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Run an Atlas Vector Search Query

Overview

You can use Atlas Vector Search to perform vector search on your data stored in Atlas. Vector search allows you to query your data based on semantic meaning rather than just keyword matches, which helps you retrieve more relevant search results. It enables your AI-powered applications to support use cases such as semantic search, hybrid search, and generative search, including Retrieval-Augmented Generation (RAG).

By using Atlas as a vector database, you can seamlessly index vector data along with your other data in Atlas. This allows you to filter on fields in your collection and perform vector search queries against vector data. You can also combine vector search with full-text search queries to return the most relevant results for your use case. You can integrate Atlas Vector Search with popular AI frameworks and services to easily implement vector search in your applications.

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

Sample Data

The examples in this guide use the sample_mflix.embedded_movies collection in the sample_mflix database. To obtain the sample dataset for this collection, see Get Started with the .NET/C# Driver.

The examples in this guide use the following sample class to model documents in the sample_mflix.embedded_movies collection:

[BsonIgnoreExtraElements]
public class Movie
{
    public ObjectId Id { get; set; }
    public string Plot { get; set; }
    public string Title { get; set; }
    [BsonElement("plot_embedding")]
    public float[] PlotEmbedding { get; set; }
}

Supported Vector Embedding Types

Vector embeddings are vectors you use to represent your data. These embeddings capture meaningful relationships in your data and enable tasks like semantic search and retrieval.

The .NET/C# Driver supports vector embeddings of several types. The following sections describe the supported vector embedding types.

Array Representations

The .NET/C# Driver supports the following representations of the array type in vector embeddings:

  • BsonArray

  • Memory

  • ReadOnlyMemory

  • float[] and double[]

The following example shows a class with properties of the preceding types:

public class BsonArrayVectors
{
    public BsonArray BsonArrayVector { get; set; }
    public Memory<float> MemoryVector { get; set; }
    public ReadOnlyMemory<float> ReadOnlyMemoryVector { get; set; }
    public float[] FloatArrayVector { get; set; }
}

Tip

To learn more about using the Memory and ReadOnlyMemory types, see the Improve Array Serialization Performance section of the Serialization guide.

Binary Vector Representations

The .NET/C# Driver supports the following binary vector representations in vector embeddings:

  • BinaryVectorFloat32 (not supported on big-endian architectures)

  • BinaryVectorInt8

  • BinaryVectorPackedBit

  • Memory<float>, Memory<byte>, Memory<sbyte>

  • ReadOnlyMemory<float>, ReadOnlyMemory<byte>, ReadOnlyMemory<sbyte>

  • float[], byte[], sbyte[]

Note

You must use the BinaryVector attribute when specifying binary vector representations of the Memory<T>, ReadOnlyMemory<T>, or array types.

The following example shows a class with properties of the preceding types:

public class BinaryVectors
{
    public BinaryVectorInt8 ValuesInt8 { get; set; }
    public BinaryVectorPackedBit ValuesPackedBit { get; set; }
    public BinaryVectorFloat32 ValuesFloat { get; set; }
    [BinaryVector(BinaryVectorDataType.Int8)]
    public Memory<byte> ValuesByte { get; set; }
    [BinaryVector(BinaryVectorDataType.Float32)]
    public float[] ValuesFloat { get; set; }
}

Binary Vector Data Serialization

You can serialize Int8 binary vector typed data as byte or sbyte. You can also serialize Float32 binary vector typed data as float. The following example serializes Int8 and Float32 binary vector data:

[BinaryVector(BinaryVectorDataType.Int8)]
public Memory<byte> ValuesByte { get; set; }
[BinaryVector(BinaryVectorDataType.Int8)]
public Memory<sbyte> ValuesSByte { get; set; }
[BinaryVector(BinaryVectorDataType.Float32)]
public float[] ValuesFloat { get; set; }

You can deserialize PackedBit vector data to a binary vector represented byte data type only if the vector data has a padding value of 0. If the vector data has a padding value not equal to 0, you can deserialize it only to a BsonVectorPackedBit.

Vector Search Query Example

You can perform a vector search query by calling the VectorSearch() method. To perform a vector search on a collection, you must first have a collection with a field that contains vector data and a vector search index that covers that field.

