These docs are for version 1.x of Rasa Open Source.

- [Rasa logo](/content/docs/index.html)

# User Guide

- [Installation](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/installation/)
- [Tutorial: Rasa Basics](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/rasa-tutorial/)
- [Tutorial: Building Assistants](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/building-assistants/)
- [Command Line Interface](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/command-line-interface/#)
- [Architecture](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/architecture/)
- [Messaging and Voice Channels](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/messaging-and-voice-channels/)
- [Testing Your Assistant](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/testing-your-assistant/)
- [Setting up CI/CD](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/setting-up-ci-cd/)
- [Validate Data](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/validate-files/)
- [Configuring the HTTP API](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/configuring-http-api/)
- [Deploying Your Rasa Assistant](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/how-to-deploy/)
- [Cloud Storage](https://legacy-docs-v1.rasa.com/1.10.25/user-guide/cloud-storage/)

# NLU

- [About](https://legacy-docs-v1.rasa.com/1.10.25/nlu/about/)
- [Using NLU Only](https://legacy-docs-v1.rasa.com/1.10.25/nlu/using-nlu-only/)
- [Training Data Format](https://legacy-docs-v1.rasa.com/1.10.25/nlu/training-data-format/)
- [Language Support](https://legacy-docs-v1.rasa.com/1.10.25/nlu/language-support/)
- [Choosing a Pipeline](https://legacy-docs-v1.rasa.com/1.10.25/nlu/choosing-a-pipeline/)
- [Components](https://legacy-docs-v1.rasa.com/1.10.25/nlu/components/)
- [Entity Extraction](https://legacy-docs-v1.rasa.com/1.10.25/nlu/entity-extraction/)

# Core

- [About](https://legacy-docs-v1.rasa.com/1.10.25/core/about/)
- [Stories](https://legacy-docs-v1.rasa.com/1.10.25/core/stories/)
- [Domains](https://legacy-docs-v1.rasa.com/1.10.25/core/domains/)
- [Responses](https://legacy-docs-v1.rasa.com/1.10.25/core/responses/)
- [Actions](https://legacy-docs-v1.rasa.com/1.10.25/core/actions/)
- [Reminders and External Events](https://legacy-docs-v1.rasa.com/1.10.25/core/reminders-and-external-events/)
- [Policies](https://legacy-docs-v1.rasa.com/1.10.25/core/policies/)
- [Slots](https://legacy-docs-v1.rasa.com/1.10.25/core/slots/)
- [Forms](https://legacy-docs-v1.rasa.com/1.10.25/core/forms/)
- [Retrieval Actions](https://legacy-docs-v1.rasa.com/1.10.25/core/retrieval-actions/)
- [Interactive Learning](https://legacy-docs-v1.rasa.com/1.10.25/core/interactive-learning/)
- [Fallback Actions](https://legacy-docs-v1.rasa.com/1.10.25/core/fallback-actions/)
- [Knowledge Base Actions](https://legacy-docs-v1.rasa.com/1.10.25/core/knowledge-bases/)

# Conversation Design

- [Dialogue Elements](https://legacy-docs-v1.rasa.com/1.10.25/dialogue-elements/dialogue-elements/)
- [Small Talk](https://legacy-docs-v1.rasa.com/1.10.25/dialogue-elements/small-talk/)
- [Completing Tasks](https://legacy-docs-v1.rasa.com/1.10.25/dialogue-elements/completing-tasks/)
- [Guiding Users](https://legacy-docs-v1.rasa.com/1.10.25/dialogue-elements/guiding-users/)

# API Reference

- [Action Server](https://legacy-docs-v1.rasa.com/1.10.25/api/action-server/)
- [HTTP API](https://legacy-docs-v1.rasa.com/1.10.25/api/http-api/)
- [Jupyter Notebooks](https://legacy-docs-v1.rasa.com/1.10.25/api/jupyter-notebooks/)
- [Agent](https://legacy-docs-v1.rasa.com/1.10.25/api/agent/)
- [Custom NLU Components](https://legacy-docs-v1.rasa.com/1.10.25/api/custom-nlu-components/)
- [Rasa SDK](https://legacy-docs-v1.rasa.com/1.10.25/api/rasa-sdk/)
- [Events](https://legacy-docs-v1.rasa.com/1.10.25/api/events/)
- [Tracker](https://legacy-docs-v1.rasa.com/1.10.25/api/tracker/)
- [Tracker Stores](https://legacy-docs-v1.rasa.com/1.10.25/api/tracker-stores/)
- [Event Brokers](https://legacy-docs-v1.rasa.com/1.10.25/api/event-brokers/)
- [Lock Stores](https://legacy-docs-v1.rasa.com/1.10.25/api/lock-stores/)
- [Training Data Importers](https://legacy-docs-v1.rasa.com/1.10.25/api/training-data-importers/)
- [Featurization of Conversations](https://legacy-docs-v1.rasa.com/1.10.25/api/core-featurization/)
- [TensorFlow Configuration](https://legacy-docs-v1.rasa.com/1.10.25/api/tensorflow_usage/)
- [Migration Guide](https://legacy-docs-v1.rasa.com/1.10.25/migration-guide/)
- [Rasa Open Source Change Log](https://legacy-docs-v1.rasa.com/1.10.25/changelog/)

# Migrate from (beta)

- [Dialogflow](https://legacy-docs-v1.rasa.com/1.10.25/migrate-from/google-dialogflow-to-rasa/)
- [Wit.ai](https://legacy-docs-v1.rasa.com/1.10.25/migrate-from/facebook-wit-ai-to-rasa/)
- [LUIS](https://legacy-docs-v1.rasa.com/1.10.25/migrate-from/microsoft-luis-to-rasa/)
- [IBM Watson](https://legacy-docs-v1.rasa.com/1.10.25/migrate-from/ibm-watson-to-rasa/)

# Reference

- [Glossary](https://legacy-docs-v1.rasa.com/1.10.25/glossary/)

# Versions

viewing: 1.10.25

# 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. |
| `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.

```bash
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.

## Train a Model

The main command is:

```bash
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.

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

```bash
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} ...
```

### 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

```bash
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.

### The full list of arguments that can be set for `rasa interactive`

```bash
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:

```bash
rasa shell
```

## Start a Server

To start a server running your Rasa model, run:

```bash
rasa run
```

## Start an Action Server

To run your action server run

```bash
rasa run actions
```

## Visualize your Stories

To open a browser tab with a graph showing your stories:

```bash
rasa visualize
```

## Evaluating a Model on Test Data

To evaluate your model on test data, run:

```bash
rasa test
```

## Create a Train-Test Split

To create a split of your NLU data, run:

```bash
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:

```bash
rasa data convert nlu
```

## Export Conversations to an Event Broker

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

```bash
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

```bash
rasa x
```

---

👋 I can help you get started with Rasa and answer your technical questions.