registry.py

"""This is a somewhat delicate package. It contains all registered components
and preconfigured templates.

Hence, it imports all of the components. To avoid cycles, no component should
import this in module scope."""

import logging
import typing
from typing import Any, Dict, List, Optional, Text, Type

from rasa.nlu.classifiers.embedding_intent_classifier import EmbeddingIntentClassifier
from rasa.nlu.classifiers.keyword_intent_classifier import KeywordIntentClassifier
from rasa.nlu.classifiers.mitie_intent_classifier import MitieIntentClassifier
from rasa.nlu.classifiers.sklearn_intent_classifier import SklearnIntentClassifier
from rasa.nlu.classifiers.custom_intent_classifier import CustomIntentClassifier
from rasa.nlu.extractors.crf_entity_extractor import CRFEntityExtractor
from rasa.nlu.extractors.duckling_http_extractor import DucklingHTTPExtractor
from rasa.nlu.extractors.entity_synonyms import EntitySynonymMapper
from rasa.nlu.extractors.mitie_entity_extractor import MitieEntityExtractor
from rasa.nlu.extractors.spacy_entity_extractor import SpacyEntityExtractor
from rasa.nlu.extractors.custom_extractors import ScriptExtractor
from rasa.nlu.extractors.custom_extractors import LatinTextExtractor
from rasa.nlu.extractors.custom_extractors import LanguageExtractor
from rasa.nlu.featurizers.count_vectors_featurizer import CountVectorsFeaturizer
from rasa.nlu.featurizers.mitie_featurizer import MitieFeaturizer
from rasa.nlu.featurizers.ngram_featurizer import NGramFeaturizer
from rasa.nlu.featurizers.regex_featurizer import RegexFeaturizer
from rasa.nlu.featurizers.spacy_featurizer import SpacyFeaturizer
from rasa.nlu.featurizers.custom_featurizer import CustomFeaturizer
from rasa.nlu.model import Metadata
from rasa.nlu.tokenizers.jieba_tokenizer import JiebaTokenizer
from rasa.nlu.tokenizers.mitie_tokenizer import MitieTokenizer
from rasa.nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
from rasa.nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
from rasa.nlu.utils.mitie_utils import MitieNLP
from rasa.nlu.utils.spacy_utils import SpacyNLP
from rasa.utils.common import class_from_module_path

if typing.TYPE_CHECKING:
    from rasa.nlu.components import Component
    from rasa.nlu.config import RasaNLUModelConfig, RasaNLUModelConfig

logger = logging.getLogger(__name__)


# Classes of all known components. If a new component should be added,
# its class name should be listed here.
component_classes = [
    # utils
    SpacyNLP,
    MitieNLP,
    # tokenizers
    MitieTokenizer,
    SpacyTokenizer,
    WhitespaceTokenizer,
    JiebaTokenizer,
    # extractors
    SpacyEntityExtractor,
    MitieEntityExtractor,
    CRFEntityExtractor,
    DucklingHTTPExtractor,
    EntitySynonymMapper,
    ScriptExtractor,
    LatinTextExtractor,
    LanguageExtractor,
    # featurizers
    SpacyFeaturizer,
    MitieFeaturizer,
    NGramFeaturizer,
    RegexFeaturizer,
    CountVectorsFeaturizer,
    CustomFeaturizer,
    # classifiers
    SklearnIntentClassifier,
    MitieIntentClassifier,
    KeywordIntentClassifier,
    EmbeddingIntentClassifier,
    CustomIntentClassifier,
]

# Mapping from a components name to its class to allow name based lookup.
registered_components = {c.name: c for c in component_classes}

