## Rasa Enterprise Feature

Rasa Enterprise allows multiple versions of an assistant to be run simultaneously and served to different users. By default, two environments are defined:

- `production`
- `worker`

This page provides a walkthrough of the best practices for managing the lifecycle of a Rasa model.

Each environment corresponds to a Rasa server which uses the model with the corresponding tag (e.g. `production`). The model training happens on the `worker` instances to ensure the training does not degrade performance.

A model can be given multiple environment tags, but each environment can only have a single model associated with it. If model A is tagged `experimental` and you promote it to `production`, the model will now have two tags, and the two environments will use the same model. This ensures that you can promote a model to an environment without affecting any others.

The name and number of environments can be customized when deploying Rasa Enterprise.

### Workflow

This workflow assumes you already added an additional deployment environment. If you are only running the production and no `experimental` environment, you can follow the guide in the next section which explains how to add a new environment.

1. The first step is to move a trained model into the `experimental` environment. To do this, first navigate to the models view. Hover over a model, and click the three dots menu which appears on the table row. Now you can select the `experimental` environment and click ‘Save’.

This model will now be used to serve users at the endpoint `/conversations/{sender_id}/messages?environment=experimental`.

1. Now that your model is running in the development environment, you can do some testing. Navigate to the ‘Talk to your bot’ view and select ‘experimental’ from the dropdown on the right. If you make any improvements, like adding more training data or updating the response templates, you need to use the ‘train’ button to create a new model and promote _that_ model to experimental.

2. Once you are satisfied with your model’s performance, repeat step 1 but instead selecting the `production` environment.

By default, only the `admin` role has the permission to modify the production environment. You can customize this in [Role-Based Access Control](https://legacy-docs-rasa-x.rasa.com/docs/rasa-x/0.42.x/enterprise/role-based-access-control)

### Customizing Deployment Environments

You currently cannot modify your deployment environments via the API or the user interface. In order to customize the environments (e.g. to create a new one), you will have to edit your deployment setup.

Rasa Enterprise lets you run multiple Rasa models in parallel, allowing you to test different environments promoting them to production. In this section we’ll show you how to:

1. Launch additional Rasa servers that run your models
2. Make the additional servers accessible in the `/conversations` tab
3. Assign tags to the Rasa models so they’re run by the right server

### Starting Additional Rasa Servers

By default, Rasa Enterprise runs two Rasa servers: one that should be used in production and one worker that executes certain long-running tasks like training models. To enable another environment, create a new Rasa server in addition to the predefined `rasa-production` and `rasa-worker` servers.

To add a development environment (`rasa-development`) to your configuration, add the following service definition under `services:` to your `docker-compose.yml` found in the `/etc/rasa` directory:

```yaml
services:
  rasa-development:
    <<:*default-rasa-service
    environment:
      <<:*rasa-credentials
      RASA_ENVIRONMENT:"development"
      DB_DATABASE:"rasa_development"
      RASA_MODEL_SERVER:"http://rasa-x:5002/api/projects/default/models/tags/development"
```

It’s important that the `RASA_ENVIRONMENT` variable is unique, and that `RASA_MODEL_SERVER` requests a unique tag, in this case `development`.

### Updating the Environments Config

We need to let Rasa Enterprise know about the newly defined Rasa servers, so that you can talk to the models running on it in the `/conversations` view. Following the example of our `rasa-experimental` service, add an entry to your environments settings in the `environments.yml` file:

```yaml
rasa:
  production: (...)
  worker: (...)
  development:
    url: http://rasa-development:5005
    token: ${RASA_TOKEN}
```

The hostname part of `url` has to match the service name defined in `docker-compose.override.yml` (in this case `rasa-development`).

### Token Authentication

By default, all Rasa servers share the same token, but you are free to define a separate token for each service. To achieve this, replace `${RASA_TOKEN}` above with your `<TOKEN>`, and add an entry in the `environments` section of the new service in `docker-compose.override.yml`: `RASA_TOKEN: "<TOKEN>"`.

### Tagging a Model

The final step is to upload a Rasa model and assign the right tag. You can upload and tag your new models in the interface or via the [HTTP API](https://legacy-docs-rasa-x.rasa.com/docs/rasa-x/pages/http-api).