These docs are for version 1.x of Rasa Open Source.

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:

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 the list of tokens, which is needed further down the pipeline.

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.

Class Methods

• required_components() - Specify which components need to be present in the pipeline.
• required_packages() - Specify which python packages need to be installed.

Instance Methods

• create(component_config, config) - Creates this component before a training is started.
• provide_context() - Initialize this component for a new pipeline.
• train(training_data, config=None, **kwargs) - Train this component.
• process(message, **kwargs) - Process an incoming message.
• persist(file_name, model_dir) - Persist this component to disk for future loading.
• prepare_partial_processing(pipeline, context) - Sets the pipeline and context used for partial processing.
• partially_process(message) - Allows the component to process messages during training.
• can_handle_language(language) - Check if component supports a specific language.