Create conda env: conda create -n rasa-app python=3.6 conda env list conda activate rasa-app
Upgrade pip: pip3 install -U pip
Install Rasa open source: pip3 install rasa
The first step is to create a new Rasa project. To do this, run: rasa init Press enter to create project in current directory
The rasa init command creates all the files that a Rasa project needs and trains a simple bot on some sample data.
This creates the following files: init.py →an empty file that helps python find your actions actions.py →code for your custom actions config.yml ‘’ →configuration of your NLU and Core models credentials.yml →details for connecting to other services data/nlu.md ‘’ →your NLU training data data/stories.md ‘’ →your stories domain.yml ‘’ →your assistant’s domain endpoints.yml →details for connecting to channels like fb messenger models/.tar.gz →your initial model The most important files are marked with a ‘*’. You will learn about all of these in this tutorial.
Next asked: Do you want to train an initial model? 💪🏽 (Y/n) Press Y
Next asked: Do you want to speak to the trained assistant on the command line? 🤖 (Y/n) Press Y
Bot loaded. Type a message and press enter (use '/stop' to exit): Your input -> Hi
Separately train: rasa train echo "Finished training."
Run test cases: rasa test echo "Finished running tests."
The next step is to try it out! If you’re following this tutorial on your local machine, start talking to your assistant by running: rasa shell
As we are heading towards building production-grade Rasa Chatbot setup, the first thing we can simply use the following command to start Rasa. rasa run
Open two terminals and execute following cmds:
conda activate rasa-app rasa run -m models --enable-api --cors "*" --debug conda activate rasa-app rasa run actions
Open index.html file