Atlas Vector Search
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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.
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[]
anddouble[]
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; } }
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; } [ ] public Memory<byte> ValuesByte { get; set; } [ ] 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:
[ ]public Memory<byte> ValuesByte { get; set; } [ ]public Memory<sbyte> ValuesSByte { get; set; } [ ]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
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]));
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:
Creates an array that contains the array-represented vector data to search for
Specifies a
VectorSearchOptions
object that contains the name of the index and the number of nearest neighbors to use during the searchCreates an aggregation pipeline that uses the
VectorSearch()
stage to perform the vector search query and aProject()
stage to filter the resultsPrints the results of the query
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-0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136 }; 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()); }
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: