Run a MongoDB Vector Search Query

Overview

In this guide, you can learn how to perform searches on your documents by using the MongoDB Vector Search feature. The C++ driver allows you to perform MongoDB Vector Search queries by using the aggregation framework. To learn more about performing aggregations, see the Aggregation guide.

Note

Deployment Compatibility

You can use the MongoDB Vector Search feature only on MongoDB Atlas clusters. This feature is not available for self-managed deployments.

To learn more about MongoDB Vector Search, see the MongoDB Vector Search Overview in the Atlas documentation.

Note

MongoDB Search

To perform advanced full-text search on your documents, you can use the MongoDB Search feature. To learn about this feature, see the MongoDB Search Overview.

MongoDB Vector Search Index

Before you can perform MongoDB Vector Search queries, you must create a MongoDB Vector Search index on your collection. To learn more about creating this index, see the MongoDB Vector Search Indexes guide.

Vector Search Aggregation Stage

To create a $vectorSearch stage in your aggregation pipeline, perform the following actions:

Create a vector to store the pipeline stages.
Specify the $vectorSearch operator and provide details about the vector search query.

You must define the following fields in your $vectorSearch stage:

Parameter	Data Type	Description
`index`	`string`	Name of the vector search index
`path`	`string`	Field that contains vector embeddings
`queryVector`	`BSON::Binary` vector subtype, `BSON::Vector`, or array of numbers	Vector representation of your query
`limit`	`number`	Number of results to return

Then, include the stage in an aggregation pipeline and pass the pipeline to the aggregate method.

MongoDB Search Query Examples

In this section, you can learn how to perform MongoDB Vector Search queries. The examples in this section use sample data from the sample_mflix.embedded_movies collection. To learn how to load this sample data, see the Load Data into Atlas tutorial in the Atlas documentation.

Basic Vector Search Query

The following code performs a MongoDB Vector Search query on the plot_embedding vector field by using a query vector that is a vector embedding of the phrase "time travel":

#include <bsoncxx/builder/basic/document.hpp>
#include <bsoncxx/builder/list.hpp>
#include <bsoncxx/json.hpp>
#include <mongocxx/client.hpp>
#include <mongocxx/instance.hpp>
#include <mongocxx/uri.hpp>
using bsoncxx::builder::basic::kvp;
using bsoncxx::builder::basic::make_document;
int main() {
  try {
    mongocxx::instance inst{};
    // Replace the placeholder with your Atlas connection string
    const auto uri = mongocxx::uri{"<connection-string>"};
    // Connect to your Atlas cluster
    auto client = mongocxx::client{uri};
    auto db = client["sample_mflix"];
    auto collection = db["embedded_movies"];
    // Define vector embeddings to search
    auto vectors = bsoncxx::builder::list{
      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024262359,0.054507218,-0.009721561,0.005816319,-0.00206687,-0.008766023,0.017365867,-0.000737426,0.018362951,-0.023597637,-0.019110762,0.021935832,0.041545134,-0.020357117,-0.017615138,0.044868745,-0.018030589,-0.032405205,-0.050186522,-0.014540797,0.005213914,-0.006688767,0.047527634,0.040714234
    };
    // Define the pipeline with vectorSearch query options
    mongocxx::pipeline stages;
    stages
        .append_stage(make_document(
            kvp("$vectorSearch",
                make_document(kvp("index", "vector_index"),
                              kvp("path", "plot_embedding_voyage_3_large"),
                              kvp("queryVector", vectors),
                              kvp("numCandidates", 150), kvp("limit", 10)))))
        .project(make_document(
            kvp("_id", 0), kvp("plot", 1), kvp("title", 1)));
    auto cursor = collection.aggregate(stages);
    for (auto&& doc : cursor) {
      std::cout << bsoncxx::to_json(doc) << std::endl;
    }
  } catch (const std::exception& e) {
    std::cout << "Exception: " << e.what() << std::endl;
  }
  return 0;
}

{ "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" }
{ "plot" : "A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.", "title" : "Retroactive" }
{ "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." }
{ "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" }
{ "plot" : "After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.", "title" : "Back to the Future Part II" }
{ "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" }
{ "plot" : "Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...", "title" : "Timerider: The Adventure of Lyle Swann" }
{ "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" }
{ "plot" : "A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.", "title" : "The Time Traveler's Wife" }
{ "plot" : "A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.", "title" : "The Final Countdown" }

This query uses the $vectorSearch stage to:

Perform an Approximate Nearest Neighbor (ANN) vector search
Search for the specified term in the plot_embedding_voyage_3_large field
Set the number of nearest neighbors used in the search to 150 by using the numCandidates option
Return a maximum of 10 documents from the query using the limit option

It uses the $project stage to:

Only include the movie plot and title fields in the results

Vector Search Score

The following code performs the same query as in the preceding example, but uses the $project stage to output only the title field and vectorSearchScore meta field, which describes how well the document matches the query vector:

// Define the pipeline with vectorSearch and project stages
mongocxx::pipeline score_pipeline;
score_pipeline
    .append_stage(make_document(
        kvp("$vectorSearch",
            make_document(kvp("queryVector", vectors),
                          kvp("index", "vector_index"),
                          kvp("path", "plot_embedding_voyage_3_large"),
                          kvp("numCandidates", 150),
                          kvp("limit", 10)))))
    .project(make_document(
        kvp("_id", 0),
        kvp("title", 1),
        kvp("score", make_document(kvp("$meta", "vectorSearchScore")))));
auto cursor = collection.aggregate(score_pipeline);
for (auto&& doc : cursor) {
  std::cout << bsoncxx::to_json(doc) << std::endl;
}

{ "title" : "About Time", "score" : 0.77101051807403564453 }
{ "title" : "Retroactive", "score" : 0.76004779338836669922 }
{ "title" : "A.P.E.X.", "score" : 0.7576859593391418457 }
{ "title" : "Timecop", "score" : 0.757656097412109375 }
{ "title" : "Back to the Future Part II", "score" : 0.75213932991027832031 }
{ "title" : "Thrill Seekers", "score" : 0.75099325180053710938 }
{ "title" : "Timerider: The Adventure of Lyle Swann", "score" : 0.75026416778564453125 }
{ "title" : "The Time Machine", "score" : 0.75025022029876708984 }
{ "title" : "The Time Traveler's Wife", "score" : 0.74949628114700317383 }
{ "title" : "The Final Countdown", "score" : 0.7469132542610168457 }

Vector Search Options

You can use a $vectorSearch stage to perform many types of Atlas Vector Search queries. Depending on your desired query, you can specify the following options in the stage definition:

Optional Parameter	Data Type	Description	Default Value
`exact`	boolean	Specifies whether to run an Exact Nearest Neighbor (`true`) or Approximate Nearest Neighbor (`false`) search	`false`
`filter`	document	Specifies a pre-filter for documents to search on	No filtering
`numCandidates`	number	Specifies the number of nearest neighbors to use during the search	No limit

To learn more about these options, see the Fields section of the $vectorSearch operator reference in the Atlas documentation.

Additional Information

To learn more about the concepts mentioned in this guide, see the following Server manual entries:

To learn more about the behavior of the aggregate method, see the Aggregation guide.

API Documentation

To learn more about the methods and types mentioned in this guide, see the following API documentation:

aggregate()

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