服务器部署¶

Botfront 是由一组精心组织好的 Docker 镜像组成，每个镜像打包一个服务，所以部署的时候你可以根据自己情况灵活选择。

服务编排框架（推荐）¶

如果你熟悉 Kubernetes 或者 Openshift， 那么在这二者之上部署 Botfront 是到目前为止最优的选择，尤其是对生产环境来说。

单服务器方案¶

在一台单独的虚机主机上部署 Botfront 也是可以的，我们推荐最低配置不要低于 1 CPU、2Gb。

::: warning 请注意！ 以下安装步骤仅限体验，并没有考虑安全性，并不适合生产环境部署 :::

1. Create a virtual machine with Ubuntu installed, and note the external IP address. For this tutorial, we'll assume the IP address is 123.99.135.3
2. Install Node.js

   wget -qO- https://raw.githubusercontent.com/nvm-sh/nvm/v0.35.1/install.sh | bash
   export NVM_DIR="$([ -z "${XDG_CONFIG_HOME-}" ] && printf %s "${HOME}/.nvm" || printf %s "${XDG_CONFIG_HOME}/nvm")"
   [ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh" # This loads nvm
   nvm install lts/erbium
   

3. Install Docker and Docker Compose

   sudo apt-get -y update
   sudo apt-get -y remove docker docker-engine docker.io
   sudo apt -y install docker.io
   sudo systemctl start docker
   sudo systemctl enable docker
   sudo apt install curl
   sudo curl -L "https://github.com/docker/compose/releases/download/1.24.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
   sudo chmod +x /usr/local/bin/docker-compose
   

4. Install Botfront

   npm install -g botfront
   botfront init # create a project
   

5. Edit the botfront.yml file

   nano .botfront/botfront.yml
   

In the env section, change the root_url to the machine IP address (leave the port 8888 unchanged)

env:
  ...
  root_url: 'http://123.99.135.3:8888'
  ...

botfront up

rasa_addons.core.channels.webchat.WebchatInput:
  session_persistence: true
  base_url: http://123.99.135.3:5005
  socket_path: '/socket.io/'

Services and Docker images¶

The table below lists all the services that can be used with Botfront.

::: tip Image tags It is not recommended to deploy the images witout tags or with the latest tag. Look in the .botfront.yml for the tags corresponding to the version of Botfront you are using. :::

Duckling (a structured entity parser developed by Facebook) is not strictly required if your NLU pipeline doesn't use it.

Also, be very careful with your choice regarding MongoDB. If you decide to just run it as a container, be sure to at least properly mount the volume on a physical disk (otherwise all your data will be gone when the container is destroyed) and seriously consider scheduling back-ups on a regular basis.

Using a hosted service such as MongoDB Atlas is highly recommended, some of them even include a free plan that will be more than enough for small projects.

Environment variables¶

The following table shows the environment variables required by each service. Be sure to make those available as arguments or in the manifest files of your deployment

Volumes¶

Although volumes are technically not required for Botfront to run and work, if you do not mount them your data will be gone when containers are destroyed.

/app/models should be mounted on the same location so when Rasa restarts it can load the latest trained model.

MongoDB database considerations¶

It is highly recommended (but optional) to provide an oplog url with MONGO_OPLOG_URL. This will improve the reactivity of the platform as well as reduce the network throughput between MongoDB and Botfront.

::: warning IMPORTANT: choose a very short database name Choose a very short database name (e.g bf) and not too long response names to avoid hitting the limits. :::

Indicative technical requirements¶

Those are the minimal requirements: