Welcome to our artificial intelligence journey series. Before you mention the information about RASA in this article, let’s get a little information about chatbots with you. The history of Chatbot actually dates back to the 1950s with the article “Computing Machinery and Intelligence” thanks to Alan Turing. If you would like to examine very detailed information about its history, I recommend that you follow our publication called Chatbot Applications, which is broadcast through the Global AI Hub.
Chat robots now appear on almost every platform. We can give many examples of this. For example Google Now, Siri, Cortana, Cleverbot, ALICE, Vodafone Chatbot TOBI and hundreds more… These chatbots, which now prepare answers for you in almost every area, can respond to the kinds of food you will eat at corporate companies. In this article, I will tell you the steps necessary for us to work on Rasa Chatbot. Let’s go🚀
RASA is one of the AI assistants that really help customers and users. It is a machine learning framework that automates text and sound-based analysis based on the information on the official site of RASA, which operates with an open-source machine learning system.
If you want to improve yourself in the RASA NLU section you can train with various exercises through the NLU Trainer.
Of course, there is a demo assistant of the RASA AI system, which has been used a lot recently! Even her name is Sara! Don’t you think that makes a lot of sense? Sara works in parallel with Rasa to provide an open-source example that developers can use to understand the workings of a complex Assistant. I just had a little chat with Sara for you guys. He answers so eloquently and erudite that it is impossible not to be impressed 🤗
🌟 Loading Via Cmd Command Screen
🌟 Installing Via Anaconda Prompt Console
I usually use Spyder and Jupyter for my Python projects, so I’ll move through Anaconda in this project. You can check whether the Tensorflow library is installed from this console as follows. Let’s perform operations together via anaconda Prompt 🚀
First of all, I created a folder in which I could install the project and provided a new environment. Then, by activating conda, I performed the installation of the necessary packages for Rasa within the environment. You can also continue with such a folder for your work.
When you type the command shown in the figure on prompt, conda will perform the installation for you. Installation may take some time, please wait patiently ⏳
When all downloads are successful, it will give you information about the operation as shown in the figure and show you specific commands to activate and disable the env environment.
As shown in the figure, you can perform operations in this way by activating the rasa2 environment.
The last step is to install the necessary libraries from the Rasa Core link “requirements.txt” you should continue by uploading the file to the folder where you are running it. If you sit back in this process, conda will make this work for you 🛫
We established Rasa and carried out our operations. How about learning a little more about AI assistants in my next post?
Hope to see you 🙏