Atlas Vector Search Quick Start
This quick start describes how to load sample documents that contain vector embeddings into an Atlas cluster or local Atlas deployment, create an Atlas Vector Search index on those embeddings, and then perform semantic search to return documents that are similar to your query.
Time required: 15 minutes
Create a Vector Search Index
In this section, you create an Atlas Vector Search index on sample data that you load into an Atlas cluster or a deployment hosted on your local computer:
1
Set up your Atlas cluster.
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 Adminor higher access to your Atlas project.If you create a new cluster, you have the necessary permissions by default.
You can create only one
M0free 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_mflixdatabase contains theembedded_moviescollection. If it doesn't, drop the sample databases and reload the sample dataset.Loading the sample dataset can take several minutes to complete.
2
Create a Vector Search index.
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_mflixdatabase and select theembedded_moviescollection.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_embeddingfield.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_embeddingfield as thevectortype. This field contains vector embeddings that represent the summary of a movie's plot.Specifies
1536vector dimensions.Measures similarity using
dotProductsimilarity.
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.
1
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.
2
Set up your local Atlas deployment.
If you don't have an existing Atlas account, run
atlas setupin your terminal or create a new account.In your terminal, run
atlas auth loginto authenticate with your Atlas login credentials. To learn more, see Connect from the Atlas CLI.Run
atlas deployments setupand 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.
3
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>
4
Create a Vector Search index.
Create a file named
vector-index.jsonCopy 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_embeddingfield as thevectortype. This field contains vector embeddings that represent the summary of a movie's plot.Specifies
1536vector dimensions.Measures similarity using
dotProductsimilarity.
Save the file, and then run the following command in your terminal, replacing
<path-to-file>with the path to thevector-index.jsonfile that you created.atlas deployments search indexes create --file <path-to-file>
Run a Vector Search Query
In this section, you run a sample vector search query on your indexed embeddings.
➤ To set the client you use to run the examples in this section, use the Select your language drop-down menu in the right navigation pane of this page.
1
Click the Collections tab to view the Data Explorer.
2
3
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
plotfield 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_embeddingfield of thesample_mflix.embedded_movescollection.Consider up to the 150 most similar movie plots and return the top 10 results.
It uses the $project stage to:
Only include the movie
plotandtitlefields in the results.Add a
scorefield to show the relevance of each result to the search term.
The Pipeline Output pane displays the results of your query.
To learn more about this pipeline stage, see Run Vector Search Queries.
1
Download and install mongosh.
2
Connect to your cluster or local deployment in mongosh.
Open mongosh in a terminal window and connect to your Atlas
cluster. For detailed instructions on connecting, see
Connect via mongosh.
In a terminal window, run atlas deployments connect and follow the prompts
to connect to your local Atlas deployment via mongosh.
For detailed instructions on connecting, see
Manage a Local Atlas Deployment.
4
Construct and run your vector search query.
Copy and paste the following sample query into your terminal and then run it using
mongosh.mongoshmight lag slightly when you paste in the query due to the number of characters in the vector embedding.This query searches for documents that include text in the
plotfield that is semantically related to the term "time travel".
1 db.embedded_movies.aggregate([ 2 { 3 "$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: 0.9332506656646729 6 }, 7 { 8 plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.', 9 title: 'About Time', 10 score: 0.9312690496444702 11 }, 12 { 13 plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', 14 title: 'The Time Machine', 15 score: 0.929530143737793 16 }, 17 { 18 plot: "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", 19 title: 'Crusade in Jeans', 20 score: 0.9290417432785034 21 }, 22 { 23 plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', 24 title: 'Timecop', 25 score: 0.9283161759376526 26 }, 27 { 28 plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', 29 title: 'A.P.E.X.', 30 score: 0.9266218543052673 31 }, 32 { 33 plot: "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", 34 title: 'Men in Black 3', 35 score: 0.9258455038070679 36 }, 37 { 38 plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', 39 title: 'Tomorrowland', 40 score: 0.9240515828132629 41 }, 42 { 43 plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', 44 title: 'Love Story 2050', 45 score: 0.923175573348999 46 }, 47 { 48 plot: 'A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...', 49 title: 'The Portal', 50 score: 0.9228089451789856 51 } 52 ]
This query uses the $vectorSearch stage to:
Compare vector embeddings of the search term against vector embeddings of movie plots in the
plot_embeddingfield of thesample_mflix.embedded_movescollection.Consider up to the 150 most similar movie plots and return the top 10 results.
It uses the $project stage to:
Only include the movie
plotandtitlefields in the results.Add a
scorefield to show the relevance of each result to the search term.
To learn more about this pipeline stage, see Run Vector Search Queries.
1
Install the MongoDB C Driver.
For detailed installation instructions, refer to the MongoDB C Driver documentation.
2
Construct your vector search query.
This query searches for documents that include text in the plot field that
is semantically related to the term "time travel".
