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https://github.com/jackdh/RasaTalk

A chatbot framework for Rasa NLU
https://github.com/jackdh/RasaTalk

bot botkit bots chatbot chatbot-framework conversational-ai nlp nodejs rasa rasa-nlu react

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A chatbot framework for Rasa NLU

Lists

README 

        





                 



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 [![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/RasaTalk/Lobby)



[![Build Status](https://travis-ci.org/jackdh/RasaTalk.svg?branch=master)](https://travis-ci.org/jackdh/RasaTalk)



Demo



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

 







Installation



**Prerequisites**



 - Database: [Mongodb](https://www.mongodb.com/) - You can run this locally or online like [mlab](https://mlab.com/)

 - Chatbot Brain: [Rasa NLU](https://rasa.com/docs/nlu/) - I recommend running with [Docker](https://hub.docker.com/r/rasa/rasa_nlu/)



```

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.




  Third Party Intregration 



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](https://github.com/jackdh/RasaTalk/wiki/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





Quickly add training data with entities



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.





Dialog management

  Updating Nodes




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.



Fine grain permission control



Training Rasa



* Convert Intents into training data.

* Accurate entity insertion (Not just search and replace)

* View current training time.

* View models currently in training.



Generate, download & train Rasa UI.



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.



Directly see NLU results and chatbot outputs.



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