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Atlas Vector Search

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.

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.

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; }
}

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; }
}

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.

You can perform a vector search query by calling the VectorSearch() method in an aggregation pipeline. 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.

Tip

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]));

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 filter the results

  4. Prints the results of the query

QueryVector vector = new[] { -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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var options = new VectorSearchOptions<Movie>()
{
IndexName = "vector_index",
NumberOfCandidates = 150
};
// run query
var results = collection.Aggregate()
.VectorSearch(movie => movie.PlotEmbedding, vector, 10, options)
.Project(Builders<Movie>.Projection
.Include(movie => movie.Title)
.Include(movie => movie.Plot))
.ToList();
// print results
foreach (var movie in results)
{
Console.WriteLine(movie.ToJson());
}

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

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