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""" @classmethod def required_components(cls) -> List[Type[Component]]: """Specify which components need to be present in the pipeline.""" 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)


<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> @classmethod<br> def required_components(cls) -> List[Type[Component]]:<br> """Specify which components need to be present in the pipeline."""<br> return []<br> defaults = {}<br> language_list = None<br> def __init__(self, component_config: Optional[Dict[Text, Any]] = None) -> None:<br> super().__init__(component_config)<br> def train(<br> self,<br> training_data: TrainingData,<br> config: Optional[RasaNLUModelConfig] = None,<br> kwargs: Any,<br> ) -> None:<br> pass<br> def process(self, message: Message, kwargs: Any) -> None:<br> pass<br> def persist(self, file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]:<br> pass<br> @classmethod<br> def load(<br> cls,<br> meta: Dict[Text, Any],<br> model_dir: Optional[Text] = None,<br> model_metadata: Optional["Metadata"] = None,<br> cached_component: Optional["Component"] = None,<br> **kwargs: Any,<br> ) -> "Component":<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 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.