Basic Overview

Rasa Talk is a Dialog Management tool built on top of Rasa NLU. It was built out of a desire for a open source on premise dialog management system. Originally inspired by Rasa UI inspiration was taken from watson conversation.

Rasa Talk can be used as just a training data generator but can also hook your chatbot up to Facebook/Telegram/Skype/Slack whatever!

Feel free to message me on

Demo

https://www.talk.jackdh.com (User: [email protected] Pass: demo1234)

Installation

Prerequisites

• Database: Mongodb - You can run this locally or online like mlab
• Chatbot Brain: Rasa NLU - I recommend running with Docker

git clone https://github.com/jackdh/RasaTalk/
Rename example.env to '.env'
Update the variables to include your MongoDB server IP and Rasa NLU IP.
yarn
yarn start

Docker

Update .env or docker-compose.yml with selected environment variables. (Mongodb volumes do not work on windows)

docker-compose up

Or view https://github.com/jackdh/RasaTalk/wiki/Setup for a more detailed setup guide

Up and Running

• Update .env with correct environment variables.
• Create a new user
• Add a new Agent
• Add some intents to the agent
• Add some expressions to the intents.
• Add entities if required.
• Start training the model
• Create a dialog node which is recognised by either and Intent or Regex.
• Populate the rest of the node
• Test it out on the right!

Features

Facebook / Skype / Third parties.

Due to the constumisable nature of RT it's possible to hook it up to practically any third party chatbot you'd like. For starters I've included a quick example of how you might use [Botkit](https://github.com/howdyai/botkit) as a middleware to get to Facebook

Both Facebook and Telegram can be easily setup within the app 🚀 Check out the telegram setup wiki for more information!

Generate Rasa NLU Training Data

• Agents - Create multiple agents to host multiple chatbots from one backend.
• Intents / Expressions - Build multiple varied expressions within the agents either manually or with the variant generator.
• Entities - Create multiple entities with their synonyms.
• Entity insertion - Highlight to insert entities into expressions

Dialog Management

• Watson Conversation style dialog management.
• Regex based or Intent based recognition.
• Dynamic recognition with multiple Intents or Entities ie: #intent OR @entity
• Smart contextual awareness
• Slot Filling with default slot or prompting
• Multiple and or varied responses.
• Jump to nodes
• Send and use REST API web hooks within nodes.
• Conditional based responses, webhooks, jump to's.
• Save user responses for future use within nodes or API's
• Create quick reply buttons.

Permission Based Editing

• Role based, Group Based & individual user permissions.
• Create secure user accounts using PassportJS
• Limit user access to certain features within the application.

Training Rasa

• Convert Intents into training data.
• Accurate entity insertion (Not just search and replace)
• View current training time.
• View models currently in training.

Built in Chatbot / Rasa parsers

• Ping the Rasa server directly to get a JSON response.
• Test the chatbot directly to see output of dialog management.

Still to come!

Further Analytics

• Fill out the front dashboard to expand on the simple analytics.

History

• View user's chats with the chatbot.
• Filter down based on criteria such as Dates, Topics or Intents.

Small Talk

• Implement simple small talk.

Todo / Help requested!

• Increase test coverage to 100%.
• Add Travis / Appveyor
• Provide autocomplete options for fields such as nodes.
• Better validation / error notifications.
• Add rename option for intents / expressions
• Add backup option for node / training data.
• Add sockets for chat as well as update notifications.

Known issues

• Prettier is picking up a non existent issue with spacing.
• Dashboard analytics need a default value.

Thanks

@Material-UI React Boilerplate