Definition
knnBetaThe
knnBetaoperator uses Hierarchical Navigable Small Worlds algorithm to perform semantic search. You can use MongoDB 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 |
|---|---|---|---|
| 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 |
| number | Number of nearest neighbors to return. You can specify a number
higher than the number of documents to return
( | Required |
| string | Indexed knnVector type field to search. | Required |
| document | Score assigned to matching documents in the results. To learn more, see scoring behavior. | Optional |
| array of numbers | Array of numbers of BSON types | Required |
Behavior
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
$limit. This overrequest pattern is the main way to trade off latency and recall in your
approximate nearest neighbor searches. Empirically, we have seen a multiplier of 5-10 work
well for many use cases, but we recommend tuning this on your specific dataset.
Example
The following query finds 150 nearest neighbors to the query and
limits the remaining number of results to 50.
1 db.<collection>.aggregate({ 2 "$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.
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
Limitations
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.
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 $project stage to:
Exclude the
_idfield and includeplotandtitlefields in the results.Add a field named
scorein the results.
1 db.embedded_movies.aggregate([ 2 { 3 "$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 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 $project
stage to:
Exclude the
_idfield and include onlyplot,title, andreleasedfields in the results.Add a field named
scorein the results.
1 db.embedded_movies.aggregate([ 2 { 3 "$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" 12 } 13 }, 14 "k": 5 15 } 16 } 17 }, 18 { 19 "$project": { 20 "_id": 0, 21 "title": 1, 22 "plot": 1, 23 "released": 1, 24 "score": { $meta: "searchScore" } 25 } 26 } 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 ]
MongoDB 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.