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:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 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 defaults = {} # Defines what language(s) this component can handle. 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.""" pass def process(self, message: Message, **kwargs: Any) -> None: """Process an incoming message.""" 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)


<br> 1<br> 2<br> 3<br> 4<br> 5<br> 6<br> 7<br> 8<br> 9<br>10<br>11<br>12<br>13<br>14<br>15<br>16<br>17<br>18<br>19<br>20<br>21<br>22<br>23<br>24<br>25<br>26<br>27<br>28<br>29<br>30<br>31<br>32<br>33<br>34<br>35<br>36<br>37<br>38<br>39<br>40<br>41<br>42<br>43<br>44<br>45<br>46<br>47<br>48<br>49<br>50<br>51<br>52<br>53<br>54<br>55<br>56<br>57<br>58<br>59<br>60<br>61<br>62<br>63<br>64<br>65<br>66<br>67<br>68<br>69<br>70<br>71<br>72<br>73<br>74<br>75<br>76<br>77<br>78<br>79<br>80<br>81<br>82<br>83<br>84<br>85<br>86<br>87<br>88<br>	<br>import typing<br>from typing import Any, Optional, Text, Dict, List, Type<br>from rasa.nlu.components import Component<br>from rasa.nlu.config import RasaNLUModelConfig<br>from rasa.nlu.training_data import Message, TrainingData<br>if typing.TYPE_CHECKING:<br> from rasa.nlu.model import Metadata<br>class MyComponent(Component):<br> """A new component"""<br> # Which components are required by this component.<br> # Listed components should appear before the component itself in the pipeline.<br> @classmethod<br> def required_components(cls) -> List[Type[Component]]:<br> """Specify which components need to be present in the pipeline."""<br> return []<br> # Defines the default configuration parameters of a component<br> defaults = {}<br> # Defines what language(s) this component can handle.<br> language_list = None<br> def __init__(self, component_config: Optional[Dict[Text, Any]] = None) -> None:<br> super().__init__(component_config)<br> def train(self, training_data: TrainingData, config: Optional[RasaNLUModelConfig] = None, kwargs: Any) -> None:<br> """Train this component."""<br> pass<br> def process(self, message: Message, kwargs: Any) -> None:<br> """Process an incoming message."""<br> pass<br> def persist(self, file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]:<br> """Persist this component to disk for future loading."""<br> pass<br> @classmethod<br> 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":<br> """Load this component from file."""<br> if cached_component:<br> return cached_component<br> else:<br> return cls(meta)<br>

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

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 processing, components can pass information to other components via the pipeline context.

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 before training.
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.