saving data in conversations with Actions on Google - node.js

I am developing an app on Actions on Google and I've noticed that when using the Dialogflow Fulfillment library I can't save data between conversations.
Here is the code using the WebhookClient:
const { WebhookClient, Card, Suggestion } = require('dialogflow-fulfillment');
exports.aog_app = functions.https.onRequest((request, response)=>{
let agent = new WebhookClient({request, response});
let intentMap = new Map();
intentMap.set('Default Welcome Intent', (agent)=>{
agent.add("hello there!") ;
});
intentMap.set('presentation', (agent)=>{
let conv = agent.conv();
let counter = conv.data.counter;
console.log("counter", counter)
if(counter){
conv.data.counter = counter+1;
}else{
conv.data.counter = 1;
}
agent.add("counter is "+counter) ;
});
agent.handleRequest(intentMap)
});
counter remains undefined on each turn.
But when using the Action on Google Nodejs Library I can save data without issues:
const {
dialogflow,
SimpleResponse,
BasicCard,
Permission,
Suggestions,
BrowseCarousel,
BrowseCarouselItem,
Button,
Carousel,
DateTime,
Image,
DialogflowConversation
} = require('actions-on-google');
const app = dialogflow({debug: true});
app.intent('Default Welcome Intent', (conv)=>{
conv.ask("hello there!");
});
app.intent('presentation', (conv)=>{
let counter = conv.data.counter;
console.log("counter", counter)
if(counter){
conv.data.counter = counter+1;
}else{
conv.data.counter = 1;
}
conv.ask("counter is "+counter)
})
exports.aog_app = functions.https.onRequest(app);
counter is incremented on each turn.
Is there a way to save data between conversations using the Dialogflow fulfillment library?

you need to add the conv back to agent after you update the conv.data
agent.add(conv);

Google's Dialogflow team published a code sample showing how to implement data persistence by integrating Google Cloud's Firebase Cloud Firestore.
You can find the sample here: https://github.com/dialogflow/fulfillment-firestore-nodejs
This is (probably) the code you're looking for:
function writeToDb (agent) {
// Get parameter from Dialogflow with the string to add to the database
const databaseEntry = agent.parameters.databaseEntry;
// Get the database collection 'dialogflow' and document 'agent' and store
// the document {entry: "<value of database entry>"} in the 'agent' document
const dialogflowAgentRef = db.collection('dialogflow').doc('agent');
return db.runTransaction(t => {
t.set(dialogflowAgentRef, {entry: databaseEntry});
return Promise.resolve('Write complete');
}).then(doc => {
agent.add(`Wrote "${databaseEntry}" to the Firestore database.`);
}).catch(err => {
console.log(`Error writing to Firestore: ${err}`);
agent.add(`Failed to write "${databaseEntry}" to the Firestore database.`);
});
}

Related

Display data from JSON external API and display to DIALOGFLOW using AXIOS

I need help with Displaying the response I get from my API to Dialogflow UI. Here is my code. I am currently using WebHook to connect Dialogflow to backend in Heroku.
My code
const functions = require('firebase-functions');
var admin = require("firebase-admin");
var serviceAccount = require("../../reactpageagent-dxug-firebase-adminsdk-26f6q-e1563ff30f.json");
admin.initializeApp({
credential: admin.credential.cert(serviceAccount),
databaseURL: "https://reactpageagent-dxug.firebaseio.com"
});
const { WebhookClient } = require('dialogflow-fulfillment');
const { Card, Suggestion } = require('dialogflow-fulfillment');
const axios = require('axios');
module.exports = (request, response) => {
const agent = new WebhookClient({ request, response });
function welcome(agent) {
agent.add('Welcome to my agent');
}
function rhymingWordHandler(agent) {
const word = agent.parameters.word;
agent.add(`Here are the rhyming words for ${word}`)
axios.get(`https://api.datamuse.com/words?rel_rhy=${word}`)
.then((result) => {
console.log(result.data);
result.data.map(wordObj => {
console.log(wordObj.word);
agent.add(JSON.stringify(wordObj.word));
return;
// agent.end(`${wordObj.word}`);
});
});
};
let intentMap = new Map();
intentMap.set('Default Welcome Intent', welcome);
intentMap.set('rhymingWord', rhymingWordHandler);
agent.handleRequest(intentMap);
}
When I console.log my the result. I get the data from the API in my console.log output, but the Data is not displayed in Dialogflow UI I also do not get any error.
Heroku log
I had real trouble with the result.data.map line of code.
In the end, I avoided it and instead processed result.data after the .then((result) => { line by checking the array length that my API returned, and if it was > 0, loop through it to output each line individually, using agent.add. If the array length was 0, I used agent.add to display a message saying 'No records found'. I used a catch to log any errors (again, using agent.add to send an error message to the user).

