First, make sure you have Python 3.6 or greater installed. Run the following commands in the Command Prompt window.
The recommended way to install Rasa Core is uing pip:
pip install rasa_core
If you already have rasa_core installed and want to udpate it, run:
pip install -U rasa_core
If you want to use the bleeding edge version of Rasa, use github + setup.py:
git clone https://github.com/RasaHQ/rasa_core.git
cd rasa_core
pip install -r requirements.txt
pip install -e .
We used Rasa NLU for intent classification & entity extraction. To get it, run
pip install rasa_nlu[tensorflow]
If you want to use the bleeding edge verion, use github + setup.py:
git clone https://github.com/RasaHQ/rasa_nlu.git
cd rasa_nlu
pip install -r requirements.txt
pip install -e .
The processing pipeline we used with rasa NLU is a combination of spaCy and scikit-learn.
Installing spacy just requires:
pip install rasa_nlu[spacy]
python -m spacy download en_core_web_md
python -m spacy link en_core_web_md en
Next, you need to clone the Github repository:
git clone https://github.com/Gooey101/Hadoop-Chatbot.git
You can now go into that directory and access all the files.
Once you have successfully installed Rasa and cloned the repo, you can now begin training. In the Command Prompt, run this command:
python bot.py train
This will run NLU and Dialogue training. If you only want to run one of these, you can use the train-nlu or train-dialogue command instead.
Note: Dialogue training will take longer than NLU training.
First, you need to install rasa-addons:
pip install rasa-addons
While in the directory, launching the website requires this command:
python website.py
The website will take a few moments to load. You can access it by clicking on the index.html file to open it in your browser. The backend will continue running until the window is closed.
Inside the Chatito folder, you will find text files that contain the settings for Chatito. Using these files on Chatito will generate the training data we used.
This is not something we looked into, but may be useful in the future if not using Rasa's paid-for platform: Rasa Dashboard.