KnnByteVectorField
A field that contains a single byte numeric vector (or none) for each document. Vectors are dense
that is, every dimension of a vector contains an explicit value, stored packed into an array (of type byte[]) whose length is the vector dimension. Values can be retrieved using [ ], which is a forward-only docID-based iterator and also offers random-access by dense ordinal (not docId). VectorSimilarityFunction may be used to compare vectors at query time (for example as part of result ranking). A KnnByteVectorField may be associated with a search similarity function defining the metric used for nearest-neighbor search among vectors of that field.
Constructors
Creates a numeric vector field with the default EUCLIDEAN_HNSW (L2) similarity. Fields are single-valued: each document has either one value or no value. Vectors of a single field share the same dimension and similarity function.
Creates a numeric vector field. Fields are single-valued: each document has either one value or no value. Vectors of a single field share the same dimension and similarity function.
Functions
Non-null if this field has a binary value
Returns the FieldType for this field.
Describes how this field should be inverted. This must return a non-null value if the field indexes terms and postings.
Non-null if this field has a numeric value
The value of the field as a Reader, or null. If null, the String value or binary value is used. Exactly one of stringValue(), readerValue(), and binaryValue() must be set.
Expert: change the value of this field. See .setStringValue.
Expert: change the value of this field. See .setStringValue.
Expert: change the value of this field. See .setStringValue.
Expert: change the value of this field. See .setStringValue.
Expert: change the value of this field. See .setStringValue.
Expert: change the value of this field. See .setStringValue.
Expert: change the value of this field. See .setStringValue.
Expert: change the value of this field. See .setStringValue.
Expert: change the value of this field. This can be used during indexing to re-use a single Field instance to improve indexing speed by avoiding GC cost of new'ing and reclaiming Field instances. Typically a single Document instance is re-used as well. This helps most on small documents.
Expert: sets the token stream to be used for indexing.
Set the vector value of this field
Stored value. This method is called to populate stored fields and must return a non-null value if the field stored.
The value of the field as a String, or null. If null, the Reader value or binary value is used. Exactly one of stringValue(), readerValue(), and binaryValue() must be set.
Creates the TokenStream used for indexing this field. If appropriate, implementations should use the given Analyzer to create the TokenStreams.
The TokenStream for this field to be used when indexing, or null. If null, the Reader value or String value is analyzed to produce the indexed tokens.
Return the vector value of this field