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Atlas Vector Search

Run Vector Search Queries

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.

Note

Optimal recall for ANN search is typically considered to be around 90-95% overlap in results with ENN search but with significantly lower latency. This provides a good balance between accuracy and performance. To achieve this with Atlas Vector Search, tune the numCandidates parameter at query time.

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.

If you enable automatic quantization, Atlas Vector Search uses only the full-fidelity vectors for ENN queries.

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.

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.

filter

document

Optional

MQL expression that compares an indexed field to use as a pre-filter. You can filter on boolean, date, objectId, numeric, string, and UUID values, including arrays of these types.

To learn which MQL 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 at least 20 times higher than the number of documents to return (limit) to increase accuracy.

This overrequest pattern is the recommended way to trade off latency and recall in your ANN searches, and we recommend tuning this parameter based on your specific dataset size and query requirements.

To learn more about other variables that might impact this parameter, see numCandidates Selection.

path

string

Required

Indexed vector type field to search.

queryVector

array of numbers

Required

Array of numbers of float32, BSON BinData vectors with subtype float32, or BSON BinData vectors with subtype int1 or int8 type that represent the query vector.

To learn more about generating BSON binData vectors with subtype float32, int8, int1, 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.

You can query your embeddings with full-fidelity vectors, as long as the vector subtype is the same. This is only possible with binData vectors with subtype float32. If you use any other subtype (int8 or int1), Atlas Vector Search doesn't return any results or errors.

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.

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:

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

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.

  • Additional fields as the 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.

The score is calculated according to the similarity measure that you specify in the index definition. 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

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

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

This algorithm 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))

This algorithm 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 result to ensure a value between 0 and 1.

Each returned document includes the score as 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

1db.<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 BSON boolean, date, objectId, numeric, string, and UUID values, including arrays of these types.

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 operators:

Type
MQL operator

Equals

$eq, $ne

Range

$gt, $lt, $gte, $lte

In set

$in, $nin

Logical

$not, $nor, $and, $or

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 ] } }
  ]
}

numCandidates Selection

For ANN search, we recommend that you specify a numCandidates number at least 20 times higher than the number of documents to return (limit) to increase accuracy. For example, if you set limit to return 5 results, consider setting numCandidates to 100 as a starting point. A higher number also reduces any discrepancies between your ANN and ENN query results. To learn more, see How to Measure the Accuracy of Your Query Results.

This overrequest pattern is the recommended way to trade off latency and recall in your ANN searches. However, we recommend tuning this parameter based on your specific dataset size and query requirements. To ensure that you get accurate results, consider the following variables:

  • Index Size: Larger collections typically require higher numCandidates values to maintain recall. A collection with millions of vectors might need significantly more candidates than one with thousands of vectors.

  • Limit Value: Because numCandidates is highly correlated with the index size, low limit values require proportionally higher numCandidates values to maintain recall.

  • Vector Quantization: Quantized vectors reduce storage at the cost of accuracy. Using quantized vectors (int8 or int1 subtypes) might require higher numCandidates values compared to full precision float32 vectors to maintain similar recall.

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_voyage_3_large field, which contains embeddings created using the voyage-3-large embedding model from Voyage AI.

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

Prerequisites

Before you run these examples, perform the following actions:

Note

If you use mongosh, pasting the queryVector from the sample code into your terminal might take a while depending on your machine.

ANN Examples

The following query uses the $vectorSearch stage to search the plot_embedding_voyage_3_large 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. Save the following embeddings in a file named query-embeddings.js:

    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  2. Load the file into mongosh to use the embeddings in your query:

    load(/<path-to-file>/query-embeddings.js');

  3. Run the following query:

    1db.embedded_movies.aggregate([
    2  {
    3    "$vectorSearch": {
    4      "index": "vector_index",
    5      "path": "plot_embedding_voyage_3_large",
    6      "queryVector": TIME_TRAVEL_EMBEDDING,
    7      "numCandidates": 150,
    8      "limit": 10,
    9      "quantization": "scalar"
    10    }
    11  },
    12  {
    13    "$project": {
    14      "_id": 0,
    15      "plot": 1,
    16      "title": 1,
    17      "score": { $meta: "vectorSearchScore" }
    18    }
    19  }
    20])
    1[
    2  {
    3    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.',
    4    title: 'About Time',
    5    score: 0.7710106372833252
    6  },
    7  {
    8    plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.',
    9    title: 'Retroactive',
    10    score: 0.760047972202301
    11  },
    12  {
    13    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...',
    14    title: 'A.P.E.X.',
    15    score: 0.7576861381530762
    16  },
    17  {
    18    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.',
    19    title: 'Timecop',
    20    score: 0.7576561570167542
    21  },
    22  {
    23    plot: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.',
    24    title: 'Back to the Future Part II',
    25    score: 0.7521393895149231
    26  },
    27  {
    28    plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.',
    29    title: 'Thrill Seekers',
    30    score: 0.7509932518005371
    31  },
    32  {
    33    plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...',
    34    title: 'Timerider: The Adventure of Lyle Swann',
    35    score: 0.7502642869949341
    36  },
    37  {
    38    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.',
    39    title: 'The Time Machine',
    40    score: 0.7502503395080566
    41  },
    42  {
    43    plot: 'A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.',
    44    title: "The Time Traveler's Wife",
    45    score: 0.749496340751648
    46  },
    47  {
    48    plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.',
    49    title: 'The Final Countdown',
    50    score: 0.7469133734703064
    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_voyage_3_large 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. Save the following embeddings in a file named query-embeddings.js:

    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  2. Load the file into mongosh to use the embeddings in your query:

    load('/<path-to-file>/query-embeddings.js');

