Atlas Vector Search
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Sample Class
The code examples in this guide demonstrate how you can use builders to
create types to interact with documents in the sample collection plants.flowers.
Documents in this collection are modeled by the following Flower class:
public class Flower { public ObjectId Id { get; set; } public string Name { get; set; } public string Category { get; set; } public double Price { get; set; } public List<string> Season { get; set; } public double Stock { get; set; } public string Description { get; set; } }
Each builder class takes a generic type parameter
TDocument which represents the type of document that you are working
with. In this guide, the Flower class is the document type used in
each builder class example.
Perform an Atlas Vector Search
You can use builders to create a $vectorSearch aggregation pipeline stage to perform an
approximate nearest neighbor search on a vector in the specified field. Your collection must
have a defined Atlas Vector Search index before you can perform a vector search on your data.
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.
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 example shows how to use builders to generate an aggregation pipeline to perform the following operations:
Performs a vector search on the Atlas Vector Search index of the
plot_embeddingfield using vector embeddings for the string"time travel"Fetches the
TitleandPlotfields from documents found in the vector search
// 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.Embedding, vector, 10, options) .Project(Builders<EmbeddedMovie>.Projection .Include(m => m.Title) .Include(m => m.Plot));
The results of the preceding example contain the following documents:
{ "_id" : ObjectId("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" } { "_id" : ObjectId("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" } { "_id" : ObjectId("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" } { "_id" : ObjectId("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" } { "_id" : ObjectId("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" } { "_id" : ObjectId("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." } { "_id" : ObjectId("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" } { "_id" : ObjectId("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" } { "_id" : ObjectId("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" } { "_id" : ObjectId("573a13e5f29313caabdc40c9"), "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal" }
To learn more about Atlas Vector Search, see Atlas Vector Search Overview in the Atlas manual.