Skip to content

Latest commit

 

History

History
307 lines (181 loc) · 8.75 KB

command-line-interface.rst

File metadata and controls

307 lines (181 loc) · 8.75 KB
Raw
desc

Command line interface for open source chatbot framework Rasa. Learn how to train, test and run your machine learning-based conversational AI assistants

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-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.

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:

rasa train --help

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:

rasa interactive --help

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

rasa shell --help

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:

rasa run --help

For more information on the additional parameters, see configuring-http-api. See the Rasa http-api docs for detailed documentation of all the endpoints.

Start an Action Server

To run your action server run

rasa run actions

The following arguments can be used to adapt the server settings:

rasa run actions --help

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:

rasa visualize --help

Evaluating 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 nlu-evaluation and core-evaluation.

The following arguments are available for rasa test:

rasa test --help

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:

rasa data split nlu --help

This command will attempt to keep the proportions of intents the same in train and test. If you have NLG data for retrieval actions, this will be saved to seperate files:

ls train_test_split

      nlg_test_data.md     test_data.json
      nlg_training_data.md training_data.json

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:

rasa data convert nlu --help

Export Conversations to an Event Broker

To export events from a tracker store using an event broker, run:

rasa export

You can specify the location of the environments file, the minimum and maximum timestamps of events that should be published, as well as the conversation IDs that should be published.

rasa export --help

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
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.

The following arguments are available for rasa x:

rasa x --help