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(MyComponent, self).__init__(component_config) def train(self, training_data, cfg, **kwargs): """Train this component.""" pass def process(self, message, **kwargs): """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>	<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(MyComponent, self).__init__(component_config)<br> def train(self, training_data, cfg, kwargs):<br> """Train this component."""<br> pass<br> def process(self, message, kwargs):<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(<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> """Load this component from file."""<br> if cached_component:<br> return cached_component<br> else:<br> return cls(meta)<br>

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.

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).

Parameters

component_config – The components configuration parameters.
config – The model 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]]

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.

Return type
None

process( message, **kwargs)

Process an incoming message.

Parameters

message – The rasa.nlu.training_data.message.Message to process.

Return type
None

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.

Returns
An optional dictionary with any information about the stored model.

Return type
Optional[Dict[str, Any]]

prepare_partial_processing( pipeline, context)

Sets the pipeline and context used for partial processing.

Parameters

pipeline – The list of components.
context – The context of processing.

Return type
None

partially_process( message)

Allows the component to process messages during training.

Parameters

message – The rasa.nlu.training_data.message.Message to process.

Returns
The processed rasa.nlu.training_data.message.Message.

Return type
Message

classmethodcan_handle_language( language)

Check if component supports a specific language.

Parameters

language – The language to check.

Returns
True if component can handle specific language, False otherwise.

Return type
bool