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2. Use Operators & Collectors

knnBeta

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  • Definition
  • Syntax
  • Options
  • Behavior
  • Examples

Definition

knnBeta

The knnBeta operator uses Hierarchical Navigable Small Worlds algorithm to perform semantic search. You can use Atlas Search support for kNN query to search similar to a selected product, search for images, etc.

Syntax

knnBeta has the following syntax:

1{
2  $search: {
3    "index": "<index name>", // optional, defaults to "default"
4    "knnBeta": {
5      "vector": [<array-of-numbers>],
6      "path": "<field-to-search>",
7      "filter": {<filter-specification>},
8      "k": <number>,
9      "score": {<options>}
10    }
11  }
12}

Options

Field
Type
Description
Necessity
filter
document
Any Atlas Search operator to filter the documents based on metadata or certain search criteria, which can help narrow down the scope of vector search.
Optional
k
number
Number of nearest neighbors to return. You can specify a number higher than the number of documents to return ($limit) to increase accuracy.
Required
path
string
Indexed knnVector type field to search. See Path Construction for more information.
Required
score
document
Score assigned to matching documents in the results. To learn more, see scoring behavior.
Optional
vector
array of numbers
Array of numbers of BSON types int or double that represent the query vector. The array size must match the number of vector dimensions specified in the index for the field.
Required

Behavior

You can run kNN queries against fields that were indexed as Atlas Search type knnVector only.

You can use $limit after the $search stage to limit the number of documents in the knnBeta query results. We recommend setting the value for k higher than the value for $limit. This overrequest pattern is the main way to trade off latency and recall in your approximate nearest neighbor searches. Empirically, we have seen a multiplier of 5-10 work well for many use cases, but we recommend tuning this on your specific dataset.

Example

The following query finds 150 nearest neighbors to the query and limits the remaining number of results to 50.

1db.<collection>.aggregate({
2  "$search": {
3    "knnBeta": {
4      "vector": <array-of-numbers-to-search>,
5      "path": <indexed-field-to-search>,
6      "k": 150
7    }
8  }
9},
10{
11  "$limit": 50
12})

Performance

To improve query performance, use the $project stage to select the fields to return in the results, unless you need all the fields in the results. We recommend excluding the vector field in the $project stage.

Scoring

You can use the score field with the $meta expression searchScore in the $project stage to return the score for the documents in the results.

Atlas Search scores the results for kNN queries in a fixed range from 0 to 1 only. For cosine and dotProduct similarities, Atlas Search normalizes the score using the following algorithm:

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

Limitations

knnBeta operator must be the top-level operator in your queries and therefore, you can't use the knnBeta operator inside the following:

  • embeddedDocument operator

  • compound operator

  • facet collector

You can't use the knnBeta operator to query fields indexed using a vectorSearch type index. You can't use the $search sort option with the knnBeta operator.

We don't recommend paginating your Atlas Search results using $skip and $limit after the $search stage.

Examples

The following queries search the sample sample_mflix.embedded_movies collection using the knnBeta operator. The queries search the plot_embedding field, which contains embeddings created using OpenAI's text-embedding-ada-002 embeddings model. If you added the sample collection to your Atlas cluster and created the sample index definition for the collection, you can switch to the sample_mflix database and run the following queries against the collection.

The following query searches the plot_embedding field using the knnBeta operator for vector dimensions and requests up to 5 nearest neighbors in the results. The query also specifies a $project stage to:

  • Exclude the _id field and include plot and title fields in the results.

  • Add a field named score in the results.

1db.embedded_movies.aggregate([
2  {
3    "$search": {
4      "index": "default",
5      "knnBeta": {
6        "vector": 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1[
2  {
3    plot: 'When two girls move to the country to be near their ailing mother, they have adventures with the wonderous forest spirits who live nearby.',
4    title: 'My Neighbor Totoro',
5    score: 0.9999986886978149
6  },
7  {
8    plot: "To escape a war, a girl flees to a remote farmhouse and becomes part of an expansive family's unusual, perhaps even supernatural lifestyle.",
9    title: 'Black Moon',
10    score: 0.8282386660575867
11  },
12  {
13    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
14    title: 'Forest Warrior',
15    score: 0.8196926712989807
16  },
17  {
18    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
19    title: 'Forest Warrior',
20    score: 0.8196901082992554
21  },
22  {
23    plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.',
24    title: 'Coraline',
25    score: 0.815956711769104
26  }
27]

The following query filters the documents for movies released before or on (≤) 30 June, 1971 (1971-06-30), then searches the plot_embedding field in the filtered documents for vector dimensions, and requests up to 5 nearest neighbors in the results. The query also specifies a $project stage to:

  • Exclude the _id field and include only plot, title, and released fields in the results.

  • Add a field named score in the results.

1db.embedded_movies.aggregate([
2  {
3    "$search": {
4      "index": "default",
5      "knnBeta": {
6        "vector": 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7        "path": "plot_embedding",
8        "filter": {
9          "range": {
10            "lte": ISODate("1971-06-30 00:27:59.177000000"),
11            "path": "released"
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14        "k": 5
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17  },
18  {
19    "$project": {
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24      "score": { $meta: "searchScore" }
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27])
1[
2  {
3    plot: 'A poor boy wins the opportunity to tour the most eccentric and wonderful candy factory of all.',
4    title: 'Willy Wonka & the Chocolate Factory',
5    released: ISODate("1971-06-30T00:00:00.000Z"),
6    score: 0.9999985694885254
7  },
8  {
9    plot: 'A down-on-his-luck inventor turns a broken-down Grand Prix car into a fancy vehicle for his children, and then they go off on a magical fantasy adventure to save their grandfather in a far-off land.',
10    title: 'Chitty Chitty Bang Bang',
11    released: ISODate("1968-12-18T00:00:00.000Z"),
12    score: 0.7576897740364075
13  },
14  {
15    plot: 'A red balloon with a life of its own follows a little boy around the streets of Paris.',
16    title: 'The Red Balloon',
17    released: ISODate("1957-03-11T00:00:00.000Z"),
18    score: 0.7515948414802551
19  },
20  {
21    plot: 'While hearing the story of "Gulliver's Travels", a young boy dreams he is the title character on the island of Lilliput .',
22    title: 'The New Gulliver',
23    released: ISODate("1935-11-03T00:00:00.000Z"),
24    score: 0.7495509386062622
25  },
26  {
27    plot: 'A living puppet, with the help of a cricket as his conscience, must prove himself worthy to become a real boy.',
28    title: 'Pinocchio',
29    released: ISODate("1940-02-23T00:00:00.000Z"),
30    score: 0.7472590804100037
31  }
32]

Atlas Search filters the documents based on the released field value that is less than or equal to 1971-06-30. It scores the document with the title Willy Wonka & the Chocolate Factory higher because it matches the search criteria in the filter option and also the vector dimensions in the query.

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