Pipeline and Component Configuration ¶

Contents

Incoming messages are processed by a sequence of components. These components are executed one after another in a so called processing pipeline. There are components for entity extraction, for intent classification, pre-processing, and others. If you want to add your own component, for example to run a spell-check or to do sentiment analysis, check out Custom Components.

Each component processes the input and creates an output. The ouput can be used by any component that comes after this component in the pipeline. There are components which only produce information that is used by other components in the pipeline and there are other components that produce Output attributes which will be returned after the processing has finished. For example, for the sentence "I am looking for Chinese food" the output

{
    "text": "I am looking for Chinese food",
    "entities": [
        {"start": 8, "end": 15, "value": "chinese", "entity": "cuisine", "extractor": "ner_crf", "confidence": 0.864}
    ],
    "intent": {"confidence": 0.6485910906220309, "name": "restaurant_search"},
    "intent_ranking": [
        {"confidence": 0.6485910906220309, "name": "restaurant_search"},
        {"confidence": 0.1416153159565678, "name": "affirm"}
    ]
}

is created as a combination of the results of the different components in the pre-configured pipeline spacy_sklearn. For example, the entities attribute is created by the ner_crf component.

Pre-configured Pipelines ¶

To ease the burden of coming up with your own processing pipelines, we provide a couple of ready to use templates which can be used by setting the pipeline configuration value to the name of the template you want to use. Here is a list of the existing templates:

spacy_sklearn ¶

To use spacy as a template:

language: "en"

pipeline: "spacy_sklearn"

See Language Support for possible values for language. To use the components and configure them separately:

language: "en"

pipeline:
- name: "nlp_spacy"
- name: "tokenizer_spacy"
- name: "intent_entity_featurizer_regex"
- name: "intent_featurizer_spacy"
- name: "ner_crf"
- name: "ner_synonyms"
- name: "intent_classifier_sklearn"

tensorflow_embedding ¶

to use it as a template:

language: "en"

pipeline: "tensorflow_embedding"

The tensorflow pipeline supports any language that can be tokenized. The current tokenizer implementation relies on words being separated by spaces, so any languages that adheres to that can be trained with this pipeline.

mitie ¶

There is no pipeline template, as you need to configure the location of mities featurizer. To use the components and configure them separately:

language: "en"

pipeline:
- name: "nlp_mitie"
  model: "data/total_word_feature_extractor.dat"
- name: "tokenizer_mitie"
- name: "ner_mitie"
- name: "ner_synonyms"
- name: "intent_entity_featurizer_regex"
- name: "intent_classifier_mitie"

mitie_sklearn ¶

There is no pipeline template, as you need to configure the location of mities featurizer. To use the components and configure them separately:

language: "en"

pipeline:
- name: "nlp_mitie"
  model: "data/total_word_feature_extractor.dat"
- name: "tokenizer_mitie"
- name: "ner_mitie"
- name: "ner_synonyms"
- name: "intent_entity_featurizer_regex"
- name: "intent_featurizer_mitie"
- name: "intent_classifier_sklearn"

keyword ¶

to use it as a template:

language: "en"

pipeline: "keyword"

to use the components and configure them separately:

language: "en"

pipeline:
- name: "intent_classifier_keyword"

Multiple Intents ¶

If you want to split intents into multiple labels, e.g. for predicting multiple intents or for modeling hierarchical intent structure, use these flags:

intent_tokenization_flag if true the algorithm will split the intent labels into tokens and use bag-of-words representations for them;

intent_split_symbol sets the delimiter string to split the intent labels. Default _

Here’s an example configuration:

language: "en"

pipeline:
- name: "tokenizer_whitespace"
- name: "ner_crf"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
  intent_tokenization_flag: true
  intent_split_symbol: "_"

Custom pipelines ¶

Creating your own pipelines is possible by directly passing the names of the ~ components to Rasa NLU in the pipeline configuration variable, e.g.

pipeline:
- name: "nlp_spacy"
- name: "ner_crf"
- name: "ner_synonyms"

This creates a pipeline that only does entity recognition, but no intent classification. Hence, the output will not contain any useful intents.