  3. Run the following query:

    1db.embedded_movies.aggregate([
    2  {
    3    "$vectorSearch": {
    4      "index": "vector_index",
    5      "path": "plot_embedding_voyage_3_large",
    6      "filter": {
    7        "$and": [
    8          {
    9            "year": {  "$gt": 1955  }
    10          }, 
    11          {
    12            "year": {  "$lt": 1975 }
    13          }
    14        ]
    15      },
    16      "queryVector": KIDS_ADVENTURE_EMBEDDING,
    17      "numCandidates": 150,
    18      "limit": 10
    19    }
    20  },
    21  {
    22    "$project": {
    23      "_id": 0,
    24      "title": 1,
    25      "plot": 1,
    26      "year": 1,
    27      "score": { $meta: "vectorSearchScore" }
    28    }
    29  }
    30])
    1[
    2  {
    3    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.',
    4    title: 'Chitty Chitty Bang Bang',
    5    year: 1968,
    6    score: 0.7489712834358215
    7  },
    8  {
    9    plot: 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.',
    10    title: 'The Little Prince',
    11    year: 1974,
    12    score: 0.7346078753471375
    13  },
    14  {
    15    plot: 'In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.',
    16    title: 'In Search of the Castaways',
    17    year: 1962,
    18    score: 0.7281590104103088
    19  },
    20  {
    21    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.',
    22    title: 'Bedknobs and Broomsticks',
    23    year: 1971,
    24    score: 0.720349133014679
    25  },
    26  {
    27    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.",
    28    title: 'Pastoral Hide and Seek',
    29    year: 1974,
    30    score: 0.7195663452148438
    31  },
    32  {
    33    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 ...',
    34    title: 'Peter Pan',
    35    year: 1960,
    36    score: 0.7195032238960266
    37  },
    38  {
    39    plot: 'When his cattle drivers abandon him for the gold fields, rancher Wil Andersen is forced to take on a collection of young boys as his drivers in order to get his herd to market in time to ...',
    40    title: 'The Cowboys',
    41    year: 1972,
    42    score: 0.717952311038971
    43  },
    44  {
    45    plot: 'When a princess is shrunken by an evil wizard, Sinbad must undertake a quest to an island of monsters to cure her and prevent a war.',
    46    title: 'The 7th Voyage of Sinbad',
    47    year: 1958,
    48    score: 0.7148033380508423
    49  },
    50  {
    51    plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.',
    52    title: 'The Red Balloon',
    53    year: 1956,
    54    score: 0.7147064805030823
    55  },
    56  {
    57    plot: 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...',
    58    title: 'Perri',
    59    year: 1957,
    60    score: 0.712087094783783
    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

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.

The following query uses the $vectorSearch stage to search the plot_embedding_voyage_3_large 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.

1using MongoDB.Bson;
2using MongoDB.Bson.Serialization.Attributes;
3using MongoDB.Bson.Serialization.Conventions;
4using MongoDB.Driver;
5
6public 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]
48public 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_voyage_3_large")]
55    public double[]? Embedding { get; set; }
56    public double Score { get; set; }
57}
1{ "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" : 0.7710106372833252 }
2{ "plot" : "A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.", "title" : "Retroactive", "score" : 0.76004797220230103 }
3{ "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" : 0.75768613815307617 }
4{ "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" : 0.75765615701675415 }
5{ "plot" : "After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.", "title" : "Back to the Future Part II", "score" : 0.7521393895149231 }
6{ "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" : 0.75099325180053711 }
7{ "plot" : "Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...", "title" : "Timerider: The Adventure of Lyle Swann", "score" : 0.75026428699493408 }
8{ "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" : 0.75025033950805664 }
9{ "plot" : "A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.", "title" : "The Time Traveler's Wife", "score" : 0.74949634075164795 }
10{ "plot" : "A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.", "title" : "The Final Countdown", "score" : 0.7469133734703064 }

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_voyage_3_large 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.

1using System.Reflection.Emit;
2using MongoDB.Bson;
3using MongoDB.Bson.Serialization.Attributes;
4using MongoDB.Bson.Serialization.Conventions;
5using MongoDB.Driver;
6using MongoDB.Driver.Search;
7
8public 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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0.037876874,0.033142265,0.041607171,-0.011477841,0.031133642,-0.045050524,-0.003981376,-0.025394723,-0.031707536,0.024533885,0.032568373,0.028694602,-0.021090532,-0.019942747,0.054806691,-0.004483532,-0.020086221,-0.01599724,0.018651491,0.020373167,0.000504397,-0.007424728,-0.063702017,-0.014203828,0.030846696,-0.006599758,-0.027259871,-0.024103465,-0.020660114,-0.004985687,0.005559579,-0.034146577,-0.011549577,-0.00173961,0.008321434,0.007030177,-0.015638558};
26    // define filter
27    var yearGtFilter = Builders<EmbeddedMovie>.Filter.Gt("year", 1955);
28    var yearLtFilter = Builders<EmbeddedMovie>.Filter.Lt("year", 1975);
29    // define options 
30    var options = new VectorSearchOptions<EmbeddedMovie>() {
31        Filter = Builders<EmbeddedMovie>.Filter.And(yearGtFilter, yearLtFilter),
32        IndexName = "vector_index",
33        NumberOfCandidates = 150
34    };
35
36    // run query
37    var results = moviesCollection.Aggregate()
38                .VectorSearch(m => m.Embedding, vector, 10, options)
39                .Project(Builders<EmbeddedMovie>.Projection
40                  .Include(m => m.Title)
41                  .Include(movie => movie.Plot)
42                  .Include(movie => movie.Year)
43                  .MetaVectorSearchScore(m => m.Score))
44                .ToList();
45
46    // print results
47    foreach (var movie in results)
48      {
49        Console.WriteLine(movie.ToJson());
50      }
51  }
52}
53
54[BsonIgnoreExtraElements]
55public class EmbeddedMovie
56{
57    [BsonIgnoreIfDefault]
58    public string Title { get; set; }
59    public string Plot { get; set; }
60    public int Year { get; set; }
61    [BsonElement("plot_embedding_voyage_3_large")]
62    public double[] Embedding { get; set; }
63    public double Score { get; set; }
64}
1{ "_id" : { "$oid" : "573a1396f29313caabce34d3" }, "plot" : "A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.", "title" : "Chitty Chitty Bang Bang", "year" : 1968, "score" : 0.74897128343582153 }
2{ "_id" : { "$oid" : "573a1396f29313caabce5648" }, "plot" : "A pilot, stranded in the desert, meets a little boy who is a prince on a planet.", "title" : "The Little Prince", "year" : 1974, "score" : 0.73460787534713745 }
3{ "_id" : { "$oid" : "573a1395f29313caabce1b8d" }, "plot" : "In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.", "title" : "In Search of the Castaways", "year" : 1962, "score" : 0.72815901041030884 }
4{ "_id" : { "$oid" : "573a1396f29313caabce43e1" }, "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.", "title" : "Bedknobs and Broomsticks", "year" : 1971, "score" : 0.72034913301467896 }
5{ "_id" : { "$oid" : "573a1396f29313caabce54e6" }, "plot" : "A young boys' coming of age tale set in a strange, carnivalesque village becomes the recreation of a memory that the director has twenty years later.", "title" : "Pastoral Hide and Seek", "year" : 1974, "score" : 0.71956634521484375 }
6{ "_id" : { "$oid" : "573a1395f29313caabce143b" }, "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 ...", "title" : "Peter Pan", "year" : 1960, "score" : 0.71950322389602661 }
7{ "_id" : { "$oid" : "573a1396f29313caabce49bf" }, "plot" : "When his cattle drivers abandon him for the gold fields, rancher Wil Andersen is forced to take on a collection of young boys as his drivers in order to get his herd to market in time to ...", "title" : "The Cowboys", "year" : 1972, "score" : 0.71795231103897095 }
8{ "_id" : { "$oid" : "573a1394f29313caabce0951" }, "plot" : "When a princess is shrunken by an evil wizard, Sinbad must undertake a quest to an island of monsters to cure her and prevent a war.", "title" : "The 7th Voyage of Sinbad", "year" : 1958, "score" : 0.71480333805084229 }
9{ "_id" : { "$oid" : "573a1394f29313caabce000d" }, "plot" : "A red balloon with a life of its own follows a little boy around the streets of Paris.", "title" : "The Red Balloon", "year" : 1956, "score" : 0.71470648050308228 }
10{ "_id" : { "$oid" : "573a1394f29313caabce074d" }, "plot" : "This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...", "title" : "Perri", "year" : 1957, "score" : 0.71208715438842773 }

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

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.

