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 when you connect to 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.

MongoDB Vector Search Index

Before you 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 C Atlas Search Indexes guide.

Vector Search Aggregation Stage

To create a $vectorSearch stage in your aggregation pipeline, include the $vectorSearch pipeline stage definition in your bson_t pipeline structure, then pass the pipeline to the mongoc_collection_aggregate() function.

Define the following fields in your $vectorSearch stage:

Parameter	Type	Description
`index`	string	Name of the vector search index
`path`	string	Field that contains vector embeddings
`queryVector`	BSON array of doubles	Vector representation of your query
`limit`	number	Number of results to return

MongoDB Search Query Examples

Tip

Sample Data

The example in this section uses sample data from the sample_mflix.embedded_movies collection. To learn how to load this sample data, see the MongoDB Get Started guide.

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". It then uses the $project stage to output only the title and plot fields and add a score field to show the relevance score of each document:

// 2048-dimensional vector embedding representation of the phrase "time travel"
double embeddings[2048] = 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// Build the query vector array for the vector embedding representation of the phrase "time travel".
bson_t query_vector = BSON_INITIALIZER;
{
    bson_array_builder_t *builder = bson_array_builder_new();
    for (int i = 0; i < 2048; i++) {
        bson_array_builder_append_double(builder, embeddings[i]);
    }
    bson_array_builder_build(builder, &query_vector);
    bson_array_builder_destroy(builder);
}
// Create aggregation pipeline for Vector Search
bson_t *pipeline = BCON_NEW(
    "pipeline", "[",
        "{",
            "$vectorSearch", "{",
                "index", "vector_index",
                "path", "plot_embedding_voyage_3_large",
                "queryVector", BCON_ARRAY(&query_vector),
                "numCandidates", BCON_INT32(150),
                "limit", BCON_INT32(10),
            "}",
        "}",
        "{",
            "$project", "{",
                "title", BCON_BOOL(true),
                "plot", BCON_BOOL(true),
                "_id", BCON_BOOL(false),
                "score", "{",
                    "$meta", "vectorSearchScore",
                "}",
            "}",
        "}",
    "]"
);
// Execute the collection aggregation command
mongoc_cursor_t *cursor = mongoc_collection_aggregate(collection, MONGOC_QUERY_NONE, pipeline, NULL, NULL );
// Print results
const bson_t *doc;
while (mongoc_cursor_next(cursor, &doc)) {
    char *str = bson_as_canonical_extended_json(doc, NULL);
    printf("%s\n", str);
    bson_free(str);
}

{ "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" : { "$numberDouble" : "0.77101063728332519531" } }
{ "plot" : "A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.", "title" : "Retroactive", "score" : { "$numberDouble" : "0.76004797220230102539" } }
{ "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" : { "$numberDouble" : "0.75768613815307617188" } }
{ "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : { "$numberDouble" : "0.75765615701675415039" } }
{ "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", "score" : { "$numberDouble" : "0.7521393895149230957" } }
{ "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : { "$numberDouble" : "0.75099325180053710938" } }
{ "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", "score" : { "$numberDouble" : "0.75026428699493408203" } }
{ "plot" : "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title" : "The Time Machine", "score" : { "$numberDouble" : "0.75025033950805664062" } }
{ "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", "score" : { "$numberDouble" : "0.74949634075164794922" } }
{ "plot" : "A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.", "title" : "The Final Countdown", "score" : { "$numberDouble" : "0.74691337347030639648" } }

Vector Search Options

You can modify your MongoDB Vector Search queries by specifying the following options in the stage definition:

Option	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. You can filter on boolean, date, objectId, numeric, string, UUID values, and arrays of these types.	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 MongoDB Server manual entries:

To learn more about aggregation pipelines in the C driver, see the C Aggregation guide.

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

API Documentation

To learn more about the functions mentioned in this guide, see the following API documentation:

mongoc_collection_aggregate()

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