Built-in Components ¶

Short explanation of every components and it’s attributes. If you are looking for more details, you should have a look at the corresponding source code for the component. Output describes, what each component adds to the final output result of processing a message. If no output is present, the component is most likely a preprocessor for another component.

nlp_mitie ¶

Short:	MITIE initializer
Outputs:	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:	The MITIE library needs a language model file, that must be specified in the configuration: pipeline: - name: "nlp_mitie" # language model to load model: "data/total_word_feature_extractor.dat" For more information where to get that file from, head over to Installation.

nlp_spacy ¶

Short:	spacy language initializer
Outputs:	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:	Language model, default will use the configured language. If the spacy model to be used has a name that is different from the language tag (`"en"`, `"de"`, etc.), the model name can be specified using this configuration variable. The name will be passed to `spacy.load(name)`. pipeline: - name: "nlp_spacy" # 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

intent_featurizer_mitie ¶

Short:	MITIE intent featurizer
Outputs:	nothing, used as an input to intent classifiers that need intent features (e.g. `intent_classifier_sklearn`)
Description:	Creates feature for intent classification using the MITIE featurizer. Note NOT used by the `intent_classifier_mitie` component. Currently, only `intent_classifier_sklearn` is able to use precomputed features.
Configuration:	pipeline: - name: "intent_featurizer_mitie"

intent_featurizer_spacy ¶

Short:	spacy intent featurizer
Outputs:	nothing, used as an input to intent classifiers that need intent features (e.g. `intent_classifier_sklearn`)
Description:	Creates feature for intent classification using the spacy featurizer.

intent_featurizer_ngrams ¶

Short:	Appends char-ngram features to feature vector
Outputs:	nothing, appends its features to an existing feature vector generated by another intent featurizer
Description:	This featurizer appends character ngram features to a feature vector. During training the component looks for the most common character sequences (e.g. `app` or `ing`). The added features represent a boolean flag if the character sequence is present in the word sequence or not. Note There needs to be another intent featurizer previous to this one in the pipeline!
Configuration:	pipeline: - name: "intent_featurizer_ngrams" # Maximum number of ngrams to use when augmenting # feature vectors with character ngrams max_number_of_ngrams: 10

intent_featurizer_count_vectors ¶

Short:	Creates bag-of-words representation of intent features
Outputs:	nothing, used as an input to intent classifiers that need bag-of-words representation of intent features (e.g. `intent_classifier_tensorflow_embedding`)
Description:	Creates bag-of-words representation of intent features using sklearn’s CountVectorizer. All tokens which consist only of digits (e.g. 123 and 99 but not a123d) will be assigned to the same feature. Note If the words in the model language cannot be split by whitespace, a language-specific tokenizer is required in the pipeline before this component (e.g. using `tokenizer_jieba` for Chinese).
Configuration:	See sklearn’s CountVectorizer docs for detailed description of the configuration parameters Handling Out-Of-Vacabulary (OOV) words: Since the training is performed on limited vocabulary data, it cannot be guaranteed that during prediction an algorithm will not encounter an unknown word (a word that were not seen during training). In order to teach an algorithm how to treat unknown words, some words in training data can be substituted by generic word `OOV_token`. In this case during prediction all unknown words will be treated as this generic word `OOV_token`. For example, one might create separate intent `outofscope` in the training data containing messages of different number of `OOV_token` s and maybe some additional general words. Then an algorithm will likely classify a message with unknown words as this intent `outofscope`. Note This featurizer creates a bag-of-words representation by counting words, so the number of `OOV_token` s might be important. `OOV_token` set a keyword for unseen words; if training data contains `OOV_token` as words in some messages, during prediction the words that were not seen during training will be substituted with provided `OOV_token`; if `OOV_token=None` (default behaviour) words that were not seen during training will be ignored during prediction time; `OOV_words` set a list of words to be treated as `OOV_token` during training; if a list of words that should be treated as Out-Of-Vacabulary is known, it can be set to `OOV_words` instead of manually changing it in trainig data or using custom preprocessor. Note Providing `OOV_words` is optional, training data can contain `OOV_token` input manually or by custom additional preprocessor. Unseen words will be substituted with `OOV_token` only if this token is present in the training data or `OOV_words` list is provided. pipeline: - name: "intent_featurizer_count_vectors" # the parameters are taken from # sklearn's CountVectorizer # regular expression for tokens "token_pattern": r'(?u)\b\w\w+\b' # remove accents during the preprocessing step "strip_accents": None # {'ascii', 'unicode', None} # list of stop words "stop_words": None # string {'english'}, list, or None (default) # min document frequency of a word to add to vocabulary # float - the parameter represents a proportion of documents # integer - absolute counts "min_df": 1 # float in range [0.0, 1.0] or int # max document frequency of a word to add to vocabulary # float - the parameter represents a proportion of documents # integer - absolute counts "max_df": 1.0 # float in range [0.0, 1.0] or int # set ngram range "min_ngram": 1 # int "max_ngram": 1 # int # limit vocabulary size "max_features": None # int or None # if convert all characters to lowercase "lowercase": true # bool # handling Out-Of-Vacabulary (OOV) words # will be converted to lowercase if lowercase is true "OOV_token": None # string or None "OOV_words": [] # list of strings

