both fields for complex queries or hybrid search. You can even store vector embeddings generated from different embedding models in the same document to streamline your workflow as you test the performance of different vector embedding models for your specific use case.

Vector Embeddings

The vector embeddings in the sample_mflix.embedded_movies collection and in the example query were created using the OpenAI text-embedding-ada-002 embedding model. Your choice of embedding model informs the vector dimensions and vector similarity function you use in your vector search index. You can use any embedding model you like, and it is worth experimenting with different models as accuracy can vary from model to model depending on your specific use case.

To learn how to create vector embeddings of your own data, see How to Create Vector Embeddings.

Vector Index Definition

An index is a data structure that holds a subset of data from a collection's documents that improves database performance for specific queries. A vector search index points to the fields that contain your vector embeddings and includes the dimensions of your vectors as well as the function used to measure similarity between vectors of queries and vectors stored in the database.

Because the text-embedding-ada-002 embedding model used in this quick start converts data into vector embeddings with 1536 dimensions and supports the cosine function, this vector search index specifies the same number of vector dimensions and similarity function.

Vector Search Query

The query you ran in this quick start is an aggregation pipeline, in which the $vectorSearch stage performs an Approximate Nearest Neighbor (ANN) search followed by a $project stage that refines the results. To see all the options for a vector search query, including using Exact Nearest Neighbor (ENN) or how to narrow the scope of your vector search with the filter option, see Run Vector Search Queries.

Next Steps

To learn how to create embeddings from data and load them into Atlas, see Create Embeddings.
To learn how to implement retrieval-augmented generation (RAG), see Retrieval-Augmented Generation (RAG) with Atlas Vector Search.
To integrate Atlas Vector Search with popular AI frameworks and services, see Integrate Vector Search with AI Technologies.
To build production ready AI chatbots using Atlas Vector Search, see the MongoDB Chatbot Framework.
To learn how implement RAG without the need for API keys or credits, see Build a Local RAG Implementation with Atlas Vector Search.

Back

Atlas Vector Search

How to Create Vector Embeddings

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

1	[
2	{
3	"$vectorSearch": {
4	"index": "vector_index",
5	"path": "plot_embedding",
6	"queryVector": 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1	{
2	"score": 0.9332506656646729,
3	"plot": "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.",
4	"title": "Thrill Seekers"
5	}
6	{
7	"plot": "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.",
8	"title": "About Time",
9	"score": 0.9312690496444702
10	}
11	{
12	"plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.",
13	"title": "The Time Machine",
14	"score": 0.929530143737793
15	}
16	{
17	"plot": "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
18	"title": "Crusade in Jeans",
19	"score": 0.9290417432785034
20	}
21	{
22	"title": "Timecop",
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24	"plot": "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past."
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26	{
27	"plot": "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...",
28	"title": "A.P.E.X.",
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32	"plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
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38	"plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.",
39	"title": "Tomorrowland"
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41	{
42	"plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.",
43	"title": "Love Story 2050",
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46	{
47	"plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...",
48	"title": "The Portal",
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12	"$project": {
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1	[
2	{
3	plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
4	title: 'Thrill Seekers',
5	score: 0.7892671227455139
6	},
7	{
8	plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.',
9	title: 'About Time',
10	score: 0.7843604683876038
11	},
12	{
13	plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.',
14	title: 'The Time Machine',
15	score: 0.7801066637039185
16	},
17	{
18	plot: "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
19	title: 'Crusade in Jeans',
20	score: 0.7789170742034912
21	},
22	{
23	plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.',
24	title: 'Timecop',
25	score: 0.7771612405776978
26	},
27	{
28	plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...',
29	title: 'A.P.E.X.',
30	score: 0.7730885744094849
31	},
32	{
33	plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
34	title: 'Men in Black 3',
35	score: 0.7712380886077881
36	},
37	{
38	plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.',
39	title: 'Tomorrowland',
40	score: 0.7669923901557922
41	},
42	{
43	plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
44	title: 'Love Story 2050',
45	score: 0.7649372816085815
46	},
47	{
48	plot: 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...',
49	title: 'The Portal',
50	score: 0.7640786170959473
51	}
52	]

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2	{ "plot" : "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title" : "About Time", "score" : { "$numberDouble" : "0.78436046838760375977" } }
3	{ "plot" : "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title" : "The Time Machine", "score" : { "$numberDouble" : "0.78010666370391845703" } }
4	{ "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : { "$numberDouble" : "0.77891707420349121094" } }
5	{ "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : { "$numberDouble" : "0.77716124057769775391" } }
6	{ "plot" : "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title" : "A.P.E.X.", "score" : { "$numberDouble" : "0.77308857440948486328" } }
7	{ "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : { "$numberDouble" : "0.77123808860778808594" } }
8	{ "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title" : "Tomorrowland", "score" : { "$numberDouble" : "0.76699239015579223633" } }
9	{ "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : { "$numberDouble" : "0.76493728160858154297" } }
10	{ "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal", "score" : { "$numberDouble" : "0.76407861709594726562" } }

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

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

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

Create a Vector Search Index

Set up your Atlas cluster.

Note

Create a Vector Search index.

Install the dependencies.

Set up your local Atlas deployment.

Load the sample data.

Create a Vector Search index.

Run a Vector Search Query

Click the Collections tab to view the Data Explorer.

Go to the Aggregation tab for the collection.

Construct and run your vector search query.

Download and install mongosh.

Connect to your cluster or local deployment in mongosh.

Switch to the sample_mflix database.

Construct and run your vector search query.

Install the MongoDB C Driver.

Construct your vector search query.

Specify the <connection-string>.

Configure CMake for your project.

Run your query.

Add the MongoDB C++ Driver to your project.

Construct your vector search query.

Specify the <connection-string>.

Compile and run your query.

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

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Add the MongoDB Go Driver to your project.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Add the MongoDB Java Sync Driver to your project.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Install the MongoDB Kotlin Coroutine Driver.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Install the MongoDB Kotlin Sync Driver.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Install the MongoDB Node.js Driver.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Install the MongoDB PHP Driver.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Install pymongo, the Python driver for MongoDB.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Install the MongoDB Ruby Driver.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Install the Rust driver for MongoDB.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Install the MongoDB Scala Driver.

Construct your vector search query.

Specify the <connection-string>.

Run your query.

Learning Summary

Sample Data

Vector Embeddings

Vector Index Definition

Vector Search Query

Next Steps

Download and install `mongosh`.

Connect to your cluster or local deployment in `mongosh`.

Switch to the `sample_mflix` database.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Install `pymongo`, the Python driver for MongoDB.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Specify the `<connection-string>`.

Specify the `<connection-string>`.