Command Line Interface

Cheat Sheet#

Log Level#

Rasa produces log messages at several different levels (eg. warning, info, error and so on). You can control which level of logs you would like to see with --verbose (same as -v) or --debug (same as -vv) as optional command line arguments. See each command below for more explanation on what these arguments mean.

In addition to CLI arguments, several environment variables allow you to control log output in a more granular way. With these environment variables, you can configure log levels for messages created by external libraries such as Matplotlib, Pika, and Kafka. These variables follow standard logging level in Python. Currently, following environment variables are supported:

1. LOG_LEVEL_LIBRARIES: This is the general environment variable to configure log level for the main libraries Rasa uses. It covers Tensorflow, asyncio, APScheduler, SocketIO, Matplotlib, RabbitMQ, Kafka.
2. LOG_LEVEL_MATPLOTLIB: This is the specialized environment variable to configure log level only for Matplotlib.
3. LOG_LEVEL_RABBITMQ: This is the specialized environment variable to configure log level only for AMQP libraries, at the moment it handles log levels from aio_pika and aiormq.
4. LOG_LEVEL_KAFKA: This is the specialized environment variable to configure log level only for kafka.
5. LOG_LEVEL_PRESIDIO: This is the specialized environment variable to configure log level only for Presidio, at the moment it handles log levels from presidio_analyzer and presidio_anonymizer.
6. LOG_LEVEL_FAKER: This is the specialized environment variable to configure log level only for Faker.

General configuration (LOG_LEVEL_LIBRARIES) has less priority than library level specific configuration (LOG_LEVEL_MATPLOTLIB, LOG_LEVEL_RABBITMQ etc); and CLI parameter sets the lowest level log messages which will be handled. This means variables can be used together with a predictable result. As an example:

The above command run will result in showing:

• messages with DEBUG level and higher by default (due to --debug)
• messages with WARNING level and higher for Matplotlib
• messages with DEBUG level and higher for kafka
• messages with ERROR level and higher for other libraries not configured

Note that CLI config sets the lowest level log messages to be handled, hence the following command will set the log level to INFO (due to --verbose) and no debug messages will be seen (library level configuration will not have any effect):

As an aside, CLI log level sets the level at the root logger (which has the important handler - coloredlogs handler); this means even if an environment variable sets a library logger to a lower level, the root logger will reject messages from that library. If not specified, the CLI log level is set to INFO.

Custom logging configuration#

You can now configure any logging formatters or handlers in a separate YAML file. The logging config YAML file must follow the Python built-in dictionary schema, otherwise it will fail validation. You can pass this file as argument to the --logging-config-file CLI option and use it with any of the rasa commands.

rasa init#

This command sets up a complete assistant for you with some example training data:

It creates the following files:

It will ask you if you want to train an initial model using this data. If you answer no, the models directory will be empty.

Any of the default CLI commands will expect this project setup, so this is the best way to get started. You can run rasa train, rasa shell and rasa test without any additional configuration.

rasa train#

The following command trains a Rasa model:

If you have existing models in your directory (under models/ by default), only the parts of your model that have changed will be re-trained. For example, if you edit your NLU training data and nothing else, only the NLU part will be trained.

If you want to train an NLU or dialogue model individually, you can run rasa train nlu or rasa train core. If you provide training data only for one one of these, rasa train will fall back to one of these commands by default.

rasa train will store the trained model in the directory defined by --out, models/ by default. The name of the model by default is <timestamp>.tar.gz. If you want to name your model differently, you can specify the name using the --fixed-model-name flag.

By default validation is run before training the model. If you want to skip validation, you can use the --skip-validation flag. If you want to fail on validation warnings, you can use the --fail-on-validation-warnings flag. The --validation-max-history is analogous to the --max-history argument of rasa data validate.

The following arguments can be used to configure the training process:

See the section on data augmentation for info on how data augmentation works and how to choose a value for the flag. Note that TEDPolicy is the only policy affected by data augmentation.

See the following section on incremental training for more information about the --epoch-fraction argument.

Incremental training#

In order to improve the performance of an assistant, it's helpful to practice CDD and add new training examples based on how your users have talked to your assistant. You can use rasa train --finetune to initialize the pipeline with an already trained model and further finetune it on the new training dataset that includes the additional training examples. This will help reduce the training time of the new model.

By default, the command picks up the latest model in the models/ directory. If you have a specific model which you want to improve, you may specify the path to this by running rasa train --finetune <path to model to finetune>. Finetuning a model usually requires fewer epochs to train machine learning components like DIETClassifier, ResponseSelector and TEDPolicy compared to training from scratch. Either use a model configuration for finetuning which defines fewer epochs than before or use the flag --epoch-fraction. --epoch-fraction will use a fraction of the epochs specified for each machine learning component in the model configuration file. For example, if DIETClassifier is configured to use 100 epochs, specifying --epoch-fraction 0.5 will only use 50 epochs for finetuning.

You can also finetune an NLU-only or dialogue management-only model by using rasa train nlu --finetune and rasa train core --finetune respectively.

To be able to fine tune a model, the following conditions must be met:

1. The configuration supplied should be exactly the same as the configuration used to train the model which is being finetuned. The only parameter that you can change is epochs for the individual machine learning components and policies.

2. The set of labels(intents, actions, entities and slots) for which the base model is trained should be exactly the same as the ones present in the training data used for finetuning. This means that you cannot add new intent, action, entity or slot labels to your training data during incremental training. You can still add new training examples for each of the existing labels. If you have added/removed labels in the training data, the pipeline needs to be trained from scratch.

