Atlas Vector Search Quick Start
On this page
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
Objectives
In this quick start, you will do the following steps:
Create an index definition for the
sample_mflix.embedded_movies
collection that indexes theplot_embedding
field as thevector
type. Theplot_embedding
field contains embeddings created using OpenAI'stext-embedding-ada-002
embedding model. The index definition specifies1536
vector dimensions and measures similarity usingdotProduct
.Run an Atlas Vector Search query that searches the sample
sample_mflix.embedded_movies
collection. The query uses the$vectorSearch
stage to search theplot_embedding
field, which contains embeddings created using OpenAI'stext-embedding-ada-002
embedding model. The query searches theplot_embedding
field using vector embeddings for the string time travel. It considers up to150
nearest neighbors, and returns10
documents in the results.
To learn more, see Learning Summary.
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 theembedded_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 theembedded_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 thevector
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, selectskip
.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 thevector
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 thevector-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.
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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7 "numCandidates": 150, 8 "limit": 10 9 } 10 }, 11 { 12 "$project": { 13 "_id": 0, 14 "plot": 1, 15 "title": 1, 16 "score": { $meta: "vectorSearchScore" } 17 } 18 } 19 ]
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 thesample_mflix.embedded_moves
collection.Consider up to the 150 most similar movie plots and return the top 10 results.
It uses the $project
stage to:
Only include the movie
plot
andtitle
fields in the results.Add a
score
field to show the relevance of each result to the search term.
The Pipeline Output pane displays the results of your query.
To learn more about this pipeline stage, see Run Vector Search Queries.
Download and install mongosh
.
Connect to your cluster or local deployment in mongosh
.
Open mongosh
in a terminal window and connect to your Atlas
cluster. For detailed instructions on connecting, see
Connect via mongosh.
In a terminal window, run atlas deployments connect
and follow the prompts
to connect to your local Atlas deployment via mongosh
.
For detailed instructions on connecting, see
Manage a Local Atlas Deployment.
Construct and run your vector search query.
Copy and paste the following sample query into your terminal and then run it using
mongosh
.mongosh
might lag slightly when you paste in the query due to the number of characters in the vector embedding.This query searches for documents that include text in the
plot
field that is semantically related to the term "time travel".
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding", 6 "queryVector": 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7 "numCandidates": 150, 8 "limit": 10 9 } 10 }, 11 { 12 "$project": { 13 "_id": 0, 14 "plot": 1, 15 "title": 1, 16 "score": { $meta: "vectorSearchScore" } 17 } 18 } 19 ])
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 thesample_mflix.embedded_moves
collection.Consider up to the 150 most similar movie plots and return the top 10 results.
It uses the $project
stage to:
Only include the movie
plot
andtitle
fields in the results.Add a
score
field to show the relevance of each result to the search term.
To learn more about this pipeline stage, see Run Vector Search Queries.
Install the MongoDB C Driver.
For detailed installation instructions, refer to the MongoDB C Driver documentation.
Construct your vector search query.
This query searches for documents that include text in the plot
field that
is semantically related to the term "time travel".
Create a file named
atlas-vector-search-quick-start.c
and copy and paste the following sample query into the file:1 2 3 4 5 int main(void) { 6 7 mongoc_client_t *client; 8 mongoc_collection_t *collection; 9 mongoc_cursor_t *cursor; 10 bson_array_builder_t *builder; 11 bson_t *pipeline; 12 bson_t query_vector; 13 bson_error_t error; 14 const bson_t *doc; 15 char *str; 16 17 int arrSize = 1536; // This collection has a vector array of 1536 dimensions; depending on your vector embedding model, this mary vary 18 double embeddings[] = 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19 20 // Initialize the MongoDB C Driver 21 mongoc_init(); 22 23 // Connect to your Atlas collection 24 client = mongoc_client_new("<connection-string>"); 25 collection = mongoc_client_get_collection (client, "sample_mflix", "embedded_movies"); 26 27 // Build query vector array 28 builder = bson_array_builder_new(); 29 30 for(int i = 0; i < arrSize; i++){ 31 bson_array_builder_append_double(builder, embeddings[i]); 32 } 33 34 bson_array_builder_build(builder, &query_vector); 35 bson_array_builder_destroy(builder); 36 37 // Create aggregation pipeline for Vector Search 38 pipeline = BCON_NEW( 39 "pipeline", 40 "[", 41 "{", 42 "$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 thesample_mflix.embedded_moves
collection.Consider up to the 150 most similar movie plots and return the top 10 results.
