sparknlp_jsl.annotator.rag.context_split_assembler#

Classes#

class ContextSplitAssembler(classname='com.johnsnowlabs.nlp.annotators.rag.ContextSplitAssembler', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

Converts and assembles VECTOR_SIMILARITY_RANKINGS type annotations into DOCUMENT type. The input annotations are expected to be of type VECTOR_SIMILARITY_RANKINGS and the output annotation type is DOCUMENT. It concatenates the results of the input annotations into a single result, separated by a join string. When explodeSplits is set to True, the splits are exploded into separate annotations. joinString parameter is used to add the delimiter between results of annotations when combining them into a single result.

Input Annotation types	Output Annotation type
VECTOR_SIMILARITY_RANKINGS	DOCUMENT

Parameters:

• joinString (str) – This parameter specifies the string that will be inserted between results of annotations when combining them into a single result. It acts as a delimiter, ensuring that the elements are properly separated and organized in the final result of annotation. Default: “ “.

• explodeSplits (bool) – Whether to explode the splits into separate annotations or not. Default: False.

explodeSplits#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

joinString#

lazyAnnotator#

name = 'ContextSplitAssembler'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'document'#

outputCol#

skipLPInputColsValidation = True#

uid = ''#

clear(param: pyspark.ml.param.Param) → None#

Clears a param from the param map if it has been explicitly set.

copy(extra: pyspark.ml._typing.ParamMap | None = None) → JP#

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters:

extra (dict, optional) – Extra parameters to copy to the new instance

Returns:

Copy of this instance

Return type:

JavaParams

explainParam(param: str | Param) → str#

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str#

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: pyspark.ml._typing.ParamMap | None = None) → pyspark.ml._typing.ParamMap#

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters:

extra (dict, optional) – extra param values

Returns:

merged param map

Return type:

dict

getInputCols()#

Gets current column names of input annotations.

getLazyAnnotator()#

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param: str) → Any#

getOrDefault(param: Param[T]) → T

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol()#

Gets output column name of annotations.

getParam(paramName: str) → Param#

Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters:

paramName (str) – Name of the parameter

hasDefault(param: str | Param[Any]) → bool#

Checks whether a param has a default value.

hasParam(paramName: str) → bool#

Tests whether this instance contains a param with a given (string) name.

inputColsValidation(value)#

isDefined(param: str | Param[Any]) → bool#

Checks whether a param is explicitly set by user or has a default value.

isSet(param: str | Param[Any]) → bool#

Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL#

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read()#

Returns an MLReader instance for this class.

save(path: str) → None#

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: Param, value: Any) → None#

Sets a parameter in the embedded param map.

setExplodeSplits(value: bool)#

Sets the value of explodeSplits. Whether to explode the splits into separate annotations or not. Default: False.

Parameters:

value (bool) – Whether to explode the splits into separate annotations or not.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*value (List[str]) – Input columns for the annotator

setJoinString(value: str)#

Sets the value of joinString. This parameter specifies the string that will be inserted between results of annotations when combining them into a single result. It acts as a delimiter, ensuring that the elements are properly separated and organized in the final result of annotation. Default: “ “.

Parameters:

value (str) – This parameter specifies the string that will be inserted between results of annotations when combining them into a single result.

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline

setOutputCol(value)#

Sets output column name of annotations.

Parameters:

value (str) – Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:

paramName (str) – Name of the parameter

setParams()#

transform(dataset: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = None) → pyspark.sql.dataframe.DataFrame#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

• dataset (pyspark.sql.DataFrame) – input dataset

• params (dict, optional) – an optional param map that overrides embedded params.

Returns:

transformed dataset

Return type:

pyspark.sql.DataFrame

write() → JavaMLWriter#

Returns an MLWriter instance for this ML instance.