Introduction
- Simplify customer interactions by integrating IBM Watson with Google Dialogflow. Leverage the strengths of both platforms to deliver enhanced AI-driven solutions.
- IBM Watson offers advanced NLP capabilities requiring contextual understanding, while Google Dialogflow excels at intent recognition and fulfillment.
Prerequisites
- Ensure you have accounts for IBM Cloud and Google Cloud Platform with active billing.
- Familiarity with both IBM Watson and Google Dialogflow.
Set Up IBM Watson
- Log in to IBM Cloud and navigate to the Watson Assistant service in the Catalog.
- Create an instance of Watson Assistant and access the API credentials from the Manage tab.
- Build and train your agent with relevant intents and entities through the Watson Assistant dashboard.
Set Up Google Dialogflow
- Log in to Google Cloud Platform, navigate to Dialogflow, and create a new agent.
- Obtain the Dialogflow API credentials by setting up a new service account from the IAM & Admin panel with Dialogflow API Client role.
- Download the JSON key file for the service account as this will be needed for authentication.
Integrate IBM Watson with Dialogflow
- Decide the intent handling strategy: centralize all intents in Watson or Dialogflow, or split them based on their strengths.
- Build a middleware using Node.js, Python, or any preferred programming language to bridge between Dialogflow and Watson.
Middleware Example in Node.js
- Use the Watson SDKs and Dialogflow client libraries. For instance, use the ibm-watson and dialogflow npm packages.
const { AssistantV2 } = require('ibm-watson/assistant/v2');
const dialogflow = require('@google-cloud/dialogflow');
// Watson Assistant Setup
const assistant = new AssistantV2({
version: '2021-11-27',
authenticator: new // Your Authenticator here,
serviceUrl: '<Your Watson Service URL>',
});
// Google Dialogflow Setup
const sessionClient = new dialogflow.SessionsClient({
keyFilename: '<Path to your Dialogflow JSON key>',
});
// Create Middleware Function
async function handleRequest(req, res) {
const dialogflowRequest = {
session: sessionClient.projectAgentSessionPath(
'<Your-Google-Project-ID>',
req.body.sessionId
),
queryInput: {
text: {
text: req.body.query,
languageCode: 'en-US',
},
},
};
const dialogflowResponses = await sessionClient.detectIntent(dialogflowRequest);
// Analyze response from Dialogflow
if (dialogflowResponses[0].queryResult.intent.displayName === 'Invoke Watson') {
const assistantResponse = await assistant.message({
assistantId: '<Your-Assistant-ID>',
sessionId: req.body.sessionId,
input: {
'message_type': 'text',
'text': req.body.query,
}
});
res.json(assistantResponse);
} else {
res.json(dialogflowResponses[0].queryResult);
}
}
Deploy the Middleware
- Host your middleware using platforms like Google Cloud Functions, AWS Lambda, or any Node.js accessible server.
- Ensure the middleware has access to both IBM Watson and Dialogflow credentials and endpoints.
Test and Iterate
- Thoroughly test the integration by querying Dialogflow and ensuring requests are redirected appropriately to Watson when needed.
- Iterate and refine the conversational design by analyzing user interaction data from both services.
Conclusion
- Maintaining regular updates and optimizations on both AI systems will ensure continued accuracy and efficiency in handling user interactions.