Send user notification every hour with Bot Framework NodeJS

I am developing a bot that sends users a message every hour on skype. It makes an API call and then sends a message containing the parsed message. I started with the welcomebot sample. I then used the switch statement to fire a setInterval()to send the message activity when the user sends a certain message but the message keeps sending even after I call clearinterval() I set to a different string. How can I make clearInterval() work? Or am I going about it the wrong way?
Index.js:
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
// Import required packages
const path = require('path');
const restify = require('restify');
const axios = require('axios');
// Import required bot services.
// See https://aka.ms/bot-services to learn more about the different parts of a bot.
const { BotFrameworkAdapter, UserState, ConversationState, MemoryStorage } = require('botbuilder');
const { WelcomeBot } = require('./bots/welcomeBot');
// Read botFilePath and botFileSecret from .env file
const ENV_FILE = path.join(__dirname, '.env');
require('dotenv').config({ path: ENV_FILE });
// Create bot adapter.
// See https://aka.ms/about-bot-adapter to learn more about bot adapter.
const adapter = new BotFrameworkAdapter({
appId: process.env.MicrosoftAppID,
appPassword: process.env.MicrosoftAppPassword
});
// Catch-all for errors.
adapter.onTurnError = async (context, error) => {
// This check writes out errors to console log .vs. app insights.
// NOTE: In production environment, you should consider logging this to Azure
// application insights.
console.error(`\n [onTurnError] unhandled error: ${ error }`);
// Send a trace activity, which will be displayed in Bot Framework Emulator
await context.sendTraceActivity(
'OnTurnError Trace',
`${ error }`,
'https://www.botframework.com/schemas/error',
'TurnError'
);
// Send a message to the user
await context.sendActivity('The bot encountered an error or bug.');
await context.sendActivity('To continue to run this bot, please fix the bot source code.');
};
// Define a state store for your bot. See https://aka.ms/about-bot-state to learn more about using MemoryStorage.
// A bot requires a state store to persist the dialog and user state between messages.
// For local development, in-memory storage is used.
// CAUTION: The Memory Storage used here is for local bot debugging only. When the bot
// is restarted, anything stored in memory will be gone.
const memoryStorage = new MemoryStorage();
const userState = new UserState(memoryStorage);
const conversationState = new ConversationState(memoryStorage);
// Create the main dialog.
const conversationReferences = {};
const bot = new WelcomeBot(userState, conversationReferences, conversationState);
// Create HTTP server
const server = restify.createServer();
server.listen(process.env.port || process.env.PORT || 3978, function() {
console.log(`\n${ server.name } listening to ${ server.url }`);
console.log('\nGet Bot Framework Emulator: https://aka.ms/botframework-emulator');
console.log('\nTo talk to your bot, open the emulator select "Open Bot"');
});
// Listen for incoming activities and route them to your bot main dialog.
server.post('/api/messages', (req, res) => {
adapter.processActivity(req, res, async (context) => {
// route to main dialog.
await bot.run(context);
});
});
const getBalance = async () => {
try {
return await axios.get('https://balance.com/getbalance', { //this returns a json object
headers: {
Authorization: xxxxx
}
});
} catch (error) {
console.error(error);
}
};
const retrieveBalance = async () => {
const balance = await getBalance();
if (balance.data.message) {
for (const conversationReference of Object.values(conversationReferences)) {
await adapter.continueConversation(conversationReference, async turnContext => {
// If you encounter permission-related errors when sending this message, see
// https://aka.ms/BotTrustServiceUrl
await turnContext.sendActivity(balance.data.message);
});
}
}
};
module.exports.retrieveBalance = retrieveBalance;
Bot.js:
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
// Import required Bot Framework classes.
const { ActionTypes, ActivityHandler, CardFactory, TurnContext } = require('botbuilder');
const balance = require('../index.js');
// Welcomed User property name
const CONVERSATION_DATA_PROPERTY = 'conversationData';
class WelcomeBot extends ActivityHandler {
/**
*
