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 from rasa.nlu.components import Component import typing from typing import Any, Optional, Text, Dict if typing.TYPE_CHECKING: from rasa.nlu.model import Metadata class MyComponent(Component): """A new component""" provides = [] requires = [] defaults = {} language_list = None def __init__(self, component_config=None): super().__init__(component_config) def train(self, training_data, cfg, **kwargs): pass def process(self, message, **kwargs): 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>	<br>from rasa.nlu.components import Component<br>import typing<br>from typing import Any, Optional, Text, Dict<br>if typing.TYPE_CHECKING:<br> from rasa.nlu.model import Metadata<br>class MyComponent(Component):<br> """A new component"""<br> provides = []<br> requires = []<br> defaults = {}<br> language_list = None<br> def __init__(self, component_config=None):<br> super().__init__(component_config)<br> def train(self, training_data, cfg, kwargs):<br> pass<br> def process(self, message, kwargs):<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>

Component

classrasa.nlu.components.``Component( component t_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.

classmethodrequired_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

This list of requirements allows us to fail early during training if a required package is not installed.

Returns

The list of required package names.

Return type

List [str]`

classmethodcreate( component_config, config)

Creates this component (e.g. before a training is started).

Method can access all configuration parameters.

Returns

The created component.

Return type Component

provide_context()

Initialize this component for a new pipeline.

Returns

The updated component configuration.

Return type

Optional [Dict [str, Any]`

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