I wrote a custom recognizer.
bot.recognizer({
recognize: function (context, done) {
var intent = { score: 1.0, intent: 'Greetings'};
if (context.message.text.toLowerCase() == 'hello' ) {
done(null, intent);
}
}
});
var bot = new builder.UniversalBot(connector, (session) => {
session.send("Sorry couldn't understand");
});
// here is the dialog
bot.dialog('Greetings', [(session, args, next) => {
sesson.send("hey there");
}]).triggerAction({
matches: 'Greetings',
onInterrupted: function (session) {
session.send('hey there');
}
});
When I type "Hello" in the emulator it replies with hey there. It works.
But when I try using Luis API it does not work. It replies "Sorry couldn't understand".
const model = "https://westus.api.cognitive.microsoft.com/luis/v2.0/apps/**?subscription-key=***&verbose=true";
var recognizer = new builder.LuisRecognizer(model);
bot.recognizer(recognizer)
I try the following in the terminal (node>) it does not work.
here is the doc I followed
var builder = require('botbuilder');
var model = "https://westus.api.cognitive.microsoft.com/luis/v2.0/apps/***?subscription-key=***&verbose=true&timezoneOffset=330&spellCheck=false";
var recognizer = new builder.LuisRecognizer(model);
recognizer.recognize(
"hello",
model,
function (err, intents, entities) {
console.log(intents);
}
)
The Luis Model url works perfectly fine, returns proper intents, tested it in the browser.
How to debug?
import globalTunnel from 'global-tunnel';
process.env.http_proxy = 'http://proxy:80';
process.env.https_proxy = 'http://proxy:80';
globalTunnel.initialize();
add globalTunnel.end() after adding the recognizer bot.recognizer(recognizer)
it worked.
Related
Alexa has released CanFulfillIntentRequest feature or Name-free Interaction for custom skills recently. I am trying to implement it in my existing skill which uses alexa-sdk. Please find my code below:
'use strict';
const Alexa = require('alexa-sdk');
var handlers = {
'LaunchRequest': function() {
var speechOutput = "You can ask me to read out quotes from Steve Jobs";
var repromptText = "Sorry I didnt understand";
this.emit(':tell', speechOutput, repromptText);
},
'RandomQuote': function() {
let data = getQuoteFunction();
const author = data[0];
const quote = data[1];
let cardTitle = "Quotation from author";
let cardContent = "Actual quote";
let speechOutput = "Actual quote";
// Speak out the output along with card information
this.emit(':tellWithCard', speechOutput, cardTitle, cardContent);
}
}
exports.handler = function (event, context, callback) {
const alexa = Alexa.handler(event, context, callback);
alexa.registerHandlers(handlers);
alexa.execute();
};
Do we need to add handler for CanFulfillIntentRequest, the way I did for other handlers ? for example:
var handlers = {
'LaunchRequest': function() {
},
'RandomQuote': function() {
},
'CanFulfillIntentRequest': function() {
//code to handle
}
}
Is this feature only available in ASK SDK v2 for Node.js ? Can we implement it in skill developed using alexa-sdk. Could anyone please let me know ?
Thanks
I am using LUIS for azure search and QnA recognizer for the first time together in the same code.
I am using intent.match to match the recognizers.
My problem is:
for the first time, If I ask a question which matches to the QnA intent, it returns the answer from the QnA base.
followed by the questions which are matching with the azuresearch intents. Azure search also deliver the result.
But if I repeat the questions which must be matched to the QnA, it says "no intent handler found for null"
var util = require('util');
var _ = require('lodash');
var builder = require('botbuilder');
var restify = require('restify');
var cognitiveservices = require('botbuilder-cognitiveservices');
/// <reference path="../SearchDialogLibrary/index.d.ts" />
var SearchLibrary = require('../SearchDialogLibrary');
var AzureSearch = require('../SearchProviders/azure-search');
var qintent;
// Setup Restify Server
var server = restify.createServer();
server.listen(process.env.port || process.env.PORT || 3978, function () {
console.log('%s listening to %s', server.name, server.url);
});
// Create chat bot and listen for messages
var connector = new builder.ChatConnector({
appId: process.env.MICROSOFT_APP_ID,
appPassword: process.env.MICROSOFT_APP_PASSWORD
});
server.post('/api/messages', connector.listen());
// Bot Storage: Here we register the state storage for your bot.
// Default store: volatile in-memory store - Only for prototyping!