* #param {UserState} //state to persist boolean flag to indicate
* if the bot had already welcomed the user
*/
constructor(userState, conversationReferences, conversationState) {
super();
// Creates a new user property accessor.
// See https://aka.ms/about-bot-state-accessors to learn more about the bot state and state accessors.
this.userState = userState;
this.conversationReferences = conversationReferences;
this.conversationDataAccessor = conversationState.createProperty(CONVERSATION_DATA_PROPERTY);
// The state management objects for the conversation and user state.
this.conversationState = conversationState;
this.onConversationUpdate(async (context, next) => {
this.addConversationReference(context.activity);
await next();
});
this.onMessage(async (context, next) => {
this.addConversationReference(context.activity);
// Add message details to the conversation data.
// Display state data.
// Read UserState. If the 'DidBotWelcomedUser' does not exist (first time ever for a user)
// set the default to false.
// const didBotWelcomedUser = await this.welcomedUserProperty.get(context, false);
// Your bot should proactively send a welcome message to a personal chat the first time
// (and only the first time) a user initiates a personal chat with your bot.
// if (didBotWelcomedUser === false) {
// // The channel should send the user name in the 'From' object
// await context.sendActivity( 'This bot will send you your paystack balance every 5 minutes. To agree reply \'ok\'');
// // Set the flag indicating the bot handled the user's first message.
// await this.welcomedUserProperty.set(context, true);
// }
// This example uses an exact match on user's input utterance.
// Consider using LUIS or QnA for Natural Language Processing.
var text = context.activity.text.toLowerCase();
var myint;
switch (text) {
case 'ok':
myint = setInterval(async () => {
await balance.retrieveBalance();
}, 1000);
// eslint-disable-next-line template-curly-spacing
break;
case 'balance':
await balance.retrieveBalance();
break;
case 'no':
clearInterval(myint);
break;
case 'hello':
case 'hi':
await context.sendActivity(`You said "${ context.activity.text }"`);
break;
case 'info':
case 'help':
await this.sendIntroCard(context);
break;
default:
await context.sendActivity('This is a simple notification bot. ');
}
// By calling next() you ensure that the next BotHandler is run.
await next();
});
// Sends welcome messages to conversation members when they join the conversation.
// Messages are only sent to conversation members who aren't the bot.
this.onMembersAdded(async (context, next) => {
// Iterate over all new members added to the conversation
for (const idx in context.activity.membersAdded) {
// Greet anyone that was not the target (recipient) of this message.
// Since the bot is the recipient for events from the channel,
// context.activity.membersAdded === context.activity.recipient.Id indicates the
// bot was added to the conversation, and the opposite indicates this is a user.
if (context.activity.membersAdded[idx].id !== context.activity.recipient.id) {
await context.sendActivity('Welcome to the Nag Bot. If you want to get nagged with your balance type \'ok\', if you want your balance alone type \'balance. for info type \'info\'');
}
}
// By calling next() you ensure that the next BotHandler is run.
await next();
});
}
/**
* Override the ActivityHandler.run() method to save state changes after the bot logic completes.
*/
async run(context) {
await super.run(context);
// Save state changes
await this.userState.saveChanges(context);
await this.conversationState.saveChanges(context, false);
}
addConversationReference(activity) {
const conversationReference = TurnContext.getConversationReference(activity);
this.conversationReferences[conversationReference.conversation.id] = conversationReference;
}
async sendIntroCard(context) {
const card = CardFactory.heroCard(
'Welcome to Nag Bot balance checker!',
'Welcome to Paystack Nagbot.',
['https://aka.ms/bf-welcome-card-image'],
[
{
type: ActionTypes.OpenUrl,
title: 'Open your dashboard',
value: 'https://dashboard.paystack.com'
},
{
type: ActionTypes.OpenUrl,
title: 'Ask a question on twitter',
value: 'https://twitter.com/paystack'
},
{
type: ActionTypes.OpenUrl,
title: 'View docs',
value: 'https://developers.paystack.co/reference'
}
]
);
await context.sendActivity({ attachments: [card] });
}
}
module.exports.WelcomeBot = WelcomeBot;

