MedBot Installation Guide

MedBot can be installed from its source code or by docker images.

Install MedBot by docker

Install docker-compose

Create your token

 echo TOKEN=$(tr -dc A-Za-z0-9 </dev/urandom | head -c 20) > .env

Create docker-compose file:

 $ cat docker-compose.yml

 services:
   rasa:
     image: medbot/medbot_server:latest
     container_name: chatbot-server
     restart: always
     ports:
       - 5005:5005
     volumes:
       - ./logs:/rasa-server/rasa/logs
     networks:
       - rasa-netowrk
     env_file:
       - .env
     command: [$TOKEN]
   app:
     image: medbot/action_server:latest
     container_name: action-server
     restart: always
     volumes:
       - ./logs:/action-server/logs
     networks:
       - rasa-netowrk
     expose: 
       - 5055
          
 networks:
   rasa-netowrk:
     driver: bridge

Enjoy your chatbot

 docker-compose -f docker-compose.yml up -d

Install from source code

Clone Rasa branch

 git clone https://github.com/arezae/chatbot --branch rasa --single-branch --depth 1

Directory tree

To run the Rasa chatbot, we need to run actions-server and chatbot-server separately. So we’ve separated actions, datasets, and the chatbot from each other.

Action-Server will contain actions and datasets, and Rasa-Server will contain the chatbot model and its autocorrect component. In the rest of the document, we will install requirements and then run chatbot and action servers.
```
 production/
 ├── action-server
 │   ├── actions
 │   ├── datasets
 │   └── docker
 └── rasa-server
     ├── autocorrect
     │   └── data
     ├── docker
     └── rasa
         ├── data
         ├── models
         └── tests
```

Install requirements

Create python environment

 pip install --user --upgrade pip
 pip install --user virtualenv
 python -m venv rasa_env

Activate your python environment
```
 source virtualenv/bin/activate
```

Install rasa chatbot requirements

 pip install --no-cache-dir -r production/rasa-server/requirements.txt

Install rasa chatbot server and actions server requirements

 pip install --no-cache-dir -r production/rasa-server/requirements.txt -r production/action-server/requirements.txt

Download Spacy weights

 python -m spacy download en_core_web_md

Download autocorrect module dictionaries. This dictionary consists of English and medical words.
```
 cd production/rasa-server
 python -c "import autocorrect; autocorrect.Speller('en_med')"
```

Train rasa model

 cd production/rasa-server/rasa
 rasa train

Run rasa server We are almost done. Now we can run the rasa and actions server.
- Run the Rasa server. If you would like to run the Rasa server with tokens to authenticate requests, You can add --auth-token YOUR_TOKEN at the end of the following command
```
  cd production/rasa-server/rasa
  mkdir logs
  rasa run --log-file logs/rasa-server.log --enable-api
```
- Run the action server
```
  cd production/action-server/actions
  mkdir logs
  rasa run --log-file logs/action-server.log actions --actions actions
```

Enjoy chatting with MedBot

curl --location --request POST 'http://localhost:5005/webhooks/rest/webhook' \
--header 'Content-Type: application/json' \
--data-raw '{
    "message" : "Can you give me dosage information of Abilify?",
    "sender" : "default"
}'

Build a Docker image

Clone Rasa branch

 git clone https://github.com/arezae/chatbot --branch rasa --single-branch --depth 1

Directory tree

To run the Rasa chatbot, we need to run actions-server and chatbot-server separately. So we’ve separated actions, datasets, and chatbots from each other.

Action-Server will contain actions and datasets, and Rasa-Server will contain the chatbot model and its autocorrect component.
```
 production/
 ├── action-server
 │   ├── actions
 │   ├── datasets
 │   └── docker
 └── rasa-server
     ├── autocorrect
     ├── docker
     └── rasa
         ├── data
         └── tests
```
In the rest of the document, we will build separated images for actions and rasa and then run them together.

Genrate token

Generate token for requests authentication. You can do this manually or use the urandom device file.
```
  echo TOKEN=$(tr -dc A-Za-z0-9 </dev/urandom | head -c 20) > production/.env
```

Check your token:

  $ cat production/.env
  TOKEN=SOME_RANDOM_STRING

Choose your Dockerfile

Default Dockerfile is Slim-based. But you can choose other Dockerfiles in the docker directory. For example for the rasa-based images:

 cp production/rasa-server/docker/Dockerfile-Rasa production/rasa-server/Dockerfile
 cp production/action-server/docker/Dockerfile-Rasa production/action-server/Dockerfile

Review docker-compose file
- In the case of using an old version of docker-compose or docker-engine, you might need to add the version key, on top of the docker-compose.yml file. For example in the case of the 1.25.0 version of docker-compose, you need to add version: "3.7" or version: "3" on top of your docker-compose file like this:
```
  version: "3.7"
  services:
    rasa:
      build:
        context: ./rasa-server
        dockerfile: Dockerfile
        args:
          - VERSION=v0.1.0
  .
  .
  .
```
- Also the version of the model weights you want to use, can be assigned by the VERSION argument in the docker-compose file. This will download the VERSION.tar.gz file from Dropbox while building a Docker images.
  
  Please note that, if you want to use the special version of our bot, you should clone that version alongside assigning that version as the VERSION argument in the docker-compose file. You can find out available versions here.

Build an image

Build a rasa chatbot and action server images

 docker-compose -f production/docker-compose.yml build

Make containers and run images

Run docker-compose to start and run the chatbot and its actions together in an isolated environment
```
 docker-compose -f production/docker-compose.yml up -d
```

Test your chatbot

Talk with your chatbot

  curl --location --request POST 'http://localhost:5005/webhooks/rest/webhook' \
  --header 'Content-Type: application/json' \
  --data-raw '{
      "message" : "Can you give me dosage information of Abilify?",
      "sender" : "default"
  }'

Debug your model. (Use your token instead of SOME_RANDOM_STRING)

  curl --location --request POST 'http://localhost:5005/model/parse?token=SOME_RANDOM_STRING' \
  --header 'Content-Type: application/json' \
  --data-raw '{
      "text" : "Can you give me dosage information of Abilify?"
  }'

Or you can use the following command. (Again, you should use your token instead of SOME_RANDOM_STRING)

  curl --location --request GET 'localhost:5005/conversations/default/tracker?token=SOME_RANDOM_STRING' \
  --header 'Content-Type: application/json' \
  --data-raw '{
      "text" : "Can you give me dosage information of Abilify?"
  }'

Monitor the chatbot
- Check for errors and warnings in logs
```
  docker-compose -f production/docker-compose.yml logs -f -t
```
- Monitor a live stream of containers resource usage statistics
```
  docker stats
```
In removing the chatbot case

Stop and remove chatbot containers
```
docker-compose -f production/docker-compose.yml down
```