Run Vector Search Queries

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Definition

ANN Search
ENN Search
Syntax
Fields
Behavior
Atlas Vector Search Index
Atlas Vector Search Score
Atlas Vector Search Pre-Filter
Considerations
Limitations
Supported Clients
Parallel Query Execution Across Segments
Examples
ANN Examples
ENN Example

Atlas Vector Search queries take the form of an aggregation pipeline stage. For the $vectorSearch queries, Atlas Vector Search returns the results of your semantic search.

Note

Atlas Vector Search supports ANN search on clusters running MongoDB v6.0.11, v7.0.2, or later and ENN search on clusters running MongoDB v6.0.16, v7.0.10, v7.3.2, or later.

Definition

The $vectorSearch stage performs an ANN or ENN search on a vector in the specified field.

ANN Search

For ANN search, Atlas Vector Search finds vector embeddings in your data that are closest to the vector embedding in your query based on their proximity in multi-dimensional space and based on the number of neighbors that it considers. It uses the Hierarchical Navigable Small Worlds algorithm and finds the vector embeddings most similar to the vector embedding in your query without scanning every vector. Therefore, ANN search is ideal for querying large datasets without significant filtering.

ENN Search

For ENN search, Atlas Vector Search exhaustively searches all the indexed vector embeddings by calculating the distance between all the embeddings and finds the exact nearest neighbor for the vector embedding in your query. This is computationally intensive and might negatively impact query latency. Therefore, we recommend ENN searches for the following use-cases:

You want to determine the recall and accuracy of your ANN query using the ideal, exact results for the ENN query.
You want to query less than 10000 documents without having to tune the number of nearest neighbors to consider.
Your want to include selective pre-filters in your query against collections where less than 5% of your data meets the given pre-filter.

Syntax

The field that you want to search must be indexed as Atlas Vector Search vector type inside a vectorSearch index type.

$vectorSearch

A $vectorSearch pipeline has the following prototype form:

{
  "$vectorSearch": {
    "exact": true | false,
    "filter": {<filter-specification>},
    "index": "<index-name>",
    "limit": <number-of-results>,
    "numCandidates": <number-of-candidates>,
    "path": "<field-to-search>",
    "queryVector": [<array-of-numbers>]
  }
}

Fields

The $vectorSearch stage takes a document with the following fields:

Field	Type	Necessity	Description
`exact`	boolean	Optional	This is required if `numCandidates` is omitted. Flag that specifies whether to run ENN or ANN search. Value can be one of the following: `false` - to run ANN search `true` - to run ENN search If omitted, defaults to `false`. ENN search is supported on Atlas clusters running MongoDB version 6.0.16, 7.0.10, 7.32, or later.
`filter`	document	Optional	Any MQL match expression that compares an indexed field with a boolean, date, objectId, number (not decimals), string, or UUID to use as a pre-filter. To learn which query and aggregation pipeline operators Atlas Vector Search supports in your filter, see Atlas Vector Search Pre-Filter.
`index`	string	Required	Name of the Atlas Vector Search index to use. Atlas Vector Search doesn't return results if you misspell the index name or if the specified index doesn't already exist on the cluster.
`limit`	number	Required	Number (of type `int` only) of documents to return in the results. This value can't exceed the value of `numCandidates` if you specify `numCandidates`.
`numCandidates`	number	Optional	This field is required if `exact` is `false` or omitted. Number of nearest neighbors to use during the search. Value must be less than or equal to (`<=`) `10000`. You can't specify a number less than the number of documents to return (`limit`). We recommend that you specify a number higher than the number of documents to return (`limit`) to increase accuracy although this might impact latency. For example, we recommend a ratio of ten to twenty nearest neighbors for a limit of only one document. This overrequest pattern is the recommended way to trade off latency and recall in your ANN searches, and we recommend tuning this on your specific dataset.
`path`	string	Required	Indexed vector type field to search. To learn more, see Path Construction.
`queryVector`	array of numbers	Required	Array of numbers of the BSON `double`, BSON BinData `vector` subtype `float32`, or BSON BinData `vector` subtype `int1` or `int8` type that represent the query vector. The number type must match the indexed field value type. Otherwise, Atlas Vector Search doesn't return any results or errors. To learn more about generating BSON BinData `vector` subtype `float32` or `vector` subtype `int1` or `int8` vectors for your query, see How to Ingest Pre-Quantized Vectors. The array size must match the number of vector `dimensions` specified in the index definition for the field. You must embed your query with the same model that you used to embed the data.

