Training Data Importers

These docs are for version 1.x of Rasa Open Source.

Overview

By default, you can use command line arguments to specify where Rasa should look for training data on your disk. Rasa then loads any potential training files and uses them to train your assistant.

If needed, you can also customize how Rasa imports training data. Potential use cases for this might be:

• using a custom parser to load training data in other formats
• using different approaches to collect training data (e.g. loading them from different resources)

You can instruct Rasa to load and use your custom importer by adding the section importers to the Rasa configuration file and specifying the importer with its full class path:

importers:
- name: "module.CustomImporter"
  parameter1: "value"
  parameter2: "value2"
- name: "module.AnotherCustomImporter"

The name key is used to determine which importer should be loaded. Any extra parameters are passed as constructor arguments to the loaded importer.

Note: You can specify multiple importers. Rasa will automatically merge their results.

RasaFileImporter (default)

By default Rasa uses the importer RasaFileImporter. If you want to use it on its own, you don’t have to specify anything in your configuration file. If you want to use it together with other importers, add it to your configuration file:

importers:
- name: "RasaFileImporter"

MultiProjectImporter (experimental)

This feature is currently experimental and might change or be removed in the future. Please share your feedback on it in the forum to help us making this feature ready for production.

With this importer you can build a contextual AI assistant by combining multiple reusable Rasa projects. You might, for example, handle chitchat with one project and greet your users with another. These projects can be developed in isolation, and then combined at train time to create your assistant.

An example directory structure could look like this:

.
├── config.yml
└── projects
    ├── GreetBot
    │   ├── data
    │   │   ├── nlu.md
    │   │   └── stories.md
    │   └── domain.yml
    └── ChitchatBot
        ├── config.yml
        ├── data
        │   ├── nlu.md
        │   └── stories.md
        └── domain.yml

In this example, the contextual AI assistant imports the ChitchatBot project which in turn imports the GreetBot project. Project imports are defined in the configuration files of each project. To instruct Rasa to use the MultiProjectImporter module, put this section in the config file of your root project:

importers:
- name: MultiProjectImporter

Then specify which projects you want to import. In our example, the config.yml in the root project would look like this:

imports:
- projects/ChitchatBot

The configuration file of the ChitchatBot in turn references the GreetBot:

imports:
- ../GreetBot

The GreetBot project does not specify further projects so the config.yml can be omitted.

Rasa uses relative paths from the referencing configuration file to import projects. These can be anywhere on your file system as long as the file access is permitted.

During the training process Rasa will import all required training files, combine them, and train a unified AI assistant. The merging of the training data happens during runtime, so no additional files with training data are created or visible.

Note: Rasa will use the policy and NLU pipeline configuration of the root project directory during training. Policy or NLU configurations of imported projects will be ignored.

Note: Equal intents, entities, slots, responses, actions and forms will be merged, e.g. if two projects have training data for an intent greet, their training data will be combined.

Writing a Custom Importer

If you are writing a custom importer, this importer has to implement the interface of TrainingDataImporter:

from typing import Optional, Text, Dict, List, Union
import rasa
from rasa.core.domain import Domain
from rasa.core.interpreter import RegexInterpreter, NaturalLanguageInterpreter
from rasa.core.training.structures import StoryGraph
from rasa.importers.importer import TrainingDataImporter
from rasa.nlu.training_data import TrainingData

class MyImporter(TrainingDataImporter):
    def __init__(self,
        config_file: Optional[Text] = None,
        domain_path: Optional[Text] = None,
        training_data_paths: Optional[Union[List[Text], Text]] = None,
        **kwargs: Dict
    ):
        pass

async def get_domain(self) -> Domain:
        path_to_domain_file = self._custom_get_domain_file()
        return Domain.load(path_to_domain_file)

async def get_stories(
        self,
        interpreter: "NaturalLanguageInterpreter" = RegexInterpreter(),
        template_variables: Optional[Dict] = None,
        use_e2e: bool = False,
        exclusion_percentage: Optional[int] = None,
    ) -> StoryGraph:
        from rasa.core.training.dsl import StoryFileReader
        path_to_stories = self._custom_get_story_file()
        return await StoryFileReader.read_from_file(path_to_stories, await self.get_domain())

async def get_config(self) -> Dict:
        path_to_config = self._custom_get_config_file()
        return rasa.utils.io.read_config_file(path_to_config)

async def get_nlu_data(self, language: Optional[Text] = "en") -> TrainingData:
        from rasa.nlu.training_data import loading
        path_to_nlu_file = self._custom_get_nlu_file()
        return loading.load_data(path_to_nlu_file)

TrainingDataImporter

classrasa.importers.importer.``TrainingDataImporter
Common interface for different mechanisms to load training data.

asyncget_domain()
Retrieves the domain of the bot.

Returns

Loaded Domain.

Return type

Domain

asyncget_config()
Retrieves the configuration that should be used for the training.

Returns

The configuration as dictionary.

Return type

Dict

asyncget_nlu_data( language='en')
Retrieves the NLU training data that should be used for training.