knn-beta

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knnBeta Operator

The Atlas Search knnVector field type and knnBeta operator are now deprecated. Instead, we recommend that you use the [vector](https://www.mongodb.com/docs/search/indexes/field-types/vector-type.md#std-label-bson-data-types-vector index type and [vectorSearch](https://www.mongodb.com/docs/search/query/operators-collectors/vectorsearch.md#std-label-fts-vectorSearch-ref operator in your $search pipeline stage.

Definition

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

Syntax

knnBeta has the following syntax:

{
  $search: {
    "index": "<index name>", // optional, defaults to "default"
    "knnBeta": {
      "vector": [<array-of-numbers>],
      "path": "<field-to-search>",
      "filter": {<filter-specification>},
      "k": <number>,
      "score": {<options>}
    }
  }
}

Options

Field Type Description Necessity
filter document Any MongoDB Search [operator](https://www.mongodb.com/docs/search/query/operators-collectors/overview.md#std-label-operators-ref 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](https://www.mongodb.com/docs/search/indexes/field-types/knn-vector.md#std-label-fts-data-types-knn-vector type field to search. Required
score document Score assigned to matching documents in the results. To learn more, see [scoring behavior.](https://www.mongodb.com/docs/search/query/operators-collectors/knn-beta.md#std-label-knn-beta-scoring Optional
vector array of numbers Array of numbers of BSON (Binary Javascript Object Notation) types int or double that represent the query vector. The array size must match the number of vector dimensions specified in the [index](https://www.mongodb.com/docs/search/indexes/field-types/knn-vector.md#std-label-fts-data-types-knn-vector for the field. Required

Behavior

You can run kNN (K-nearest neighbors) queries against fields that were indexed as MongoDB Search type [knnVector](https://www.mongodb.com/docs/search/indexes/field-types/knn-vector.md#std-label-fts-data-types-knn-vector only.

You can use [$limit](https://www.mongodb.com/docs/manual/reference/operator/aggregation/limit.md#mongodb-pipeline-pipe.-limit after the [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-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.

db.<collection>.aggregate({
  "$search": {
    "knnBeta": {
      "vector": <array-of-numbers-to-search>,
      "path": <indexed-field-to-search>,
      "k": 150
    }
  }
},
{
  "$limit": 50
})

Performance

To improve query performance, use the [$project](https://www.mongodb.com/docs/manual/reference/operator/aggregation/project.md#mongodb-pipeline-pipe.-project stage to select the fields to return in the results, unless you need all the fields in the results. We recommend excluding the vector field in the [$project](https://www.mongodb.com/docs/manual/reference/operator/aggregation/project.md#mongodb-pipeline-pipe.-project stage.

Scoring

You can use the score field with the [$meta](https://www.mongodb.com/docs/manual/reference/operator/aggregation/meta.md#mongodb-expression-exp.-meta expression searchScore in the [$project](https://www.mongodb.com/docs/manual/reference/operator/aggregation/project.md#mongodb-pipeline-pipe.-project stage to return the score for the documents in the results.

MongoDB Search scores the results for kNN (K-nearest neighbors) queries in a fixed range from 0 to 1 only. For cosine and dotProduct [similarities](https://www.mongodb.com/docs/search/indexes/field-types/knn-vector.md#std-label-fts-knn-vector-type-options, 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:

  • [embeddedDocument](https://www.mongodb.com/docs/search/query/operators-collectors/embedded-document.md#std-label-embedded-document-ref

  • [compound](https://www.mongodb.com/docs/search/query/operators-collectors/compound.md#std-label-compound-ref

  • [facet (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/facet.md#std-label-fts-facet-ref

You can't use the knnBeta operator to query fields indexed using a vectorSearch.

We don't recommend paginating your MongoDB Search results using [$skip](https://www.mongodb.com/docs/manual/reference/operator/aggregation/skip.md#mongodb-pipeline-pipe.-skip and [$limit](https://www.mongodb.com/docs/manual/reference/operator/aggregation/limit.md#mongodb-pipeline-pipe.-limit after the [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-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](https://www.mongodb.com/docs/manual/sample-data/sample-mflix.md#std-label-mflix-embedded_movies to your cluster and created the [sample index definition](https://www.mongodb.com/docs/search/indexes/field-types/knn-vector.md#std-label-knn-vector-egs for the collection, you can switch to the sample_mflix database and run the following queries against the collection.

Basic Example

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](https://www.mongodb.com/docs/manual/reference/operator/aggregation/project.md#mongodb-pipeline-pipe.-project stage to:

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

  • Add a field named score in the results.

db.embedded_movies.aggregate([
  {
    "$search": {
      "index": "default",
      "knnBeta": {
        "vector": 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        "path": "plot_embedding",
        "k": 5
      }
    }
  },
  {
    "$project": {
      "_id": 0,
      "plot": 1,
      "title": 1,
      "score": { $meta: "searchScore" }
    }
  }
])

Output:

[
  {
    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.',
    title: 'My Neighbor Totoro',
    score: 0.9999986886978149
  },
  {
    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.",
    title: 'Black Moon',
    score: 0.8282386660575867
  },
  {
    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
    title: 'Forest Warrior',
    score: 0.8196926712989807
  },
  {
    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
    title: 'Forest Warrior',
    score: 0.8196901082992554
  },
  {
    plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.',
    title: 'Coraline',
    score: 0.815956711769104
  }
]

Migrate to the vectorSearch Operator

The [vectorSearch (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/vectorsearch.md#std-label-fts-vectorSearch-ref adds analyzed text capabilities to your semantic search. To use the [vectorSearch (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/vectorsearch.md#std-label-fts-vectorSearch-ref in your [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search queries against a vector field instead of knnBeta, complete the following steps:

  1. Ensure that your index definition uses the vector type.

If your index definition uses the [knnVector](https://www.mongodb.com/docs/search/indexes/field-types/knn-vector.md#std-label-fts-data-types-knn-vector type to index vector fields, replace knnVector in your index definition with the [vector](https://www.mongodb.com/docs/search/indexes/field-types/vector-type.md#std-label-bson-data-types-vector type. To learn more, see [Migrate to vector Type.](https://www.mongodb.com/docs/search/indexes/field-types/knn-vector.md#std-label-migrate-knnVector-to-vector

  1. Modify your queries to use the vectorSearch operator.

The [vectorSearch (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/vectorsearch.md#std-label-fts-vectorSearch-ref 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](https://www.mongodb.com/docs/search/query/operators-collectors/knn-beta.md#std-label-knn-beta-egs with the following:

```javascript db.embedded_movies.aggregate([ { "$search": { "index": "default", "vectorSearch": { "filter": { "range": { "lte": ISODate("1971-06-30 00:27:59.177000000"), "path": "released" } }, "path": "plot_embedding", "queryVector": 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