# 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/1.9.7/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/1.9.7/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 = {}
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. 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, components can pass information to other components.
### Methods
- **`required_components()`**: Specify which components need to be present in the pipeline.
- **`required_packages()`**: Specify which python packages need to be installed.
- **`create(component_config, config)`**: Creates this component.
- **`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.