# 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 [Tutorial: Rasa Basics](https://legacy-docs-v1.rasa.com/1.7.0/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/1.7.0/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. This means that files you create on your computer will be visible inside the container, and files created in the container will get synced back to your computer. - `rasa/rasa` is the name of the docker image to run. - The Docker image has the `rasa` command as its entrypoint, which means you don’t have to type `rasa init`, just `init` is enough. 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. #### Note By default Docker runs containers as `root` user. Hence, all files created by these containers will be owned by `root`. See the [documentation of docker](https://docs.docker.com/v17.12/edge/engine/reference/commandline/run/) and [docker-compose](https://docs.docker.com/compose/compose-file/) if you want to run the containers with a different user. ### 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. Note that this command includes the flags `-it`, which means that you are running Docker interactively, and you are able to give input via the command line. For commands which require interactive input, like `rasa shell` and `rasa interactive`, you need to pass the `-it` flags. ## 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. See [Choosing a Pipeline](https://legacy-docs-v1.rasa.com/1.7.0/nlu/choosing-a-pipeline/#choosing-a-pipeline) for more information about dependencies. All tags start with a version – the `latest` tag corresponds to the current master build. The tags are: - `{version}` - `{version}-spacy-en` - `{version}-spacy-de` - `{version}-mitie-en` - `{version}-full` The plain `{version}` tag includes all the dependencies you need to run the `supervised_embeddings` pipeline. If you are using components with pre-trained word vectors, you need to choose the corresponding tag. Alternatively, you can use the `-full` tag, which includes all pipeline dependencies. #### Note You can see a list of all the versions and tags of the Rasa Docker image [here](https://hub.docker.com/r/rasa/rasa/). ### 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 ``` Here’s what’s happening in that command: - `-v $(pwd):/app`: Mounts your project directory into the Docker container so that Rasa can train a model on your training data. - `rasa/rasa:latest-full`: Use the Rasa image with the tag `latest-full`. - `train`: Execute the `rasa train` command within the container. For more information see [Command Line Interface](https://legacy-docs-v1.rasa.com/1.7.0/user-guide/command-line-interface/#command-line-interface). In this case, we’ve also passed values for the location of the domain file, training data, and the models output directory to show how these can be customized. You can also leave these out since we are passing the default values. #### Note If you are using a custom NLU component or policy, you have to add the module file to your Docker container. You can do this by either mounting the file or by including it in your own custom image (e.g. if the custom component or policy has extra dependencies). Make sure that your module is in the Python module search path by setting the environment variable `PYTHONPATH=$PYTHONPATH:`. ## Running the Rasa Server To run your AI assistant in production, configure your required [Messaging and Voice Channels](https://legacy-docs-v1.rasa.com/1.7.0/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. After, run the trained model with: ``` 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/). Docker Compose provides an easy way to run multiple containers together without having to run multiple commands. 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 ``` The file starts with the version of the Docker Compose specification that you want to use. Each container is declared as a `service` within the docker-compose file. The first service is the `rasa` service. The command is similar to the `docker run` command. The `ports` part defines a port mapping between the container and your host system. In this case it makes `5005` of the `rasa` service available on port `5005` of your host. This is the port of the [REST Channel](https://legacy-docs-v1.rasa.com/1.7.0/user-guide/connectors/your-own-website/#rest-channels) interface of Rasa. #### Note Since Docker Compose starts a set of Docker containers, it is no longer possible to connect to the command line of a single container after executing the `run` command. 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/1.7.0/core/actions/#custom-actions). Continuing the example from above, you might want to add an action which tells the user a joke to cheer them up. ### 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 ``` Then build a custom action using the Rasa SDK, e.g.: ```python import requests import json from rasa_sdk import Action class ActionJoke(Action): def name(self): return "action_joke" def run(self, dispatcher, tracker, domain): request = requests.get('http://api.icndb.com/jokes/random').json() # make an api call joke = request['value']['joke'] # extract a joke from returned json response dispatcher.utter_message(text=joke) # send the message back to the user return [] ``` Next, add the custom action in your stories and your domain file. Continuing with the example bot from `rasa init`, replace `utter_cheer_up` in `data/stories.md` with the custom action `action_joke`, and add `action_joke` to the actions in the domain file. ### 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`: ``` 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 ``` This pulls the image for the Rasa SDK which includes the action server, mounts your custom actions into it, and starts the server. To instruct Rasa to use the action server you have to tell Rasa its location. Add this to your `endpoints.yml` (if it does not exist, create it): ``` action_endpoint: url: http://action_server:5055/webhook ``` Run `docker-compose up` to start the action server together with Rasa. ### Adding Custom Dependencies If your custom action has additional dependencies of systems or Python libraries, you can add these by extending the official image. To do so, create a file named `Dockerfile` in which you extend the official image and add your custom dependencies. For example: ``` # Extend the official Rasa SDK image FROM rasa/rasa-sdk:latest # Add a custom system library (e.g. git) RUN apt-get update && \ apt-get install -y git # Add a custom python library (e.g. jupyter) RUN pip install --no-cache-dir jupyter ``` You can then build the image via the following command, and use it in your `docker-compose.yml` instead of the `rasa/rasa-sdk` image. ``` docker build . -t : ``` ## 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/1.7.0/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 MongoDB as Tracker Store Start by adding MongoDB to your docker-compose file. The following example adds the MongoDB as well as a UI (you can skip this), which will be available at `localhost:8081`. Username and password for the MongoDB instance are specified as `rasa` and `example`. ``` mongo: image: mongo environment: MONGO_INITDB_ROOT_USERNAME: rasa MONGO_INITDB_ROOT_PASSWORD: example mongo-express: image: mongo-express ports: - 8081:8081 environment: ME_CONFIG_MONGODB_ADMINUSERNAME: rasa ME_CONFIG_MONGODB_ADMINPASSWORD: example ``` Then add the MongoDB to the `tracker_store` section of your endpoints configuration `endpoints.yml`: ``` racker_store: type: mongod url: mongodb://mongo:27017 username: rasa password: example ``` Then start all components with `docker-compose up`. ### 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/1.7.0/api/tracker-stores/#tracker-stores). If you want the tracker store component (e.g. a certain database) to be part of your Docker Compose file, add a corresponding service and configuration there.