It uses the
$project
stage to:Only include the movie
plot
andtitle
fields in the results.Add a
score
field to show the relevance of each result to the search term.
Specify the <connection-string>
.
Replace
<connection-string>
with the connection string for your Atlas cluster or local deployment.Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true Save the file.
Configure CMake for your project.
In your project directory, create a file named
CMakeLists.txt
and copy the following code into the file.cmake_minimum_required(VERSION 3.30) project(atlas-vector-search-quick-start) # Specify the minimum version you require. find_package (mongoc-1.0 1.27.6 REQUIRED) add_executable(atlas-vector-search-quick-start atlas-vector-search-quick-start.c ) # The "atlas-vector-search-quick-start.c" sample program is shared among four tests. target_link_libraries (atlas-vector-search-quick-start PRIVATE mongo::mongoc_shared) Create a directory in your project folder called
build
.mkdir build Enter the
build
directory and run thecmake
command to create your Makefile.cd build cmake ../
Run your query.
From your build
directory, compile and run the atlas-vector-search-quick-start.c
file.
cmake --build . ./atlas-vector-search-quick-start
1 { "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : { "$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 2 3 4 5 6 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, 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-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 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 thesample_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
andtitle
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 [ ]48 public class EmbeddedMovie 49 { 50 [ ]51 public ObjectId Id { get; set; } 52 public string Title { get; set; } 53 public string Plot { get; set; } 54 [ ]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 thesample_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
andtitle
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 $vectorSearch query options 32 queryVectorValues := []float64{ 33 -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 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38 {"$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 thesample_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
andtitle
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, -0.0058480743d, -0.020153364d, -0.0032805866d, 0.004248601d, 0.0051449724d, 0.006791097d, 0.007650814d, 0.003458861d, -0.0031223053d, -0.01932697d, -0.033615597d, 0.00745088d, 0.006321252d, -0.0038154104d, 0.014555207d, 0.027697546d, -0.02828402d, 0.0066711367d, 0.0077107945d, 0.01794076d, 0.011349596d, -0.0052715978d, 0.014755142d, -0.019753495d, -0.011156326d, 0.011202978d, 0.022126047d, 0.00846388d, 0.030549942d, -0.0041386373d, 0.018847128d, -0.00033655585d, 0.024925126d, -0.003555496d, -0.019300312d, 0.010749794d, 0.0075308536d, -0.018287312d, -0.016567878d, -0.012869096d, -0.015528221d, 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-0.011982721d, -0.016967745d, -0.0060913274d, -0.007130985d, -0.013109017d, -0.009710136d)); 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 thesample_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
andtitle
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, 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-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, 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 thesample_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
andtitle
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, 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, 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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 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 thesample_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
andtitle
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, 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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 thesample_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
andtitle
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 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, 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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 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 thesample_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
andtitle
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, 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, 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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, 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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], 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 thesample_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
andtitle
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 yourGemfile
:Gemfilegem "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, 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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, 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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 thesample_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
andtitle
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:
use mongodb::{ bson::{Document, doc}, Client }; use futures::TryStreamExt; 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, 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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 results = coll.aggregate(pipeline).await?; while let Some(result) = results.try_next().await? { println!("{}", result); } Ok(()) }
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! 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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 thesample_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
andtitle
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, 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} }
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 thesample_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
andtitle
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.