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
- 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
- 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
- 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
- Type in some messages to see how the assistant responds. For example:
> Hi Hello! <br> Hey! How are you?<br>
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 CollectingDispatcherclass ActionHelloWorld(Action):
def name(self):
return "action_hello_world"def run(self, dispatcher, tracker, domain): dispatcher.utter_message(text="Hello, world!") return []
- Update domain.yml to include your action:
actions:
- action_hello_world
- Start the action server:
rasa run actions
References ?
Rasa Official Documentation
Python Download
Virtualenv Documentation
Happy coding
The above is the detailed content of Setting Up Your First Rasa Project. For more information, please follow other related articles on the PHP Chinese website!

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