You are viewing documentation for our open source project which is maintained by the community. If you want to get started building assistants with Rasa please check out our latest [documentation here](/content/docs/index.html). ## Message Objects ### classMessage() Container for data that can be used to describe a conversation turn. The turn is described by a set of attributes such as e.g. `TEXT` and `INTENT` when describing a user utterance or e.g. `ACTION_NAME` for describing a bot action. The container includes raw information (`self.data`) as well as features (`self.features`) for each such attribute. Moreover, the message has a timestamp and can keep track about information on a specific subset of attributes (`self.output_properties`). ### __init__ ```python def __init__(data: Optional[Dict[Text, Any]]=None, output_properties: Optional[Set]=None, time: Optional[int]=None, features: Optional[List["Features"]]=None, **kwargs: Any)->None ``` Creates an instance of Message. ### add_features ```python def add_features(features: Optional["Features"])->None ``` Add more vectorized features to the message. ### add_diagnostic_data ```python def add_diagnostic_data(origin: Text, data: Dict[Text, Any])->None ``` Adds diagnostic data from the `origin` component. **Arguments**: - `origin` - Name of the component that created the data. - `data` - The diagnostic data. ### set ```python def set(prop: Text, info: Any, add_to_output:bool=False)->None ``` Sets the message's property to the given value. **Arguments**: - `prop` - Name of the property to be set. - `info` - Value to be assigned to that property. - `add_to_output` - Decides whether to add `prop` to the `output_properties`. ### get ```python def get(prop: Text, default: Optional[Any]=None)-> Any ``` Retrieve message property. ### as_dict_nlu ```python def as_dict_nlu()->dict ``` Get dict representation of message as it would appear in training data ### as_dict ```python def as_dict(only_output_properties:bool=False)-> Dict ``` Gets dict representation of message. ### __hash__ ```python def __hash__()->int ``` Calculate a hash for the message. **Returns**: Hash of the message. ### fingerprint ```python def fingerprint()-> Text ``` Calculate a string fingerprint for the message. **Returns**: Fingerprint of the message. ### build ```python @classmethod def build(cls, text: Text, intent: Optional[Text]=None, entities: Optional[List[Dict[Text, Any]]]=None, intent_metadata: Optional[Any]=None, example_metadata: Optional[Any]=None, **kwargs: Any)->"Message" ``` Builds a Message from `UserUttered` data. **Arguments**: - `text` - text of a user's utterance - `intent` - an intent of the user utterance - `entities` - entities in the user's utterance - `intent_metadata` - optional metadata for the intent - `example_metadata` - optional metadata for the intent example **Returns**: Message. ### get_full_intent ```python def get_full_intent()-> Text ``` Get intent as it appears in training data. ### separate_intent_response_key ```python @staticmethod def separate_intent_response_key( original_intent: Text)-> Tuple[Text, Optional[Text]] ``` Splits intent into main intent name and optional sub-intent name. For example, "FAQ/how_to_contribute" would be split into `("FAQ", "how_to_contribute")`. The response delimiter can take different values (not just "/") and depends on the constant - `RESPONSE_IDENTIFIER_DELIMITER`. If there is no response delimiter in the intent, the second tuple item is None, e.g. "FAQ" would be mapped to `("FAQ", None)`. ### get_sparse_features ```python def get_sparse_features( attribute: Text, featurizers: Optional[List[Text]]=None )-> Tuple[Optional["Features"], Optional["Features"]] ``` Gets all sparse features for the attribute given the list of featurizers. If no featurizers are provided, all available features will be considered. **Arguments**: - `attribute` - message attribute - `featurizers` - names of featurizers to consider **Returns**: Sparse features. ### get_sparse_feature_sizes ```python def get_sparse_feature_sizes( attribute: Text, featurizers: Optional[List[Text]]=None)-> Dict[Text, List[int]] ``` Gets sparse feature sizes for the attribute given the list of featurizers. If no featurizers are provided, all available features will be considered. **Arguments**: - `attribute` - message attribute - `featurizers` - names of featurizers to consider **Returns**: Sparse feature sizes. ### get_dense_features ```python def get_dense_features( attribute: Text, featurizers: Optional[List[Text]]=None )-> Tuple[Optional["Features"], Optional["Features"]] ``` Gets all dense features for the attribute given the list of featurizers. If no featurizers are provided, all available features will be considered. **Arguments**: - `attribute` - message attribute - `featurizers` - names of featurizers to consider **Returns**: Dense features. ### get_all_features ```python def get_all_features( attribute: Text, featurizers: Optional[List[Text]]=None)-> List["Features"] ``` Gets all features for the attribute given the list of featurizers. If no featurizers are provided, all available features will be considered. **Arguments**: - `attribute` - message attribute - `featurizers` - names of featurizers to consider **Returns**: Features. ### features_present ```python def features_present(attribute: Text, featurizers: Optional[List[Text]]=None)->bool ``` Checks if there are any features present for the attribute and featurizers. If no featurizers are provided, all available features will be considered. **Arguments**: - `attribute` - Message attribute. - `featurizers` - Names of featurizers to consider. **Returns**: `True`, if features are present, `False` otherwise. ### is_core_or_domain_message ```python def is_core_or_domain_message()->bool ``` Checks whether the message is a core message or from the domain. E.g. a core message is created from a story or a domain action, not from the NLU data. **Returns**: True, if message is a core or domain message, false otherwise. ### is_e2e_message ```python def is_e2e_message()->bool ``` Checks whether the message came from an e2e story. **Returns**: `True`, if message is a from an e2e story, `False` otherwise. ### find_overlapping_entities ```python def find_overlapping_entities()-> List[Tuple[Dict[Text, Any], Dict[Text, Any]]] ``` Finds any overlapping entity annotations.