// We provide adapters for Azure Table, CosmosDb, SQL Azure, or you can implement your own!
// For samples and documentation, see: https://github.com/Microsoft/BotBuilder-Azure
var inMemoryStorage = new builder.MemoryBotStorage();
var qnarecognizer = new cognitiveservices.QnAMakerRecognizer({
knowledgeBaseId: '6b30ac2a-5ce9-4576-a1cb-c89abfb73889',
authKey: 'd457c897-71cf-46bf-a3d9-9a1f51246fad',
endpointHostName: 'https://qnadispatchwg.azurewebsites.net/qnamaker'
});
const LuisModelUrl = 'https://westeurope.api.cognitive.microsoft.com/luis/v2.0/apps/180a9aaa-9d67-4d40-b3d3-121917f4dbb8?subscription-key=39155bb750dc4b2abd84d410d80fce21&timezoneOffset=0&q=';
var recognizer = new builder.LuisRecognizer(LuisModelUrl);
// Bot with main dialog that triggers search and display its results
var bot = new builder.UniversalBot(connector);
bot.set('storage', inMemoryStorage);
var intents = new builder.IntentDialog({ recognizers: [recognizer, qnarecognizer] });
bot.dialog('/', intents);
intents.matches('Greeting', (session) => {
session.send('You reached the Greeting intent. You said \'%s\'.', session.message.text);
session.endDialog();
});
intents.matches('qna', [
function (session, args) {
console.log("my args in qna:=========================================================================================== %o", args);
var answerEntity = builder.EntityRecognizer.findEntity(args.entities, 'answer');
session.send(answerEntity.entity);
}
]);
intents.matches('Search.Aco', [
function (session, args, next) {
console.log("my args in SearchDialog:=========================================================================================== %o", args);
var intent = args.intent;
console.log("our intent is " + intent);
var entities = builder.EntityRecognizer.findEntity(args.entities, 'businessterm');
console.log("recognnized entitiy is: "+ entities.entity);
// qintent = title;
//session.send('You reached the Search.Aco intent. You enquire for the entitiy \'%s\'.', qintent);
//console.log(" SearchDialog: before updating the session.message.text------------->" + session.message.text);
session.message.text= entities.entity;
//console.log(" SearchDialog: after updating the session.message.text------------->" + session.message.text);
SearchLibrary.begin(session);
},
function (session, args, results) {
// Process selected search results
session.send(
'Done! For future reference, you selected these properties: %s',
args.selection.map(function (i) { return i.key; }).join(', '));
}
]);
var azureSearchClient = AzureSearch.create('aco-intel2', '4105C6676D0CDD9B2E7891952B9E9E00', 'azureblob-index');
var jobsResultsMapper = SearchLibrary.defaultResultsMapper(jobToSearchHit);
// Register Search Dialogs Library with bot
bot.library(SearchLibrary.create({
multipleSelection: true,
search: function (query) { return azureSearchClient.search(query).then(jobsResultsMapper); },
refiners: ['people', 'content', 'location']
}));
// Maps the AzureSearch Job Document into a SearchHit that the Search Library can use
function jobToSearchHit(acosearch) {
console.log("inside jobToSearchHit");
console.log("inside acosearch.DocUrl" + acosearch.DocUrl + "-------" + acosearch.metadata_storage_name);
return {
key: acosearch.id,
title: acosearch.metadata_storage_name,
description: acosearch.content.substring(0, 100)+"...",
documenturl:acosearch.DocUrl,
imageUrl: acosearch.imageurl
};
}
module.exports = { qintent: "qintent"};
for the second time if I am mixing the
This is how it is showing:
Emulator output
can you please help me to understand what is the difference between the intent.match and dialog' matches. I tried the following way too but it does not recognize the answering entity. How can be QnA be called from the following code?
var util = require('util');
var _ = require('lodash');
var builder = require('botbuilder');
var restify = require('restify');
var cognitiveservices = require('botbuilder-cognitiveservices');
/// <reference path="../SearchDialogLibrary/index.d.ts" />
var SearchLibrary = require('../SearchDialogLibrary');
var AzureSearch = require('../SearchProviders/azure-search');
var qintent;
// Setup Restify Server
var server = restify.createServer();
server.listen(process.env.port || process.env.PORT || 3978, function () {
console.log('%s listening to %s', server.name, server.url);
});
// Create chat bot and listen for messages
var connector = new builder.ChatConnector({
appId: process.env.MICROSOFT_APP_ID,
appPassword: process.env.MICROSOFT_APP_PASSWORD
});
server.post('/api/messages', connector.listen());
// Bot Storage: Here we register the state storage for your bot.
