Run a MongoDB Vector Search Query

Automated embedding is available as a Preview feature only for MongoDB Community Edition v8.2 and later. The feature and the corresponding documentation might change at anytime during the Preview period. To learn more, see Preview Features.

Overview

In this guide, you can learn how to use the MongoDB Vector Search feature in the Go driver by using the $vectorSearch pipeline stage. This pipeline stage allows you to perform a semantic search on your documents. A semantic search is a type of search which locates information that is similar in meaning, but not necessarily identical, to your provided search term or phrase.

Important

Feature Compatibility

To learn what versions of MongoDB Atlas support this feature, see Limitations in the MongoDB Atlas documentation.

Sample Data

The example on this page queries the plot_embedding field from the embedded_movies collection, found in the sample_mflix database of the Atlas sample datasets.

The plot_embedding field contains vector embeddings with 1536 dimensions, created using OpenAI's text-embedding-ada-002 embedding model.

To learn how to create a free MongoDB Atlas cluster and load the sample datasets, see Get Started with Atlas.

Run a MongoDB Vector Search Query

To use this feature, you must create a vector search index and index your vector embeddings. To learn about how to programmatically create a vector search index, see MongoDB Search and MongoDB Vector Search Indexes. To learn more about vector embeddings, see How to Index Vector Embeddings for Vector Search in the Atlas documentation.

After you create a vector search index on your vector embeddings, you can reference this index in your aggregation pipeline to run your vector search query.

The following sections demonstrate how to create a BSON binary vector for your query vector and how to use your vector search index to run a vector search query by using the plot_embedding field.

Create a BSON Binary Vector

In this example, you can create a 1536 dimensional vector to use as the query vector for your vector search query on the plot_embedding field. The query searches the plot_embedding field by using a vector embedding for the string "time travel".

The following example shows how to translate this vector embedding to a BSON binary vector that you can use as the query vector:

// Converts a float32 array to a BSON vector
vectorFloat := bson.NewVector([]float32{-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, 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// Converts the BSON vector to a BSON binary vector
queryVector := vectorFloat.Binary()

If you need to access a slice of the original vector, you can also deserialize your query vector back to a BSON vector.

The following example shows how to convert the query vector from a BSON binary vector to a BSON vector by using the NewVectorFromBinary() method:

// Converts the BSON binary vector back to a BSON vector
backToVectorFloat, err := bson.NewVectorFromBinary(queryVector)
if err != nil {
	log.Fatal(err)
}

Tip

Query Vector Type

The preceding example creates an instance of a BSON binary vector to serve as the query vector, but you can also use an array of BSON double values. However, we recommend that you use a BSON binary vector to improve storage efficiency.

Run the Vector Search Query

The following example shows how to build an aggregation pipeline that uses the $vectorSearch and $project methods to perform an Approximate Nearest Neighbor (ANN) vector search with the following specifications:

Queries the plot_embedding field with the BSON binary queryVector
Sets the number of nearest neighbors used in the search to 150 by using the numCandidates option
Uses the vector_search index created on the plot_embedding field
Returns 5 documents with the specified plot, title, and score fields

// Creates the aggregation pipeline
vectorSearchStage := bson.D{
	{"$vectorSearch", bson.D{
		{"index", "vector_index"},
		{"path", "plot_embedding"},
		{"queryVector", queryVector},
		{"numCandidates", 150},
		{"limit", 5},
	}}}
projectStage := bson.D{
	{"$project", bson.D{
		{"_id", 0},
		{"plot", 1},
		{"title", 1},
		{"score", bson.D{{"$meta", "vectorSearchScore"}}},
	}}}
// Runs the aggregation pipeline
cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
if err != nil {
	log.Fatalf("failed to retrieve data from the server: %v", err)
}
// Displays the results
type ProjectedMovieResult struct {
	Title string  `bson:"title"`
	Plot  string  `bson:"plot"`
	Score float64 `bson:"score"`
}
var results []ProjectedMovieResult
if err = cursor.All(ctx, &results); err != nil {
	log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
}
for _, result := range results {
	fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score)
}

Title: Thrill Seekers
Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.
Score: 0.92730712890625
Title: About Time
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.
Score: 0.926605224609375
Title: The Time Machine
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.
Score: 0.9239959716796875
Title: Timecop
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.
Score: 0.923583984375
Title: Crusade in Jeans
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...
Score: 0.9222412109375

Query an Auto-Embedding Index

You can automate vector generation for text searches by querying an auto-embedding MongoDB Vector Search index. To learn how to create an auto-embedding index by using the Go driver, see the MongoDB Auto-Embedding Search Index Model section of the Indexes guide.

