This is the example chatbot used in my blogpost Building a Chatbot.
The post discusses:
- what chatbots are
- what Rasa is at a high level
- Terminology like intents, entities, slots and stories
- training and testing a chatbot with Rasa.
This example chatbot simply collects a user's contact information. A straightforward conversation, called a happy path, would look something like this:
👨 : Hey
🤖 : Please provide your contact information
👨 : Sure. My name is John and email is john@email.com
🤖 : thanks John for the info.
Clearly, this is a very simple bot. And most users won't be so cooperating! For a more complex example, where some unexpected cases are properly handled, called sad paths, please checkout this example bot.
This project follows the format of a standard Rasa project. There's a directory called data for training data like nlu, stories, and rules.
There's a directory called actions, which contains all your custom actions.
You'll also find the domain.yml file, which mentions all your intents, entities, slots, responses and actions.
Finally, there's the config.yml file, which specifies the components your bot is comprised of.
- Clone this repo
- Navigate to the RasaChatbot directory
- Install rasa>=2.6.2 in an env.
Modify the files in data/ or the domain.yml file to play around.
Before training the bot, a good practice is to check for any inconsistencies in the stories and rules, though in a project this simple, it's unlikely to occur.
$ rasa data validate
To train the bot, we simply use the rasa train command. We'll provide a name to the model for better organization, but it's not necessary.
$ rasa train --fixed-model-name contact_bot
To test your bot, open a new terminal window and start a rasa shell session.
$ rasa shell
This will let you chat with your bot in your terminal. If you want a more interactive UI and a little more debugging information like what intents were identified and what entities were extracted, you can use Rasa X.
You can find me on medium here.