// Default store: volatile in-memory store - Only for prototyping!
// We provide adapters for Azure Table, CosmosDb, SQL Azure, or you can implement your own!
// For samples and documentation, see: https://github.com/Microsoft/BotBuilder-Azure
var inMemoryStorage = new builder.MemoryBotStorage();
var qnarecognizer = new cognitiveservices.QnAMakerRecognizer({
knowledgeBaseId: '6b30ac2a-5ce9-4576-a1cb-c89abfb73889',
authKey: 'd457c897-71cf-46bf-a3d9-9a1f51246fad',
endpointHostName: 'https://qnadispatchwg.azurewebsites.net/qnamaker'
});
const LuisModelUrl = 'https://westeurope.api.cognitive.microsoft.com/luis/v2.0/apps/180a9aaa-9d67-4d40-b3d3-121917f4dbb8?subscription-key=39155bb750dc4b2abd84d410d80fce21&timezoneOffset=0&q=';
var recognizer = new builder.LuisRecognizer(LuisModelUrl);
// Bot with main dialog that triggers search and display its results
var bot = new builder.UniversalBot(connector, function (session, args) {
session.send('You reached the default message handler. You said \'%s\'.', session.message.text);
}).set('storage', inMemoryStorage);
bot.recognizer(recognizer, qnarecognizer);
bot.dialog('GreetingDialog',
(session) => {
session.send('You reached the Greeting intent. You said \'%s\'.', session.message.text);
session.endDialog();
}
).triggerAction({
matches: 'Greeting'
})
bot.dialog('HelpDialog',
(session) => {
session.send('You reached the Help intent. You said \'%s\'.', session.message.text);
session.endDialog();
}
).triggerAction({
matches: 'Help'
})
bot.dialog('CancelDialog',
(session) => {
session.send('You reached the Cancel intent. You said \'%s\'.', session.message.text);
session.endDialog();
}
).triggerAction({
matches: 'Cancel'
})
bot.dialog('QnADialog',[
function (session, args, next) {
var answerEntity = builder.EntityRecognizer.findEntity(args.entities, 'answer');
console.log(answerEntity);
session.send(answerEntity.entity);
}
]).triggerAction({
matches: 'Search.QnA'
})
bot.dialog('SearchDialog', [
function (session, args) {
console.log("my args in SearchDialog:=========================================================================================== %o", args);
var intent = args.intent;
title = builder.EntityRecognizer.findEntity(intent.entities, 'businessterm');
console.log(title.entity);
qintent = title.entity;
//session.send('You reached the Search.Aco intent. You enquire for the entitiy \'%s\'.', qintent);
//console.log(" SearchDialog: before updating the session.message.text------------->" + session.message.text);
session.message.text= qintent;
//console.log(" SearchDialog: after updating the session.message.text------------->" + session.message.text);
SearchLibrary.begin(session);
},
function (session, args, results) {
// Process selected search results
session.send(
'Done! For future reference, you selected these properties: %s',
args.selection.map(function (i) { return i.key; }).join(', '));
}
]).triggerAction({
matches: 'Search.Aco'
});
var azureSearchClient = AzureSearch.create('aco-intel2', '4105C6676D0CDD9B2E7891952B9E9E00', 'azureblob-index');
var jobsResultsMapper = SearchLibrary.defaultResultsMapper(jobToSearchHit);
// Register Search Dialogs Library with bot
bot.library(SearchLibrary.create({
multipleSelection: true,
search: function (query) { return azureSearchClient.search(query).then(jobsResultsMapper); },
refiners: ['people', 'content', 'location']
}));
// Maps the AzureSearch Job Document into a SearchHit that the Search Library can use
function jobToSearchHit(acosearch) {
console.log("inside jobToSearchHit");
console.log("inside acosearch.DocUrl" + acosearch.DocUrl + "-------" + acosearch.metadata_storage_name);
return {
key: acosearch.id,
title: acosearch.metadata_storage_name,
description: acosearch.content.substring(0, 100)+"...",
documenturl:acosearch.DocUrl,
imageUrl: acosearch.imageurl
};
}
module.exports = { qintent: "qintent"};
This code gives me traces like following:
logs
Please help me understand whats wrong in the above codes. Would be a great help.