# DEPRECATED ensures compatibility, will be remove in future versions
old_style_names = {
    "nlp_spacy": "SpacyNLP",
    "nlp_mitie": "MitieNLP",
    "ner_spacy": "SpacyEntityExtractor",
    "ner_mitie": "MitieEntityExtractor",
    "ner_crf": "CRFEntityExtractor",
    "ner_duckling_http": "DucklingHTTPExtractor",
    "script_extractor": "ScriptExtractor",
    "latin_text_extractor": "LatinTextExtractor",
    "language_extractor": "LanguageExtractor",
    "ner_synonyms": "EntitySynonymMapper",
    "intent_featurizer_spacy": "SpacyFeaturizer",
    "intent_featurizer_mitie": "MitieFeaturizer",
    "intent_featurizer_ngrams": "NGramFeaturizer",
    "intent_entity_featurizer_regex": "RegexFeaturizer",
    "intent_featurizer_count_vectors": "CountVectorsFeaturizer",
    "custom_featurizer": "CustomFeaturizer",
    "tokenizer_mitie": "MitieTokenizer",
    "tokenizer_spacy": "SpacyTokenizer",
    "tokenizer_whitespace": "WhitespaceTokenizer",
    "tokenizer_jieba": "JiebaTokenizer",
    "intent_classifier_sklearn": "SklearnIntentClassifier",
    "intent_classifier_mitie": "MitieIntentClassifier",
    "intent_classifier_keyword": "KeywordIntentClassifier",
    "intent_classifier_tensorflow_embedding": "EmbeddingIntentClassifier",
    "custom_intent_classifier": "CustomIntentClassifier",
}

# To simplify usage, there are a couple of model templates, that already add
# necessary components in the right order. They also implement
# the preexisting `backends`.
registered_pipeline_templates = {
    "pretrained_embeddings_spacy": [
        "SpacyNLP",
        "SpacyTokenizer",
        "SpacyFeaturizer",
        "RegexFeaturizer",
        "CRFEntityExtractor",
        "EntitySynonymMapper",
        "SklearnIntentClassifier",
    ],
    "keyword": ["KeywordIntentClassifier"],
    "supervised_embeddings": [
        "WhitespaceTokenizer",
        "RegexFeaturizer",
        "CRFEntityExtractor",
        "EntitySynonymMapper",
        "CountVectorsFeaturizer",
        "EmbeddingIntentClassifier",
    ],
    "custom_pipeline": [
        "ScriptExtractor",
        "LatinTextExtractor",
        "CustomFeaturizer",
        "LanguageExtractor",
        "CustomIntentClassifier", ]

}


def pipeline_template(s: Text) -> Optional[List[Dict[Text, Text]]]:
    components = registered_pipeline_templates.get(s)

    if components:
        # converts the list of components in the configuration
        # format expected (one json object per component)
        return [{"name": c} for c in components]

    else:
        return None


def get_component_class(component_name: Text) -> Type["Component"]:
    """Resolve component name to a registered components class."""

    if component_name not in registered_components:
        if component_name not in old_style_names:
            try:
                return class_from_module_path(component_name)
            except Exception:
                raise Exception(
                    "Failed to find component class for '{}'. Unknown "
                    "component name. Check your configured pipeline and make "
                    "sure the mentioned component is not misspelled. If you "
                    "are creating your own component, make sure it is either "
                    "listed as part of the `component_classes` in "
                    "`rasa.nlu.registry.py` or is a proper name of a class "
                    "in a module.".format(component_name)
                )
        else:
            # DEPRECATED ensures compatibility, remove in future versions
            logger.warning(
                "DEPRECATION warning: your nlu config file "
                "contains old style component name `{}`, "
                "you should change it to its class name: `{}`."
                "".format(component_name, old_style_names[component_name])
            )
            component_name = old_style_names[component_name]

    return registered_components[component_name]


def load_component_by_meta(
    component_meta: Dict[Text, Any],
    model_dir: Text,
    metadata: Metadata,
    cached_component: Optional["Component"],
    **kwargs: Any
) -> Optional["Component"]:
    """Resolves a component and calls its load method.

    Inits it based on a previously persisted model.
    """

    # try to get class name first, else create by name
    component_name = component_meta.get("class", component_meta["name"])
    component_class = get_component_class(component_name)
    return component_class.load(
        component_meta, model_dir, metadata, cached_component, **kwargs
    )


def create_component_by_config(
    component_config: Dict[Text, Any], config: "RasaNLUModelConfig"
) -> Optional["Component"]:
    """Resolves a component and calls it's create method.

    Inits it based on a previously persisted model.
    """

    # try to get class name first, else create by name
    component_name = component_config.get("class", component_config["name"])
    component_class = get_component_class(component_name)
    return component_class.create(component_config, config)