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Automatic suggestions for an operators. AI powered by DeepPavlov.

See image

Install instructions

  • First you have to install extension itself
  • After you have installed extension and added few most common questions. You can proceed with DeepPavlov training.

Live Helper Chat instructions install

After you have cloned repository you can copy extension/lhcchatbot to extensions folders of Live Helper chat

so it should look like lhc_web\extension/lhcchatbot

Install database

You can either run this file SQL directly or run this command

php cron.php -s site_admin -e lhcchatbot -c cron/update_structure

Copy extension settings file

extension/lhcchatbot/settings/settings.ini.default.php to extension/lhcchatbot/settings/settings.ini.php

Activate extension in main APP settings file

Edit main application settigns.gile

'extensions' => 
      array (
        'lhcchatbot'
      ),

Enter some common questions in back office

  • First you have to create at-least one Context (Modules -> Reply predictions)
  • Edit department and choose your newly created Context to be server for edited department.
  • Adding questions
    • Questions can be added by selecting visitor message with mouse direclty in the chat and clicking plus icon.
    • Questions can also be added from left menu Modules -> Reply predictions
  • After you have added few questions you can run this command
/usr/bin/php cron.php -s site_admin -e lhcchatbot -c cron/deeppavlov_train

After that you should see csv file (train_1.csv most likely if you have one context) in extension/lhcchatbot/train folder.

Copy those files to deeppavlov/Dockerfiles/deep/train folder of cloned repository.

Rasa AI workflow

MeiliSearch setup

MeiliSearch allows instant auto completion suggestions based on chats history and canned messages.

Navigate to deeppavlov and copy .env.default to .env

Edit .env file LHC_MEILI_SEARCH_MASTER_KEY value and set your own master key value.

Start a docker service

Start one time

docker-compose -f docker-meilisearch-compose.yml up

Start as a service

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

Export data for auto completion

If you are planning to update constantly auto completion data it makes sense to run this command once a week.

/usr/bin/php cron.php -s site_admin -e lhcchatbot -c cron/auto_complete

After above command is execute you will see in extension/lhcchatbot/train folder autocomplete_hash_<dep_id>.json and autocomplete_text_<dep_id>.json files. If you wish you can always adjust file manually or just modify script itself.

Now run in shell. It will feed auto complete data to MeiliSearch. It will print also Public Key

cd extension/lhcchatbot && ./doc/update_autocomplete.sh "http://localhost:7700/" <master_key>

Configure Live Helper chat

In Reply Predictions module you will find menu item called Auto complete and set Public key. Public key you will get from above command. Auto completion has to be enabled per department. Edit department and enable it in Reply Predictions tab.

Nginx configuration example

location /msearch/ {
    proxy_pass  http://127.0.0.1:7700/;
}

How to use?

  • Start typing your regular sentences, and you will see possible sentence endings at the bottom.
  • To replace all what you typed you can use #<your search query> also
  • In messages you can also use placeholders {nick}, {operator}, {year}, {month}, {demail}, {email} just start typing any of these keywords.

See image

DeepPavlov setup

Navigate to deeppavlov and copy .env.default to .env

Training is always happening on a docker image startup.

There is a two ways DeepPavlov can work. Wither with spellchecker or without.

Without spellchecker

# Optional to build an image
# docker-compose -f docker-do-compose.yml build

# Train and run image
docker-compose -f docker-dp-compose.yml up

Run as service once it's build.

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

To test does it works you can use CURL command

curl -X POST "http://localhost:5000/model" -H  "accept: application/json" -H  "Content-Type: application/json" -d "{\"q\":[\"hi\"]}"

To rebuild image

docker-compose -f docker-dp-compose.yml build --no-cache

To restart with forcing to recreate it

docker-compose -f docker-dp-compose.yml up --force-recreate

With spellchecker

With spellchecker visitor messages before running against your questions will be checked against spelling errors.

Spellchecker requires these changes.

  • Edit .env file and change LHC_API=train_tfidf_logreg_en_faq.json to LHC_API=riseapi.json
  • Navigate to deeppavlov/Dockerfiles/deep/data/downloads/language_models and see README.md file content. You will need to download file which is 6GB file size!

Automating retraining

The Easiest way is just to have some shell which would run daily something like that. This is just an example adopt it to your needs.

# Export trainings Adjust paths!
cd `lhc_web/` && /usr/bin/php cron.php -s site_admin -e lhcchatbot -c cron/deeppavlov_train

# Copy trainings. Adjust paths!
cd ../ && cp extension/lhcchatbot/train/* /deeppavlov/Dockerfiles/deep/train

# Restart docker image
docker-compose restart deeppavlov-lhcchatbot

Supporting more than one context

The easiest way is just to modify docker-compose.yml file and add more than one service with different configuration

Possible workflow

  • You should modify ports sections of docker-compose.yml
  • Create a copy of deeppavlov/Dockerfiles/deep/data like deeppavlov/Dockerfiles/deep/data_2
  • Modify volumes: section - ./Dockerfiles/deep/data:/base/deep to something like - ./Dockerfiles/deep/data_2:/base/deep
  • Modify volumes: section - "./Dockerfiles/deep/train/${LHC_TRAIN_FILE}:/base/train/train.csv" to something like - "./Dockerfiles/deep/train/train_2.csv:/base/train/train.csv"
  • Modify container_name from deeppavlov-lhcchatbot to deeppavlov-lhcchatbot-german as an example
  • Modify - LHC_API=${LHC_API} if you are using spellchecker. As most likelu it will not setup for other langauge than english. Put there train_tfidf_logreg_en_faq.json

After that don't forget to modify your new context and set host to new url with a new port.

Examples configuration

  deeppavlov-lhcchatbot-german:
    build: ./Dockerfiles/deep
    environment:
      - LHC_API=train_tfidf_logreg_en_faq.json
    container_name: deeppavlov-lhcchatbot-german
    image: remdex/deeppavlov-lhcchatbot:latest
    ports:
      - "5005:5000"
    volumes:
      - ./Dockerfiles/deep/data_2:/base/deep
      - ./Dockerfiles/deep/config:/base/config
      - "./Dockerfiles/deep/train/train_9.csv:/base/train/train.csv"
    networks:
      - code-network
    restart: always