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knnBeta Operator

knnBeta

The knnBeta operator uses the Hierarchical Navigable Small Worlds algorithm to perform semantic search. You can use MongoDB Search support for kNN queries to search for similar products, images, and other content.

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}
Field
Type
Description
Necessity

filter

document

Any MongoDB 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.

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

You can run kNN queries against fields that were indexed as MongoDB 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})

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.

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.

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

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

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

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 MongoDB Search results using $skip and $limit after the $search stage.

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 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 "$search": {
4 "index": "default",
5 "knnBeta": {
6 "vector": 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7 "path": "plot_embedding",
8 "k": 5
9 }
10 }
11 },
12 {
13 "$project": {
14 "_id": 0,
15 "plot": 1,
16 "title": 1,
17 "score": { $meta: "searchScore" }
18 }
19 }
20])
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 vectorSearch (MongoDB Search Operator) adds analyzed text capabilities to your semantic search. To use the vectorSearch (MongoDB Search Operator) in your $search queries against a vector field instead of knnBeta, complete the following steps:

1

If your index definition uses the knnVector type to index vector fields, replace knnVector in your index definition with the vector type. To learn more, see Migrate to vector Type.

2

The vectorSearch (MongoDB Search Operator) adds analyzed text capabilities alongside semantic search. To use the vectorSearch operator, modify your queries against vector type fields to use vectorSearch operator options.

Example

Replace the query in the Filter Example with the following:

1db.embedded_movies.aggregate([
2 {
3 "$search": {
4 "index": "default",
5 "vectorSearch": {
6 "filter": {
7 "range": {
8 "lte": ISODate("1971-06-30 00:27:59.177000000"),
9 "path": "released"
10 }
11 },
12 "path": "plot_embedding",
13 "queryVector": 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14 "limit": 10,
15 "numCandidates": 1000
16 }
17 }
18 },
19 {
20 "$project": {
21 "_id": 0,
22 "title": 1,
23 "plot": 1,
24 "released": 1,
25 "score": { $meta: "searchScore" }
26 }
27 }
28])

For other examples that demonstrate advanced filtering options such fuzzy or phrase matching, see vectorSearch operator Examples.