The following query uses the $vectorSearch stage to search the plot_embedding_voyage_3_large 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.

1package main
2
3import (
4	"context"
5	"fmt"
6	"log"
7
8	"go.mongodb.org/mongo-driver/v2/bson"
9	"go.mongodb.org/mongo-driver/v2/mongo"
10	"go.mongodb.org/mongo-driver/v2/mongo/options"
11)
12
13func main() {
14	ctx := context.Background()
15
16	// Replace the placeholder with your Atlas connection string
17	const uri = "<connection-string>"
18
19	// Connect to your Atlas cluster
20	clientOptions := options.Client().ApplyURI(uri)
21	client, err := mongo.Connect(clientOptions)
22	if err != nil {
23		log.Fatalf("failed to connect to the server: %v", err)
24	}
25	defer func() { _ = client.Disconnect(ctx) }()
26
27	// Set the namespace
28	coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30	queryVector := [2048]float64{
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32	}
33
34	vectorSearchStage := bson.D{
35		{"$vectorSearch", bson.D{
36			{"index", "vector_index"},
37			{"path", "plot_embedding_voyage_3_large"},
38			{"queryVector", queryVector},
39			{"numCandidates", 150},
40			{"limit", 10},
41		}}}
42
43	projectStage := bson.D{
44		{"$project", bson.D{
45			{"_id", 0},
46			{"plot", 1},
47			{"title", 1},
48			{"score", bson.D{{"$meta", "vectorSearchScore"}}},
49		}}}
50
51	cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
52	if err != nil {
53		log.Fatalf("failed to retrieve data from the server: %v", err)
54	}
55	// Display the results
56	type ProjectedMovieResult struct {
57		Title string  `bson:"title"`
58		Plot  string  `bson:"plot"`
59		Score float64 `bson:"score"`
60	}
61
62	var results []ProjectedMovieResult
63	if err = cursor.All(ctx, &results); err != nil {
64		log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
65	}
66	for _, result := range results {
67		fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score)
68	}
69}
1Title: About Time 
2Plot: 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. 
3Score: 0.7710106372833252 
4
5Title: Retroactive 
6Plot: A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband. 
7Score: 0.760047972202301 
8
9Title: A.P.E.X. 
10Plot: 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... 
11Score: 0.7576861381530762 
12
13Title: Timecop 
14Plot: 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. 
15Score: 0.7576561570167542 
16
17Title: Back to the Future Part II 
18Plot: After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip. 
19Score: 0.7521393895149231 
20
21Title: Thrill Seekers 
22Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters. 
23Score: 0.7509932518005371 
24
25Title: Timerider: The Adventure of Lyle Swann 
26Plot: Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ... 
27Score: 0.7502642869949341 
28
29Title: The Time Machine 
30Plot: 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. 
31Score: 0.7502503395080566 
32
33Title: The Time Traveler's Wife 
34Plot: A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage. 
35Score: 0.749496340751648 
36
37Title: The Final Countdown 
38Plot: A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor. 
39Score: 0.7469133734703064

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_voyage_3_large 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.

1package main
2
3import (
4	"context"
5	"fmt"
6	"log"
7
8	"go.mongodb.org/mongo-driver/v2/bson"
9	"go.mongodb.org/mongo-driver/v2/mongo"
10	"go.mongodb.org/mongo-driver/v2/mongo/options"
11)
12
13func main() {
14	ctx := context.Background()
15
16	// Replace the placeholder with your Atlas connection string
17	const uri = "<connection-string>"
18
19	// Connect to your Atlas cluster
20	clientOptions := options.Client().ApplyURI(uri)
21	client, err := mongo.Connect(clientOptions)
22	if err != nil {
23		log.Fatalf("failed to connect to the server: %v", err)
24	}
25	defer func() { _ = client.Disconnect(ctx) }()
26
27	// Set the namespace
28	coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30	queryVector := [2048]float64{
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32	}
33
34	vectorSearchStage := bson.D{
35		{"$vectorSearch", bson.D{
36			{"index", "vector_index"},
37			{"path", "plot_embedding_voyage_3_large"},
38			{"filter", bson.D{{
39				"$and", bson.A{
40					bson.D{{"year", bson.D{{"$gt", 1955}}}},
41					bson.D{{"year", bson.D{{"$lt", 1975}}}},
42				},
43			}}},
44			{"queryVector", queryVector},
45			{"numCandidates", 150},
46			{"limit", 10},
47		}}}
48
49	projectStage := bson.D{
50		{"$project", bson.D{
51			{"_id", 0},
52			{"plot", 1},
53			{"title", 1},
54			{"year", 1},
55			{"score", bson.D{{"$meta", "vectorSearchScore"}}},
56		}}}
57
58	cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
59	if err != nil {
60		log.Fatalf("failed to retrieve data from the server: %v", err)
61	}
62	// display the results
63	type ProjectedMovieResult struct {
64		Title string  `bson:"title"`
65		Plot  string  `bson:"plot"`
66		Year  int32   `bson:"year"`
67		Score float64 `bson:"score"`
68	}
69
70	var results []ProjectedMovieResult
71	if err = cursor.All(ctx, &results); err != nil {
72		log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
73	}
74	for _, result := range results {
75		fmt.Printf("Title: %v \nPlot: %v \nYear: %v \nScore: %v \n\n", result.Title, result.Plot, result.Year, result.Score)
76	}
77}
1Title: Chitty Chitty Bang Bang 
2Plot: 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. 
3Year: 1968 
4Score: 0.7489712834358215 
5
6Title: The Little Prince 
7Plot: A pilot, stranded in the desert, meets a little boy who is a prince on a planet. 
8Year: 1974 
9Score: 0.7346078753471375 
10
11Title: In Search of the Castaways 
12Plot: In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast. 
13Year: 1962 
14Score: 0.7281590104103088 
15
16Title: Bedknobs and Broomsticks 
17Plot: An apprentice witch, three kids and a cynical conman search for the missing component to a magic spell useful to the defense of Britain. 
18Year: 1971 
19Score: 0.720349133014679 
20
21Title: Pastoral Hide and Seek 
22Plot: 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. 
23Year: 1974 
24Score: 0.7195663452148438 
25
26Title: Peter Pan 
27Plot: 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 ... 
28Year: 1960 
29Score: 0.7195032238960266 
30
31Title: The Cowboys 
32Plot: When his cattle drivers abandon him for the gold fields, rancher Wil Andersen is forced to take on a collection of young boys as his drivers in order to get his herd to market in time to ... 
33Year: 1972 
34Score: 0.717952311038971 
35
36Title: The 7th Voyage of Sinbad 
37Plot: When a princess is shrunken by an evil wizard, Sinbad must undertake a quest to an island of monsters to cure her and prevent a war. 
38Year: 1958 
39Score: 0.7148033380508423 
40
41Title: The Red Balloon 
42Plot: A red balloon with a life of its own follows a little boy around the streets of Paris. 
43Year: 1956 
44Score: 0.7147064805030823 
45
46Title: Perri 
47Plot: This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten... 
48Year: 1957 
49Score: 0.7120871543884277

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

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.

