# 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/1.9.4/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)
  ...
```

## 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.

## Talk to your Assistant

To start a chat session with your assistant on the command line, run:

```
rasa shell
```

The model that should be used to interact with your bot can be specified by `--model`. If you start the shell with an NLU-only model, `rasa shell` allows you to obtain the intent and entities of any text you type on the command line. If your model includes a trained Core model, you can chat with your bot and see what the bot predicts as a next action. If you have trained a combined Rasa model but nevertheless want to see what your model extracts as intents and entities from text, you can use the command `rasa shell nlu`.

## Start a Server

To start a server running your Rasa model, run:

```
rasa run
```

### Start an Action Server

To run your action server run

```
rasa run actions
```

### Visualize your Stories

To open a browser tab with a graph showing your stories:

```
rasa visualize
```

Normally, training stories in the directory `data` are visualized. If your stories are located somewhere else, you can specify their location with `--stories`.

## Evaluating a Model on Test Data

To evaluate your model on test data, run:

```
rasa test
```