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

Short: MITIE initializer

Outputs: nothing

Requires: nothing

Description: Initializes mitie structures. Every mitie component relies on this, hence this should be put at the beginning of every pipeline that uses any mitie components.

Configuration:

pipeline:
- name: "MitieNLP"
  # language model to load
  model: "data/total_word_feature_extractor.dat"

SpacyNLP

Short: spacy language initializer

Outputs: nothing

Requires: nothing

Description: Initializes spacy structures. Every spacy component relies on this, hence this should be put at the beginning of every pipeline that uses any spacy components.

Configuration:

pipeline:
- name: "SpacyNLP"
  # language model to load
  model: "en_core_web_md"

# when retrieving word vectors, this will decide if the casing
  # of the word is relevant. E.g. `hello` and `Hello` will
  # retrieve the same vector, if set to `false`. For some
  # applications and models it makes sense to differentiate
  # between these two words, therefore setting this to `true`.
  case_sensitive: false

Text Featurizers

Text featurizers are divided into two different categories: sparse featurizers and dense featurizers. Sparse featurizers are featurizers that return feature vectors with a lot of missing values, e.g. zeros. As those feature vectors would normally take up a lot of memory, we store them as sparse features.

MitieFeaturizer

Short: MITIE intent featurizer

Outputs: nothing, used as an input to intent classifiers that need intent features (e.g. SklearnIntentClassifier)

Requires: MitieNLP

Type: Dense featurizer

Description: Creates feature for intent classification using the MITIE featurizer.

Configuration:

pipeline:
- name: "MitieFeaturizer"

SpacyFeaturizer

Short: spacy intent featurizer

Outputs: nothing, used as an input to intent classifiers that need intent features (e.g. SklearnIntentClassifier)

Requires: SpacyNLP

Type: Dense featurizer

Description: Creates feature for intent classification using the spacy featurizer.

Configuration:

pipeline:
- name: "SpacyFeaturizer"

Intent Classifiers

MitieIntentClassifier

Short: MITIE intent classifier

Outputs: intent

Requires: A tokenizer and a featurizer

Output-Example:

{
    "intent": {"name": "greet", "confidence": 0.98343}
}

Configuration:

pipeline:
- name: "MitieIntentClassifier"

SklearnIntentClassifier

Short: sklearn intent classifier

Outputs: intent and intent_ranking

Requires: A featurizer

Output-Example:

{
    "intent": {"name": "greet", "confidence": 0.78343},
    "intent_ranking": [
        {
            "confidence": 0.1485910906220309,
            "name": "goodbye"
        },
        {
            "confidence": 0.08161531595656784,
            "name": "restaurant_search"
        }
    ]
}

Configuration:

pipeline:
- name: "SklearnIntentClassifier"
  # Specifies the list of regularization values to
  # cross-validate over for C-SVM.
  # This is used with the ``kernel`` hyperparameter in GridSearchCV.
  C: [1, 2, 5, 10, 20, 100]
  # Specifies the kernel to use with C-SVM.
  # This is used with the ``C`` hyperparameter in GridSearchCV.
  kernels: ["linear"]

Selectors

Response Selector

Short: Response Selector

Outputs: A dictionary with key as direct_response_intent and value containing response and ranking

Requires: A featurizer

Output-Example:

{
    "text": "What is the recommend python version to install?",
    "entities": [],
    "intent": {
        "confidence": 0.6485910906220309,
        "name": "faq"
    },
    "intent_ranking": [
        {
            "confidence": 0.6485910906220309,
            "name": "faq"
        },
        {
            "confidence": 0.1416153159565678,
            "name": "greet"
        }
    ],
    "response_selector": {
      "faq": {
        "response": {
            "confidence": 0.7356462617,
            "name": "Supports 3.5, 3.6 and 3.7, recommended version is 3.6"
        },
        "ranking": [
            {
                "confidence": 0.7356462617,
                "name": "Supports 3.5, 3.6 and 3.7, recommended version is 3.6"
            },
            {
                "confidence": 0.2134543431,
                "name": "You can ask me about how to get started"
            }
        ]
      }
    }
}

Tokenizers

WhitespaceTokenizer

Short: Tokenizer using whitespaces as a separator

Outputs: nothing

Requires: nothing

Description: Creates a token for every whitespace separated character sequence.

Configuration:

pipeline:
- name: "WhitespaceTokenizer"
  case_sensitive: false

MitieTokenizer

Short: Tokenizer using MITIE

Outputs: nothing

Requires: MitieNLP

Description: Creates tokens using the MITIE tokenizer.

Configuration:

pipeline:
- name: "MitieTokenizer"

Entity Extractors

MitieEntityExtractor

Short: MITIE entity extraction

Outputs: appends entities

Requires: MitieNLP

Output-Example:

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

Configuration:

pipeline:
- name: "MitieEntityExtractor"