Also, the difference between the intent.match and dialogs match. As per my understanding, bot recognizes the session.message and match it with the 'match:' argurments and call the dialogs. so it can jump to and fro between dialogs.
in the first case, its strange for me because it doesnot do it second time.
Thanks in Advance,
Vivek
Using the IntentDialog class (e.g. var intents = new builder.IntentDialog( ... ) does not allow for intent interruption in the way that registering the dialog directly to the bot does.
Given the following example dialogs:
const bot = new UniversalBot(connector); // Assume an already created ChatConnector
bot.recognizer(new LuisRecognizer(LuisModelUrl); // LUIS model with HowAreYou and Weather intents.
bot.dialog('HowAreYou',[
function (session, args, next) {
builder.Prompts.text(session, `I\'m doing great! How are you?`);
},
function (session, args) {
var result = session.message.text;
session.send(`I\'m glad to hear you\'re doing ${result}!`);
]).triggerAction({
matches: 'HowAreYou'
});
bot.dialog('Weather', [
function (session, args) {
builder.Prompts.text(session `Which city do you want to find the weather forecast for?`);
},
function (session, args) {
// some logic to handle the response and call a weather api
}
]).triggerAction({
matches: 'Weather'
});
When a user asks "How are you?" the 'HowAreYou' intent is detected by LUIS and the bot begins the corresponding dialog. The bot then prompts the user "I'm doing great! How are you?'. If the user then says to the bot, "Show me the weather", this bot will receive that message and detect the "Weather" intent. After doing so, it will add the "Weather" dialog to the top of the dialog stack and begin the Weather dialog.
The UniversalBot doesn't prevent you from switching dialogs when a new intent is detected. In the code for the intent dialog, it continues any dialog you were already in and doesn't support switching to a new intent/dialog.
I am making a chat bot using the QnA template (Microsoft Azure). Basically, a user asks a question and the bot will try and find the answer in an FAQ document. If it fails, I want it to run a Bing search with the user's query and replies with the most accurate answer. I found this example that uses Bing Web Search API: https://learn.microsoft.com/en-us/azure/cognitive-services/bing-web-search/quickstarts/nodejs. For now, I just want the bot to reply with the first link of the search for example.
However, I don't know how to merge the code in the link, with the generated code for the QnA Bot (in Node.js):
var restify = require('restify');
var builder = require('botbuilder');
var botbuilder_azure = require("botbuilder-azure");
var builder_cognitiveservices = require("botbuilder-cognitiveservices");
var request = require('request');
// Setup Restify Server
var server = restify.createServer();
server.listen(process.env.port || process.env.PORT || 3978, function () {
console.log('%s listening to %s', server.name, server.url);
});
// Create chat connector for communicating with the Bot Framework Service
var connector = new builder.ChatConnector({
appId: process.env.MicrosoftAppId,
appPassword: process.env.MicrosoftAppPassword,
openIdMetadata: process.env.BotOpenIdMetadata
});
// Listen for messages from users
server.post('/api/messages', connector.listen());
var tableName = 'botdata';
var azureTableClient = new botbuilder_azure.AzureTableClient(tableName, process.env['AzureWebJobsStorage']);
var tableStorage = new botbuilder_azure.AzureBotStorage({ gzipData: false }, azureTableClient);
// Create your bot with a function to receive messages from the user
var bot = new builder.UniversalBot(connector);
bot.set('storage', tableStorage);
// Recognizer and and Dialog for GA QnAMaker service
var recognizer = new builder_cognitiveservices.QnAMakerRecognizer({
knowledgeBaseId: process.env.QnAKnowledgebaseId,
authKey: process.env.QnAAuthKey || process.env.QnASubscriptionKey, // Backward compatibility with QnAMaker (Preview)
endpointHostName: process.env.QnAEndpointHostName
});
var basicQnAMakerDialog = new builder_cognitiveservices.QnAMakerDialog({
recognizers: [recognizer],
defaultMessage: 'Sorry, I cannot find anything on that topic',
qnaThreshold: 0.3
});
// Override the invokeAnswer function from QnAMakerDialog
builder_cognitiveservices.QnAMakerDialog.prototype.invokeAnswer = function (session, recognizeResult, threshold, noMatchMessage) {
var qnaMakerResult = recognizeResult;
