If you want to build a conversational ai we need a bot that should be created so that a person can interact with it. Every time that a person interacts with the computer a chatbot as an assistant can guild a person on that particular website and so that is why chatbot is important.
A Conversation chatbot understands the context of the conversation and can handle any user goal gracefully and help accomplish it as best as possible. This doesn’t always mean that the bot will be able to answer all questions but it can handle the conversation well.
Making a chatbot or virtual assistance is used to transform the user experience. Nowadays, chatbots are gaining attraction, big or small entities such as IBM, Google, Facebook? are working on it and building their in-house products.
What is rasa?
Rasa is a machine learning conversational ai which is used to build a chatbot. It is one of the best open-source machine learning toolkits which is used to developer complex chatbot with minimal training data.
It is based on two frameworks-
1.Rasa nlu– a library for natural language understanding (NLU) which does the classification of intent and extracts the entity from the user input.
2.Rasa Core– A chatbot framework that predicts the next best response or action based on conversational history.
We majorly require the installation of Rasa Stack and a language model. The language model is going to be used to parse incoming text messages and extract the necessary information. We will be working with the SpaCy language model.
2.How it works
First, let us understand how it works (rasa chatbot).
- When Rasa receives a message from the user, it predicts or extracts the intent and entities present in the. This part is handled by Rasa NLU (mentioned previously).
- Rasa then tries to predict the next action (what it should do next). This decision is taken considering multiple factors. This part is handled by Rasa Core
- Once the user?s intent is identified, Rasa performs an action called action_request_certificate to get the request the certificate in question.
3 Installing Rasa and Dependencies
1. Install the python development environment.
run this in the terminal or the command prompt cmd
this is to ensure that the python is installed in the system.
2. Install rasa open source and virtual environment
this is to activate the virtual environment. If you want to build in more functionality in the rasa bot then you should install the virtual machine using these commands.
You should first install all the packages of python and TensorFlow and Keras should be installed first then only install rasa open source and virtual environment. Install rasa open source and virtual environment.
1.pip install -U pip
2.pip install rasa(cmd)
3. You also need to install a spaCy English language model:
Python -m spacy download en
(write this after installing rasa)
Or you can download a language model using this command
2.2 Make sure you also install rasa -SDK by writing the code
pip install rasa rasa-SDK
(As this will help to create the rasa chatbot)
When you have done all the things then now we can set up the chatbot in rasa. All the steps work best in cmd or pycharm as in the next steps are all in pycharm.
4. Rasa chatbot
Step -1: Create a folder where you want to set up the bot
Step -2: Open an editor like pycharm and when you have installed the rasa SDK then run the below command to set up the project
After running this command the required files will automatically be created and also it will train the nlu & core Models.
Files which will be created are-
Step-3 Then train the bot by using the command
Open the terminal in the pycharm and then to train the bot that you create to run this command.
Step-4 Run the bot by using the command
when we are finished with training the bot then we can run the rasa bot using the command. Run this in the terminal. (you should check the version of python and its packages so that you can use the chatbot in rasa).