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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

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

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

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

    Note

    You can create only one M0 free cluster per project.

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

  4. 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.

2
  1. When the sample data finishes loading, click Create Search Index.

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

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

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

  4. In the Index Name field, specify vector_index.

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

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

    This index definition:

  6. Click Next.

  7. Click Create Search Index.

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

1
  1. Install the Atlas CLI and MongoDB Database Tools.

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

    brew install mongodb-atlas-cli
    brew tap mongodb/brew && brew install mongodb-database-tools
  2. Install Docker.

For detailed instructions, see Prerequisites.

2
  1. If you don't have an existing Atlas account, run atlas setup in your terminal or create a new account.

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

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

    For detailed instructions, see Create a Local Atlas Deployment.

3
  1. Run the following command in your terminal to download the sample data:

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

    mongorestore --archive=sampledata.archive --port=<port-number>
4
  1. Create a file named vector-index.json

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

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

    This index definition:

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

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

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


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


1
2
  1. Expand sample_mflix under the list of databases and select embedded_movies from the list of collections in that database.

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

3
  1. In the aggregation pipeline pane, click the </> Text toggle to enable text mode for pipeline editing.

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

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

1[
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding",
6 "queryVector": 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1{
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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",
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11{
12 "plot": "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.",
13 "title": "The Time Machine",
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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
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21{
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24 "plot": "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past."
25}
26{
27 "plot": "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...",
28 "title": "A.P.E.X.",
29 "score": 0.9266218543052673
30}
31{
32 "plot": "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
33 "title": "Men in Black 3",
34 "score": 0.9258455038070679
35}
36{
37 "score": 0.9240515828132629,
38 "plot": "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.",
39 "title": "Tomorrowland"
40}
41{
42 "plot": "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.",
43 "title": "Love Story 2050",
44 "score": 0.923175573348999
45}
46{
47 "plot": "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...",
48 "title": "The Portal",
49 "score": 0.9228089451789856
50}

This query uses the $vectorSearch stage to:

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

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

It uses the $project stage to:

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

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

The Pipeline Output pane displays the results of your query.

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

1
2

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

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

3
use sample_mflix
switched to db sample_mflix
4
  1. Copy and paste the following sample query into your terminal and then run it using mongosh. mongosh might lag slightly when you paste in the query due to the number of characters in the vector embedding.

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

1db.embedded_movies.aggregate([
2 {
3 "$vectorSearch": {
4 "index": "vector_index",
5 "path": "plot_embedding",
6 "queryVector": 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1[
2 {
3 plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
4 title: 'Thrill Seekers',
5 score: 0.9332506656646729
6 },
7 {
8 plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.',
9 title: 'About Time',
10 score: 0.9312690496444702
11 },
12 {
13 plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.',
14 title: 'The Time Machine',
15 score: 0.929530143737793
16 },
17 {
18 plot: "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...",
19 title: 'Crusade in Jeans',
20 score: 0.9290417432785034
21 },
22 {
23 plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.',
24 title: 'Timecop',
25 score: 0.9283161759376526
26 },
27 {
28 plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...',
29 title: 'A.P.E.X.',
30 score: 0.9266218543052673
31 },
32 {
33 plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.",
34 title: 'Men in Black 3',
35 score: 0.9258455038070679
36 },
37 {
38 plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.',
39 title: 'Tomorrowland',
40 score: 0.9240515828132629
41 },
42 {
43 plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
44 title: 'Love Story 2050',
45 score: 0.923175573348999
46 },
47 {
48 plot: 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...',
49 title: 'The Portal',
50 score: 0.9228089451789856
51 }
52]

This query uses the $vectorSearch stage to:

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

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

It uses the $project stage to:

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

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

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

1

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

2

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

  1. Create a file named atlas-vector-search-quick-start.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
    5int 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.

3
  1. Replace <connection-string> with the connection string for your Atlas cluster or local deployment.

    Your connection string should use the following format:

    mongodb+srv://<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
  2. Save the file.

4
  1. 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)
  2. Create a directory in your project folder called build.

    mkdir build
  3. Enter the build directory and run the cmake command to create your Makefile.

    cd build
    cmake ../
5

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" } }
1
  1. Install the C++ Driver. For detailed installation instructions, refer to the MongoDB C++ Driver documentation.

  2. 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)
2

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

  1. Create a file named atlas_vector_search_quick_start.cpp .

  2. 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
    8using bsoncxx::builder::basic::kvp;
    9using bsoncxx::builder::basic::make_document;
    10
    11int 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, 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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.

3
  1. Replace <connection-string> with the connection string for your Atlas cluster or local deployment.

    Your connection string should use the following format:

    mongodb+srv://<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
  2. Save the file.

4
  1. Create and enter the /build directory:

    mkdir build && cd build
  2. Prepare the project.

    cmake ../
  3. Build the app.

    cmake --build .
  4. 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":0.9332506656646729}'
    2'{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.9312690496444702}'
    3'{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.929530143737793}'
    4'{"plot":"After using his mother’s newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children’s crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.9290417432785034}';
    5'{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.9283161759376526}'
    6'{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.9266218543052673}'
    7'{"plot":"Agent J travels in time to M.I.B.’s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.9258455038070679}'
    8'{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.9240515828132629}'
    9'{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.923175573348999}'
    10'{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.9228089451789856}'
1
  1. Create a new directory called query-quick-start and initialize your project with the dotnet new command.

    mkdir query-quick-start
    cd query-quick-start
    dotnet new console
  2. Add the .NET/C# Driver to your project as a dependency.

    dotnet add package MongoDB.Driver

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

2

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

  1. Edit the Program.cs file.

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

    1using MongoDB.Bson;
    2using MongoDB.Bson.Serialization.Attributes;
    3using MongoDB.Bson.Serialization.Conventions;
    4using MongoDB.Driver;
    5
    6public class vectorSearchBasicQuery
    7{
    8 // define connection to your Atlas cluster
    9 private const string MongoConnectionString = "<connection-string>";
    10
    11 public static void Main(string[] args){
    12 var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
    13 ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
    14
    15 // connect to your Atlas cluster
    16 var mongoClient = new MongoClient(MongoConnectionString);
    17
    18 // define namespace
    19 var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
    20 var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
    21
    22 // define vector embeddings to search
    23 var vector = new[] 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