Behavior

$vectorSearch must be the first stage of any pipeline where it appears.

Atlas Vector Search Index

You must index the fields to search using the $vectorSearch stage inside a vectorSearch type index definition. You can index the following types of fields in an Atlas Vector Search vectorSearch type index definition:

Fields that contain vector embeddings as vector type.
Fields that contain boolean, date, objectId, numeric, string, and UUID values as filter type to enable vector search on pre-filtered data.

To learn more about these Atlas Vector Search field types, see How to Index Fields for Vector Search.

Atlas Vector Search Score

Atlas Vector Search assigns a score, in a fixed range from 0 to 1 (where 0 indicates low similarity and 1 indicates high similarity), to every document that it returns. For cosine and dotProduct similarities, Atlas Vector Search normalizes the score to ensure that the score is not negative.

For cosine, Atlas Vector Search uses the following algorithm to normalize the score:

score = (1 + cosine(v1,v2)) / 2

For dotProduct, Atlas Vector Search uses the following algorithm to normalize the score:

score = (1 + dotProduct(v1,v2)) / 2

These algorithms show that Atlas Vector Search normalizes the score by considering the similarity score of the document vector (v1) and the query vector (v2), which has the range [-1, 1]. Atlas Vector Search adds 1 to the similarity score to normalize the score to a range [0, 2] and then divides by 2 to ensure a value between 0 and 1.

For euclidean similarity, Atlas Vector Search uses the following algorithm to normalize the score to ensure a value between 0 and 1:

score = 1 / (1 + euclidean(v1,v2))

The preceding algorithm shows that Atlas Vector Search normalizes the score by calculating the euclidean distance, which is the distance between the document vector (v1) and the query vector (v2), which has the range [0, ∞]. Atlas Vector Search then transforms the distance to a similarity score by adding 1 to the distance and then divides 1 by the similarity score to ensure a value between 0 and 1.

The score assigned to a returned document is part of the document's metadata. To include each returned document's score along with the result set, use a $project stage in your aggregation pipeline.

To retrieve the score of your Atlas Vector Search query results, use vectorSearchScore as the value in the $meta expression. That is, after the $vectorSearch stage, in the $project stage, the score field takes the $meta expression. The expression requires the vectorSearchScore value to return the score of documents for the vector search.

Example

1 db.<collection>.aggregate([
2   {
3     "$vectorSearch": {
4       <query-syntax>
5     }
6   },
7   {
8     "$project": {
9       "<field-to-include>": 1,
10       "<field-to-exclude>": 0,
11       "score": { "$meta": "vectorSearchScore" }
12     }
13   }
14 ])

Note

Pre-filtering your data doesn't affect the score that Atlas Vector Search returns using $vectorSearchScore for $vectorSearch queries.

Atlas Vector Search Pre-Filter

The $vectorSearch filter option matches only BSON boolean, date, objectId, numeric, string, and UUID values. You must index the fields that you want to filter your data by as the filter type in a vectorSearch type index definition. Filtering your data is useful to narrow the scope of your semantic search and ensure that not all vectors are considered for comparison.

Atlas Vector Search supports the $vectorSearch filter option for the following MQL match expressions:

The $vectorSearch filter option supports only the following aggregation pipeline operators:

Note

The $vectorSearch filter option doesn't support other query operators, aggregation pipeline operators, or Atlas Search operators.

Considerations

Atlas Vector Search supports the short form of $eq. In the short form, you don't need to specify $eq in the query.

Example

For example, consider the following filter with $eq:

"filter": { "_id": { "$eq": ObjectId("5a9427648b0beebeb69537a5") }

You can run the preceding query using the short form of $eq in the following way:

"filter": { "_id": ObjectId("5a9427648b0beebeb69537a5") }

You can also specify an array of filters in a single query by using the $and operator.

Example

For example, consider the following pre-filter for documents with a genres field equal to Action and a year field with the value 1999, 2000, or 2001:

"filter": {
  "$and": [
    { "genres": "Action" },
    { "year": { "$in": [ 1999, 2000, 2001 ] } }
  ]
}

Limitations

$vectorSearch is supported only on Atlas clusters running the following MongoDB versions:

v6.0.11
v7.0.2 and later (including RCs).

