Command Line Interface

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 Running the Server 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 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.

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)

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.

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]

positional arguments:
  {core}
    core                Starts an interactive learning session model to create
                        new training data for a Rasa Core model by chatting.
                        Uses the 'RegexInterpreter', i.e. `/<intent>` input
                        format.
  model-as-positional-argument
                        Path to a trained Rasa model. If a directory is
                        specified, it will use the latest model in this
                        directory. (default: None)

optional arguments:
  -h, --help            show this help message and exit
  --e2e                 Save story files in e2e format. In this format user
                        messages will be included in the stories. (default:
                        False)
  -m MODEL, --model MODEL
                        Path to a trained Rasa model. If a directory is
                        specified, it will use the latest model in this
                        directory. (default: None)
  --data DATA [DATA ...]
                        Paths to the Core and NLU data files. (default:
                        ['data'])
  --skip-visualization  Disable plotting the visualization during interactive
                        learning. (default: False)
  --conversation-id CONVERSATION_ID
                        Specify the id of the conversation the messages are
                        in. Defaults to a UUID that will be randomly
                        generated. (default: ff94bce1b32043d687c0dc1a99fbc005)
  --endpoints ENDPOINTS
                        Configuration file for the model server and the
                        connectors as a yml file. (default: None)

## 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`.

To increase the logging level for debugging, run:

rasa shell --debug


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]

positional arguments: {nlu} nlu Interprets messages on the command line using your NLU model. model-as-positional-argument Path to a trained Rasa model. If a directory is specified, it will use the latest model in this directory. (default: None)

optional arguments: -h, --help show this help message and exit --conversation-id CONVERSATION_ID Set the conversation ID. (default: 7c76d1b3782544cfb2d6a9bdf6dd5c46) -m MODEL, --model MODEL Path to a trained Rasa model. If a directory is specified, it will use the latest model in this directory. (default: models) --log-file LOG_FILE Store logs in specified file. (default: None) --endpoints ENDPOINTS Configuration file for the model server and the connectors as a yml file. (default: None)

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)

Server Settings: -p PORT, --port PORT Port to run the server at. (default: 5005) -t AUTH_TOKEN, --auth-token AUTH_TOKEN Enable token based authentication. Requests need to provide the token to be accepted. (default: None) --cors [CORS [CORS ...]] Enable CORS for the passed origin. Use * to whitelist all origins. (default: None) --enable-api Start the web server API in addition to the input channel. (default: False) --response-timeout RESPONSE_TIMEOUT Maximum time a response can take to process (sec). (default: 3600) --remote-storage REMOTE_STORAGE Set the remote location where your Rasa model is stored, e.g. on AWS. (default: None) --ssl-certificate SSL_CERTIFICATE Set the SSL Certificate to create a TLS secured server. (default: None) --ssl-keyfile SSL_KEYFILE Set the SSL Keyfile to create a TLS secured server. (default: None) --ssl-ca-file SSL_CA_FILE If your SSL certificate needs to be verified, you can specify the CA file using this parameter. (default: None) --ssl-password SSL_PASSWORD If your ssl-keyfile is protected by a password, you can specify it using this paramer. (default: None)

Channels: --credentials CREDENTIALS Authentication credentials for the connector as a yml file. (default: None) --connector CONNECTOR Service to connect to. (default: None)

JWT Authentication: --jwt-secret JWT_SECRET Public key for asymmetric JWT methods or shared secretfor symmetric methods. Please also make sure to use --jwt-method to select the method of the signature, otherwise this argument will be ignored. (default: None) --jwt-method JWT_METHOD Method used for the signature of the JWT authentication payload. (default: HS256)


## Start a Server

To start a server running your Rasa model, run:

rasa run


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]

positional arguments: {actions} actions Runs the action server. model-as-positional-argument Path to a trained Rasa model. If a directory is specified, it will use the latest model in this directory. (default: None)

optional arguments: -h, --help show this help message and exit -m MODEL, --model MODEL Path to a trained Rasa model. If a directory is specified, it will use the latest model in this directory. (default: models) --log-file LOG_FILE Store logs in specified file. (default: None) --endpoints ENDPOINTS Configuration file for the model server and the connectors as a yml file. (default: None)

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.