To learn more about configuring a collection for vector search, see the Atlas Vector Search guide in the MongoDB Atlas documentation.

You can convert BinaryVectorFloat32, BinaryVectorInt8, and BinaryVectorPackedBit data to the BsonBinaryData type to use in a vector search query by using the ToQueryVector() method. The following example converts BinaryVectorInt8 into a BsonBinaryData object:

var binaryVector = new BinaryVectorInt8(new sbyte[] { 0, 1, 2, 3, 4 });
var queryVector = binaryVector.ToQueryVector();

You can specify your array-represented vector data as an instance of the QueryVector class to use in a vector search query. The following example creates an array of ReadOnlyMemory<float> values as a QueryVector object to use in a vector search query:

QueryVector v = new QueryVector(new ReadOnlyMemory<float>([1.2f, 2.3f]));

Consider the embedded_movies collection in the sample_mflix database. You can use a $vectorSearch stage to perform a semantic search on the plot_embedding field of the documents in the collection. The following sections describe different methods for performing Atlas Vector Search operations on this collection.

Tip

To obtain the sample dataset used in the following example, see Get Started with the .NET/C# Driver. To create the sample Atlas Vector Search index used in the following example, see Create an Atlas Vector Search Index in the Atlas manual.

Aggregation Pipeline Example

This example performs the following steps to run an Atlas Vector Search query on a collection that contains vector data and a vector search index on the PlotEmbedding field:

  1. Creates an array that contains the array-represented vector data to search for

  2. Specifies a VectorSearchOptions object that contains the name of the index and the number of nearest neighbors to use during the search

  3. Creates an aggregation pipeline that uses the VectorSearch() stage to perform the vector search query and a Project() stage to display only the Title, Plot, and Score fields

  4. Prints the results of the query

// Defines vector embeddings for the string "time travel"
var vector = new[] 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// Specifies that the vector search will consider the 150 nearest neighbors
// in the specified index
var options = new VectorSearchOptions<EmbeddedMovie>()
{
   IndexName = "vector_index",
   NumberOfCandidates = 150
};
// Builds aggregation pipeline and specifies that the $vectorSearch stage
// returns 10 results
var pipeline = new EmptyPipelineDefinition<EmbeddedMovie>()
   .VectorSearch(m => m.PlotEmbedding, vector, 10, options)
   .Project(Builders<EmbeddedMovie>.Projection
      .Include(m => m.Title)
      .Include(m => m.Plot)
      .MetaVectorSearchScore(m => m.Score);

The results of the preceding example contain the following documents:

{ "_id" : { "$oid" : "573a13a0f29313caabd04a4f" }, "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : 0.926971435546875 }
{ "_id" : { "$oid" : "573a13d8f29313caabda6557" }, "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. 9267120361328125 }
{ "_id" : { "$oid" : "573a1399f29313caabceec0e" }, "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : 0.9235687255859375 }
{ "_id" : { "$oid" : "573a13a5f29313caabd13b4b" }, "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" : 0.9228668212890625 }
{ "_id" : { "$oid" : "573a13aef29313caabd2e2d7" }, "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans", "score" : 0.9228515625 }
{ "_id" : { "$oid" : "573a1399f29313caabcee36f" }, "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" : 0.9199066162109375 }
{ "_id" : { "$oid" : "573a13c6f29313caabd715d3" }, "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3", "score" : 0.919403076171875 }
{ "_id" : { "$oid" : "573a13d4f29313caabd98c13" }, "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" : 0.9191131591796875 }
{ "_id" : { "$oid" : "573a13b6f29313caabd477fa" }, "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050", "score" : 0. 917755126953125 }
{ "_id" : { "$oid" : "573a13b3f29313caabd3ebd4" }, "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" : 0.9172210693359375 }

LINQ Example

The following code sample performs the same vector search query as the preceding example, but uses the LINQ syntax instead of the aggregation pipeline syntax:

var results = collection.AsQueryable()
    .VectorSearch(m => m.PlotEmbedding, vector, 10, options)
    .Select(m => new { m.Title, m.Plot });

Additional Information

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

API Documentation

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

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