$vectorSearch can't be used in view definition and the following pipeline stages:

$lookup sub-pipeline
$unionWith sub-pipeline
$facet pipeline stage

You can pass the results of $vectorSearch to this stage.

Supported Clients

You can run $vectorSearch queries by using the Atlas UI, mongosh, and any MongoDB driver.

You can also use Atlas Vector Search with local Atlas deployments that you create with the Atlas CLI. To learn more, see Create a Local Atlas Deployment.

Parallel Query Execution Across Segments

We recommend dedicated Search Nodes to isolate vector search query processing. You might see improved query performance on the dedicated Search Nodes. Note that the high-CPU systems might provide more performance improvement. When Atlas Vector Search runs on search nodes, Atlas Vector Search parallelizes query execution across segments of data.

Parallelization of query processing improves the response time in many cases, such as queries on large datasets. Using intra-query parallelism during Atlas Vector Search query processing utilizes more resources, but improves latency for each individual query.

Note

Atlas Vector Search doesn't guarantee that each query will run concurrently. For example, when too many concurrent queries are queued, Atlas Vector Search might fall back to single-threaded execution.

You might see inconsistent results for the same successive queries. To mitigate this, increase the value of numCandidates in your $vectorSearch queries.

Examples

The following queries search the sample sample_mflix.embedded_movies collection using the $vectorSearch stage. The queries search the plot_embedding field, which contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. If you added the sample collection to your Atlas cluster and created the sample indexes for the collection, you can run the following queries against the collection.

Note

Pasting the queryVector from the sample code into your terminal might take a while depending on your machine.

➤ Use the Select your language drop-down menu to set the language of the examples in this page.

ANN Examples

The following query uses the $vectorSearch stage to search the plot_embedding field using vector embeddings for the string time travel. It considers up to 150 nearest neighbors, and returns 10 documents in the results. The query also specifies a $project stage to do the following:

Exclude the _id field and include only the plot and title fields in the results.
Add a field named score that shows the vector search score for each document in the results.

1 db.embedded_movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "vector_index",
5       "path": "plot_embedding",
6       "queryVector": 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8       "limit": 10
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10   },
11   {
12     "$project": {
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16       "score": { $meta: "vectorSearchScore" }
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1 [
2   {
3     plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
4     title: 'Thrill Seekers',
5     score: 0.7892671227455139
6   },
7   {
8     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.',
9     title: 'About Time',
10     score: 0.7843604683876038
11   },
12   {
13     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.',
14     title: 'The Time Machine',
15     score: 0.7801066637039185
16   },
17   {
18     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...",
19     title: 'Crusade in Jeans',
20     score: 0.7789170742034912
21   },
22   {
23     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.',
24     title: 'Timecop',
25     score: 0.7771612405776978
26   },
27   {
28     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...',
29     title: 'A.P.E.X.',
30     score: 0.7730885744094849
31   },
32   {
33     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.",
34     title: 'Men in Black 3',
35     score: 0.7712380886077881
36   },
37   {
38     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.',
39     title: 'Tomorrowland',
40     score: 0.7669923901557922
41   },
42   {
43     plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
44     title: 'Love Story 2050',
45     score: 0.7649372816085815
46   },
47   {
48     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 ...',
49     title: 'The Portal',
50     score: 0.7640786170959473
51   }
52 ]

The following query filters the documents for movies released between January 01, 1955 and January 01, 1975 before performing the semantic search against the sample vector data. It uses the $and operator to perform a logical AND operation of the specified dates. It then searches the plot_embedding field in the filtered documents for 150 nearest neighbors using the vector embeddings for the string kids adventure, and returns 10 documents in the results. The query also specifies a $project stage to do the following:

Exclude the _id field and include only plot, title, and year fields in the results.
Add a field named score that shows the vector search score of the documents in the results.