intent_classifier_keyword ¶

Short:	Simple keyword matching intent classifier.
Outputs:	`intent`
Output-Example:	{ "intent": {"name": "greet", "confidence": 0.98343} }
Description:	This classifier is mostly used as a placeholder. It is able to recognize hello and goodbye intents by searching for these keywords in the passed messages.

intent_classifier_mitie ¶

Short:	MITIE intent classifier (using a text categorizer)
Outputs:	`intent`
Output-Example:	{ "intent": {"name": "greet", "confidence": 0.98343} }
Description:	This classifier uses MITIE to perform intent classification. The underlying classifier is using a multi class linear SVM with a sparse linear kernel (see mitie trainer code).
Configuration:	pipeline: - name: "intent_classifier_mitie"

intent_classifier_sklearn ¶

Short:	sklearn intent classifier
Outputs:	`intent` and `intent_ranking`
Output-Example:	{ "intent": {"name": "greet", "confidence": 0.78343}, "intent_ranking": [ { "confidence": 0.1485910906220309, "name": "goodbye" }, { "confidence": 0.08161531595656784, "name": "restaurant_search" } ] }
Description:	The sklearn intent classifier trains a linear SVM which gets optimized using a grid search. In addition to other classifiers it also provides rankings of the labels that did not “win”. The spacy intent classifier needs to be preceded by a featurizer in the pipeline. This featurizer creates the features used for the classification.
Configuration:	During the training of the SVM a hyperparameter search is run to find the best parameter set. In the config, you can specify the parameters that will get tried pipeline: - name: "intent_classifier_sklearn" # 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"]

