Components

Note

For clarity, we have renamed the pre-defined pipelines to reflect what they do rather than which libraries they use as of Rasa NLU 0.15. The tensorflow_embedding pipeline is now called supervised_embeddings, and spacy_sklearn is now known as pretrained_embeddings_spacy. Please update your code if you are using these.

This is a reference of the configuration options for every built-in component in Rasa NLU. If you want to build a custom component, check out Custom NLU Components.

Word Vector Sources

• MitieNLP
• SpacyNLP

Text Featurizers

• MitieFeaturizer
• SpacyFeaturizer
• ConveRTFeaturizer
• RegexFeaturizer
• CountVectorsFeaturizer

Intent Classifiers

• MitieIntentClassifier
• SklearnIntentClassifier
• EmbeddingIntentClassifier
• KeywordIntentClassifier

Selectors

• Response Selector

Tokenizers

• WhitespaceTokenizer
• JiebaTokenizer
• MitieTokenizer
• SpacyTokenizer
• ConveRTTokenizer

Entity Extractors

• MitieEntityExtractor
• SpacyEntityExtractor
• EntitySynonymMapper
• CRFEntityExtractor
• DucklingHTTPExtractor

Configuration Examples

MitieNLP

pipeline:
  - name: "MitieNLP"
    model: "data/total_word_feature_extractor.dat"

SpacyNLP

pipeline:
  - name: "SpacyNLP"
    model: "en_core_web_md"
    case_sensitive: false

MitieFeaturizer

pipeline:
  - name: "MitieFeaturizer"

SpacyFeaturizer

pipeline:
  - name: "SpacyFeaturizer"

CountVectorsFeaturizer

pipeline:
  - name: "CountVectorsFeaturizer"
    use_shared_vocab: False
    analyzer: 'word'
    stop_words: None
    min_df: 1
    max_df: 1.0
    ngram_range: [1, 1]
    lowercase: true
    OOV_token: None

Entity Extractor Output Example

{
    "entities": [{
        "value": "New York City",
        "start": 20,
        "end": 33,
        "entity": "city",
        "extractor": "MitieEntityExtractor"
    }]
}