Create a file named
atlas-vector-search-quick-start.cand copy and paste the following sample query into the file:1 2 3 4 5 int main(void) { 6 7 mongoc_client_t *client; 8 mongoc_collection_t *collection; 9 mongoc_cursor_t *cursor; 10 bson_array_builder_t *builder; 11 bson_t *pipeline; 12 bson_t query_vector; 13 bson_error_t error; 14 const bson_t *doc; 15 char *str; 16 17 int arrSize = 1536; // This collection has a vector array of 1536 dimensions; depending on your vector embedding model, this mary vary 18 double embeddings[] = 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19 20 // Initialize the MongoDB C Driver 21 mongoc_init(); 22 23 // Connect to your Atlas collection 24 client = mongoc_client_new("<connection-string>"); 25 collection = mongoc_client_get_collection (client, "sample_mflix", "embedded_movies"); 26 27 // Build query vector array 28 builder = bson_array_builder_new(); 29 30 for(int i = 0; i < arrSize; i++){ 31 bson_array_builder_append_double(builder, embeddings[i]); 32 } 33 34 bson_array_builder_build(builder, &query_vector); 35 bson_array_builder_destroy(builder); 36 37 // Create aggregation pipeline for Vector Search 38 pipeline = BCON_NEW( 39 "pipeline", 40 "[", 41 "{", 42 "$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
$vectorSearchstage to:Compare vector embeddings of the search term against vector embeddings of movie plots in the
plot_embeddingfield of thesample_mflix.embedded_movescollection.Consider up to the 150 most similar movie plots and return the top 10 results.
It uses the
$projectstage to:Only include the movie
plotandtitlefields in the results.Add a
scorefield to show the relevance of each result to the search term.
3
Specify the <connection-string>.
Replace
<connection-string>with the connection string for your Atlas cluster or local deployment.Your connection string should use the following format:
mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net Ensure that your connection string includes your database user's credentials. To learn more about finding your connection string, see Connect via Drivers.
Your connection string should use the following format:
mongodb://localhost:<port-number>/?directConnection=true Save the file.
4
Configure CMake for your project.
In your project directory, create a file named
CMakeLists.txtand copy the following code into the file.cmake_minimum_required(VERSION 3.30) project(atlas-vector-search-quick-start) # Specify the minimum version you require. find_package (mongoc-1.0 1.27.6 REQUIRED) add_executable(atlas-vector-search-quick-start atlas-vector-search-quick-start.c ) # The "atlas-vector-search-quick-start.c" sample program is shared among four tests. target_link_libraries (atlas-vector-search-quick-start PRIVATE mongo::mongoc_shared) Create a directory in your project folder called
build.mkdir build Enter the
builddirectory and run thecmakecommand to create your Makefile.cd build cmake ../
5
Run your query.
From your build directory, compile and run the atlas-vector-search-quick-start.c file.
cmake --build . ./atlas-vector-search-quick-start
1 { "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : { "$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" } }
1
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.txtfile.cd query-quick-start touch CMakeLists.txt Copy and paste the following lines into the
CMakeLists.txtfile:cmake_minimum_required(VERSION 3.30) project(query_quick_start) set(CMAKE_CXX_STANDARD 17) find_package(mongocxx REQUIRED) find_package(bsoncxx REQUIRED) add_executable(query_quick_start atlas_vector_search_quick_start.cpp ) target_link_libraries(query_quick_start PRIVATE mongo::mongocxx_shared) target_link_libraries(query_quick_start PRIVATE mongo::bsoncxx_shared)
2
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.cppfile: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, 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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_embeddingfield of thesample_mflix.embedded_movescollection.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
plotandtitlefields in the results.Add a
scorefield to show the relevance of each result to the search term.
To learn more about this pipeline stage, see Run Vector Search Queries.
3
Specify the <connection-string>.
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.
4
Compile and run your query.
Create and enter the
/builddirectory: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":0.9332506656646729}' 2 '{"plot":"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.","title":"About Time","score":0.9312690496444702}' 3 '{"plot":"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.","title":"The Time Machine","score":0.929530143737793}' 4 '{"plot":"After using his mother’s newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children’s crusade where he confronts his new friends with modern techniques...","title":"Crusade in Jeans","score":0.9290417432785034}'; 5 '{"plot":"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.","title":"Timecop","score":0.9283161759376526}' 6 '{"plot":"A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...","title":"A.P.E.X.","score":0.9266218543052673}' 7 '{"plot":"Agent J travels in time to M.I.B.’s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.","title":"Men in Black 3","score":0.9258455038070679}' 8 '{"plot":"Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.","title":"Tomorrowland","score":0.9240515828132629}' 9 '{"plot":"With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.","title":"Love Story 2050","score":0.923175573348999}' 10 '{"plot":"A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...","title":"The Portal","score":0.9228089451789856}'
1
Set up and initialize the .NET/C# project for the query.
Create a new directory called
query-quick-startand 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.
2
Construct your vector search query.
This query searches for documents that include text in the plot field that
is semantically related to the term "time travel".
Edit the
Program.csfile.Copy and paste the following sample query into the
Program.csfile: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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