intent_classifier_tensorflow_embedding ¶

Short:	Embedding intent classifier
Outputs:	`intent` and `intent_ranking`
Output-Example:	{ "intent": {"name": "greet", "confidence": 0.8343}, "intent_ranking": [ { "confidence": 0.385910906220309, "name": "goodbye" }, { "confidence": 0.28161531595656784, "name": "restaurant_search" } ] }
Description:	The embedding intent classifier embeds user inputs and intent labels into the same space. Supervised embeddings are trained by maximizing similarity between them. This algorithm is based on the starspace idea from: https://arxiv.org/abs/1709.03856. However, in this implementation the `mu` parameter is treated differently and additional hidden layers are added together with dropout. This algorithm also provides similarity rankings of the labels that did not “win”. The embedding intent classifier needs to be preceded by a featurizer in the pipeline. This featurizer creates the features used for the embeddings. It is recommended to use `intent_featurizer_count_vectors` that can be optionally preceded by `nlp_spacy` and `tokenizer_spacy`. Note If during prediction time a message contains only words unseen during training, and no Out-Of-Vacabulary preprocessor was used, empty intent `""` is predicted with confidence `0.0`.
Configuration:	If you want to split intents into multiple labels, e.g. for predicting multiple intents or for modeling hierarchical intent structure, use these flags: tokenization of intent labels: `intent_tokenization_flag` if `true` the algorithm will split the intent labels into tokens and use bag-of-words representations for them, default `false`; `intent_split_symbol` sets the delimiter string to split the intent labels, default `_`. The algorithm also has hyperparameters to control: neural network’s architecture: `num_hidden_layers_a` and `hidden_layer_size_a` set the number of hidden layers and their sizes before embedding layer for user inputs; `num_hidden_layers_b` and `hidden_layer_size_b` set the number of hidden layers and their sizes before embedding layer for intent labels; training: `batch_size` sets the number of training examples in one forward/backward pass, the higher the batch size, the more memory space you’ll need; `epochs` sets the number of times the algorithm will see training data, where `one epoch` = one forward pass and one backward pass of all the training examples; embedding: `embed_dim` sets the dimension of embedding space; `mu_pos` controls how similar the algorithm should try to make embedding vectors for correct intent labels; `mu_neg` controls maximum negative similarity for incorrect intents; `similarity_type` sets the type of the similarity, it should be either `cosine` or `inner`; `num_neg` sets the number of incorrect intent labels, the algorithm will minimize their similarity to the user input during training; `use_max_sim_neg` if `true` the algorithm only minimizes maximum similarity over incorrect intent labels; regularization: `C2` sets the scale of L2 regularization `C_emb` sets the scale of how important is to minimize the maximum similarity between embeddings of different intent labels; `droprate` sets the dropout rate, it should be between `0` and `1`, e.g. `droprate=0.1` would drop out `10%` of input units; Note For `cosine` similarity `mu_pos` and `mu_neg` should be between `-1` and `1`. Note There is an option to use linearly increasing batch size. The idea comes from https://arxiv.org/abs/1711.00489. In order to do it pass a list to `batch_size`, e.g. `"batch_size": [64, 256]` (default behaviour). If constant `batch_size` is required, pass an `int`, e.g. `"batch_size": 64`. In the config, you can specify these parameters: pipeline: - name: "intent_classifier_tensorflow_embedding" # nn architecture "num_hidden_layers_a": 2 "hidden_layer_size_a": [256, 128] "num_hidden_layers_b": 0 "hidden_layer_size_b": [] "batch_size": [64, 256] "epochs": 300 # embedding parameters "embed_dim": 20 "mu_pos": 0.8 # should be 0.0 < ... < 1.0 for 'cosine' "mu_neg": -0.4 # should be -1.0 < ... < 1.0 for 'cosine' "similarity_type": "cosine" # string 'cosine' or 'inner' "num_neg": 20 "use_max_sim_neg": true # flag which loss function to use # regularization "C2": 0.002 "C_emb": 0.8 "droprate": 0.2 # flag if to tokenize intents "intent_tokenization_flag": false "intent_split_symbol": "_" # visualization of accuracy "evaluate_every_num_epochs": 10 # small values may hurt performance "evaluate_on_num_examples": 1000 # large values may hurt performance Note Parameter `mu_neg` is set to a negative value to mimic the original starspace algorithm in the case `mu_neg = mu_pos` and `use_max_sim_neg = False`. See starspace paper for details.

intent_entity_featurizer_regex ¶

Short:	regex feature creation to support intent and entity classification
Outputs:	`text_features` and `tokens.pattern`
Description:	During training, the regex intent featurizer creates a list of regular expressions defined in the training data format. For each regex, a feature will be set marking whether this expression was found in the input, which will later be fed into intent classifier / entity extractor to simplify classification (assuming the classifier has learned during the training phase, that this set feature indicates a certain intent). Regex features for entity extraction are currently only supported by the `ner_crf` component! .. note:: There needs to be a tokenizer previous to this featurizer in the pipeline!

