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 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.

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: 99d26848553a43ed8fc11af658547ec9)
  --endpoints ENDPOINTS
                        Configuration file for the model server and the
                        connectors as a yml file. (default: None)

## [Talk to your Assistant](https://legacy-docs-v1.rasa.com/1.4.6/user-guide/command-line-interface/#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: 6c36f7b16be34c52a0092f98c4b29a5f) -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)


## [Start a Server](https://legacy-docs-v1.rasa.com/1.4.6/user-guide/command-line-interface/#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)

Start an Action Server

To run your action server run

rasa run actions

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]

optional arguments:
  -h, --help            show this help message and exit
  -p PORT, --port PORT  port to run the server at (default: 5055)
  --cors [CORS [CORS ...]]
                        enable CORS for the passed origin. Use * to whitelist
                        all origins (default: None)
  --actions ACTIONS     name of action package to be loaded (default: None)
  --ssl-keyfile SSL_KEYFILE
                        Set the SSL certificate to create a TLS secured
                        server. (default: None)
  --ssl-certificate SSL_CERTIFICATE
                        Set the SSL certificate to create a TLS secured
                        server. (default: None)
  --ssl-password SSL_PASSWORD
                        If your ssl-keyfile is protected by a password, you
                        can specify it using this paramer. (default: None)
  --auto-reload         Enable auto-reloading of modules containing Action
                        subclasses. (default: False)

## [Visualize your Stories](https://legacy-docs-v1.rasa.com/1.4.6/user-guide/command-line-interface/#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 and Evaluating a Core Model.

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 <config-1> <config-2>'. 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](https://legacy-docs-v1.rasa.com/1.4.6/user-guide/command-line-interface/#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 tool that helps you build, improve, and deploy AI Assistants that are powered by the Rasa framework. 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 installed (instruction can be found here) 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.