Class SchemaNode

rasa/engine/graph.py:26–67

Represents one node in the schema. Args: needs: describes which parameters in `fn` (or `constructor_name` if `eager==False`) are filled by which parent nodes. uses: The class which models the behavior of this specific graph node. constructor_name: The name of

@dataclass
class SchemaNode:
    """Represents one node in the schema.

    Args:
        needs: describes which parameters in `fn` (or `constructor_name`
            if `eager==False`) are filled by which parent nodes.
        uses: The class which models the behavior of this specific graph node.
        constructor_name: The name of the constructor which should be used to
            instantiate the component. If `eager==False` then the `constructor` can
            also specify parameters which are filled by parent nodes. This is e.g.
            useful if a parent node returns a `Resource` and this node wants to
            directly load itself from this resource.
        fn: The name of the function which should be called on the instantiated
            component when the graph is executed. The parameters from `needs` are
            filled from the parent nodes.
        config: The user's configuration for this graph node. This configuration
            does not need to be specify all possible parameters; the default values
            for missing parameters will be filled in later.
        eager: If `eager` then the component is instantiated before the graph is run.
            Otherwise it's instantiated as the graph runs (lazily). Usually we always
            instantiated lazily during training and eagerly during inference (to
            avoid that the first prediction takes longer).
        is_target: If `True` then this node can't be pruned during fingerprinting
            (it might be replaced with a cached value though). This is e.g. used for
            all components which train as their result always needs to be added to
            the model archive so that the data is available during inference.
        is_input: Nodes with `is_input` are _always_ run (also during the fingerprint
            run). This makes sure that we e.g. detect changes in file contents.
        resource: If given, then the graph node is loaded from an existing resource
            instead of instantiated from scratch. This is e.g. used to load a trained
            component for predictions.
    """

    needs: Dict[Text, Text]
    uses: Type[GraphComponent]
    constructor_name: Text
    fn: Text
    config: Dict[Text, Any]
    eager: bool = False
    is_target: bool = False
    is_input: bool = False
    resource: Optional[Resource] = None


@dataclass