The following query uses the $vectorSearch stage to search the plot_embedding_voyage_3_large 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.

1import com.mongodb.client.MongoClient;
2import com.mongodb.client.MongoClients;
3import com.mongodb.client.MongoCollection;
4import com.mongodb.client.MongoDatabase;
5import com.mongodb.client.model.search.FieldSearchPath;
6import org.bson.Document;
7import org.bson.conversions.Bson;
8
9import java.util.List;
10
11import static com.mongodb.client.model.Aggregates.project;
12import static com.mongodb.client.model.Aggregates.vectorSearch;
13import static com.mongodb.client.model.Projections.fields;
14import static com.mongodb.client.model.Projections.include;
15import static com.mongodb.client.model.Projections.exclude;
16import static com.mongodb.client.model.Projections.metaVectorSearchScore;
17import static com.mongodb.client.model.search.SearchPath.fieldPath;
18import static com.mongodb.client.model.search.VectorSearchOptions.approximateVectorSearchOptions;
19import static java.util.Arrays.asList;
20
21public class BasicQuery {
22  public static void main( String[] args ) {
23    // specify connection
24    String uri = "<connection-string>";
25
26    // establish connection and set namespace
27    try (MongoClient mongoClient = MongoClients.create(uri)) {
28      MongoDatabase database = mongoClient.getDatabase("sample_mflix");
29      MongoCollection<Document> collection = database.getCollection("embedded_movies");
30      
31
32      // define $vectorSearch query options
33      List<Double> queryVector = 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34      String indexName = "vector_index";
35      FieldSearchPath fieldSearchPath = fieldPath("plot_embedding_voyage_3_large");
36      int limit = 10;
37      int numCandidates = 150;
38      
39      // define pipeline 
40      List<Bson> pipeline = asList(
41        vectorSearch(
42          fieldSearchPath,
43          queryVector,
44          indexName,
45          limit,
46          approximateVectorSearchOptions(numCandidates)),
47        project(
48          fields(exclude("_id"), include("title"), include("plot"),
49          metaVectorSearchScore("score"))));
50      
51      // run query and print results
52      collection.aggregate(pipeline)
53        .forEach(doc -> System.out.println(doc.toJson()));	
54    }
55  }
56}
1{"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": 0.7710106372833252}
2{"plot": "A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.", "title": "Retroactive", "score": 0.760047972202301}
3{"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": 0.7576861381530762}
4{"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": 0.7576561570167542}
5{"plot": "After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.", "title": "Back to the Future Part II", "score": 0.7521393895149231}
6{"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": 0.7509932518005371}
7{"plot": "Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...", "title": "Timerider: The Adventure of Lyle Swann", "score": 0.7502642869949341}
8{"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": 0.7502503395080566}
9{"plot": "A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.", "title": "The Time Traveler's Wife", "score": 0.749496340751648}
10{"plot": "A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.", "title": "The Final Countdown", "score": 0.7469133734703064}

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_voyage_3_large 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.

1import static com.mongodb.client.model.Aggregates.*;  
2import static com.mongodb.client.model.Projections.*;  
3import static com.mongodb.client.model.search.SearchPath.fieldPath;  
4import static java.util.Arrays.asList;  
5import static com.mongodb.client.model.Filters.*;  
6  
7import com.mongodb.client.MongoClient;  
8import com.mongodb.client.MongoClients;  
9import com.mongodb.client.MongoCollection;  
10import com.mongodb.client.MongoDatabase;  
11import com.mongodb.client.model.search.FieldSearchPath;  
12import com.mongodb.client.model.search.VectorSearchOptions;  
13import org.bson.Document;  
14import org.bson.conversions.Bson;  
15  
16import java.util.List;  
17  
18public class FilterQuery {  
19    public static void main(String[] args) {  
20        // Specify connection URI  
21        String uri = "<connection-string>";
22  
23        // Establish connection and set namespace  
24        try (MongoClient mongoClient = MongoClients.create(uri)) {  
25            MongoDatabase database = mongoClient.getDatabase("sample_mflix");  
26            MongoCollection<Document> collection = database.getCollection("embedded_movies");  
27  
28            // Parameters  
29            List<Double> queryVector = 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 
30            String indexName = "vector_index";  
31            FieldSearchPath fieldSearchPath = fieldPath("plot_embedding_voyage_3_large");  
32            long numCandidates = 150;  
33            long limit = 10;  
34  
35            // Define filter criteria  
36            Bson criteria = and(gt("year", 1955), lt("year", 1975));  
37  
38            // Create VectorSearchOptions  
39            VectorSearchOptions options = VectorSearchOptions.approximateVectorSearchOptions(numCandidates)  
40                .filter(criteria);  
41  
42            // Define the pipeline  
43            List<Bson> pipeline = asList(  
44                vectorSearch(  
45                    fieldSearchPath,  
46                    queryVector,  
47                    indexName,  
48                    limit,  
49                    options  
50                ),  
51                project(  
52                    fields(  
53                        exclude("_id"),  
54                        include("title"),  
55                        include("plot"),  
56                        include("year"),  
57                        metaVectorSearchScore("score")  
58                    )  
59                )  
60            );  
61  
62            // Run query and print results  
63            collection.aggregate(pipeline).forEach(doc -> System.out.println(doc.toJson()));  
64        }  
65    }  
66}  
1{"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.", "title": "Chitty Chitty Bang Bang", "year": 1968, "score": 0.7489712834358215}
2{"plot": "A pilot, stranded in the desert, meets a little boy who is a prince on a planet.", "title": "The Little Prince", "year": 1974, "score": 0.7346078753471375}
3{"plot": "In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.", "title": "In Search of the Castaways", "year": 1962, "score": 0.7281590104103088}
4{"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.", "title": "Bedknobs and Broomsticks", "year": 1971, "score": 0.720349133014679}
5{"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.", "title": "Pastoral Hide and Seek", "year": 1974, "score": 0.7195663452148438}
6{"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 ...", "title": "Peter Pan", "year": 1960, "score": 0.7195032238960266}
7{"plot": "When his cattle drivers abandon him for the gold fields, rancher Wil Andersen is forced to take on a collection of young boys as his drivers in order to get his herd to market in time to ...", "title": "The Cowboys", "year": 1972, "score": 0.717952311038971}
8{"plot": "When a princess is shrunken by an evil wizard, Sinbad must undertake a quest to an island of monsters to cure her and prevent a war.", "title": "The 7th Voyage of Sinbad", "year": 1958, "score": 0.7148033380508423}
9{"plot": "A red balloon with a life of its own follows a little boy around the streets of Paris.", "title": "The Red Balloon", "year": 1956, "score": 0.7147064805030823}
10{"plot": "This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...", "title": "Perri", "year": 1957, "score": 0.7120871543884277}

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

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.

