By utilizing the controls and features of Rasa and Rasa X, developers can develop and customize powerful, feature-rich AI chatbots. Read our blog to find out how!

1. A Guide to Rasa and Rasa X
Vasishtha Ingale
Software Engineer
Vasishtha is a Software Engineer at Nitor Infotech. He has a keen interest in
assimilating statistical approaches for Data Science. He is p... Read More

2. I hope you read and enjoyed my previous blog titled ‘Introduction to
Rasa X’ since it is a precursor to this one. In case you haven’t, you can
read it here.
In this blog, I am going to lead you through the installation, folder
structure, controls, and features of Rasa as well as Rasa X to develop an
assistant.
Let’s first dive into installing Rasa.
Installation of Rasa
To install Rasa, you require Python 3.7 or Python 3.8. Firstly, you need to
create and activate a new virtual environment by giving this command:
C:> python3 -m venv ./rasaenv
Activate it with: C:> .rasaenvScriptsactivate
Once the installation is done, generate a folder structure for the Rasa
project by typing rasa init in command prompt.
After initializing the structure, a developer can start customizing the
basic chatbot that is included in the initialized folder.
Training Data in Different Files of Rasa
Folder
1. NLU File: Intent is a class for a given set of examples which helps the
assistant identify the user query. These examples are specified in the
nlu.yml file. For example, if a user wants to stop a conversation, then you
can define the examples as follows.

3. 2. Domain file: The file domain.yml includes the following things:
1. Entities and slots: These are examples like City, Size of Pizza, Email
Addresses, etc.
2. Form: This includes a structure to fill information automatically from user input
3. Responses: Here all the possible responses are defined, and a name is given
to each one of them. Responses are named starting with utter for e.g. If a
response is ‘order’, then conventionally it is written as utter_order.
3. Stories file: This file includes all the conversational flows designed
and developed by a developer which helps the chatbot to make
decisions in accordance with the stories. Stories are some ideal paths
which illustrate the flow of the conversation can be. By gaining expertise
in writing these stories, you can improve the performance of an
assistant.
4. Rules file: The rules.yml file has rules for a specific conversation. For
example, if a user wants something which is out of scope for the bot to
answer, then any rule can handle the situation. Once the training data is

4. updated, you must train the model on top of it which is saved in the
folder.
The following image shows the folder structure of a Rasa chatbot. Every
time the training is done, the new model is saved so all the past models
can also be utilized.
Now it’s time to explore the second tool in our arsenal – Rasa X!
Installation of Rasa X
For Rasa X installation in Windows OS, we need to first install the
Microsoft Visual C++ compiler. After that, we can go to the command
prompt, activate the virtual environment created while installing Rasa
and type the following command:
To check whether it has been installed correctly or not, navigate to the
chatbot folder, open the command prompt and type rasa x. It will open a
window in your default browser with GUI.
Features of Rasa X

5. 1. Easy writing of training data:
Instead of writing the training examples in a code editor or IDE, you can
directly specify your data using GUI. In the above example, we defined
the book_flight intent and gave an example of what a user can say. You
can add as many examples as you want and save them.
2. Interactive learning:
Interactive learning involves looking into what the intent was (identified
by the bot) and then correcting it and manually guiding the bot to give
responses. This way the bot creates automatic stories and can train
itself further.
3. Records all previous conversations:
To analyze earlier conversations and their flow and performance, Rasa X
stores all conversations. In a live deployment monitoring user response
and accordingly making changes in the training stories is a crucial step
to have a successful chatbot in place.
4. Visualizes what’s going on in backend:

6. The above image shows the Rasa X chatbot in the image. When the user
said ‘Hello’, you can see that the bot identified the user intent as ‘greet’
which can be seen through this beautiful UI. Also, the next intent is to
book a flight and then at the end you can see that a form is initialized to
ask the user about the city from which he wants to book a flight and so
on.
5. Slots identification:

7. In the above example, once the user has confirmed that he wants to
book a flight and the form has started to fill, the assistant asks about the
departing city and automatically extracts the city from user input text
with a confidence of (1.0) which means 100 percent sure.
6. Buttons:
In the above image, you can see that once the user gives all the details
about cities that he wants to depart and land at, the assistant gives a
query in backend and communicates the dates available and shows the
buttons of available dates and asks the user to select a suitable date
which makes the conversation very easy. In case the user is not
interested in these dates, then the bot can identify it and will

8. recommend some new flights or just close the conversation as per the
story.
7. Actions:
actions.py is a Python file which handles connection with external
sources like database, csv file or API. It tracks the slots and can read or
write to files in accordance.
All the actions are defined as a class in the file. The above code snippet
is an example of saving all the slot information in a csv file once the
conversation is done.
I hope that after reading this blog, you are quite familiar with the
controls and features of Rasa and Rasa X, and a sample as well. Now
once you have understood the structure, you can develop and customize
a chatbot according to your use case.
Do read this case study about how we came up with an AI-based chatbot
solution to efficiently log, track, and monitor compliance requests for a
leading European business management solutions company.
Feel free to get in touch with us at Nitor Infotech if you’d like to share your
experience and suggestions. You can also discover more about how

9. artificial intelligence and machine learning can make a real difference
for your products, solutions, and services here.