# 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 └── .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 `.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](/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.