How to create tables in firebase with api.ai

I have a question for dialogflow. I want to know if it's possible to have the agent create new fields or tables in the firebase database (firestore or realtime) All the code I find is about changing the values ​​of a table and not creating them.
I do not know where to start, I've done integrations with the server and everything is working.
function writeToDb (agent) {
const databaseEntry = agent.parameters.databaseEntry;
const dialogflowAgentRef = db.collection('dialogflow').doc('agent');
return db.runTransaction(t => {
t.set(dialogflowAgentRef, {entry: databaseEntry});
return Promise.resolve('Write complete');
I need a explication to create new tables or fields by the agent
Google's Dialogflow Firestore sample on Github demonstrates how to connect Dialogflow to the Firestore database.
Check out the writeToDb() function below, and remember to require the same dependencies:
'use strict';
const functions = require('firebase-functions');
const admin = require('firebase-admin');
const {WebhookClient} = require('dialogflow-fulfillment');
process.env.DEBUG = 'dialogflow:*'; // enables lib debugging statements
admin.initializeApp(functions.config().firebase);
const db = admin.firestore();
exports.dialogflowFirebaseFulfillment = functions.https.onRequest((request, response) => {
const agent = new WebhookClient({ request, response });
function writeToDb (agent) {
// Get parameter from Dialogflow with the string to add to the database
const databaseEntry = agent.parameters.databaseEntry;
// Get the database collection 'dialogflow' and document 'agent' and store
// the document {entry: "<value of database entry>"} in the 'agent' document
const dialogflowAgentRef = db.collection('dialogflow').doc('agent');
return db.runTransaction(t => {
t.set(dialogflowAgentRef, {entry: databaseEntry});
return Promise.resolve('Write complete');
}).then(doc => {
agent.add(`Wrote "${databaseEntry}" to the Firestore database.`);
}).catch(err => {
console.log(`Error writing to Firestore: ${err}`);
agent.add(`Failed to write "${databaseEntry}" to the Firestore database.`);
});
}
function readFromDb (agent) {
// Get the database collection 'dialogflow' and document 'agent'
const dialogflowAgentDoc = db.collection('dialogflow').doc('agent');
// Get the value of 'entry' in the document and send it to the user
return dialogflowAgentDoc.get()
.then(doc => {
if (!doc.exists) {
agent.add('No data found in the database!');
} else {
agent.add(doc.data().entry);
}
return Promise.resolve('Read complete');
}).catch(() => {
agent.add('Error reading entry from the Firestore database.');
agent.add('Please add a entry to the database first by saying, "Write <your phrase> to the database"');
});
}
// Map from Dialogflow intent names to functions to be run when the intent is matched
let intentMap = new Map();
intentMap.set('ReadFromFirestore', readFromDb);
intentMap.set('WriteToFirestore', writeToDb);
agent.handleRequest(intentMap);
});

How to connect Dialogflow to Cloud Firestore via the Inline Editor in Dialogflow?

