GLPI Helpdesk bot

AI assistant for the GLPI IT Helpdesk. It includes an integration with its API to open incident reports.

Supported features

• Login & Auth validation using Google OAuth (TODO) (login)
• Open an incident ticket on GLPI (open_incident)
• Get the current status of a ticket (get_incident_status)
• Reset my password (password_reset)
• Issues with email (problem_email)
• Request biometrics report (request_biometrics_report)
• Request a VM (request_vm)
• FAQ: How to connect to the WiFi? (connect_wifi)
• FAQ: How to create a user? (create_user)

MISC Intents

• greet
• goodbye
• thank
• bot_challenge
• help
• show_menu
• inform
• confirm
• deny
• out_of_scope

Lookup Tables

• Software
• Faculties
• Departments

Requirements

• Python 3.7+
• Pip3
• Virtualenv or Conda (Recommended for isolated env creation)

Setup and installation

If you haven’t installed Rasa NLU and Rasa Core yet, you can do it by navigating to the project directory and running:

pip install -r requirements.txt

You also need to install the spaCy Spanish language model. You can install it by running:

python -m spacy download es_core_news_md
python -m spacy link es_core_news_md es

Development instructions

Validate training data

rasa data validate

Training Rasa Core & NLU models

The following command trains both the Core and NLU models

rasa train

NLU model evaluation

The following command performs a model evaluation of the latest trained NLU model under the models directory

rasa data split nlu
rasa test nlu -u train_test_split/test_data.md --out nlu_metrics/
# or 5 (default -f) cross validation
rasa test nlu -u data/nlu.md --cross-validation --out nlu_metrics/

Finally, check the following files for results:

• Intent Confusion Matrix
• [Intent Confidence Prediction][nlu_metrics/hist.png]
• Intent/Entity Metrics Report

Within the nlu_metrics folder there are also other reportes for each the NLU pipeline components (e.g. DIETClassifier report and errors)

Dialogue (CORE) model evaluation

The following command performs a model evaluation of the latest trained dialogue model under the models directory

rasa test core --stories tests/e2e-stories.md --out core_metrics/

Finally, check the results directory for a summary of the performed evaluation

Visualizing stories

rasa visualize -d domain.yml --stories data/stories.md -u data/nlu.md --out core_metrics/graph.html

Chatbot Deployment

Deploying custom actions locally

rasa run actions --actions actions -p 5055

or as a Docker container

docker build . --tag glpi-action-server
docker run -p 5055:5055 glpi-action-server

or as a background process

sh scripts/startActions.sh

Deploy DucklingHTTPExtractor (Optional if Enabled on the NLU pipeline)

docker run -p 8000:8000 rasa/duckling

Test your chatbot locally

rasa shell --endpoints endpoints.yml

Run Chatbot + Rasa X Locally (DEPRECATED)

Rasa X is a tool designed to make it easier to deploy and improve Rasa-powered assistants by learning from real conversations

rasa x --data data/train/ --endpoints endpoints.yml --cors '*' --enable-api --port 5005 --rasa-x-port 5002

or as a background process

sh scripts/startRasaX.sh

Update admin password

python scripts/manage_user.py create me $RASA_X_PASSWORD admin --update

Deploy the chatbot without RasaX & enabled connection to a web channel through sockets.io

rasa run --endpoints endpoints.yml --credentials credentials.yml --enable-api --cors "*" --port 5005

Deploy the web client

cd client
npm install
npm start

or as a background process

sh scripts/startClient.sh

Deployment on server

• Install Rasa + RasaX using Docker Compose Quick Start Guide

• To upgrade the components to their latest release, follow this instructions

• To update the admin password run the following command within RASA_HOME

python rasa_x_commands.py create --update admin me glpi@dmin

• Build the Action Server Docker Image (in case you want to build your own locally)

export GLPI_DOCKER_IMAGE=santteegt/glpi-chatbot-actions:latest
docker build -t $GLPI_DOCKER_IMAGE .

• Each time a commit is pushed to the develop branch, the DockerHub registry deploys a new image with the latest changes

• To do a manual push to DockerHub (Optional)

docker login --username santteegt
docker push $GLPI_DOCKER_IMAGE

• Copy socketio settings from credentials.yml to the credentials.yml file in the RASA_HOME directory

• Copy the docker-compose.override.yml file to the RASA_HOME directory

• Set the following environment variables on the .env file within the RASA_HOME directory

  • GLPI_API_URI: (Setup > General > API) <- where you can find the info on your GLPI instance
  • GLPI_APP_TOKEN: (Setup > General > API)
  • GLPI_AUTH_TOKEN: (User Preferences > API Token)
  • GLPI_LOCALMODE: false (true if you don't want )

• Start Docker Compose:

docker-compose up -d

• If you started the service for the first time or just updated it, you need to install the ES Spacy language model in both rasa-production and rasa-worker containers:

docker-compose exec -u 0 rasa-production bash -c "python -m spacy download es_core_news_md && python -m spacy link es_core_news_md es"
docker-compose exec -u 0 rasa-worker bash -c "python -m spacy download es_core_news_md && python -m spacy link es_core_news_md es"

Workaround with connection timeout error when trying to add your Git project to RasaX

1. Access the rasa-x container shell

docker exec -u 0 -it <rasa-x-container-id> bash

1. Add an SSH config file in /app/.ssh/config with the following:

Host github.com
 Hostname ssh.github.com
 Port 443

1. Add -F /app/.ssh/config as parameter on the ssh executable script /usr/local/lib/python3.7/site-packages/rasax/community/services/integrated_version_control/git_service.py:

def _save_ssh_executable(path: Path, path_to_key: Path) -> None:
    command = f"""
        #!/bin/sh
        ID_RSA={path_to_key}
        # Kubernetes tends to reset file permissions on restart. Hence, re-apply the required
        # permissions whenever using the key.
        chmod 600 $ID_RSA
        exec /usr/bin/ssh -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no -i $ID_RSA -F /app/.ssh/config "$@"
        """

1. Restart the compose environment

MISC Suggestions / Instructions

Caveat for using the Webchat client (DEPRECATED)

See this issue

pip install git+git://github.com/RasaHQ/rasa.git

Workaround to make Rasa work if AVX is not compatible with your CPU (DEPRECATED)

You may experience the following error on an on-premise and/or cloud server (deployed with Kubernetes): The TensorFlow library was compiled to use AVX instructions

In order to fix this issue, execute the following commands after installing rasa dependencies as shown above:

pip uninstall tensorflow -y
conda create --name glpi-rasax python=3.6.8
conda activate glpi-rasax
conda install -c anaconda -n glpi-rasax tensorflow==1.15.0
conda deactivate glpi-rasax
export PYTHONPATH='${HOME}/anaconda3/envs/glpi-rasax/lib/python3.6/site-packages'

Prepare dataset for training/testing (DEPRECATED)

If you're (re-)generating data using Chatito, paste the related files under the data/nlu_chatito folder and then execute the following commands:

rasa data convert nlu --data data/nlu_chatito/train/ --out data/train/nlu.md -l es -f md
rasa data convert nlu --data data/nlu_chatito/test/ --out data/test/nlu.md -l es -f md