# 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/1.10.21/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/1.10.21/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.
### 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.
### _classmethod_ `required_components()`
Specify which components need to be present in the pipeline.
#### Returns
The list of class names of required components.
### _classmethod_ `required_packages()`
Specify which python packages need to be installed.
### _classmethod_ `create`(_component_config_, _config_)
Creates this component (e.g. before training is started).
#### Parameters
- **component_config** – The components configuration parameters.
- **config** – The model configuration parameters.
### `provide_context()`
Initialize this component for a new pipeline.
### `train`(_training_data_, _config=None_, _**kwargs_)
Train this component.
#### Parameters
- **training_data** – The `rasa.nlu.training_data.training_data.TrainingData`.
- **config** – The model configuration parameters.
### `process`(_message_, _**kwargs_)
Process an incoming message.
#### Parameters
- **message** – The `rasa.nlu.training_data.message.Message` to process.
### `persist`(_file_name_, _model_dir_)
Persist this component to disk for future loading.
#### Parameters
- **file_name** – The file name of the model.
- **model_dir** – The directory to store the model to.
### `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 (e.g. external training data).
### _classmethod_ `can_handle_language`(_language_)
Check if the component supports a specific language.