The following query uses the $vectorSearch stage to search the plot_embedding_voyage_3_large 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.

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5
6const client = new MongoClient(uri);
7
8async function run() {
9    try {
10        await client.connect();
11
12        // set namespace
13        const database = client.db("sample_mflix");
14        const coll = database.collection("embedded_movies");
15
16        // define pipeline
17        const agg = [
18            {
19              '$vectorSearch': {
20                'index': 'vector_index',
21                'path': 'plot_embedding_voyage_3_large',
22                'queryVector': [
23                  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024262359,0.054507218,-0.009721561,0.005816319,-0.00206687,-0.008766023,0.017365867,-0.000737426,0.018362951,-0.023597637,-0.019110762,0.021935832,0.041545134,-0.020357117,-0.017615138,0.044868745,-0.018030589,-0.032405205,-0.050186522,-0.014540797,0.005213914,-0.006688767,0.047527634,0.040714234
24                ],
25                'numCandidates': 150,
26                'limit': 10
27              }
28            }, {
29              '$project': {
30                '_id': 0,
31                'plot': 1,
32                'title': 1,
33                'score': {
34                  '$meta': 'vectorSearchScore'
35                }
36              }
37            }
38          ];
39        // run pipeline
40        const result = coll.aggregate(agg);
41
42        // print results
43        await result.forEach((doc) => console.dir(JSON.stringify(doc)));
44    } finally {
45        await client.close();
46    }
47}
48run().catch(console.dir);
1'{"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":0.7710106372833252}'
2'{"plot":"A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.","title":"Retroactive","score":0.760047972202301}'
3'{"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":0.7576861381530762}'
4'{"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":0.7576561570167542}'
5'{"plot":"After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.","title":"Back to the Future Part II","score":0.7521393895149231}'
6'{"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":0.7509932518005371}'
7'{"plot":"Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...","title":"Timerider: The Adventure of Lyle Swann","score":0.7502642869949341}'
8'{"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":0.7502503395080566}'
9`{"plot":"A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.","title":"The Time Traveler's Wife","score":0.749496340751648}`
10'{"plot":"A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.","title":"The Final Countdown","score":0.7469133734703064}'

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_voyage_3_large 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.

1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5    
6const client = new MongoClient(uri);
7
8async function run() {
9  try {
10    await client.connect();
11
12    // set namespace
13    const database = client.db("sample_mflix");
14    const coll = database.collection("embedded_movies");
15
16    // define pipeline
17    const agg = [
18      {
19        '$vectorSearch': {
20          'index': 'vector_index', 
21          'path': 'plot_embedding_voyage_3_large', 
22          'filter': {
23            '$and': [
24              {
25                'year': {
26                  '$gt': 1955
27                }, 
28                'year': {
29                  '$lt': 1975
30                }
31              }
32            ]
33          }, 
34          'queryVector': 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
35          'numCandidates': 150, 
36          'limit': 10
37        }
38      }, {
39        '$project': {
40          '_id': 0, 
41          'title': 1, 
42          'plot': 1, 
43          'year': 1, 
44          'score': {
45            '$meta': 'vectorSearchScore'
46          }
47        }
48      }
49    ];
50  // run pipeline
51  const result = coll.aggregate(agg);
52
53  // print results
54    await result.forEach((doc) => console.dir(JSON.stringify(doc)));
55  } finally {
56    await client.close();
57  }
58}
59run().catch(console.dir);
1'{"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.","title":"Chitty Chitty Bang Bang","year":1968,"score":0.7489712834358215}'
2`{"plot":"While hearing the story of \\"Gulliver's Travels\\", a young boy dreams he is the title character on the island of Lilliput .","title":"The New Gulliver","year":1935,"score":0.7459166049957275}`
3'{"plot":"A pilot, stranded in the desert, meets a little boy who is a prince on a planet.","title":"The Little Prince","year":1974,"score":0.7346078753471375}'
4'{"plot":"Pistol-packing tomboy, and grandfather come to discover band of bank robbing bandits taking refuge in the neighboring ghost town.","title":"Yellow Sky","year":1948,"score":0.7290171384811401}'
5'{"plot":"A boy raised by wolves tries to adapt to human village life.","title":"Jungle Book","year":1942,"score":0.7287350296974182}'
6'{"plot":"In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.","title":"In Search of the Castaways","year":1962,"score":0.7281590104103088}'
7'{"plot":"Peter Pan enters the nursery of the Darling children and, with the help of fairy dust, leads them off to Never Never Land, where they meet the nefarious Captain Hook.","title":"Peter Pan","year":1924,"score":0.7273794412612915}'
8`{"plot":"In Africa, the girl Jill Young trades a baby gorilla with two natives and raises the animal. Twelve years later, the talkative and persuasive promoter Max O'Hara organizes a safari to ...","title":"Mighty Joe Young","year":1949,"score":0.7219074368476868}`
9'{"plot":"A living puppet, with the help of a cricket as his conscience, must prove himself worthy to become a real boy.","title":"Pinocchio","year":1940,"score":0.721661388874054}'
10'{"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.","title":"Bedknobs and Broomsticks","year":1971,"score":0.720349133014679}'

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

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.

The following query uses the $vectorSearch stage to search the plot_embedding_voyage_3_large 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.

Work with a runnable version of this example as a Python notebook.

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8  {
9    '$vectorSearch': {
10      'index': 'vector_index', 
11      'path': 'plot_embedding_voyage_3_large', 
12      'queryVector': 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
13      'numCandidates': 150, 
14      'limit': 10
15    }
16  }, {
17    '$project': {
18      '_id': 0, 
19      'plot': 1, 
20      'title': 1, 
21      'score': {
22        '$meta': 'vectorSearchScore'
23      }
24    }
25  }
26]
27
28# run pipeline
29result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
30
31# print results
32for i in result:
33    print(i)
34 
1{'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': 0.7710106372833252}
2{'plot': 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', 'title': 'Retroactive', 'score': 0.760047972202301}
3{'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': 0.7576861381530762}
4{'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': 0.7576561570167542}
5{'plot': 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.', 'title': 'Back to the Future Part II', 'score': 0.7521393895149231}
6{'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': 0.7509932518005371}
7{'plot': 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...', 'title': 'Timerider: The Adventure of Lyle Swann', 'score': 0.7502642869949341}
8{'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': 0.7502503395080566}
9{'plot': 'A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.', 'title': "The Time Traveler's Wife", 'score': 0.749496340751648}
10{'plot': 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', 'title': 'The Final Countdown', 'score': 0.7469133734703064}

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_voyage_3_large 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.

