Bot Interface and Conversational Toolkit

Boticot is an open source natural language understanding toolkit. You can use it to create virtual assistant, chatbot and NLU agents. Boticot helps to manage conversations contexts and manage multiple agents in the same time. It helps also to improve your user experience based on analytics and tracking tools provided with Boticot Admin.

Features provided by Boticot:

• Manage training data and creating intents and entities using user interface
• Track in real time users inputs and improve you agent model based on real user data
• Manage automatic responses for intents: texts, suggestions, links and images
• Manage synonyms
• Supervise agent performance with provided analytics
• Manage multiple agents
• Manage users with roles

Boticot is based on rasa NLU (version 1.5.3).

Boticot API QuickStart

Project setup

You should be in api folder and follow the next steps:

install dependencies

pip install rasa==1.5.3
pip install Tensorflow-datasets==1.3.0
pip install Flask-PyMongo==2.3.0
pip install flask==1.1.2
pip install flask_cors==3.0.8
pip install flask-compress==1.5.0
pip install flask_jwt_extended==3.24.1
pip install isodate==0.6.0
pip install bcrypt==3.2.0

Set up authentication configuration

Before running the project for the first time and for a security purpose, you should replace the default values of environment variables inside api:

• JWT_SECRET_KEY
• ADMIN_LOGIN
• ADMIN_PWD The ADMIN_LOGIN and ADMIN_PWD will be used for authentication inside Boticot Admin

Run project

flask run --port 8010

Local Environment variables

To use your local environment variables for development, you need to create a new file .env in api folder and override default exisiting value in .flaskenv file. Your local .env file will be ignored by git.

Boticot Admin QuickStart

You should be in admin folder and follow the next steps:

Prerequisite

Node 12

Project setup

yarn install

Compiles and hot-reloads for development

yarn serve

Boticot Trainer QuickStart

Project setup

You should be in trainer folder and follow the next steps:

install dependencies

pip install rasa==1.5.3
pip install Tensorflow-datasets==1.3.0
pip install Flask-PyMongo==2.3.0
pip install flask==1.1.2
pip install flask_cors==3.0.8
pip install flask-compress==1.5.0
pip install isodate==0.6.0

Run project

flask run --port 8011

Local Environment variables

To use your local environment variables for development, you need to create a new file .env in trainer folder and override default exisiting value in .flaskenv file. Your local .env file will be ignored by git.