RASA CHATBOT APP DEVELOPMENT HOW TO
RASA CHATBOT APP DEVELOPMENT CODE

To distinguish these two “courses” folders, though, courses (project) will refer to the project we are working on and courses (application) will refer to this sub-folder that contains the application’s core files.īefore adjusting the core files, we need to create another application within the project. It has some important files that we will work with. You’ll see another folder called “courses.” This sub-folder is the main application of the project. > django-admin startproject coursesĭjango created a new folder for our project – courses.

RASA CHATBOT APP DEVELOPMENT CODE

Luckily Django will help us set up a lot of the base code that we need. Now, in courses_project, we need to create our Django project. We will be working in a folder called courses_project, so you can set that up on your system. It also assumes you have a working terminal, such as anaconda prompt or Git Bash. This tutorial assumes you have already set up Python 3.9 on your system. Finally, we will develop a Rasa chatbot with intents and actions that handle a user’s request for open courses. It will help us extend our web application to handle requests for information from our chatbot.

Django Rest Framework will help us build an Application Programming Interface (API) within Django. We will use this to create and interact with our database. Django is a Python framework for developing web applications. Pretty basic, right? But to get this conversation working, we are going to have to look at three different systems. The conversation we are aiming for users to have is as follows:Ĭhatbot: Hey, how can I help you with enrollment?Ĭhatbot: Here are a list of courses you could enroll in. The system will be very simple and barebones. Our focus will be on developing a system to look for courses to enroll in.

RASA CHATBOT APP DEVELOPMENT HOW TO

In this article, we will look at how to retrieve data from a Django web application with a Rasa chatbot. Also, we can automate processes more easily if information is stored in one place in a clear, standardized structure. Additionally, we are less likely to make mistakes updating content once as opposed to multiple times. Having such information in a database means that any platform/channel can get the same information from one source. For example, contact information should probably be the same on both a website and a chat platform. Often, important information is the same across all those platforms. Storing information in a database helps ease the burden of ensuring a chatbot aligns with other platforms and channels. As an application grows and deals with more complex queries, it will likely be easier to centralize important information into a database. Any mistake will lead to frustration when users find out the chatbot gave them outdated content. When we need to add or change information, we’ll have to rely on our memory as we manually adjust each instance in the bot. Hardcoding information into a prototype might make the initial development faster, but manually updating that information is going to be prone to error. Chatbots need to provide up-to-date information to users, but it can be difficult to manage changes as an application’s scope increases.

Rasa chatbot app development