session.privateConversationData.qnaFeedbackUserQuestion = session.message.text;
if (qnaMakerResult.score >= threshold && qnaMakerResult.answers.length > 0) {
if (this.isConfidentAnswer(qnaMakerResult) || this.qnaMakerTools == null) {
this.respondFromQnAMakerResult(session, qnaMakerResult);
this.defaultWaitNextMessage(session, qnaMakerResult);
}
else {
this.qnaFeedbackStep(session, qnaMakerResult);
}
}
else {
this.noMatch(session, noMatchMessage, qnaMakerResult);
}
};
// API call to Bing
basicQnAMakerDialog.noMatch = function (session, noMatchMessage, qnaMakerResult) {
var term = session.message.text;
var key = 'i hid it';
var options = {
url: "https://api.cognitive.microsoft.com/bing/v7.0/search?q=" + term,
method: 'GET',
headers : {
'Ocp-Apim-Subscription-Key' : key
}
};
request(options, function(err,res, body){
if(err){
console.error(err);
session.send(noMatchMessage);
}
body = JSON.parse(body);
session.send("I found a thing: " + body["webPages"]["value"][0]["name"]);
});
};
bot.dialog('basicQnAMakerDialog', basicQnAMakerDialog);
bot.dialog('/', //basicQnAMakerDialog);
[
function (session, results) {
session.replaceDialog('basicQnAMakerDialog');
},
]);
In the function inside the bot.dialog, I think I should add a condition such as: if the bot returns the default message, open a web page and let the "term" to search be the user's last message. However, I don't know how to code this exactly, and where to do so. Also, I don't know how to exit from the replaceDialog function in order to reply with something other than the default message or the answers in the FAQ.
PS: I don't have much experience with javascript or web development in general. Any help will be appreciated :)
What you are implementing involves two steps, extracting out the "noMatch" case to a method so you can make a full response message, and then the API call itself.
The "noMatch" method.
First, you will want to override the invokeAnswer function from QnAMakerDialog (This override changes nothing but adding a call to a separate method for the noMatch case.)
builder_cognitiveservices.QnAMakerDialog.prototype.invokeAnswer = function (session, recognizeResult, threshold, noMatchMessage) {
var qnaMakerResult = recognizeResult;
session.privateConversationData.qnaFeedbackUserQuestion = session.message.text;
if (qnaMakerResult.score >= threshold && qnaMakerResult.answers.length > 0) {
if (this.isConfidentAnswer(qnaMakerResult) || this.qnaMakerTools == null) {
this.respondFromQnAMakerResult(session, qnaMakerResult);
this.defaultWaitNextMessage(session, qnaMakerResult);
}
else {
this.qnaFeedbackStep(session, qnaMakerResult);
}
}
else {
this.noMatch(session, noMatchMessage, qnaMakerResult);
}
};
After this you will define your basicQnAMakerDialog as usual, but consider making the default message field something for an error case or "no results found". (This doesn't really matter, as you can arbitrarily decide what you send back within the noMatch method, but it's nice to have the default there)
var basicQnAMakerDialog = new builder_cognitiveservices.QnAMakerDialog({
recognizers: [recognizer],
defaultMessage: 'Sorry, I can't find anything on that topic'
qnaThreshold: 0.3
});
After this you can define the noMatch method seperately.
basicQnAMakerDialog.noMatch = function (session, noMatchMessage, qnaMakerResult) {
session.send(noMatchMessage); //Contains the default message
this.defaultWaitNextMessage(session, qnaMakerResult);
}
Within this method we can call the API or do basically whatever we want.
Credit to gliddell on this override
The API call.
Within the noMatch method, we will make an API call to Bing
basicQnAMakerDialog.noMatch = function (session, noMatchMessage, qnaMakerResult) {
var term = session.message.text;
var key = <Environment Var containing the Bing key>;
var options = {
url: "https://api.cognitive.microsoft.com/bing/v7.0/search?q=" + term,
method: 'GET',
headers : {
'Ocp-Apim-Subscription-Key' : key
}
};
request(options, function(err,res, body){
if(err){
console.error(err);
session.send(noMatchMessage);
}
body = JSON.parse(body);
session.send("I found a thing: " + body["webPages"]["value"][0]["name"]);
});
};
You can customize the message you are sending back as much as you like after you get the results from the search.
I am new to Microsoft Bot Framework. Earlier I was using Gupshup to build my bots. Gupshup had designed the workflow in a very nice manner. I had used api.ai NLP engine with Gupshup. I want to switch and try MS Bot Framework now with api.ai.
