Unleashing the power of automation with conversational AI

Google Cloud Platform offers the Dialogflow service with two editions, Essentials and CX. 

Lex is the Conversional AI service delivered by AWS. As of now, two versions are available, V1 and V2.

The first edition (V1) is a good choice for small to medium chatbot applications. It must be noted that it will eventually migrate to Lex V2 and the AWS team is working towards allowing an easy migration from the older service to the newer. 

Figure 4. AWS Lex V1 web browser interface 

The V2 version is a refactoring effort by AWS to enable better workflows and advanced features for developers. Those familiar with AWS V2 will find the web interface adheres to the latest UI style chosen by AWS. Due to the nature of the services, and unlike Dialogflow, AWS intentions are to replace V2 with V1 hence the complexity of their chatbots is intended for small to medium sizes. 

• Power Virtual Agents, a no code solution powered by the Power ecosystem 
• Microsoft Bot composer, a GUI application 
• Microsoft bot framework API, a modular and extensible SDK for developing chatbots in Python or C# 

Here are some examples with code shown by using Dialogflow Essentials, Lex V1, Bot composer. 

The main demo features shown are: 

Figure 8. From left to right: Google Dialogflow Essentials integrated on Telegram, AWS Lex V1 embedded test tool, Microsoft Bot Framework Composer embedded test tool 

Dialogflow is presented through a Telegram integration and the UI refers to the Android application. For Lex and Bot composer, you can see the service test implementation displayed. 

All three platforms make it easy to build this kind of conversations with no-code process. 

Figure 10. Google Dialogflow inline cloud function written in JS 

The code is written in Node.js and can also be edited on the GCP Console. The following code connects the chatbot to Firebase. 

'use strict';const functions = require('firebase-functions'); 
const {WebhookClient} = require('dialogflow-fulfillment');const admin = require('firebase-admin'); 
admin.initializeApp(); 
const db = admin.firestore(); 
exports.dialogflowFirebaseFulfillment = functions.https.onRequest((request, response) => { 
const agent = new WebhookClient({ request, response }); 
var last_body = request.body.queryResult;function send_book_data_firebase(agent) {let email = agent.parameters.email; 
let depart = last_body.outputContexts[0].parameters.Department; 
let date_sel = last_body.outputContexts[0].parameters.date;db.collection('dialogflow_firestore').add({ 
email: email, 
depart: depart, 
date: date_sel 
}); 
} 
let intentMap = new Map(); 
intentMap.set('set-call-email', send_book_data_firebase); 
agent.handleRequest(intentMap); 
}); 

Below, we will explain each block of code. 

'use strict';const functions = require('firebase-functions'); 
const {WebhookClient} = require('dialogflow-fulfillment'); 

‘use strict’ indicates that the code should be executed in the ‘strict mode’. With strict mode, you cannot, for example, use undeclared variables. The two imports allow you to connect to the Dialogflow service. 

… 
const admin = require('firebase-admin'); 
admin.initializeApp(); 
const db = admin.firestore(); 
exports.dialogflowFirebaseFulfillment = functions.https.onRequest((request, response) => { 
const agent = new WebhookClient({ request, response }); 
var last_body = request.body.queryResult; 
… 

The first three lines initialize the connection with Firestore, the latter lines of code handle the message sent from Dialogflow when the user types text along with several other pieces of information (such as the parameters collected so far). 

… 
function send_book_data_firebase(agent) {let email = agent.parameters.email; 
let depart = last_body.outputContexts[0].parameters.Department; 
let date_sel = last_body.outputContexts[0].parameters.date;db.collection('dialogflow_firestore').add({ 
email: email, 
depart: depart, 
date: date_sel 
}); 
}let intentMap = new Map(); 
intentMap.set('set-call-email', send_book_data_firebase); 
agent.handleRequest(intentMap); 
}); 

Figure 11. Data stored through integration between AWS Lex V1 with the AWS Lambda function and Firestore database 

This means we are not bound only to the service cloud platform, but it can virtually expand to other services. To achieve those results, the AWS lambda function should be employed by using the node package ‘firebase-admin’. 

Likewise, Firestore database integration, the ‘intentMap.set’ methods trigger the ‘luxoft_career’ function when the correct intent is matched. In this case, the rich response is added by instantiating a Card object and filling the dictionary with the required information. 

Bot composer uses the Hero card response where through a special syntax of Bot composer, the response should be declared as ‘HeroCard’ and each newline must be filled with a piece of key information and its value. 

Figure 14. Steps to integrate a rich response in Bot Composer 

The pricing with regard to texts varies between services.