# Command Line Interface

The command line interface (CLI) gives you easy-to-remember commands for common tasks.

| Command                          | Effect                                                                                     |
|----------------------------------|---------------------------------------------------------------------------------------------|
| `rasa init`                      | Creates a new project with example training data, actions, and config files.              |
| `rasa train`                     | Trains a model using your NLU data and stories, saves trained model in `./models`.        |
| `rasa interactive`               | Starts an interactive learning session to create new training data by chatting.            |
| `rasa shell`                     | Loads your trained model and lets you talk to your assistant on the command line.          |
| `rasa run`                       | Starts a Rasa server with your trained model. See the [Configuring the HTTP API](https://legacy-docs-v1.rasa.com/1.8.2/user-guide/configuring-http-api/#configuring-http-api) docs for details. |
| `rasa run actions`               | Starts an action server using the Rasa SDK.                                               |
| `rasa visualize`                 | Visualizes stories.                                                                        |
| `rasa test`                      | Tests a trained Rasa model using your test NLU data and stories.                          |
| `rasa data split nlu`           | Performs a split of your NLU data according to the specified percentages.                  |
| `rasa data convert nlu`         | Converts NLU training data between different formats.                                       |
| `rasa export`                    | Export conversations from a tracker store to an event broker.                             |
| `rasa x`                         | Launch Rasa X locally.                                                                     |
| `rasa -h`                        | Shows all available commands.                                                               |

## Create a new project

A single command sets up a complete project for you with some example training data.

```
rasa init
```

This creates the following files:

```
.
├── __init__.py
├── actions.py
├── config.yml
├── credentials.yml
├── data
│   ├── nlu.md
│   └── stories.md
├── domain.yml
├── endpoints.yml
└── models
    └── <timestamp>.tar.gz
```

The `rasa init` command will ask you if you want to train an initial model using this data. If you answer no, the `models` directory will be empty.

With this project setup, common commands are very easy to remember. To train a model, type `rasa train`, to talk to your model on the command line, `rasa shell`, to test your model type `rasa test`.

## Train a Model

The main command is:

```
rasa train
```

This command trains a Rasa model that combines a Rasa NLU and a Rasa Core model. If you only want to train an NLU or a Core model, you can run `rasa train nlu` or `rasa train core`. However, Rasa will automatically skip training Core or NLU if the training data and config haven’t changed.

`rasa train` will store the trained model in the directory defined by `--out`. The name of the model is per default `<timestamp>.tar.gz`. If you want to name your model differently, you can specify the name using `--fixed-model-name`.

The following arguments can be used to configure the training process:

```
usage: rasa train [-h] [-v] [-vv] [--quiet] [--data DATA [DATA ...]]
                  [-c CONFIG] [-d DOMAIN] [--out OUT]
                  [--augmentation AUGMENTATION] [--debug-plots]
                  [--fixed-model-name FIXED_MODEL_NAME] [--persist-nlu-data]
                  [--force]
                  {core,nlu} ...

positional arguments:
  {core,nlu}
    core                Trains a Rasa Core model using your stories.
    nlu                 Trains a Rasa NLU model using your NLU data.

optional arguments:
  -h, --help            show this help message and exit
  --data DATA [DATA ...]
                        Paths to the Core and NLU data files. (default:
                        ['data'])
  -c CONFIG, --config CONFIG
                        The policy and NLU pipeline configuration of your bot.
                        (default: config.yml)
  -d DOMAIN, --domain DOMAIN
                        Domain specification (yml file). (default: domain.yml)
  --out OUT             Directory where your models should be stored.
                        (default: models)
  --augmentation AUGMENTATION
                        How much data augmentation to use during training.
                        (default: 50)
  --debug-plots         If enabled, will create plots showing checkpoints and
                        their connections between story blocks in a file
                        called `story_blocks_connections.html`. (default:
                        False)
  --fixed-model-name FIXED_MODEL_NAME
                        If set, the name of the model file/directory will be
                        set to the given name. (default: None)
  --persist-nlu-data    Persist the nlu training data in the saved model.
                        (default: False)
  --force               Force a model training even if the data has not
                        changed. (default: False)

Python Logging Options:
  -v, --verbose         Be verbose. Sets logging level to INFO. (default:
                        None)
  -vv, --debug          Print lots of debugging statements. Sets logging level
                        to DEBUG. (default: None)
  --quiet               Be quiet! Sets logging level to WARNING. (default:
                        None)
```

Note: Make sure training data for Core and NLU are present when training a model using `rasa train`. If training data for only one model type is present, the command automatically falls back to `rasa train nlu` or `rasa train core` depending on the provided training files.

## Interactive Learning

To start an interactive learning session with your assistant, run

```
rasa interactive
```

If you provide a trained model using the `--model` argument, the interactive learning process is started with the provided model. If no model is specified, `rasa interactive` will train a new Rasa model with the data located in `data/` if no other directory was passed to the `--data` flag. After training the initial model, the interactive learning session starts. Training will be skipped if the training data and config haven’t changed.