class PairwiseVectorSimilarity extends AnnotatorModel[PairwiseVectorSimilarity] with HasSimpleAnnotate[PairwiseVectorSimilarity] with ParamsAndFeaturesWritable

Computes pairwise vector similarity between two sets of sentence embeddings.

The annotator takes **two** SENTENCE_EMBEDDINGS input columns (e.g. query embeddings and document embeddings already joined on the same row) and, for every row, scores all N×M pairs between the embeddings in column A and the embeddings in column B. Each pair produces one VECTOR_SIMILARITY output annotation whose result holds the score as a String, and whose metadata holds typed fields for easy extraction.

Sign conventions

method        range         higher means
----------    ----------    ------------
cosine        [-1.0, 1.0]   more similar
dotProduct    (−∞, +∞)      more similar
euclidean     (−∞, 0.0]     more similar (0.0 = identical vectors)

The euclidean method returns the **negative** L2 distance so that "higher is better" holds uniformly across all three methods. A score of 0.0 means the two vectors are identical; more negative values indicate less similarity.

Important: input data shape

Each input column should contain **exactly one** embedding per row for standard document retrieval. If a column contains N > 1 embeddings (e.g. produced by SentenceDetector + embedder), all N×M cross-pairs are scored and returned as separate annotations. To compare queries against a corpus, join them first:

Example

import com.johnsnowlabs.nlp.annotators.similarity.PairwiseVectorSimilarity
import org.apache.spark.sql.functions.{col, desc, explode}

// Assume embeddingPipeline produces a "embeddings" column of SENTENCE_EMBEDDINGS.
val queryDf  = embeddingPipeline.transform(queries).select(col("embeddings").as("query_emb"))
val corpusDf = embeddingPipeline.transform(corpus).select(col("embeddings").as("doc_emb"), col("id"))

// CrossJoin to get one (query, document) pair per row, then score.
val paired = queryDf.crossJoin(corpusDf)

val pvs = new PairwiseVectorSimilarity()
  .setInputCols("query_emb", "doc_emb")
  .setOutputCol("similarity")
  .setSimilarityMethod("cosine")

pvs.transform(paired)
  .select(explode(col("similarity")).as("s"))
  .select(
    col("s.metadata")("sentence_a_text").as("query"),
    col("s.metadata")("sentence_b_text").as("document"),
    col("s.result").cast("double").as("score"))
  .orderBy(desc("score"))
  .show(false)
Linear Supertypes
HasSimpleAnnotate[PairwiseVectorSimilarity], AnnotatorModel[PairwiseVectorSimilarity], CanBeLazy, RawAnnotator[PairwiseVectorSimilarity], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[PairwiseVectorSimilarity], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. PairwiseVectorSimilarity
  2. HasSimpleAnnotate
  3. AnnotatorModel
  4. CanBeLazy
  5. RawAnnotator
  6. HasOutputAnnotationCol
  7. HasInputAnnotationCols
  8. HasOutputAnnotatorType
  9. ParamsAndFeaturesWritable
  10. HasFeatures
  11. DefaultParamsWritable
  12. MLWritable
  13. Model
  14. Transformer
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new PairwiseVectorSimilarity()
  2. new PairwiseVectorSimilarity(uid: String)

Type Members

  1. type AnnotationContent = Seq[Row]

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def annotate(annotations: Seq[Annotation]): Seq[Annotation]

    Not supported — LightPipeline flattens both input columns into a single Seq, making it impossible to distinguish col A from col B.

    Not supported — LightPipeline flattens both input columns into a single Seq, making it impossible to distinguish col A from col B. Use transform() on a DataFrame instead.

    annotations

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    PairwiseVectorSimilarity → HasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  14. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  15. final def clear(param: Param[_]): PairwiseVectorSimilarity.this.type
    Definition Classes
    Params
  16. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  17. def copy(extra: ParamMap): PairwiseVectorSimilarity

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  18. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  19. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  20. def dfAnnotate: UserDefinedFunction

    Overrides the default dfAnnotate so the two input columns are kept separate rather than being flattened into a single Seq.

    Overrides the default dfAnnotate so the two input columns are kept separate rather than being flattened into a single Seq. The first element of rows corresponds to col A (index 0 in getInputCols) and the second to col B (index 1).

    returns

    udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation

    Definition Classes
    PairwiseVectorSimilarity → HasSimpleAnnotate
  21. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  22. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  23. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  24. def explainParams(): String
    Definition Classes
    Params
  25. final val extraInputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  26. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  27. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  28. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  29. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  30. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  31. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  32. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  33. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  34. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  35. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  36. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  37. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  38. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  39. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  40. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  41. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  42. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  43. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  44. def getSimilarityMethod: String

  45. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  46. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  47. def hasParent: Boolean
    Definition Classes
    Model
  48. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  49. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  50. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator types: two SENTENCE_EMBEDDINGS columns (e.g.

