# Installation

BrainWise comes as a collection of services delivered as Docker images that you need to orchestrate so they can work together. You have different options.

Note that using docker-compose in production is generally not recommended as a best practice.

# 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, ...)

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.

# Indicative technical requirements

Those are the minimal requirements:

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