Below is my Gupshup's code:
function MessageHandler(context, event) {
sendMessageToApiAi({
message : event.message,
sessionId : new Date().getTime() +'api',
nlpToken : "74c04b2c16284c738a8dbcf6bb343f",
callback : function(res){
if(JSON.parse(res).result.parameters.Ent_1=="Hello"){
context.sendResponse("Hello");
}
}
},context);
};
function sendMessageToApiAi(options,botcontext) {
var message = options.message; // Mandatory
var sessionId = options.sessionId || ""; // optinal
var callback = options.callback;
if (!(callback && typeof callback == 'function')) {
return botcontext.sendResponse("ERROR : type of options.callback should be function and its Mandatory");
}
var nlpToken = options.nlpToken;
if (!nlpToken) {
if (!botcontext.simpledb.botleveldata.config || !botcontext.simpledb.botleveldata.config.nlpToken) {
return botcontext.sendResponse("ERROR : token not set. Please set Api.ai Token to options.nlpToken or context.simpledb.botleveldata.config.nlpToken");
} else {
nlpToken = botcontext.simpledb.botleveldata.config.nlpToken;
}
}
var query = '?v=20150910&query='+ encodeURIComponent(message) +'&sessionId='+context.simpledb.roomleveldata.session+'&timezone=Asia/Calcutta&lang=en '
var apiurl = "https://api.api.ai/api/query"+query;
var headers = { "Authorization": "Bearer " + nlpToken};
botcontext.simplehttp.makeGet(apiurl, headers, function(context, event) {
if (event.getresp) {
callback(event.getresp);
} else {
callback({})
}
});
}
I have started off with MS bot Framework and linked with api.ai. Below is my code:
var builder = require('botbuilder');
var restify = require('restify');
var apiairecognizer = require('api-ai-recognizer');
var request = require('request');
//=========================================================
// Bot Setup
//=========================================================
// Setup Restify Server
var server = restify.createServer();
server.listen(process.env.port || process.env.PORT || 3978, function () {
console.log('%s listening to %s', server.name, server.url);
});
// Create chat bot
var connector = new builder.ChatConnector({
appId: "8c9f2d7b-dfa6-4116-ac45-po34eeb1d25c",
appPassword: "7CCO8vBGtdcTr9PoiUVy98tO"
});
server.post('/api/messages', connector.listen());
var bot = new builder.UniversalBot(connector);
var recognizer = new apiairecognizer("74c04b2c16284c738a8dbcf6bb343f");
var intents = new builder.IntentDialog({
recognizers: [recognizer]
});
bot.dialog('/',intents);
intents.matches('Flow_1',function(session, args){
var fulfillment = builder.EntityRecognizer.findEntity(args.entities, 'fulfillment');
if (fulfillment){
var speech = fulfillment.entity;
session.send(speech);
console.log("Inside fulfillment");
}else{
session.send('Sorry...not sure how to respond to that');
}
});
intents.matches('Intro',function(session, args){
var fulfillment = builder.EntityRecognizer.findEntity(args.entities, 'fulfillment');
if (fulfillment){
var speech = fulfillment.entity;
session.send(speech);
}else{
session.send('Sorry...not sure how to respond to that');
}
});
intents.matches('Default Fallback Intent',function(session, args){
var fulfillment = builder.EntityRecognizer.findEntity(args.entities, 'fulfillment');
if (fulfillment){
var speech = fulfillment.entity;
session.send(speech);
}else{
session.send('Sorry...not sure how to respond to that');
}
});
Now here is what I want to achieve:
JSON.parse(res).result.parameters.Ent_1 was a easy of parsing and getting the paramerters. How can I achieve something similar to that in Bot Framework? Do I have to construct a function sendMessageToApiAi() or is there a different way to achieve in MS Bot Framework?
Actually, Gupshup's template doesn't care about the intent which is sending the response. The template just gets the response from the API call and allows you to parse the response as desired.
Now in MSbot framework, if you want to get the value of Ent_1 then you can use the below sample code considering Flow_1 is the intent which will contain the entity Ent_1
intents.matches('Flow_1',function(session, args){
var fulfillment = builder.EntityRecognizer.findEntity(args.entities, 'Ent_1');
if (fulfillment){
var speech = fulfillment.entity;
session.send(speech);
console.log("Inside fulfillment");
}else{
session.send('Sorry...not sure how to respond to that');
}
});
You can also go through this blog which will help.
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);
});