Custom Rasa 1.x Training and Testing

This document explains how you can test the Rasa X and Custon Rasa 1.x with Expertflow's chat solution

Training Rasa Hybrid Bot Via Rasa X

Rasa X is a tool that helps you build, improve, and deploy AI Assistants that are powered by the Rasa framework.

Deploy Rasa X

To deploy Rasa X on the server Please follow below mentioned steps

  1.  Clone the Skeleton Project from gitlab and checkout the branch 3.9.0.CSN-1631
    $ git clone https://gitlab.expertflow.com/chat-solution/chat-solution.git --branch 3.9.0.CSN-1631
  2. Go to the folder chat-solution and run deployment script
    $ ./docker_deployment_local_script.sh
    
  3. Run the docker-compose-all-with-rasa-x.yml profile if you want to deploy all the chat solution or if you want to run only Rasa-x run docker-compose-rasa-x.yml
     1)  docker-compose-all-with-kb.yml
    
     2)  docker-compose-all-with-rasa.yml
    
     3)  docker-compose-all-with-rasa-x.yml
    
     4)  docker-compose-core.yml
    
     5)  docker-compose-external-reporting.yml
    
     6)  docker-compose-kb-integration.yml
    
     7)  docker-compose-rasa-bot.yml
    
     8)  docker-compose-rasa-x.yml
    

Access Rasa X GUI and Train a Bot

Following are the steps and features of Rasa X gui

  • To Access the Rasa X gui Enter the http://IP-of-machine:5002 on which Rasa X is deployed. The login screen will appear on the browser write the Default password admin123
  • Following Dashboard will appear after login in which there are options to train a bot

  • In Config Tab You can add the configuration of your NLU Pipeline, Language and Action Policies


  • In NLU Training Tab You can add example assign intents and can change the intents of already assigned example

You can also watch already labeled examples by switching into Training Data Tab


  • In Stories Tab You can View, Edit and Compare the stories. Currently in Rasa X CE there is not option to delete the story



  • You Can Edit the Domain of your Bot in Domain Tab.


  • Add Templates in Responses Tabs. To add the response first its name has to be added in Domain Tab with prefix utter_ (e.g utter_greet)




  • Train the the Bot here

  • After training is Done Go To Models Tab You will see the newly trained model make it active


  • After Training Run the basic Customer Agent chat scenario and you will be able to see 3 actions against 3 intents from the bot

  • Conversations between customer and agent (if suggestion is selected) are shown in Rasa-X conversation tab. You can label the data there and make the story for it




Limitation

  1. Talk to bot feature of Rasa-X is not compatible with Experflow's Custom Rasa 1.x only the strict learning feature is available