Setting Up Your First Rasa Project

#python #development #beginners

Rasa is an open source framework for creating conversational AI and chatbots. If you are a looking to configure your first project in Rasa, you’ve come to the right place. In this blog, Iwe will set up a Rasa project from the ground up, step by step.

What Is Rasa?

Before diving in, let’s clarify what Rasa is. Rasa consists of two primary components:

Rasa Open Source: A framework for building natural language understanding (NLU) and dialogue management.
Rasa X: A tool for improving and managing your assistant over time.

Rasa is written in Python and allows for flexible customisation, making it a popular choice among developers.

Prerequisites

To set up Rasa, you need:

Python 3.8 or 3.9 (Rasa currently doesn’t support 3.10+)
pip (Python package manager)
Virtual Environment (Optional but recommended)

Familiarity with Python and basic command-line usage is helpful but not required.

Step 1: Installing Python and Creating Virtual Environment

Download Python:

Head over to the Python website and download Python 3.8 or 3.9.

Create a Virtual Environment: Using a virtual environment keeps your Rasa project dependencies isolated from your global Python setup.

python -m venv venv
source venv/bin/activate

Step 2: Install Rasa

Install Rasa via pip:

pip install rasa
1. Verify the installation:
rasa --version

You should see the Rasa version and Python version displayed.

Step 3: Create Your First Rasa Project

Now, let’s create your Rasa project:

Run the following command:

rasa init
1. Follow the prompts:

Rasa will set up a new project directory with the following structure:

my_project/
├── actions/
├── data/
├── models/
├── tests/
├── config.yml
├── credentials.yml
├── domain.yml
└── endpoints.yml
We will be prompted to train a model and test your assistant. Go ahead and try it!

Step 4: Understand the Key Files in Rasa

Here’s a breakdown of the key files in your project:

domain.yml: Defines your bot’s personality, intents, responses, and entities.
data/nlu.yml: Contains training examples for intent recognition.
data/stories.yml: Defines conversation flows to train the dialogue model.
config.yml: Specifies the machine learning pipeline for intent classification and entity recognition.
endpoints.yml: Configures where to find external services (e.g., action server).
credentials.yml: Configures third-party integrations like Slack or Telegram.

Step 5: Train Your Rasa Model

Use the following command to train your assistant if not already trained:

rasa train
1. This will generate a model file in the models/ directory, ready to power your chatbot!

Step 6: Test Your Assistant

Test your assistant locally:

rasa shell
1. Type in some messages to see how the assistant responds. For example:

> Hi Hello! 

Hey! How are you?

Step 7: Add Custom Actions

Want your bot to perform actions like fetching data from an API? Add custom actions!

Open the actions/ directory and create a Python file (e.g., actions.py).
Write your custom action:

from rasa_sdk import Action
from rasa_sdk.executor import CollectingDispatcher

class ActionHelloWorld(Action):
def name(self):
return "action_hello_world"
```
def run(self, dispatcher, tracker, domain):
    dispatcher.utter_message(text="Hello, world!")
    return []
```
1. Update domain.yml to include your action:
actions:
- action_hello_world
  1. Start the action server:
rasa run actions