# Training Data Importers

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

**Warning:** This document is for an old version of Rasa. The latest version is 1.10.26.

## 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)

### Using Custom Importers
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:

```yaml
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. 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:

```yaml
importers:
- name: "RasaFileImporter"
```

## MultiProjectImporter (experimental)
Warning: This feature is currently experimental and might change or be removed in the future. With this importer you can build a contextual AI assistant by combining multiple reusable Rasa projects.

An example directory structure could look like this:

```plaintext
.
├── 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.

### Writing a Custom Importer
If you are writing a custom importer, this importer has to implement the interface of `TrainingDataImporter`:

```python
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:
        # Implementation details...
    async def get_stories(self, interpreter: "NaturalLanguageInterpreter" = RegexInterpreter()):
        # Implementation details...
    async def get_config(self) -> Dict:
        # Implementation details...
    async def get_nlu_data(self, language: Optional[Text] = "en") -> TrainingData:
        # Implementation details...
```

### TrainingDataImporter
Common interface for different mechanisms to load training data.