Version: 3.x

rasa.shared.nlu.training_data.formats.rasa_yaml

RasaYAMLReader Objects

class RasaYAMLReader(TrainingDataReader)

Reads YAML training data and creates a TrainingData object.

validate

def validate(string: Text) -> None

Check if the string adheres to the NLU yaml data schema.

If the string is not in the right format, an exception will be raised.

reads

def reads(string: Text, **kwargs: Any) -> "TrainingData"

Reads TrainingData in YAML format from a string.

Arguments:

  • string - String with YAML training data.
  • **kwargs - Keyword arguments.

Returns:

New TrainingData object with parsed training data.

is_yaml_nlu_file

@staticmethod
def is_yaml_nlu_file(filename: Union[Text, Path]) -> bool

Checks if the specified file possibly contains NLU training data in YAML.

Arguments:

  • filename - name of the file to check.

Returns:

True if the filename is possibly a valid YAML NLU file, False otherwise.

Raises:

  • YamlException - if the file seems to be a YAML file (extension) but can not be read / parsed.

RasaYAMLWriter Objects

class RasaYAMLWriter(TrainingDataWriter)

Writes training data into a file in a YAML format.

dumps

def dumps(training_data: "TrainingData") -> Text

Turns TrainingData into a string.

dump

def dump(target: Union[Text, Path, StringIO],
training_data: "TrainingData") -> None

Writes training data into a file in a YAML format.

Arguments:

  • target - Name of the target object to write the YAML to.
  • training_data - TrainingData object.

training_data_to_dict

@classmethod
def training_data_to_dict(
cls, training_data: "TrainingData") -> Optional[OrderedDict]

Represents NLU training data to a dict/list structure ready to be serialized as YAML.

Arguments:

  • training_data - TrainingData to convert.

Returns:

OrderedDict containing all training data.

process_intents

@classmethod
def process_intents(cls, training_data: "TrainingData") -> List[OrderedDict]

Serializes the intents.

process_synonyms

@classmethod
def process_synonyms(cls, training_data: "TrainingData") -> List[OrderedDict]

Serializes the synonyms.

process_regexes

@classmethod
def process_regexes(cls, training_data: "TrainingData") -> List[OrderedDict]

Serializes the regexes.

process_lookup_tables

@classmethod
def process_lookup_tables(cls,
training_data: "TrainingData") -> List[OrderedDict]

Serializes the look up tables.

Arguments:

  • training_data - The training data object with potential look up tables.

Returns:

The serialized lookup tables.

process_training_examples_by_key

@staticmethod
def process_training_examples_by_key(
training_examples: Dict[Text, List[Union[Dict, Text]]], key_name: Text,
key_examples: Text,
example_extraction_predicate: Callable[[Dict[Text, Any]], Text]
) -> List[OrderedDict]

Prepares training examples to be written to YAML.

This can be any NLU training data (intent examples, lookup tables, etc.)

Arguments:

  • training_examples - Multiple training examples. Mappings in case additional values were specified for an example (e.g. metadata) or just the plain value.
  • key_name - The top level key which the examples belong to (e.g. intents)
  • key_examples - The sub key which the examples should be added to (e.g. examples).
  • example_extraction_predicate - Function to extract example value (e.g. the the text for an intent example)

Returns:

NLU training data examples prepared for writing to YAML.