# 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。

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
  1. 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
  1. Install Botfront
npm install -g botfront
botfront init # create a project
  1. 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'
  ...
  1. Launch Botfront
botfront up
  1. Open Botfront in your browser (http://123.99.135.3:8888) and setup your project
  2. Go to settings/credentials and change the base_url host to the IP address (keep the host unchanged)
rasa_addons.core.channels.webchat.WebchatInput:
  session_persistence: true
  base_url: http://123.99.135.3:5005
  socket_path: '/socket.io/'
  1. Botfront is ready to use.

# Services and Docker images

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

Service Docker image
botftont botfront/botfront
botfront-api botfront/botfront-api
rasa botfront/rasa-for-botfront
duckling botfront/duckling
actions Build your own
mongo mongo or hosted service (mLab, Mongo Atlas, Compose, ...)

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

Environment variable Description Required by
ROOT_URL The Botfront app URL (e.g. https://botfront.your.domain) botfront
MONGO_URL The mongoDB connection string (e.g. mongodb://user:pass@server:port/database) botfront botfront-api
MONGO_OPLOG_URL The mongoDB Oplog connection string botfront (optional)
MAIL_URL An SMTP url if you want to use the password reset feature botfront
BF_PROJECT_ID The Botfront project ID (typically my-first-project) rasa
BF_URL The botfront-api root url rasa actions
MODELS_LOCAL_PATH Where the trained model returned by Rasa is stored locally. Defaults to /app/models/ and should not be changed in a containerized environment. The Botfront Dockerfile exposes a volume with that path botfront (optional)

# 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.

Volume Description Used by
/app/models Where Botfront stores the model retured by Rasa when the training is completed botfront
/app/models Where Rasa loads a model from when it starts rasa
/data/db Where MongoDB persists your data mongo

/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.

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:

Service RAM CPU
botfront 1 Gb 1
botfront-api 128 Mb 0.5
duckling 512 Mb 0.5
rasa (supervised_embeddings) 1 Gb 1