# 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/1.3.10/user-guide/rasa-tutorial/#rasa-tutorial).

## Installing Docker

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

```
docker -v && docker-compose -v
# Docker version 18.09.2, build 6247962
# docker-compose version 1.23.2, build 1110ad01
```

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/1.3.10/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:

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

What does this command mean?

- `-v $(pwd):/app` mounts your current working directory to the working directory in the Docker container.
- `rasa/rasa` is the name of the docker image to run.

Running this command will produce a lot of output. What happens is:

- A Rasa project is created
- An initial model is trained using the project’s training data.

### Talking to Your Assistant

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

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

This will start a shell where you can chat to your assistant.

## 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.

The tags are:
- `{version}`
- `{version}-spacy-en`
- `{version}-spacy-de`
- `{version}-mitie-en`
- `{version}-full`

### 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:

```
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/1.3.10/user-guide/messaging-and-voice-channels/#messaging-and-voice-channels) in `credentials.yml`. If this file does not exist, create it using:

```
touch credentials.yml
```

Then edit it according to your connected channels.

### 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`:

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

Add the following content to the file:

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

To run the services configured in your `docker-compose.yml` execute:

```
docker-compose up
```

## Adding Custom Actions

To create more sophisticated assistants, you will want to use [Custom Actions](https://legacy-docs-v1.rasa.com/1.3.10/core/actions/#custom-actions).

### Creating a Custom Action

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

```
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

To spin it up together with the Rasa instance, add a service `action_server` to the `docker-compose.yml`:

```
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 Custom Dependencies

If your custom action has additional dependencies of systems or Python libraries, you can add these by extending the official image.

## 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/1.3.10/api/tracker-stores/#tracker-stores).

### Using PostgreSQL as Tracker Store

Start by adding PostgreSQL to your docker-compose file:

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

Then add PostgreSQL to the `tracker_store` section of your endpoint configuration `config/endpoints.yml`:

```
tracker_store:
  type: sql
  dialect: "postgresql"
  url: postgres
  db: rasa
```

### Using Redis as Tracker Store

Start by adding Redis to your docker-compose file:

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

Then add Redis to the `tracker_store` section of your endpoint configuration `endpoints.yml`:

```
tracker_store:
  type: redis
  url: redis
```

### 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

Then add the required configuration to your endpoint configuration `endpoints.yml` as it is described in [Tracker Stores](https://legacy-docs-v1.rasa.com/1.3.10/api/tracker-stores/#tracker-stores).