# 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/1.10.18/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:
```yaml
pipeline:
- name: "sentiment.SentimentAnalyzer"
```
Also be sure to read the section on the [Component Lifecycle](https://legacy-docs-v1.rasa.com/1.10.18/nlu/choosing-a-pipeline/#component-lifecycle).
To get started, you can use this skeleton that contains the most important methods that you should implement:
| | |
| --- | --- |
| ```
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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"""
@classmethod
def required_components(cls) -> List[Type[Component]]:
return []
defaults = {}
supported_language_list = None
not_supported_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:
pass
def process(self, message: Message, **kwargs: Any) -> None:
pass
def persist(self, file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]:
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":
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.
**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`
### _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.
Returns
The list of required package names.
**Return type**
`List`
### _classmethod_`create`( _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`
### _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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