Work with a runnable version of this example as a Python notebook.

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8  {
9    '$vectorSearch': {
10      'index': 'vector_index', 
11      'path': 'plot_embedding_voyage_3_large', 
12      'filter': {
13        '$and': [
14          {
15            'year': {
16              '$gt': 1955
17            }, 
18            'year': {
19              '$lt': 1975
20            }
21          }
22        ]
23      }, 
24      'queryVector': 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
25      'numCandidates': 150, 
26      'limit': 10
27    }
28  }, {
29    '$project': {
30      '_id': 0, 
31      'title': 1, 
32      'plot': 1, 
33      'year': 1, 
34      'score': {
35        '$meta': 'vectorSearchScore'
36      }
37    }
38  }
39]
40
41# run pipeline
42result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
43
44# print results
45for i in result:
46    print(i)
47 
1{'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.', 'title': 'Chitty Chitty Bang Bang', 'year': 1968, 'score': 0.7489712834358215}
2{'plot': 'While hearing the story of "Gulliver\'s Travels", a young boy dreams he is the title character on the island of Lilliput .', 'title': 'The New Gulliver', 'year': 1935, 'score': 0.7459166049957275}
3{'plot': 'A pilot, stranded in the desert, meets a little boy who is a prince on a planet.', 'title': 'The Little Prince', 'year': 1974, 'score': 0.7346078753471375}
4{'plot': 'Pistol-packing tomboy, and grandfather come to discover band of bank robbing bandits taking refuge in the neighboring ghost town.', 'title': 'Yellow Sky', 'year': 1948, 'score': 0.7290171384811401}
5{'plot': 'A boy raised by wolves tries to adapt to human village life.', 'title': 'Jungle Book', 'year': 1942, 'score': 0.7287350296974182}
6{'plot': 'In 19th century England young Mary Grant and her brother Robert Grant embark on a dangerous quest to find their missing father,sea captain Grant,who vanished somewhere along the Chilean coast.', 'title': 'In Search of the Castaways', 'year': 1962, 'score': 0.7281590104103088}
7{'plot': 'Peter Pan enters the nursery of the Darling children and, with the help of fairy dust, leads them off to Never Never Land, where they meet the nefarious Captain Hook.', 'title': 'Peter Pan', 'year': 1924, 'score': 0.7273794412612915}
8{'plot': "In Africa, the girl Jill Young trades a baby gorilla with two natives and raises the animal. Twelve years later, the talkative and persuasive promoter Max O'Hara organizes a safari to ...", 'title': 'Mighty Joe Young', 'year': 1949, 'score': 0.7219074368476868}
9{'plot': 'A living puppet, with the help of a cricket as his conscience, must prove himself worthy to become a real boy.', 'title': 'Pinocchio', 'year': 1940, 'score': 0.721661388874054}
10{'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.', 'title': 'Bedknobs and Broomsticks', 'year': 1971, 'score': 0.720349133014679}

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

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.

ENN Example

The following query uses the $vectorSearch stage to search the plot_embedding_voyage_3_large field using vector embeddings for the string world war. It requests exact matches and limits the results to 10 documents only. The query also specifies a $project stage to do the following:

  • Exclude the _id field and include only the 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. Save the following embeddings in a file named query-embeddings.js:

    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  2. Load the file into mongosh to use the embeddings in your query:

    load('/<path-to-file>/query-embeddings.js');