The following example builds an aggregation pipeline that searches for semantic similarity to the phrase "time travel" in the plot field. The query uses an auto-embedding MongoDB Vector Search index on the plot field named auto_embedding_index:

// Creates the $vectorSearch stage that queries the
// auto-embedding index named "auto_embedding_index"
vectorSearchStage := bson.D{
	{"$vectorSearch", bson.D{
		{"index", "auto_embedding_index"},
		{"path", "plot"},
		{"query", bson.D{{"text", "time travel"}}},
		{"model", "voyage-4"},
		{"numCandidates", 150},
		{"limit", 10},
	}},
}
// Projects only the title and plot fields
projectStage := bson.D{
	{"$project", bson.D{
		{"_id", 0},
		{"title", 1},
		{"plot", 1},
	}},
}
// Runs the aggregation pipeline
cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
if err != nil {
	log.Fatalf("failed to retrieve data from the server: %v", err)
}
defer cursor.Close(ctx)
type ProjectedMovieResult struct {
	Title string `bson:"title"`
	Plot  string `bson:"plot"`
}
var results []ProjectedMovieResult
if err := cursor.All(ctx, &results); err != nil {
	log.Fatalf("failed to unmarshal retrieved docs: %v", err)
}
for _, result := range results {
	fmt.Printf("Title: %v\nPlot: %v\n---\n\n", result.Title, result.Plot)
}

Title: Manuel on the Island of Wonders
Plot: Manuel's fantasy travel through Time goes from Long Ago (Episode 1 - O jardim proibido / Le Jardin interdit) through Now (Episode 2 - O pique-nique dos sonhos / Le Pique-nique des rèves), ...
---
Title: 11 Minutes Ago
Plot: Traveling in 11-minute increments, a time-tumbler from 48-years in the future spends two years of his life weaving through a two-hour wedding reception.
---
Title: Time Freak
Plot: A neurotic inventor creates a time machine and gets lost traveling around yesterday.
---
Title: Timecrimes
Plot: A man accidentally gets into a time machine and travels back in time nearly an hour. Finding himself will be the first of a series of disasters of unforeseeable consequences.
---
Title: The Little Girl Who Conquered Time
Plot: A high-school girl acquires the ability to time travel.
---
Title: The Little Girl Who Conquered Time
Plot: A high-school girl acquires the ability to time travel.
---
Title: Time Traveller
Plot: A high-school girl acquires the ability to time travel.
---
Title: Je t'aime je t'aime
Plot: Recovering from an attempted suicide, a man is selected to participate in a time travel experiment that has only been tested on mice. A malfunction in the experiment causes the man to ...
---
Title: A.P.E.X.
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: The Ah of Life
Plot: Theoretical mathematician, Nigel Kline finds himself the subject of his own vertical time study. Entering into Einstein's relativity, three versions of Nigel face off with each other, weaving time and space in a world of fluid moments.
---
Title: About Time
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.
---

Note

When you query an auto-embedding MongoDB Vector Search index, provide the text to search in the query field (for example {"text", "time travel"}) and a compatible model name in the model field. The supported models are voyage-4-lite, voyage-4, voyage-4-large, and voyage-code-3. You do not need to compute or pass a queryVector value.

Additional Information

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

To learn more about the syntax of the $vectorSearch pipeline stage, see the Syntax and Fields sections of the Create and Run Queries guide in the MongoDB Vector Search section of the MongoDB Atlas documentation.

API Documentation

To learn more about any of the methods or types discussed in this guide, see the following API Documentation:

Back

MongoDB Search

Monitoring