Run an Atlas Vector Search Query

On this page

Overview
Sample Data
Perform a Vector Search
Create a BSON Binary Vector
Run the Vector Search Query
Additional Information
API Documentation

Overview

In this guide, you can learn how to use the Atlas 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 the Get Started with Atlas guide.

Perform a Vector Search

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 the Atlas Search and Atlas Vector Search Indexes section in the Indexes guide. 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

Additional Information

To learn more about Atlas Vector Search, see the Atlas 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 Atlas 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

Modify CRUD Execution

Aggregation