  3. Run the following query.

    1db.embedded_movies.aggregate([
    2  {
    3    "$vectorSearch": {
    4      "index": "vector_index",
    5      "path": "plot_embedding_voyage_3_large",
    6      "queryVector": WORLD_WAR_EMBEDDING,
    7      "exact": true,
    8      "limit": 10
    9    }
    10  },
    11  {
    12    "$project": {
    13      "_id": 0,
    14      "plot": 1,
    15      "title": 1,
    16      "score": { $meta: "vectorSearchScore" }
    17    }
    18  }
    19])
    1[
    2  {
    3    plot: 'During WW2 the Russian Army sent a special group named "Zvezda" to fight the Nazis in their backyard.',
    4    title: 'The Star',
    5    score: 0.7459506392478943
    6  },
    7  {
    8    plot: "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.",
    9    title: 'P-51 Dragon Fighter',
    10    score: 0.7430292367935181
    11  },
    12  {
    13    plot: 'April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.',
    14    title: 'Fury',
    15    score: 0.7428240776062012
    16  },
    17  {
    18    plot: "It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...",
    19    title: 'Memphis Belle',
    20    score: 0.7340154647827148
    21  },
    22  {
    23    plot: 'The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...',
    24    title: 'The Young Lions',
    25    score: 0.7327103614807129
    26  },
    27  {
    28    plot: 'Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.',
    29    title: 'White Tiger',
    30    score: 0.732368528842926
    31  },
    32  {
    33    plot: 'The story of ordinary men during WWII as seen from three different points of View.',
    34    title: 'The Fallen',
    35    score: 0.7301670908927917
    36  },
    37  {
    38    plot: 'A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.',
    39    title: 'War Pigs',
    40    score: 0.7290490865707397
    41  },
    42  {
    43    plot: 'Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...',
    44    title: 'The Red Baron',
    45    score: 0.728823184967041
    46  },
    47  {
    48    plot: 'In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.',
    49    title: 'Battle of Britain',
    50    score: 0.7270985245704651
    51  }
    52]
1using MongoDB.Bson;
2using MongoDB.Bson.Serialization.Attributes;
3using MongoDB.Bson.Serialization.Conventions;
4using MongoDB.Driver;
5using MongoDB.Driver.Search;
6
7public class vectorSearchBasicQuery 
8{
9  // define connection to your Atlas cluster
10  private const string MongoConnectionString = "<connection-string>";
11
12  public static void Main(string[] args){
13    var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
14    ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
15
16    // connect to your Atlas cluster
17    var mongoClient = new MongoClient(MongoConnectionString);
18
19    // define namespace
20    var moviesDatabase = mongoClient.GetDatabase("sample_mflix");
21    var moviesCollection = moviesDatabase.GetCollection<EmbeddedMovie>("embedded_movies");
22
23    // define vector embeddings to search
24    var vector = new[] 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7938282,-0.001239097,0.00737414,0.029335376,0.014828872,0.043519516,-0.029335376,-0.04287478,0.017166032,-0.015957156,0.004210916,0.029335376,-0.035299163,0.032559045,-0.02095384,0.026756443,-0.023210408,-0.001037618,0.008220353,-0.031914312,0.002216272,-0.030141294,0.007092069,0.015554197,-0.001229023,-0.014022955,-0.002981893,-0.006326448,-0.034976795,0.012411121,-0.024499875,-0.026111709,0.015070647,0.036427446,0.01523183,0.060282588};
25    var options = new VectorSearchOptions<EmbeddedMovie>() {
26        IndexName = "vector_index",
27        Exact = true
28    };
29
30    // run query
31    var results = moviesCollection.Aggregate()
32                .VectorSearch(m => m.Embedding, vector, 10, options)
33                .Project(Builders<EmbeddedMovie>.Projection
34                  .Include(m => m.Title)
35                  .Include(movie => movie.Plot))
36                .ToList();
37
38    // print results
39    foreach (var movie in results)
40      {
41        Console.WriteLine(movie.ToJson());
42      }
43  }
44}
45
46[BsonIgnoreExtraElements]
47public class EmbeddedMovie
48{
49    [BsonIgnoreIfDefault]
50    public string Title { get; set; }
51    public string Plot { get; set; }
52    [BsonElement("plot_embedding_voyage_3_large")]
53    public double[] Embedding { get; set; }
54}
1{ "_id" : { "$oid" : "573a13aaf29313caabd2144d" }, "plot" : "During WW2 the Russian Army sent a special group named \"Zvezda\" to fight the Nazis in their backyard.", "title" : "The Star" }
2{ "_id" : { "$oid" : "573a13e1f29313caabdbc094" }, "plot" : "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.", "title" : "P-51 Dragon Fighter" }
3{ "_id" : { "$oid" : "573a13e1f29313caabdbbce8" }, "plot" : "April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.", "title" : "Fury" }
4{ "_id" : { "$oid" : "573a1399f29313caabcec130" }, "plot" : "It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...", "title" : "Memphis Belle" }
5{ "_id" : { "$oid" : "573a1394f29313caabce0d70" }, "plot" : "The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...", "title" : "The Young Lions" }
6{ "_id" : { "$oid" : "573a13daf29313caabdad76b" }, "plot" : "Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.", "title" : "White Tiger" }
7{ "_id" : { "$oid" : "573a13aef29313caabd2e8d0" }, "plot" : "The story of ordinary men during WWII as seen from three different points of View.", "title" : "The Fallen" }
8{ "_id" : { "$oid" : "573a13f1f29313caabddb924" }, "plot" : "A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.", "title" : "War Pigs" }
9{ "_id" : { "$oid" : "573a13adf29313caabd2a8cf" }, "plot" : "Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...", "title" : "The Red Baron" }
10{ "_id" : { "$oid" : "573a1396f29313caabce39a8" }, "plot" : "In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.", "title" : "Battle of Britain" }
1package main
2
3import (
4	"context"
5	"fmt"
6	"log"
7
8	"go.mongodb.org/mongo-driver/v2/bson"
9	"go.mongodb.org/mongo-driver/v2/mongo"
10	"go.mongodb.org/mongo-driver/v2/mongo/options"
11)
12
13func main() {
14	ctx := context.Background()
15
16	// Replace the placeholder with your Atlas connection string
17	const uri = "<connection-string>"
18
19	// Connect to your Atlas cluster
20	clientOptions := options.Client().ApplyURI(uri)
21	client, err := mongo.Connect(clientOptions)
22	if err != nil {
23		log.Fatalf("failed to connect to the server: %v", err)
24	}
25	defer func() { _ = client.Disconnect(ctx) }()
26
27	// Set the namespace
28	coll := client.Database("sample_mflix").Collection("embedded_movies")
29
30	queryVector := [2048]float64{
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32	}
33
34	vectorSearchStage := bson.D{
35		{"$vectorSearch", bson.D{
36			{"index", "vector_index"},
37			{"path", "plot_embedding_voyage_3_large"},
38			{"queryVector", queryVector},
39			{"exact", true},
40			{"limit", 10},
41		}}}
42
43	projectStage := bson.D{
44		{"$project", bson.D{
45			{"_id", 0},
46			{"plot", 1},
47			{"title", 1},
48			{"score", bson.D{{"$meta", "vectorSearchScore"}}},
49		}}}
50
51	cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
52	if err != nil {
53		log.Fatalf("failed to retrieve data from the server: %v", err)
54	}
55	// display the results
56	type ProjectedMovieResult struct {
57		Title string  `bson:"title"`
58		Plot  string  `bson:"plot"`
59		Score float64 `bson:"score"`
60	}
61
62	var results []ProjectedMovieResult
63	if err = cursor.All(ctx, &results); err != nil {
64		log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
65	}
66	for _, result := range results {
67		fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score)
68	}
69}
1Title: The Star 
2Plot: During WW2 the Russian Army sent a special group named "Zvezda" to fight the Nazis in their backyard. 
3Score: 0.7459506392478943 
4
5Title: P-51 Dragon Fighter 
6Plot: As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons. 
7Score: 0.7430292367935181 
8
9Title: Fury 
10Plot: April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany. 
11Score: 0.7428240776062012 
12
13Title: Memphis Belle 
14Plot: It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ... 
15Score: 0.7340154647827148 
16
17Title: The Young Lions 
18Plot: The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ... 
19Score: 0.7327103614807129 
20
21Title: White Tiger 
22Plot: Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction. 
23Score: 0.732368528842926 
24
25Title: The Fallen 
26Plot: The story of ordinary men during WWII as seen from three different points of View. 
27Score: 0.7301670908927917 
28
29Title: War Pigs 
30Plot: A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary. 
31Score: 0.7290490865707397 
32
33Title: The Red Baron 
34Plot: Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ... 
35Score: 0.728823184967041 
36
37Title: Battle of Britain 
38Plot: In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain. 
39Score: 0.7270985245704651 
1import static com.mongodb.client.model.Aggregates.*;  
2import static com.mongodb.client.model.Projections.*;  
3import static com.mongodb.client.model.search.SearchPath.fieldPath;  
4import static java.util.Arrays.asList;  
5  
6import com.mongodb.client.MongoClient;  
7import com.mongodb.client.MongoClients;  
8import com.mongodb.client.MongoCollection;  
9import com.mongodb.client.MongoDatabase;  
10import com.mongodb.client.model.search.FieldSearchPath;  
11import com.mongodb.client.model.search.VectorSearchOptions;  
12import org.bson.Document;
13import org.bson.conversions.Bson;
14
15import java.util.List;  
16  
17public class FilterQuery {  
18    public static void main(String[] args) {  
19        // Specify connection URI  
20        String uri = "<connection-string>";  
21  
22        // Establish MongoDB connection  
23        try (MongoClient mongoClient = MongoClients.create(uri)) {  
24            MongoDatabase database = mongoClient.getDatabase("sample_mflix");  
25            MongoCollection<Document> collection = database.getCollection("embedded_movies");  
26  
27            // Parameters  
28            List<Double> queryVector = 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 
29            String indexName = "vector_index";  
30            FieldSearchPath fieldSearchPath = fieldPath("plot_embedding_voyage_3_large");  
31            long limit = 10; 
32  
33            // Create ExactVectorSearchOptions for precise matching  
34            VectorSearchOptions options = VectorSearchOptions.exactVectorSearchOptions();  
35  
36            // Define the aggregation pipeline  
37            List<Bson> pipeline = asList(  
38                vectorSearch(  
39                    fieldSearchPath,  
40                    queryVector,  
41                    indexName,  
42                    limit,  
43                    options  
44                ),  
45                project(  
46                    fields(  
47                        exclude("_id"),        
48                        include("title"),         
49                        include("plot"),         
50                        metaVectorSearchScore("score") 
51                    )  
52                )  
53            );  
54  
55            // Run aggregation query and print results  
56            collection.aggregate(pipeline).forEach(doc -> System.out.println(doc.toJson()));  
57        }  
58    }  
59}  
1{"plot": "During WW2 the Russian Army sent a special group named \"Zvezda\" to fight the Nazis in their backyard.", "title": "The Star", "score": 0.7459505200386047}
2{"plot": "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.", "title": "P-51 Dragon Fighter", "score": 0.7430291771888733}
3{"plot": "April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.", "title": "Fury", "score": 0.7428240180015564}
4{"plot": "It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...", "title": "Memphis Belle", "score": 0.7340154647827148}
5{"plot": "The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...", "title": "The Young Lions", "score": 0.7327103614807129}
6{"plot": "Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.", "title": "White Tiger", "score": 0.7323684692382812}
7{"plot": "The story of ordinary men during WWII as seen from three different points of View.", "title": "The Fallen", "score": 0.7301670908927917}
8{"plot": "A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.", "title": "War Pigs", "score": 0.7290489673614502}
9{"plot": "Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...", "title": "The Red Baron", "score": 0.7288231253623962}
10{"plot": "In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.", "title": "Battle of Britain", "score": 0.7270984649658203}
1const { MongoClient } = require("mongodb");
2
3// connect to your Atlas cluster
4const uri = "<connection-string>";
5    
6const client = new MongoClient(uri);
7
8async function run() {
9    try {
10        await client.connect();
11
12        // set namespace
13        const database = client.db("sample_mflix");
14        const coll = database.collection("embedded_movies");
15
16        // define pipeline
17        const agg = [
18            {
19              '$vectorSearch': {
20                'index': 'vector_index', 
21                'path': 'plot_embedding_voyage_3_large', 
22                'queryVector': [
23                  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24                ], 
25                'exact': true, 
26                'limit': 10
27              }
28            }, {
29              '$project': {
30                '_id': 0, 
31                'plot': 1, 
32                'title': 1, 
33                'score': {
34                  '$meta': 'vectorSearchScore'
35                }
36              }
37            }
38          ];
39        // run pipeline
40        const result = coll.aggregate(agg);
41
42        // print results
43        await result.forEach((doc) => console.dir(JSON.stringify(doc)));
44    } finally {
45        await client.close();
46    }
47}
48run().catch(console.dir);
1'{"plot":"During WW2 the Russian Army sent a special group named \\"Zvezda\\" to fight the Nazis in their backyard.","title":"The Star","score":0.7459506392478943}'
2`{"plot":"As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.","title":"P-51 Dragon Fighter","score":0.7430292367935181}`
3'{"plot":"April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.","title":"Fury","score":0.7428240776062012}'
4`{"plot":"It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...","title":"Memphis Belle","score":0.7340154647827148}`
5'{"plot":"The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...","title":"The Young Lions","score":0.7327103614807129}'
6'{"plot":"Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.","title":"White Tiger","score":0.732368528842926}'
7'{"plot":"The story of ordinary men during WWII as seen from three different points of View.","title":"The Fallen","score":0.7301670908927917}'
8'{"plot":"A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.","title":"War Pigs","score":0.7290490865707397}'
9'{"plot":"Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...","title":"The Red Baron","score":0.728823184967041}'
10'{"plot":"In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.","title":"Battle of Britain","score":0.7270985245704651}'