I have a Cloud Firestore database that stores the number of inhabitants of all cities in England in 2017.
Then I have a Dialogflow. Whenever I tell the name of a city to Dialogflow, I want it to get the number of inhabitants in that city from Firestore and return it to Dialogflow.
Specifically, I want to implement this via the Inline Editor.
Question: What lines of code do I need to add to the code below in order to make this happen?
So here is the code that I write in the Inline Editor in Dialogflow > Fulfillment > index.js:
'use strict';
const functions = require('firebase-functions');
const firebaseAdmin = require('firebase-admin');
const {WebhookClient} = require('dialogflow-fulfillment');
const {Card, Suggestion} = require('dialogflow-fulfillment');
const App = require('actions-on-google').DialogflowApp;
process.env.DEBUG = 'dialogflow:debug'; // enables lib debugging statements
exports.dialogflowFirebaseFulfillment = functions.https.onRequest((request, response) => {
const agent = new WebhookClient({ request, response });
console.log('Dialogflow Request headers: ' + JSON.stringify(request.headers));
console.log('Dialogflow Request body: ' + JSON.stringify(request.body));
function welcome(agent) {
agent.add(`Hello and welcome!`);
}
function fallback(agent) {
agent.add(`I didn't understand`);
agent.add(`I'm sorry, can you try again?`);
}
let intentMap = new Map();
intentMap.set('Default Welcome Intent', welcome);
intentMap.set('Default Fallback Intent', fallback);
agent.handleRequest(intentMap);
});
Here is some sample code showing how to connect Firebase's Firestore database to Dialogflow fulfillment hosting on Firebase functions:
'use strict';
const functions = require('firebase-functions');
const admin = require('firebase-admin');
const {WebhookClient} = require('dialogflow-fulfillment');
process.env.DEBUG = 'dialogflow:*'; // enables lib debugging statements
admin.initializeApp(functions.config().firebase);
const db = admin.firestore();
exports.dialogflowFirebaseFulfillment = functions.https.onRequest((request, response) => {
const agent = new WebhookClient({ request, response });
function writeToDb (agent) {
// Get parameter from Dialogflow with the string to add to the database
const databaseEntry = agent.parameters.databaseEntry;
// Get the database collection 'dialogflow' and document 'agent' and store
// the document {entry: "<value of database entry>"} in the 'agent' document
const dialogflowAgentRef = db.collection('dialogflow').doc('agent');
return db.runTransaction(t => {
t.set(dialogflowAgentRef, {entry: databaseEntry});
return Promise.resolve('Write complete');
}).then(doc => {
agent.add(`Wrote "${databaseEntry}" to the Firestore database.`);
}).catch(err => {
console.log(`Error writing to Firestore: ${err}`);
agent.add(`Failed to write "${databaseEntry}" to the Firestore database.`);
});
}
function readFromDb (agent) {
// Get the database collection 'dialogflow' and document 'agent'
const dialogflowAgentDoc = db.collection('dialogflow').doc('agent');
// Get the value of 'entry' in the document and send it to the user
return dialogflowAgentDoc.get()
.then(doc => {
if (!doc.exists) {
agent.add('No data found in the database!');
} else {
agent.add(doc.data().entry);
}
return Promise.resolve('Read complete');
}).catch(() => {
agent.add('Error reading entry from the Firestore database.');
agent.add('Please add a entry to the database first by saying, "Write <your phrase> to the database"');
});
}
// Map from Dialogflow intent names to functions to be run when the intent is matched
let intentMap = new Map();
intentMap.set('ReadFromFirestore', readFromDb);
intentMap.set('WriteToFirestore', writeToDb);
agent.handleRequest(intentMap);
});
This came from Dialogflow's Firestore sample located here: https://github.com/dialogflow/fulfillment-firestore-nodejs

How can I properly prompt a user without having LUIS attempt to recognize the response?