Additional arguments are:

usage: rasa visualize [-h] [-v] [-vv] [--quiet] [-d DOMAIN] [-s STORIES]
                      [-c CONFIG] [--out OUT] [--max-history MAX_HISTORY]
                      [-u NLU]

optional arguments:
  -h, --help            show this help message and exit
  -d DOMAIN, --domain DOMAIN
                        Domain specification (yml file). (default: domain.yml)
  -s STORIES, --stories STORIES
                        File or folder containing your training stories.
                        (default: data)
  -c CONFIG, --config CONFIG
                        The policy and NLU pipeline configuration of your bot.
                        (default: config.yml)
  --out OUT             Filename of the output path, e.g. 'graph.html'.
                        (default: graph.html)
  --max-history MAX_HISTORY
                        Max history to consider when merging paths in the
                        output graph. (default: 2)
  -u NLU, --nlu NLU     File or folder containing your NLU data, used to
                        insert example messages into the graph. (default:
                        None)

## Evaluate a Model on Test Data

To evaluate your model on test data, run:

rasa test


Specify the model to test using `--model`.
Check out more details in [Evaluating an NLU Model](https://legacy-docs-v1.rasa.com/1.6.2/user-guide/evaluating-models/#nlu-evaluation) and [Evaluating a Core Model](https://legacy-docs-v1.rasa.com/1.6.2/user-guide/evaluating-models/#core-evaluation).

The following arguments are available for `rasa test`:

usage: rasa test [-h] [-v] [-vv] [--quiet] [-m MODEL] [-s STORIES] [--max-stories MAX_STORIES] [--endpoints ENDPOINTS] [--fail-on-prediction-errors] [--url URL] [--evaluate-model-directory] [-u NLU] [--out OUT] [--successes] [--no-errors] [--histogram HISTOGRAM] [--confmat CONFMAT] [-c CONFIG [CONFIG ...]] [--cross-validation] [-f FOLDS] [-r RUNS] [-p PERCENTAGES [PERCENTAGES ...]] [--no-plot] {core,nlu} ...

positional arguments: {core,nlu} core Tests Rasa Core models using your test stories. nlu Tests Rasa NLU models using your test NLU data.

optional arguments: -h, --help show this help message and exit -m MODEL, --model MODEL Path to a trained Rasa model. If a directory is specified, it will use the latest model in this directory. (default: models)

Core Test Arguments: -s STORIES, --stories STORIES File or folder containing your test stories. (default: tests) --max-stories MAX_STORIES Maximum number of stories to test on. (default: None) --endpoints ENDPOINTS Configuration file for the connectors as a yml file. (default: None) --fail-on-prediction-errors If a prediction error is encountered, an exception is thrown. This can be used to validate stories during tests, e.g. on travis. (default: False) --url URL If supplied, downloads a story file from a URL and trains on it. Fetches the data by sending a GET request to the supplied URL. (default: None) --evaluate-model-directory Should be set to evaluate models trained via 'rasa train core --config '. All models in the provided directory are evaluated and compared against each other. (default: False)

NLU Test Arguments: -u NLU, --nlu NLU File or folder containing your NLU data. (default: data) --out OUT Output path for any files created during the evaluation. (default: results) --successes If set successful predictions (intent and entities) will be written to a file. (default: False) --no-errors If set incorrect predictions (intent and entities) will NOT be written to a file. (default: False) --histogram HISTOGRAM Output path for the confidence histogram. (default: hist.png) --confmat CONFMAT Output path for the confusion matrix plot. (default: confmat.png) -c CONFIG [CONFIG ...], --config CONFIG [CONFIG ...] Model configuration file. If a single file is passed and cross validation mode is chosen, cross-validation is performed, if multiple configs or a folder of configs are passed, models will be trained and compared directly. (default: None) --no-plot Don't render evaluation plots (default: False)


## Create a Train-Test Split

To create a split of your NLU data, run:

rasa data split nlu


You can specify the training data, the fraction, and the output directory using the following arguments:

usage: rasa data split nlu [-h] [-v] [-vv] [--quiet] [-u NLU] [--training-fraction TRAINING_FRACTION] [--random-seed RANDOM_SEED] [--out OUT]

optional arguments: -h, --help show this help message and exit -u NLU, --nlu NLU File or folder containing your NLU data. (default: data) --training-fraction TRAINING_FRACTION Percentage of the data which should be in the training data. (default: 0.8) --random-seed RANDOM_SEED Seed to generate the same train/test split. (default: None) --out OUT Directory where the split files should be stored. (default: train_test_split)

This command will attempt to keep the proportions of intents the same in train and test.

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

You can specify the input file, output file, and the output format with the following arguments:

usage: rasa data convert nlu [-h] [-v] [-vv] [--quiet] --data DATA --out OUT
                             [-l LANGUAGE] -f {json,md}

optional arguments:
  -h, --help            show this help message and exit
  --data DATA           Path to the file or directory containing Rasa NLU
                        data. (default: None)
  --out OUT             File where to save training data in Rasa format.
                        (default: None)
  -l LANGUAGE, --language LANGUAGE
                        Language of data. (default: en)
  -f {json,md}, --format {json,md}
                        Output format the training data should be converted
                        into. (default: None)

## 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](/content/docs/rasa-x/index.html).

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.

Note
By default Rasa X runs on the port 5002. Using the argument `--rasa-x-port` allows you to change it to
any other port.