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.
`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.

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.

Arguments

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} ...

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)

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.

Arguments

The full list of arguments that can be set for rasa interactive is:

usage: rasa interactive [-h] [-v] [-vv] [--quiet] [--e2e] [-m MODEL]
                        [--data DATA [DATA ...]] [--skip-visualization]
                        [--conversation-id CONVERSATION_ID]
                        [--endpoints ENDPOINTS] [-c CONFIG] [-d DOMAIN]
                        [--out OUT] [--augmentation AUGMENTATION]
                        [--debug-plots] [--force] [--persist-nlu-data]
                        {core} ... [model-as-positional-argument]

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.

Debugging

To increase the logging level for debugging, run:

rasa shell --debug

Arguments

The full list of options for rasa shell is:

usage: rasa shell [-h] [-v] [-vv] [--quiet]
                  [--conversation-id CONVERSATION_ID] [-m MODEL]
                  [--log-file LOG_FILE] [--endpoints ENDPOINTS] [-p PORT]
                  [-t AUTH_TOKEN] [--cors [CORS [CORS ...]]] [--enable-api]
                  [--response-timeout RESPONSE_TIMEOUT]
                  [--remote-storage REMOTE_STORAGE]
                  [--ssl-certificate SSL_CERTIFICATE]
                  [--ssl-keyfile SSL_KEYFILE] [--ssl-ca-file SSL_CA_FILE]
                  [--ssl-password SSL_PASSWORD] [--credentials CREDENTIALS]
                  [--connector CONNECTOR] [--jwt-secret JWT_SECRET]
                  [--jwt-method JWT_METHOD]
                  {nlu} ... [model-as-positional-argument]

Start a Server

To start a server running your Rasa model, run:

rasa run

Arguments

The following arguments can be used to configure your Rasa server:

usage: rasa run [-h] [-v] [-vv] [--quiet] [-m MODEL] [--log-file LOG_FILE]
                [--endpoints ENDPOINTS] [-p PORT] [-t AUTH_TOKEN]
                [--cors [CORS [CORS ...]]] [--enable-api]
                [--response-timeout RESPONSE_TIMEOUT]
                [--remote-storage REMOTE_STORAGE]
                [--ssl-certificate SSL_CERTIFICATE]
                [--ssl-keyfile SSL_KEYFILE] [--ssl-ca-file SSL_CA_FILE]
                [--ssl-password SSL_PASSWORD] [--credentials CREDENTIALS]
                [--connector CONNECTOR] [--jwt-secret JWT_SECRET]
                [--jwt-method JWT_METHOD]
                {actions} ... [model-as-positional-argument]

Start an Action Server

To run your action server run

rasa run actions

Arguments

The following arguments can be used to adapt the server settings:

usage: rasa run actions [-h] [-v] [-vv] [--quiet] [-p PORT]
                        [--cors [CORS [CORS ...]]] [--actions ACTIONS]
                        [--ssl-keyfile SSL_KEYFILE]
                        [--ssl-certificate SSL_CERTIFICATE]
                        [--ssl-password SSL_PASSWORD] [--auto-reload]

Visualize your Stories

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

rasa visualize

Evaluating a Model on Test Data

To evaluate your model on test data, run:

rasa test

Specify the model to test using --model.

Create a Train-Test Split

To create a split of your NLU data, run:

rasa data split nlu

Convert Data Between Markdown and JSON

To convert NLU data from LUIS data format, WIT data format, Dialogflow data format, JSON, or Markdown to JSON or Markdown, run:

rasa data convert nlu

Export Conversations to an Event Broker

To export events from a tracker store using an event broker, run:

rasa export

Start Rasa X

Rasa X is a toolset that helps you leverage conversations to improve your assistant. You can find more information about it here.

You can start Rasa X locally by executing

rasa x

To be able to start Rasa X you need to have Rasa X local mode installed and you need to be in a Rasa project.