1 db.embedded_movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "vector-search-tutorial",
5       "path": "plot_embedding",
6       "filter": {
7         "$and": [
8           {
9             "year": {  "$gt": 1955  }
10           }, 
11           {
12             "year": {  "$lt": 1975 }
13           }
14         ]
15       },
16       "queryVector": 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21   {
22     "$project": {
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27       "score": { $meta: "vectorSearchScore" }
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1 [
2   {
3     plot: 'In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...',
4     title: 'Peter Pan',
5     year: 1960,
6     score: 0.748110830783844
7   },
8   {
9     plot: 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.',
10     title: 'Chitty Chitty Bang Bang',
11     year: 1968,
12     score: 0.7442465424537659
13   },
14   {
15     plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.',
16     title: 'That Man from Rio',
17     year: 1964,
18     score: 0.7416020035743713
19   },
20   {
21     plot: 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.',
22     title: 'The Little Prince',
23     year: 1974,
24     score: 0.7378944158554077
25   },
26   {
27     plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.',
28     title: 'The Red Balloon',
29     year: 1956,
30     score: 0.7342712879180908
31   },
32   {
33     plot: 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.',
34     title: 'Willy Wonka & the Chocolate Factory',
35     year: 1971,
36     score: 0.7342107892036438
37   },
38   {
39     plot: 'An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.',
40     title: 'Bedknobs and Broomsticks',
41     year: 1971,
42     score: 0.7339356541633606
43   },
44   {
45     plot: "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.",
46     title: 'Pastoral Hide and Seek',
47     year: 1974,
48     score: 0.733299970626831
49   },
50   {
51     plot: 'A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.',
52     title: 'The Three Musketeers',
53     year: 1973,
54     score: 0.7331198453903198
55   },
56   {
57     plot: 'A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.',
58     title: 'Frosty',
59     year: 1964,
60     score: 0.7318308353424072
61   }
62 ]

Atlas Vector Search filters the documents based on the year field value that ranges between 1955 and 1975. It returns documents that summarize children's adventures in the plot for movies released between 1955 and 1975.

Tip

See also: Additional Filter Examples

The How to Perform Semantic Search Against Data in Your Atlas Cluster tutorial demonstrates other pre-filters in semantic search queries against the embedded data in the sample_mflix.embedded_movies collection.

Exclude the _id field and include only the plot and title fields in the results.
Add a field named score that shows the vector search score for each document in the results.

1 using MongoDB.Bson;
2 using MongoDB.Bson.Serialization.Attributes;
3 using MongoDB.Bson.Serialization.Conventions;
4 using MongoDB.Driver;
5 
6 public class vectorSearchBasicQuery 
7 {
8   // define connection to your Atlas cluster
9   private const string MongoConnectionString = "<connection-string>";
10 
11   public static void Main(string[] args){
12     var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
13     ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
14 
15     // connect to your Atlas cluster
16     var mongoClient = new MongoClient(MongoConnectionString);
17 
18     // define namespace
19     var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
20     var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
21 
22     // define vector embeddings to search
23     var vector = new[] 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24     var options = new VectorSearchOptions<EmbeddedMovie>() {
25         IndexName = "vector_index",
26         NumberOfCandidates = 150
27     };
28 
29     // run query
30     var results = moviesCollection.Aggregate()
31                 .VectorSearch(movie => movie.Embedding, vector, 10, options)
32                 .Project(Builders<EmbeddedMovie>.Projection
33                   .Include(movie => movie.Title)
34                   .Include(movie => movie.Plot)
35                   .Exclude(movie => movie.Id)
36                   .MetaVectorSearchScore(movie => movie.Score))
37                 .ToList();
38 
39     // print results
40     foreach (var movie in results)
41       {
42         Console.WriteLine(movie.ToJson());
43       }
44   }
45 }
46 
47 [BsonIgnoreExtraElements]
48 public class EmbeddedMovie
49 {
50     [BsonIgnoreIfDefault]
51     public ObjectId Id { get; set; }
52     public string? Title { get; set; }
53     public string? Plot { get; set; }
54     [BsonElement("plot_embedding")]
55     public double[]? Embedding { get; set; }
56     public double Score { get; set; }
57 }

1 { "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" : 0.78926712274551392 }
2 { "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" : 0.78436046838760376 }
3 { "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" : 0.78010666370391846 }
4 { "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" : 0.77891707420349121 }
5 { "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" : 0.77716124057769775 }
6 { "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" : 0.77308857440948486 }
7 { "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" : 0.77123808860778809 }
8 { "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" : 0.76699239015579224 }
9 { "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" : 0.76493728160858154 }
10 { "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", "score" : 0.76407861709594727 }

Exclude the _id field and include only plot, title, and year fields in the results.
Add a field named score that shows the vector search score of the documents in the results.

