Atlas Vector Search

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Overview

Atlas Vector Search Index
Vector Search Aggregation Stage
Atlas Search Query Examples
Basic Vector Search Query
Vector Search Score
Vector Search Options
Additional Information
API Documentation

Overview

In this guide, you can learn how to perform searches on your documents by using the Atlas Vector Search feature. The Rust driver allows you to perform Atlas Vector Search queries by using the aggregation framework. To learn more about performing aggregations, see the Aggregation guide.

Note

Deployment Compatibility

You can use the Atlas Vector Search feature only when you connect to MongoDB Atlas clusters. This feature is not available for self-managed deployments.

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

Note

Atlas Search

To perform advanced full-text search on your documents, you can use the Atlas Search feature. To learn about this feature, see the Atlas Search Overview.

Atlas Vector Search Index

Before you can perform Atlas Vector Search queries, you must create an Atlas Vector Search index on your collection. To learn more about creating this index, see the Atlas Search and Vector Search Indexes guide.

Vector Search Aggregation Stage

To create a $vectorSearch stage in your aggregation pipeline, perform the following actions:

Create a vector to store the pipeline stages.
Specify the $vectorSearch operator and provide details about the vector search query.

You must define the following fields in your $vectorSearch stage:

Parameter	Type	Description
`index`	string	Name of the vector search index
`path`	string	Field that contains vector embeddings
`queryVector`	array of numbers	Vector representation of your query
`limit`	number	Number of results to return

Atlas Search Query Examples

In this section, you can learn how to perform Atlas Vector Search queries. The examples in this section use sample data from the sample_mflix.embedded_movies collection. To learn how to load this sample data, see the Load Data into Atlas tutorial in the Atlas documentation.

Basic Vector Search Query

The following code performs an Atlas Vector Search query on the plot_embedding vector field by using a query vector that represents the term time travel:

let query_vector = Vector::Float32(vec![-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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0.011782787, 0.014541878, -0.015701497, -0.029350337, 0.021979429, 0.01332228, -0.026244693, -0.0123492675, -0.003895384, 0.0071576433, -0.035454992, -0.00046984528, 0.0033522295, 0.039347045, 0.0005119148, 0.00476843, -0.012995721, 0.0024042083, -0.006931051, -0.014461905, -0.0127558, 0.0034555288, -0.0074842023, -0.030256703, -0.007057676, -0.00807734, 0.007804097, -0.006957709, 0.017181009, -0.034575284, -0.008603834, -0.005008351, -0.015834786, 0.02943031, 0.016861115, -0.0050849924, 0.014235313, 0.0051449724, 0.0025924798, -0.0025741523, 0.04289254, -0.002104307, 0.012969063, -0.008310596, 0.00423194, 0.0074975314, 0.0018810473, -0.014248641, -0.024725191, 0.0151016945, -0.017527562, 0.0018727167, 0.0002830318, 0.015168339, 0.0144219175, -0.004048667, -0.004358565, 0.011836103, -0.010343261, -0.005911387, 0.0022825818, 0.0073175905, 0.00403867, 0.013188991, 0.03334902, 0.006111321, 0.008597169, 0.030123414, -0.015474904, 0.0017877447, -0.024551915, 0.013155668, 0.023525586, 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0.02071318, -0.03156294, -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136]);
let pipeline = vec![
    doc! {
        "$vectorSearch": doc! {
            "queryVector": &query_vector,
            "path": "plot_embedding",
            "numCandidates": 150,
            "index": "vector_index",
            "limit": 5
        }
    },
    doc! {
        "$project": doc! {
            "_id": 0,
            "title": 1,
            "plot": 1,
        }
    },
];
let mut results = my_coll.aggregate(pipeline).await?;
while let Some(result) = results.try_next().await? {
    println!("{}", result);
}

{ "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" }
{ "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" }
{ "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" }
{ "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" }
{ "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" }

Tip

Query Vector Type

The preceding example creates an instance of bson::binary::Vector, introduced in bson v2.14, to serve as the query vector. You can also use a vec of numbers as a query vector, but we recommend that you use the Vector type to improve storage efficiency.

Vector Search Score

The following code performs the same query as in the preceding example, but outputs only the title field and vectorSearchScore meta field, which describes how well the document matches the query vector:

let pipeline = vec![
    doc! {
        "$vectorSearch": doc! {
            "queryVector": &query_vector,
            "path": "plot_embedding",
            "numCandidates": 150,
            "index": "vector_index",
            "limit": 5
        }
    },
    doc! {
        "$project": doc! {
            "_id": 0,
            "title": 1,
            "score": doc! { "$meta": "vectorSearchScore" },
        }
    },
];
let mut results = my_coll.aggregate(pipeline).await?;
while let Some(result) = results.try_next().await? {
    println!("{}", result);
}

{ "title": "Thrill Seekers", "score": 0.9332504272460938 }
{ "title": "About Time", "score": 0.9312690496444702 }
{ "title": "The Time Machine", "score": 0.9295310378074646 }
{ "title": "Crusade in Jeans", "score": 0.9290415048599243 }
{ "title": "Timecop", "score": 0.9283164739608765 }

Vector Search Options

You can use a $vectorSearch stage to perform many types of Atlas Vector Search queries. Depending on your desired query, you can specify the following options in the stage definition:

Optional Parameter	Type	Description	Default Value
`exact`	boolean	Specifies whether to run an Exact Nearest Neighbor (`true`) or Approximate Nearest Neighbor (`false`) search	`false`
`filter`	document	Specifies a pre-filter for documents to search on	No filtering
`numCandidates`	number	Specifies the number of nearest neighbors to use during the search	No limit

To learn more about these options, see the Fields section of the $vectorSearch operator reference in the Atlas documentation.

Additional Information

To learn more about the concepts mentioned in this guide, see the following Server manual entries:

To learn more about the behavior of the aggregate() method, see the Aggregation Operations section of the Retrieve Data guide.

API Documentation

To learn more about the methods and types mentioned in this guide, see the following API documentation:

Back

Aggregation

Indexes