# Running Rasa with Docker

This is a guide on how to build a Rasa assistant with Docker. If you haven’t used Rasa before, we’d recommend that you start with the [Rasa Tutorial](https://legacy-docs-v1.rasa.com/1.5.3/user-guide/rasa-tutorial/#rasa-tutorial).

## Installing Docker

If you’re not sure if you have Docker installed, you can check by running:

```bash
docker -v && docker-compose -v
```

If Docker is installed on your machine, the output should show you your installed versions of Docker and Docker Compose. If the command doesn’t work, you’ll have to install Docker. See [Docker Installation](https://docs.docker.com/install/) for details.

## Building an Assistant with Rasa and Docker

This section will cover the following:

- Setting up your Rasa project and training an initial model
- Talking to your AI assistant via Docker
- Choosing a Docker image tag
- Training your Rasa models using Docker
- Talking to your assistant using Docker
- Running a Rasa server with Docker

### Setup

Just like in the [tutorial](https://legacy-docs-v1.rasa.com/1.5.3/user-guide/rasa-tutorial/#rasa-tutorial), you’ll use the `rasa init` command to create a project. The only difference is that you’ll be running Rasa inside a Docker container, using the image `rasa/rasa`. To initialize your project, run:

```bash
docker run -v $(pwd):/app rasa/rasa init --no-prompt
```

### Talking to Your Assistant

To talk to your newly-trained assistant, run this command:

```bash
docker run -it -v $(pwd):/app rasa/rasa shell
```

## Customizing your Model

### Choosing a Tag

To keep images as small as possible, we publish different tags of the `rasa/rasa` image with different dependencies installed.

### Training a Custom Rasa Model with Docker

Edit the `config.yml` file to use the pipeline you want, and place your NLU and Core data into the `data/` directory. Now you can train your Rasa model by running:

```bash
docker run -v $(pwd):/app rasa/rasa:latest-full train --domain domain.yml --data data --out models
```

## Running the Rasa Server

To run your AI assistant in production, configure your required [Messaging and Voice Channels](https://legacy-docs-v1.rasa.com/1.5.3/user-guide/messaging-and-voice-channels/#messaging-and-voice-channels) in `credentials.yml`. If this file does not exist, create it using:

```bash
touch credentials.yml
```

Then edit it according to your connected channels. After, run the trained model with:

```bash
docker run -v $(pwd)/models:/app/models rasa/rasa:latest-full run
```

## Using Docker Compose to Run Multiple Services

To run Rasa together with other services, such as a server for custom actions, it is recommended to use [Docker Compose](https://docs.docker.com/compose/).

Start by creating a file called `docker-compose.yml`:

```bash
touch docker-compose.yml
```

Add the following content to the file:

```yaml
version: '3.0'
services:
  rasa:
    image: rasa/rasa:latest-full
    ports:
      - 5005:5005
    volumes:
      - ./:/app
    command:
      - run
```

## Adding Custom Actions

To create more sophisticated assistants, you will want to use [Custom Actions](https://legacy-docs-v1.rasa.com/1.5.3/core/actions/#custom-actions).

### Creating a Custom Action

Start by creating the custom actions in a directory `actions`:

```bash
mkdir actions
# Rasa SDK expects a python module.
# Therefore, make sure that you have this file in the directory.
touch actions/__init__.py
touch actions/actions.py
```

### Adding the Action Server

The custom actions are run by the action server. To spin it up together with the Rasa instance, add a service `action_server` to the `docker-compose.yml`:

```yaml
version: '3.0'
services:
  rasa:
    image: rasa/rasa:latest-full
    ports:
      - 5005:5005
    volumes:
      - ./:/app
    command:
      - run
  action_server:
    image: rasa/rasa-sdk:latest
    volumes:
      - ./actions:/app/actions
```

## Adding a Custom Tracker Store

By default, all conversations are saved in memory. This means that all conversations are lost as soon as you restart the Rasa server. If you want to persist your conversations, you can use a different [Tracker Store](https://legacy-docs-v1.rasa.com/1.5.3/api/tracker-stores/#tracker-stores).

### Using PostgreSQL as Tracker Store

Start by adding PostgreSQL to your docker-compose file:

```yaml
postgres:
  image: postgres:latest
```

### Using MongoDB as Tracker Store

Start by adding MongoDB to your docker-compose file.

### Using Redis as Tracker Store

Start by adding Redis to your docker-compose file:

```yaml
redis:
  image: redis:latest
```

### Using a Custom Tracker Store Implementation

If you have a custom implementation of a tracker store you have two options to add this store to Rasa:

- extending the Rasa image
- mounting it as volume

Add the required configuration to your endpoint configuration `endpoints.yml`.