tokenizer_whitespace ¶

Short:	Tokenizer using whitespaces as a separator
Outputs:	nothing
Description:	Creates a token for every whitespace separated character sequence. Can be used to define tokens for the MITIE entity extractor.

tokenizer_jieba ¶

Short:	Tokenizer using Jieba for Chinese language
Outputs:	nothing
Description:	Creates tokens using the Jieba tokenizer specifically for Chinese language. For language other than Chinese, Jieba will work as `tokenizer_whitespace`. Can be used to define tokens for the MITIE entity extractor. Make sure to install Jieba, `pip install jieba`.
Configuration:	User’s custom dictionary files can be auto loaded by specific the files’ directory path via `dictionary_path` pipeline: - name: "tokenizer_jieba" dictionary_path: "path/to/custom/dictionary/dir" # or None (which is default value) means don't use custom dictionaries

tokenizer_mitie ¶

Short:	Tokenizer using MITIE
Outputs:	nothing
Description:	Creates tokens using the MITIE tokenizer. Can be used to define tokens for the MITIE entity extractor.
Configuration:	pipeline: - name: "tokenizer_mitie"

tokenizer_spacy ¶

Short:	Tokenizer using spacy
Outputs:	nothing
Description:	Creates tokens using the spacy tokenizer. Can be used to define tokens for the MITIE entity extractor.

ner_mitie ¶

Short:	MITIE entity extraction (using a mitie ner trainer)
Outputs:	appends `entities`
Output-Example:	{ "entities": [{"value": "New York City", "start": 20, "end": 33, "confidence": null, "entity": "city", "extractor": "ner_mitie"}] }
Description:	This uses the MITIE entitiy extraction to find entities in a message. The underlying classifier is using a multi class linear SVM with a sparse linear kernel and custom features. The MITIE component does not provide entity confidence values.
Configuration:	pipeline: - name: "ner_mitie"

ner_spacy ¶

Short:	spacy entity extraction
Outputs:	appends `entities`
Output-Example:	{ "entities": [{"value": "New York City", "start": 20, "end": 33, "entity": "city", "confidence": null, "extractor": "ner_spacy"}] }
Description:	Using spacy this component predicts the entities of a message. spacy uses a statistical BILOU transition model. As of now, this component can only use the spacy builtin entity extraction models and can not be retrained. This extractor does not provide any confidence scores.

ner_synonyms ¶

Short:

Maps synonymous entity values to the same value.

Outputs:

modifies existing entities that previous entity extraction components found

Description:

If the training data contains defined synonyms (by using the value attribute on the entity examples). this component will make sure that detected entity values will be mapped to the same value. For example, if your training data contains the following examples:

[{
  "text": "I moved to New York City",
  "intent": "inform_relocation",
  "entities": [{"value": "nyc",
                "start": 11,
                "end": 24,
                "entity": "city",
               }]
},
{
  "text": "I got a new flat in NYC.",
  "intent": "inform_relocation",
  "entities": [{"value": "nyc",
                "start": 20,
                "end": 23,
                "entity": "city",
               }]
}]

this component will allow you to map the entities New York City and NYC to nyc. The entitiy extraction will return nyc even though the message contains NYC. When this component changes an exisiting entity, it appends itself to the processor list of this entity.

