These docs are for version 1.x of Rasa Open Source. # 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 [Running the Server](https://legacy-docs-v1.rasa.com/1.7.1/user-guide/running-the-server/#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 └── .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 for Configuring 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) ``` ## 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] 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. `/` 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: 846497fe3cc54084b67a59f691c23bf4) --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 ``` ## 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 ``` ## Evaluate 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 ``` ## Start Rasa X Rasa X is a toolset that helps you leverage conversations to improve your assistant. You can start Rasa X locally by executing ``` rasa x ```