Command Line Interface

Cheat Sheet

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 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.

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.

Interactive Learning

To start an interactive learning session with your assistant, run

rasa interactive

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

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

Evaluating a Model on Test Data

To evaluate your model on test data, run:

rasa test

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

👋 I can help you get started with Rasa and answer your technical questions.