Work with a runnable version of this example as a Python notebook.

1import pymongo
2
3# connect to your Atlas cluster
4client = pymongo.MongoClient("<connection-string>")
5
6# define pipeline
7pipeline = [
8  {
9    '$vectorSearch': {
10      'index': 'vector_index', 
11      'path': 'plot_embedding_voyage_3_large', 
12      'queryVector': 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
13      'exact': True, 
14      'limit': 10
15    }
16  }, {
17    '$project': {
18      '_id': 0, 
19      'plot': 1, 
20      'title': 1, 
21      'score': {
22        '$meta': 'vectorSearchScore'
23      }
24    }
25  }
26]
27
28# run pipeline
29result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline)
30
31# print results
32for i in result:
33    print(i)
34 
1{'plot': 'During WW2 the Russian Army sent a special group named "Zvezda" to fight the Nazis in their backyard.', 'title': 'The Star', 'score': 0.7459506392478943}
2{'plot': "As World War Two rages on, the allies are about to push the Nazis out of North Africa. That's when the Nazis turn up the heat, unleashing their secret Weapon - dragons.", 'title': 'P-51 Dragon Fighter', 'score': 0.7430292367935181}
3{'plot': 'April, 1945. As the Allies make their final push in the European Theatre, a battle-hardened Army sergeant named Wardaddy commands a Sherman tank and his five-man crew on a deadly mission behind enemy lines. Outnumbered, out-gunned, and with a rookie soldier thrust into their platoon, Wardaddy and his men face overwhelming odds in their heroic attempts to strike at the heart of Nazi Germany.', 'title': 'Fury', 'score': 0.7428240776062012}
4{'plot': "It's May 1943 at a US Army Air Corps base in England. The four officers and six enlisted men of the Memphis Belle - a B-17 bomber so nicknamed for the girlfriend of its stern and stoic ...", 'title': 'Memphis Belle', 'score': 0.7340154647827148}
5{'plot': 'The destiny of three soldiers during World War II. The German officer Christian Diestl approves less and less of the war. Jewish-American Noah Ackerman deals with antisemitism at home and ...', 'title': 'The Young Lions', 'score': 0.7327103614807129}
6{'plot': 'Great Patriotic War, early 1940s. After barely surviving a battle with a mysterious, ghostly-white Tiger tank, Red Army Sergeant Ivan Naydenov becomes obsessed with its destruction.', 'title': 'White Tiger', 'score': 0.732368528842926}
7{'plot': 'The story of ordinary men during WWII as seen from three different points of View.', 'title': 'The Fallen', 'score': 0.7301670908927917}
8{'plot': 'A rag tag unit of misfits known as the War Pigs must go behind enemy lines to exterminate Nazis by any means necessary.', 'title': 'War Pigs', 'score': 0.7290490865707397}
9{'plot': 'Baron Manfred von Richthofen is the most feared and celebrated pilot of the German air force in World War I. To him and his companions, air combats are events of sporty nature, technical ...', 'title': 'The Red Baron', 'score': 0.728823184967041}
10{'plot': 'In 1940, the British Royal Air Force fights a desperate battle vs. the Nazi Germany Air Force for control of British air space to prevent a Nazi invasion of Britain.', 'title': 'Battle of Britain', 'score': 0.7270985245704651}

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