1 using System.Reflection.Emit;
2 using MongoDB.Bson;
3 using MongoDB.Bson.Serialization.Attributes;
4 using MongoDB.Bson.Serialization.Conventions;
5 using MongoDB.Driver;
6 using MongoDB.Driver.Search;
7 
8 public class vectorSearchFilterQuery 
9 {
10   // define connection to your Atlas cluster
11   private const string MongoConnectionString = "<connection-string>";
12 
13   public static void Main(string[] args){
14     var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
15     ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
16 
17     // connect to your Atlas cluster
18     var mongoClient = new MongoClient(MongoConnectionString);
19 
20     // define namespace
21     var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
22     var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
23 
24     // define vector embeddings to search
25     var vector = new[] {0.02421053,-0.022372592,-0.006231137,-0.02168502,-0.020375984,0.037552103,-0.010505334,-0.027026938,0.0070674648,-0.020032197,0.01783725,0.016303431,0.014584498,-0.018736385,0.009031017,-0.0045981496,0.02750295,-0.028322749,0.010624337,-0.024236975,-0.0048659067,0.015153068,-0.000490888,-0.022161031,-0.0024560927,-0.007411252,0.009745035,-0.01886861,0.02112967,-0.011939983,0.015153068,0.0025800543,0.017824028,-0.02410475,-0.016633997,-0.0018214093,-0.008323609,-0.009222744,0.009388026,-0.0028296304,0.0017536436,0.0065517845,-0.011635863,-0.028454976,-0.018934723,0.012951509,-0.0032015154,-0.005880739,-0.03115238,0.012951509,0.0057749585,0.009202911,-0.0069352393,0.00205611,0.0063732797,0.0039700773,-0.007100521,-0.0077087595,0.011596196,-0.010207825,-0.007100521,-0.0051006074,-0.01670011,0.012773004,-0.035304267,-0.0074971984,0.0025800543,-0.006118745,0.030253245,-0.0010751605,0.039456155,0.007821151,-0.0017189344,-0.0010801188,0.0062575825,-0.011490415,-0.022637043,0.004743598,-0.012601111,0.0197413,-0.0015255542,-0.025942687,-0.03284487,0.020389207,0.009797926,0.0141217075,-0.0015172901,0.025982354,-0.011589585,-0.001138794,0.0006131968,0.016832335,0.017916586,0.014412603,-0.0027155858,0.011854036,-0.02169824,0.02112967,-0.020680103,-0.007391418,-0.012872174,0.021473458,0.0047766543,-0.0048394613,-0.024395647,0.0065418677,0.009797926,-0.00449898,0.041836217,0.0023833686,-0.021737909,0.0136721395,0.014148152,-0.028772317,-0.027899627,-0.015695194,-0.012521776,0.02205525,-0.01927851,-0.022068473,0.020971,0.02317917,0.030544141,-0.011827591,0.0075170323,0.023086611,-0.02164535,-0.01732157,0.007510421,-0.027635176,0.016263764,-0.0008801275,0.033109322,-0.014505162,-0.029909458,0.036679417,0.0074971984,0.0059137954,-0.031178825,-0.012634167,0.008416167,0.030491251,-0.016832335,-0.009507029,0.010016099,0.009778093,0.007840985,0.010928456,-0.009685534,-0.027661622,0.024752656,-0.024871659,0.01516629,0.002778393,0.0059501575,0.022042029,0.0005441915,0.0076889256,-0.009883873,-0.019966085,0.008508725,0.0098045375,0.0091169635,-0.02750295,0.012501942,0.03480181,0.021751132,0.020746216,0.003546955,-0.014690278