3. The model to be finetuned is trained with MINIMUM_COMPATIBLE_VERSION of the currently installed rasa version.

rasa interactive#

You can start an interactive learning session by running:

This will first train a model and then start an interactive shell session. You can then correct your assistants predictions as you talk to it. If UnexpecTEDIntentPolicy is included in the pipeline, action_unlikely_intent can be triggered at any conversation turn. Subsequently, the following message will be displayed:

As the message states, this is an indication that you have explored a conversation path which is unexpected according to the current set of training stories and hence adding this path to training stories is recommended. Like other bot actions, you can choose to confirm or deny running this action.

If you provide a trained model using the --model argument, training is skipped and that model will be loaded instead.

During interactive learning, Rasa will plot the current conversation and a few similar conversations from the training data to help you keep track of where you are. You can view the visualization at http://localhost:5005/visualization.html as soon as the session has started. This diagram can take some time to generate. To skip the visualization, run rasa interactive --skip-visualization.

The following arguments can be used to configure the interactive learning session:

rasa shell#

You can start a chat session by running:

By default, this will load up the latest trained model. You can specify a different model to be loaded by using the --model flag.

If you start the shell with an NLU-only model, rasa shell will output the intents and entities predicted for any message you enter.

If you have trained a combined Rasa model but only 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:

The following arguments can be used to configure the command. Most arguments overlap with rasa run; see the following section for more info on those arguments.

Note that the --connector argument will always be set to cmdline when running rasa shell. This means all credentials in your credentials file will be ignored, and if you provide your own value for the --connector argument it will also be ignored.

rasa run#

To start a server running your trained model, run:

By default the Rasa server uses HTTP for its communication. To secure the communication with SSL and run the server on HTTPS, you need to provide a valid certificate and the corresponding private key file. You can specify these files as part of the rasa run command. If you encrypted your keyfile with a password during creation, you need to add the --ssl-password as well.

Rasa by default listens on each available network interface. You can limit this to a specific network interface using the -i command line option.

Rasa will by default connect to all channels specified in your credentials file. To connect to a single channel and ignore all other channels in your credentials file, specify the name of the channel in the --connector argument.

The name of the channel should match the name you specify in your credentials file. For supported channels see the page about messaging and voice channels.

The following arguments can be used to configure your Rasa server:

For more information on the additional parameters, see Model Storage. See the Rasa HTTP API page for detailed documentation of all the endpoints.

rasa run actions#

To start an action server with the Rasa SDK, run:

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

rasa visualize#

To generate a graph of your stories in the browser, run:

If your stories are located somewhere other than the default location data/, you can specify their location with the --stories flag.

The following arguments can be used to configure this command:

rasa test#

To evaluate a model on your test data, run:

This will test your latest trained model on any end-to-end stories you have defined in files with the test_ prefix. If you want to use a different model, you can specify it using the --model flag.

To evaluate the dialogue and NLU models separately, use the commands below:

and

You can find more details on specific arguments for each testing type in Evaluating an NLU Model and Evaluating a Dialogue Management Model.

The following arguments are available for rasa test:

rasa test e2e#

To run end-to-end testing on your trained model, run:

This will test your latest trained model on any end-to-end test cases you have. If you want to use a different model, you can specify it using the --model flag.

The following arguments are available for rasa test e2e:

rasa data split#

To create a train-test split of your NLU training data, run:

This will create a 80/20 split of train/test data by default. You can specify the training data, the fraction, and the output directory using the following arguments:

If you have NLG data for retrieval actions, this will be saved to separate files:

To split your stories, you can use the following command:

It has the same arguments as split nlu command, but loads yaml files with stories and perform random splitting. Directory train_test_split will contain all yaml files processed with prefixes train_ or test_ containing train and test parts.

rasa data convert nlu#

You can convert NLU data from

• LUIS data format,
• WIT data format,
• Dialogflow data format, or
• JSON

to

• YAML or
• JSON

You can start the converter by running:

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

rasa data migrate#

The domain is the only data file whose format changed between 2.0 and 3.0. You can automatically migrate a 2.0 domain to the 3.0 format.

You can start the migration by running:

You can specify the input file or directory and the output file or directory with the following arguments:

If no arguments are specified, the default domain path (domain.yml) will be used for both input and output files.

This command will also back-up your 2.0 domain file(s) into a different original_domain.yml file or directory labeled original_domain.

Note that the slots in the migrated domain will contain mapping conditions if these slots are part of a form's required_slots.

You can learn more about this command by running:

rasa data validate#

You can check your domain, NLU data, or story data for mistakes and inconsistencies. To validate your data, run this command:

The validator searches for errors in the data, e.g. two intents that have some identical training examples. The validator also checks if you have any stories where different assistant actions follow from the same dialogue history. Conflicts between stories will prevent a model from learning the correct pattern for a dialogue.

If you pass a max_history value to one or more policies in your config.yml file, provide the smallest of those values in the validator command using the --max-history <max_history> flag.

You can also validate only the story structure by running this command:

To interrupt validation even for minor issues such as unused intents or responses, use the --fail-on-warnings flag.

You can use rasa data validate with additional arguments, e.g. to specify the location of your data and domain files:

rasa export#

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

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 evaluate markers#

The following command applies the markers you defined in your marker configuration file, to pre-existing dialogues stored in your tracker store, and produces .csv files containing the extracted markers and summary statistics:

Use the following arguments to configure the marker extraction process:

rasa markers upload#

This command applies to markers and their real-time processing. Running this command validates the marker configuration file against the domain file and uploads the configuration to Analytics Data Pipeline

rasa license#

Use rasa license to display information about licensing in Rasa Pro, especially information about 3rd party dependencies licenses.

Here is the list of all possible arguments: