# 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`](https://legacy-docs-v1.rasa.com/1.8.0/api/custom-nlu-components/#rasa.nlu.components.Component "rasa.nlu.components.Component") class with the methods you’ll need to implement.
**Note**
There is a detailed tutorial on building custom components [here](https://blog.rasa.com/enhancing-rasa-nlu-with-custom-components/).
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](https://legacy-docs-v1.rasa.com/1.8.0/nlu/choosing-a-pipeline/#section-component-lifecycle).
To get started, you can use this skeleton that contains the most important methods that you should implement:
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| ```
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``` | ```
import typing
from typing import Any, Optional, Text, Dict, List, Type
from rasa.nlu.components import Component
from rasa.nlu.config import RasaNLUModelConfig
from rasa.nlu.training_data import Message, TrainingData
if typing.TYPE_CHECKING:
from rasa.nlu.model import Metadata
class MyComponent(Component):
"""A new component"""
# Which components are required by this component.
# Listed components should appear before the component itself in the pipeline.
@classmethod
def required_components(cls) -> List[Type[Component]]:
"""Specify which components need to be present in the pipeline."""
return []
# Defines the default configuration parameters of a component
# these values can be overwritten in the pipeline configuration
# of the model. The component should choose sensible defaults
# and should be able to create reasonable results with the defaults.
defaults = {}
# Defines what language(s) this component can handle.
# This attribute is designed for instance method: `can_handle_language`.
# Default value is None which means it can handle all languages.
# This is an important feature for backwards compatibility of components.
language_list = None
def __init__(self, component_config: Optional[Dict[Text, Any]] = None) -> None:
super().__init__(component_config)
def train(
self,
training_data: TrainingData,
config: Optional[RasaNLUModelConfig] = None,
**kwargs: Any,
) -> None:
"""Train this component.
This is the components chance to train itself provided
with the training data. The component can rely on
any context attribute to be present, that gets created
by a call to :meth:`components.Component.pipeline_init`
of ANY component and
on any context attributes created by a call to
:meth:`components.Component.train`
of components previous to this one."""
pass
def process(self, message: Message, **kwargs: Any) -> None:
"""Process an incoming message.
This is the components chance to process an incoming
message. The component can rely on
any context attribute to be present, that gets created
by a call to :meth:`components.Component.pipeline_init`
of ANY component and
on any context attributes created by a call to
:meth:`components.Component.process`
of components previous to this one."""
pass
def persist(self, file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]:
"""Persist this component to disk for future loading."""
pass
@classmethod
def load(
cls,
meta: Dict[Text, Any],
model_dir: Optional[Text] = None,
model_metadata: Optional["Metadata"] = None,
cached_component: Optional["Component"] = None,
**kwargs: Any,
) -> "Component":
"""Load this component from file."""
if cached_component:
return cached_component
else:
return cls(meta)
``` |
**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,
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
_class_`rasa.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.
_classmethod_`required_components`()
Specify which components need to be present in the pipeline.
**Returns**
The list of class names of required components.
**Return type**
`List`[`Type`[`Component`](https://legacy-docs-v1.rasa.com/1.8.0/api/custom-nlu-components/#rasa.nlu.components.Component "rasa.nlu.components.Component")]
_classmethod_`required_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.
This list of requirements allows us to fail early during training if a required package is not installed.
**Returns**
The list of required package names.
**Return type**
`List`[`str`]
_classmethod_`create`( _component_config_, _config_)
Creates this component (e.g. before a training is started).
**Returns**
The created component.
**Return type**
[`Component`](https://legacy-docs-v1.rasa.com/1.8.0/api/custom-nlu-components/#rasa.nlu.components.Component "rasa.nlu.components.Component")
`provide_context`()
Initialize this component for a new pipeline.
**Returns**
The updated component configuration.
**Return type**
`Optional`[`Dict`[`str`, `Any`]]
`train`( _training_data_, _config=None_, _**kwargs_)
Train this component.
**Return type**
`None`
`process`( _message_, _**kwargs_)
Process an incoming message.
**Return type**
`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`[`Dict`[`str`, `Any`]]
`prepare_partial_processing`( _pipeline_, _context_)
Sets the pipeline and context used for partial processing.
**Return type**
`None`
`partially_process`( _message_)
Allows the component to process messages during training (e.g. external training data).
**Returns**
The processed `rasa.nlu.training_data.message.Message`.
**Return type**
`Message`
_classmethod_`can_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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