## Architecture Rasa Enterprise allows multiple versions of an assistant to be run simultaneously. By default, two environments are defined: - `production` - `worker` Each environment corresponds to a Rasa Open Source server which uses the model with the corresponding tag (e.g. `production`). Model training always happens on the `worker` instances to ensure the training does not degrade performance of the bot itself. 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 `development` 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](https://legacy-docs-enterprise.rasa.com/docs/rasa-enterprise/user-guide/deployment-environments/#customizing-deployment-environments) when deploying Rasa Enterprise. ## Workflow ##### note This workflow assumes, you already added an additional deployment environment called `development`. If you are only running the production and no `development` 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 `development` 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 `development` environment and click ‘Save’. This model will now be used when you select the `development` environment in the Share your Bot link generation screen. 2. Now that your model is running in the development environment, you can do some testing. Navigate to the Conversations screen, open the ‘Share your Assistant’ options and select ‘development’ from the environments dropdown. 3. 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 development. 4. Once you are satisfied with your model’s performance, repeat step 1 but instead selecting the `production` environment. ##### note 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-enterprise.rasa.com/docs/rasa-enterprise/user-guide/role-based-access-control) ## Customizing Deployment Environments An additional Rasa Open Source server must first be created before it can be made available to Rasa Enterprise. Once it is available, you can add it as a deployment environment to Rasa Enterprise in the user interface on the Models screen. ### Starting Additional Rasa Servers Any Rasa Open Source deployment can be configured as a deployment environment given the correct settings and credentials. Please follow the [instructions for connecting an existing deployment via the event broker](https://legacy-docs-enterprise.rasa.com/docs/rasa-enterprise/installation-and-setup/connect-rasa-to-rasa-enterprise#connection-via-the-event-broker) to set up event forwarding from your Rasa Open Source server to Rasa Enterprise. ##### note If the event broker for your Rasa Open Source server is not configured correctly, your deployment environment will still receive and respond to messages sent to it. However, you will not be able to see the bots responses in the Rasa Enterprise `Share your Assistant` and `Talk to your Bot` screens. In addition to correctly configuring the event broker, the following settings are required for a Rasa Open Soure server to act as a deployment environment: 1. The environment variable `RASA_ENVIRONMENT` must be set to the name of the environment e.g. before running the server: ``` export RASA_ENVIRONMENT=development ``` This value must match the target model tag and the name specified in the UI when adding the deployment environment. 2. The [model server](/content/docs/rasa/model-storage#load-model-from-server/index.html) URL must point to the target model endpoint of your Rasa Enterprise deployment. The URL should be in the format `/api/projects/default/models/tags/` For example: ```yaml endpoints.yml models: url: /api/projects/default/models/tags/development token: ${RASA_X_TOKEN} wait_time_between_pulls: 10 ``` `RASA_X_TOKEN` refers to the environment variable set in your Rasa Enterprise deployment. 3. The `rasa` channel credentials must point to your Rasa Enterprise deployment. ```yaml credentials.yml rasa: url: /api ``` 4. The server must be secured with an [authentication token](/content/docs/rasa/http-api#token-based-auth/index.html). You will need to provide this token to Rasa Enterprise when you add the server as a deployment environment. For example, in the startup command for your Rasa Open Source server: ``` rasa run --auth-token RASA_TOKEN ``` ### Configuring Deployment Environments in the UI Once you have started and configured a Rasa Open Source server, you can add it as a deployment environment in the Rasa Enterprise UI. On the models screen, go to `Configure Environments`: If you are editing an existing environment, hover over the environment and choose "Edit". If you are adding a new environment, choose `Add an Environment`: Add the environment name, url, and authentication token for the Rasa Open Source server the environment should point to. The `url` field should be set to the base address of the Rasa Open Source server. ##### note The `name` field of a deployment environment **must match** the value of the `RASA_ENVIRONMENT` environment variable in the target Rasa Open Source Server.