,0.010445832,0.008469057,-0.00007535833,0.0059600743,-0.013526691,0.029539226,-0.011126795,0.025400562,-0.025466675,-0.0046080663,-0.013923368,-0.009011183,0.019318178,0.019053727,-0.012085431,-0.0074707535,0.0013024234,0.0076624807,0.0060460214,-0.0023007276,0.017757915,0.031258162,0.0008768218,-0.003695709,-0.6981518,-0.012058986,0.008931847,-0.02914255,0.00833022,0.028349195,0.013857256,0.0029668147,-0.008164939,-0.001494977,-0.0011197866,0.0104855,0.014610942,-0.0066608707,0.000643774,0.0020676798,0.008607894,-0.023787407,0.020494986,0.015443964,-0.019833859,0.012905231,0.013387854,-0.020918109,0.0035800114,0.026775708,0.005920407,-0.018233927,-0.008759954,0.0005437783,-0.022081695,0.0071996907,-0.002963509,0.004092386,0.057967756,-0.015285294,-0.008978127,0.027740957,0.015853863,0.047178138,-0.018366152,-0.0064889775,0.029777233,0.0141217075,0.007847597,0.02200236,0.031125935,0.010611114,-0.00663112,-0.005940241,0.017215788,-0.019992528,-0.01644888,-0.013447356,0.001490845,0.007893875,0.016276987,-0.0062939445,0.00032333322,0.0020230536,-0.025360893,0.028587202,-0.009645866,0.01459772,-0.012376328,0.03202507,-0.006059244,0.010888788,0.014518384,-0.034405135,0.023364285,0.018895056,-0.009361581,-0.0011255714,0.00663112,0.016885225,0.01609187,-0.006750123,-0.035304267,0.0022660184,0.027714511,0.01680589,-0.03686453,-0.008045935,0.052943178,-0.0091169635,-0.0066840104,0.018405821,0.00027374856,0.0005235312,0.0138969235,0.018075256,0.0005850988,-0.0074971984,0.0011255714,-0.011054071,-0.0022048638,0.0043931995,0.021142893,-0.02472621,-0.007232747,0.0014858865,-0.00062228733,-0.017903363,-0.0013495288,-0.0001454483,0.0027370725,0.0060129645,0.0364943,-0.04601455,-0.008713675,-0.017215788,-0.017784359,-0.007100521,-0.014610942,-0.027978962,0.0046179835,-0.010267328,0.036785197,-0.019542962,0.028719427,0.004343615,0.0067765685,-0.018075256,-0.004462618,0.010121879,-0.0024957606,-0.00883929,0.0017007533,-0.011371412,-0.007788095,0.002418078,-0.01053839,-0.018458711,-0.0048328503,0.0035072872,0.0043568374,-0.006389808,0.027635176,-0.002768476,-0.033479553,-0.0069749067,-0.00096276856,-0.0034048124,0.012773004,-0.01979419,-0.003675875,-0.011655698,-0.026709596,-0.0009206216,-0.009295468,0.011391246,0.0050510224,0.0027486421,0.0024246892,-0.01264739,0.004687402,-0.0058377655,0.0117945345,-0.009388026,0.010545001,0.020481765,-0.000089768866,-0.022425482,-0.013487023,-0.008316998,-0.019503294,0.025942687,0.0076889256,-0.03355889,-0.0071071326,-0.019106617,-0.015430742,0.021724686,0.0019652047,0.011113572,-0.019410737,-0.023615515,0.000523118,0.019027282,-0.015853863,-0.011887092,-0.021804022,-0.013473801,-0.0049518533,-0.00071773777,-0.003194904,0.046411227,-0.0108689545,0.04003795,-0.0026626955,0.03146972,-0.005804709,-0.013645695,0.0046973187,-0.010148324,0.02292794,0.0310466,0.018709939,0.020005751,0.028534312,-0.02134123,0.044031166,-0.00021548661,0.018458711,-0.038795028,-0.00930208,-0.013738252,0.029486336,-0.0019503294,0.008812845,-0.02755584,0.004852684,-0.013301908,