I have a bot configured with a LUIS intent recognizer and although the initial root dialog starts as expected when LUIS recognizes the intent, if I prompt a user from within that dialog, the response the user sends back does not resume the suspended dialog but instead is sent back through the LUIS recognizer and begins a new dialog entirely.
This is my bot setup:
const connector = new builder.ChatConnector({
appId: config.get('bot.id'),
appPassword: config.get('bot.secret')
})
super(connector, (session) => this.help(session))
this.set('storage', new BotStorage())
this.use(builder.Middleware.sendTyping(), new MessageSanitizer())
this.on('conversationUpdate', (message: any) => {
console.log(message)
})
const recognizer = new builder.LuisRecognizer(config.get('bot.model'))
this.recognizer(recognizer)
My dialog setup:
this.dialog('/send', [(session, context, next) => {
const amount = builder.EntityRecognizer.findEntity(context.intent.entities, 'builtin.currency')
const recipient = builder.EntityRecognizer.findEntity(context.intent.entities, 'recipient')
const product = builder.EntityRecognizer.findEntity(context.intent.entities, 'product')
session.send('Sure, I can do that.')
session.beginDialog('/send/product', product ? product.entity : null)
}]).triggerAction({
matches: 'send'
})
this.dialog('/send/product', [(session, query, next) => {
if (query) {
session.dialogData.productQuery = query
next()
} else {
builder.Prompts.text(session, 'What type of product did you want to send?')
}
}, (session, results) => {
if (results && results.response) {
session.dialogData.productQuery = results.response
}
session.sendTyping()
ProductService.search(session.dialogData.productQuery).then(products => {
if (!products.length) {
session.send('Sorry, I couldn\'t find any products by that name.')
session.replaceDialog('/send/product')
}
const attachments = products.map(product => {
const image = builder.CardImage.create(session, product.configuration.image)
const valueLine = `$${product.value.min} - $${product.value.max}`
const card = new builder.HeroCard(session)
.title(product.name)
.images([image])
.text(product.description)
.subtitle(valueLine)
.tap(builder.CardAction.postBack(session, product.id))
return card
})
const message = new builder.Message(session)
.text('Okay, I found the following products. Please select the one you\'d like to send.')
.attachments(attachments)
.attachmentLayout(builder.AttachmentLayout.carousel)
builder.Prompts.text(session, message)
}).catch((err: Error) => {
session.error(err)
})
}, (session, response, next) => {
console.log(response)
}])
At the suggestion of a reply below, I also tried to set up the recognizer as part of an IntentDialog rather than on the bot itself and then map the subsequent dialogs to that root dialog like so:
const recognizer = new builder.LuisRecognizer(config.get('bot.model'))
this.intents = new builder.IntentDialog({ recognizers: [recognizer] })
this.intents.matches('send', '/send')
this.dialog('/', this.intents)
this.dialog('/send', [(session, context, next) => {
const amount = builder.EntityRecognizer.findEntity(context.entities, 'builtin.currency')
const recipient = builder.EntityRecognizer.findEntity(context.entities, 'recipient')
const product = builder.EntityRecognizer.findEntity(context.entities, 'product')
session.send('Sure, I can do that.')
session.beginDialog('/send/product', product ? product.entity : null)
}, (session) => {
console.log('hello')
}])
However, this did not help, I am still getting the following error:
/ - WARN: IntentDialog - no intent handler found for None
Indicating that the response is attempting to match a new intent rather than resume the suspended dialog.
An example interaction with the bot that illustrates this issue:
The 'typing' indicator will just continue indefinitely because its trying to match the 'None' intent but none exists.
A match in the triggerAction will take precedence as every message received by the bot is sent through the routing system. You can customize it, but you can also try using the IntentDialog to isolate some of your flows from it.
More: Botframework No Interruptions from other Intent Dialogs until done with current Intent Dialog
In the latest SDKs, you can achieve this without using IntentDialog. All you have to do is use .onEnabled on the LuisRecognizer without modifying any of your existing dialogs:
var recognizer = new builder.LuisRecognizer(url)
.onEnabled(function (context, callback) {
// LUIS is only ON when there are no tasks pending(e.g. Prompt text)
var enabled = context.dialogStack().length === 0;
callback(null, enabled);
});

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