Custom NLU Components

Custom NLU Components

You can create a custom component to perform a specific task which NLU doesn’t currently offer (for example, sentiment analysis). Below is the specification of the rasa.nlu.components.Component class with the methods you’ll need to implement.

Note
There is a detailed tutorial on building custom components here.

You can add a custom component to your pipeline by adding the module path.
So if you have a module called sentiment
containing a SentimentAnalyzer class:

pipeline:
- name: "sentiment.SentimentAnalyzer"

Also be sure to read the section on the Component Lifecycle.

To get started, you can use this skeleton that contains the most important methods that you should implement:
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Note
If you create a custom tokenizer you should implement the methods of rasa.nlu.tokenizers.tokenizer.Tokenizer.
The train and process methods are already implemented and you simply need to overwrite the tokenize method.
train and process will automatically add a special token __CLS__ to the end of list of tokens.

Note
If you create a custom featurizer you should return a sequence of features.
E.g. your featurizer should return a matrix of size (number-of-tokens x feature-dimension).
The feature vector of the __CLS__ token should contain features for the complete message.

Component

classrasa.nlu.components.``Component( component_config=None)

A component is a message processing unit in a pipeline.

Components are collected sequentially in a pipeline. Each component is called one after another. This holds for initialization, training, persisting and loading the components. If a component comes first in a pipeline, its methods will be called first.

E.g. to process an incoming message, the process method of each component will be called. During the processing (as well as the training, persisting and initialization) components can pass information to other components. The information is passed to other components by providing attributes to the so called pipeline context. The pipeline context contains all the information of the previous components a component can use to do its own processing. For example, a featurizer component can provide features that are used by another component down the pipeline to do intent classification.

classmethodrequired_components()

Specify which components need to be present in the pipeline.

Returns

The list of class names of required components.

Return type
List

classmethodrequired_packages()

Specify which python packages need to be installed.

E.g. ['spacy']. More specifically, these should be importable python package names e.g. sklearn and not package names in the dependencies sense e.g. scikit-learn.

Returns

The list of required package names.

Return type
List

classmethodcreate( component_config, config)

Creates this component (e.g. before a training is started).

Returns

The created component.

Return type
Component

provide_context()

Initialize this component for a new pipeline.

Returns

The updated component configuration.

Return type
Optional

train( training_data, config=None, **kwargs)

Train this component.

Parameters

training_data – The rasa.nlu.training_data.training_data.TrainingData.

Returns

None

process( message, **kwargs)

Process an incoming message.

Parameters

message – The rasa.nlu.training_data.message.Message to process.

Returns

None

persist( file_name, model_dir)

Persist this component to disk for future loading.

Returns

An optional dictionary with any information about the stored model.

Return type
Optional

prepare_partial_processing( pipeline, context)

Sets the pipeline and context used for partial processing.

Returns

None

partially_process( message)

Allows the component to process messages during training.

Returns

The processed rasa.nlu.training_data.message.Message.

Return type
Message

classmethodcan_handle_language( language)

Check if component supports a specific language.

Returns

True if component can handle specific language, False otherwise.

Return type
bool

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