    Input annotator types: two SENTENCE_EMBEDDINGS columns (e.g. queries and documents).

    Definition Classes
    PairwiseVectorSimilarity → HasInputAnnotationCols
  52. final val inputCols: StringArrayParam

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  53. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  54. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  55. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  56. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  57. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  58. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  59. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  66. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  71. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  72. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  73. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  74. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  75. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  76. val outputAnnotatorType: AnnotatorType

    Output annotator type: VECTOR_SIMILARITY

    Output annotator type: VECTOR_SIMILARITY

    Definition Classes
    PairwiseVectorSimilarity → HasOutputAnnotatorType
  77. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  78. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  79. var parent: Estimator[PairwiseVectorSimilarity]
    Definition Classes
    Model
  80. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  81. def set[T](feature: StructFeature[T], value: T): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  82. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  83. def set[T](feature: SetFeature[T], value: Set[T]): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  84. def set[T](feature: ArrayFeature[T], value: Array[T]): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  85. final def set(paramPair: ParamPair[_]): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    Params
  86. final def set(param: String, value: Any): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    Params
  87. final def set[T](param: Param[T], value: T): PairwiseVectorSimilarity.this.type
    Definition Classes
    Params
  88. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  89. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  90. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  91. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  92. final def setDefault(paramPairs: ParamPair[_]*): PairwiseVectorSimilarity.this.type
    Attributes
    protected
    Definition Classes
    Params
  93. final def setDefault[T](param: Param[T], value: T): PairwiseVectorSimilarity.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  94. def setExtraInputCols(value: Array[String]): PairwiseVectorSimilarity.this.type
    Definition Classes
    HasInputAnnotationCols
  95. final def setInputCols(value: String*): PairwiseVectorSimilarity.this.type
    Definition Classes
    HasInputAnnotationCols
  96. def setInputCols(value: Array[String]): PairwiseVectorSimilarity.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  97. def setLazyAnnotator(value: Boolean): PairwiseVectorSimilarity.this.type
    Definition Classes
    CanBeLazy
  98. final def setOutputCol(value: String): PairwiseVectorSimilarity.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  99. def setParent(parent: Estimator[PairwiseVectorSimilarity]): PairwiseVectorSimilarity
    Definition Classes
    Model
  100. def setSimilarityMethod(value: String): PairwiseVectorSimilarity.this.type

  101. val similarityMethod: Param[String]

    Similarity function to use when scoring pairs.

    Similarity function to use when scoring pairs.

    Supported values:

    • "cosine" (default) — cosine similarity in [-1.0, 1.0]; higher = more similar.
    • "dotProduct" — dot-product score in (−∞, +∞); higher = more similar.
    • "euclidean" — **negative** L2 distance in (−∞, 0.0]; 0.0 = identical, more negative \= less similar. The sign is negated so that "higher is better" holds uniformly across all three methods.
  102. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  103. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  104. final def transform(dataset: Dataset[_]): DataFrame

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    dataset

    Dataset[Row]

    Definition Classes
    AnnotatorModel → Transformer
  105. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  106. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  107. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    RawAnnotator → PipelineStage
  108. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  109. val uid: String
    Definition Classes
    PairwiseVectorSimilarity → Identifiable
  110. def validate(schema: StructType): Boolean

    Verifies that exactly two named input columns are present, each carrying the SENTENCE_EMBEDDINGS annotator type.

    Verifies that exactly two named input columns are present, each carrying the SENTENCE_EMBEDDINGS annotator type.

    The base RawAnnotator.validate uses exists (any column of the right type satisfies each entry in inputAnnotatorTypes), which would pass validation even when only one SENTENCE_EMBEDDINGS column is present, and the generic failure message only mentions annotator types. This override checks each column by name and raises a specific error so the "two distinct columns" requirement is clear, rather than returning false and surfacing the generic base message.

    schema

    to be validated

    returns

    True if all the required types are present, else false

    Attributes
    protected
    Definition Classes
    PairwiseVectorSimilarity → RawAnnotator
  111. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  112. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  113. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  114. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  115. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from HasSimpleAnnotate[PairwiseVectorSimilarity]

Inherited from AnnotatorModel[PairwiseVectorSimilarity]

Inherited from CanBeLazy

Inherited from RawAnnotator[PairwiseVectorSimilarity]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[PairwiseVectorSimilarity]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

Annotator types

Required input and expected output annotator types

Parameter setters

Parameter getters

Ungrouped