1	db.<collection>.aggregate([
2	{
3	"$vectorSearch": {
4	<query-syntax>
5	}
6	},
7	{
8	"$project": {
9	"<field-to-include>": 1,
10	"<field-to-exclude>": 0,
11	"score": { "$meta": "vectorSearchScore" }
12	}
13	}
14	])

1	db.embedded_movies.aggregate([
2	{
3	"$vectorSearch": {
4	"index": "vector_index",
5	"path": "plot_embedding",
6	"queryVector": 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12	"$project": {
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1	[
2	{
3	plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
4	title: 'Thrill Seekers',
5	score: 0.7892671227455139
6	},
7	{
8	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.',
9	title: 'About Time',
10	score: 0.7843604683876038
11	},
12	{
13	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.',
14	title: 'The Time Machine',
15	score: 0.7801066637039185
16	},
17	{
18	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...",
19	title: 'Crusade in Jeans',
20	score: 0.7789170742034912
21	},
22	{
23	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.',
24	title: 'Timecop',
25	score: 0.7771612405776978
26	},
27	{
28	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...',
29	title: 'A.P.E.X.',
30	score: 0.7730885744094849
31	},
32	{
33	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.",
34	title: 'Men in Black 3',
35	score: 0.7712380886077881
36	},
37	{
38	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.',
39	title: 'Tomorrowland',
40	score: 0.7669923901557922
41	},
42	{
43	plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.',
44	title: 'Love Story 2050',
45	score: 0.7649372816085815
46	},
47	{
48	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 ...',
49	title: 'The Portal',
50	score: 0.7640786170959473
51	}
52	]

1	db.embedded_movies.aggregate([
2	{
3	"$vectorSearch": {
4	"index": "vector-search-tutorial",
5	"path": "plot_embedding",
6	"filter": {
7	"$and": [
8	{
9	"year": { "$gt": 1955 }
10	},
11	{
12	"year": { "$lt": 1975 }
13	}
14	]
15	},
16	"queryVector": 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1	[
2	{
3	plot: 'In this magical tale about the boy who refuses to grow up, Peter Pan and his mischievous fairy sidekick Tinkerbell visit the nursery of Wendy, Michael, and John Darling. With a sprinkling ...',
4	title: 'Peter Pan',
5	year: 1960,
6	score: 0.748110830783844
7	},
8	{
9	plot: 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.',
10	title: 'Chitty Chitty Bang Bang',
11	year: 1968,
12	score: 0.7442465424537659
13	},
14	{
15	plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.',
16	title: 'That Man from Rio',
17	year: 1964,
18	score: 0.7416020035743713
19	},
20	{
21	plot: 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.',
22	title: 'The Little Prince',
23	year: 1974,
24	score: 0.7378944158554077
25	},
26	{
27	plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.',
28	title: 'The Red Balloon',
29	year: 1956,
30	score: 0.7342712879180908
31	},
32	{
33	plot: 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.',
34	title: 'Willy Wonka & the Chocolate Factory',
35	year: 1971,
36	score: 0.7342107892036438
37	},
38	{
39	plot: 'An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain.',
40	title: 'Bedknobs and Broomsticks',
41	year: 1971,
42	score: 0.7339356541633606
43	},
44	{
45	plot: "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.",
46	title: 'Pastoral Hide and Seek',
47	year: 1974,
48	score: 0.733299970626831
49	},
50	{
51	plot: 'A young swordsman comes to Paris and faces villains, romance, adventure and intrigue with three Musketeer friends.',
52	title: 'The Three Musketeers',
53	year: 1973,
54	score: 0.7331198453903198
55	},
56	{
57	plot: 'A fairy-tale about a conceited young man and a young woman with a tyrannical step-mother, who must overcome magical trials in order to be together.',
58	title: 'Frosty',
59	year: 1964,
60	score: 0.7318308353424072
61	}
62	]

1	{ "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" : 0.78926712274551392 }
2	{ "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" : 0.78436046838760376 }
3	{ "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" : 0.78010666370391846 }
4	{ "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" : 0.77891707420349121 }
5	{ "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" : 0.77716124057769775 }
6	{ "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" : 0.77308857440948486 }
7	{ "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" : 0.77123808860778809 }
8	{ "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" : 0.76699239015579224 }
9	{ "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" : 0.76493728160858154 }
10	{ "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", "score" : 0.76407861709594727 }

1	using System.Reflection.Emit;
2	using MongoDB.Bson;
3	using MongoDB.Bson.Serialization.Attributes;
4	using MongoDB.Bson.Serialization.Conventions;
5	using MongoDB.Driver;
6	using MongoDB.Driver.Search;
7
8	public class vectorSearchFilterQuery
9	{
10	// define connection to your Atlas cluster
11	private const string MongoConnectionString = "<connection-string>";
12
13	public static void Main(string[] args){
14	var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
15	ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
16
17	// connect to your Atlas cluster
18	var mongoClient = new MongoClient(MongoConnectionString);
19
20	// define namespace
21	var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
22	var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
23
24	// define vector embeddings to search
25	var vector = new[] 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