Prerequisites
- Ensure you have Python 3.x installed on your system.
- Set up a Rasa environment by installing Rasa Open Source.
- Ensure you have a Zoom account, and access to Zoom’s Developer Console to create an app.
Set Up Rasa Chatbot
- Create a new Rasa project with `rasa init` and follow the prompts to set up a basic bot.
- Design your conversational flows using Rasa stories and create the necessary `nlu.yml`, `stories.yml`, and `domain.yml` files.
- Run `rasa train` to train your bot with the NLU data and stories configured.
- Ensure your bot is working locally by executing `rasa shell` and testing interactions.
Create a Zoom App
- Sign in to Zoom's Developer Console and click on 'Develop' > 'Build App'.
- Choose the 'OAuth' app type and fill in basic information such as app name and description.
- For Redirect URL, input a URL where the integration service is hosted. Use a placeholder if your service is not yet deployed.
- Note down the Client ID and Client Secret once the app is created, as these are required for OAuth authentication.
Develop a Middleware Service
- Set up a Flask or FastAPI application to act as middleware between Rasa and Zoom.
- Implement OAuth 2.0 flow in your service by using Zoom API’s OAuth endpoints in Python.
- Authorize the Rasa service to interact with your Zoom app using the retrieved tokens.
- Example code snippet for OAuth setup:
from requests_oauthlib import OAuth2Session
zoom_client_id = 'YOUR_CLIENT_ID'
zoom_client_secret = 'YOUR_CLIENT_SECRET'
redirect_uri = 'YOUR_REDIRECT_URL'
oauth = OAuth2Session(zoom_client_id, redirect_uri=redirect_uri)
authorization_url, state = oauth.authorization_url('https://zoom.us/oauth/authorize')
# Redirect the user to the Zoom authorization URL
print('Please visit this link to authorize: ', authorization_url)
# Fetch the access token
token = oauth.fetch_token(
'https://zoom.us/oauth/token',
client_secret=zoom_client_secret,
authorization_response=input('Enter the full callback URL: ')
)
print("Access token received: ", token)
Connect Rasa Actions to Zoom
- Create custom actions in Rasa to handle interaction logic and send responses to Zoom.
- Define these actions in `actions.py` and iterate over possible conversational commands like scheduling a meeting, retrieving meeting details, etc.
- Ensure action server is running with `rasa run actions` during chatbot operation.
Deploy the Middleware Service
- Utilize platforms like Heroku, AWS, or any server configured to support Python apps to deploy your middleware service.
- Ensure your deployed service URL is updated in the Zoom App Redirect URL settings for proper OAuth flow.
- Use Ngrok to expose your local Rasa and middleware applications for testing purposes.
Test the Integration
- Trigger interactions from Zoom and verify that your middleware correctly relays requests and responses between Zoom and your Rasa bot.
- Log detailed responses from both Zoom and Rasa to identify discrepancies or errors.
- Smooth out any conversational or logical issues within Rasa to optimize the chatbot experience.
Monitor and Maintain
- Implement logging and monitoring mechanisms in your middleware to track usage and errors.
- Regularly update Rasa training data and Zoom interactions to improve user engagement and satisfaction.
- Ensure compliance with Zoom's API usage policies and guidelines to avoid service disruptions.