ner_crf ¶

Short:	conditional random field entity extraction
Outputs:	appends `entities`
Output-Example:	{ "entities": [{"value":"New York City", "start": 20, "end": 33, "entity": "city", "confidence": 0.874, "extractor": "ner_crf"}] }
Description:	This component implements conditional random fields to do named entity recognition. CRFs can be thought of as an undirected Markov chain where the time steps are words and the states are entity classes. Features of the words (capitalisation, POS tagging, etc.) give probabilities to certain entity classes, as are transitions between neighbouring entity tags: the most likely set of tags is then calculated and returned. If POS features are used (pos or pos2), spaCy has to be installed.
Configuration:	pipeline: - name: "ner_crf" # The features are a ``[before, word, after]`` array with # before, word, after holding keys about which # features to use for each word, for example, ``"title"`` # in array before will have the feature # "is the preceding word in title case?". # Available features are: # ``low``, ``title``, ``suffix5``, ``suffix3``, ``suffix2``, # ``suffix1``, ``pos``, ``pos2``, ``prefix5``, ``prefix2``, # ``bias``, ``upper`` and ``digit`` features: [["low", "title"], ["bias", "suffix3"], ["upper", "pos", "pos2"]] # The flag determines whether to use BILOU tagging or not. BILOU # tagging is more rigorous however # requires more examples per entity. Rule of thumb: use only # if more than 100 examples per entity. BILOU_flag: true # This is the value given to sklearn_crfcuite.CRF tagger before training. max_iterations: 50 # This is the value given to sklearn_crfcuite.CRF tagger before training. # Specifies the L1 regularization coefficient. L1_c: 0.1 # This is the value given to sklearn_crfcuite.CRF tagger before training. # Specifies the L2 regularization coefficient. L2_c: 0.1

ner_duckling_http ¶

Short:	Duckling lets you extract common entities like dates, amounts of money, distances, and others in a number of languages.
Outputs:	appends `entities`
Output-Example:	{ "entities": [{"end": 53, "entity": "time", "start": 48, "value": "2017-04-10T00:00:00.000+02:00", "confidence": 1.0, "extractor": "ner_duckling_http"}] }
Description:	To use this component you need to run a duckling server. The easiest option is to spin up a docker container using `docker run -p 8000:8000 rasa/duckling`. Alternatively, you can install duckling directly on your machine and start the server. Duckling allows to recognize dates, numbers, distances and other structured entities and normalizes them (for a reference of all available entities see the duckling documentation). Please be aware that duckling tries to extract as many entity types as possible without providing a ranking. For example, if you specify both `number` and `time` as dimensions for the duckling component, the component will extract two entities: `10` as a number and `in 10 minutes` as a time from the text `I will be there in 10 minutes`. In such a situation, your application would have to decide which entity type is be the correct one. The extractor will always return 1.0 as a confidence, as it is a rule based system.
Configuration:	Configure which dimensions, i.e. entity types, the duckling component to extract. A full list of available dimensions can be found in the duckling documentation. pipeline: - name: "ner_duckling_http" # url of the running duckling server url: "http://localhost:8000" # dimensions to extract dimensions: ["time", "number", "amount-of-money", "distance"] # allows you to configure the locale, by default the language is # used locale: "de_DE" # if not set the default timezone of Duckling is going to be used # needed to calculate dates from relative expressions like "tomorrow" timezone: "Europe/Berlin"

Component Lifecycle ¶

Every component can implement several methods from the Component base class; in a pipeline these different methods will be called in a specific order. Lets assume, we added the following pipeline to our config: "pipeline": ["Component A", "Component B", "Last Component"]. The image shows the call order during the training of this pipeline :

Before the first component is created using the create function, a so called context is created (which is nothing more than a python dict). This context is used to pass information between the components. For example, one component can calculate feature vectors for the training data, store that within the context and another component can retrieve these feature vectors from the context and do intent classification.

Initially the context is filled with all configuration values, the arrows in the image show the call order and visualize the path of the passed context. After all components are trained and persisted, the final context dictionary is used to persist the model’s metadata.

Returned Entities Object ¶

In the object returned after parsing there are two fields that show information about how the pipeline impacted the entities returned. The extractor field of an entity tells you which entity extractor found this particular entity. The processors field contains the name of components that altered this specific entity.

The use of synonyms can also cause the value field not match the text exactly. Instead it will return the trained synonym.

{
  "text": "show me chinese restaurants",
  "intent": "restaurant_search",
  "entities": [
    {
      "start": 8,
      "end": 15,
      "value": "chinese",
      "entity": "cuisine",
      "extractor": "ner_crf",
      "confidence": 0.854,
      "processors": []
    }
  ]
}

Note

The confidence will be set by the CRF entity extractor (ner_crf component). The duckling entity extractor will always return 1. The ner_spacy extractor does not provide this information and returns null.