Dialogflow

How to Build easy conversational AI – GCP Dialogflow

Dialogflow is a GCP framework that enables users to develop easy human-computer interaction technologies that can support Natural Language Processing (NLP).

Basically, Dialogflow handles the job of translating natural language to machine-readable data using machine-learning models trained by your examples.

Why use GCP Dialogflow

A few reasons to use Dialogflow are –

1. It can be easily integrated with messaging applications such as Twitter, Skype, Kik, Viber, Facebook Messenger, Telegram, Slack, and Twilio, and also for several voice assistants such as Microsoft Cortana, Amazon Alexa, and Google Assistant.
2. If you want to learn how to build a chatbot, the free version is also available.
3. It uses the concept of NLP (Natural Language Processing).
4. It also offers us with Knowledge base feature, where we can upload the knowledge document through an HTML link or CSV file. And that can be used for end-user conversation.
5. It can integrate with automated phone services also.
6. It supports 20 languages with 14 different platforms for deployment.

Building Blocks of GCP Dialogflow

AGENTS

The agent is basically your entire chatbot application which goes like collecting what users are saying mapping it to intent and performing certain actions on it. Followed by producing the response for the users. This all starts with a trigger event called an utterance.

INTENTS

Let’s take an example to understand what are intents – “Hey Google, what’s the weather like today?”. Here, “what’s the weather like today” is the intent.

Dialogflow basically trains the model with such intents and with many similar ones, then maps the user intent with the right intent. This process is known as intent matching.

The following things are comprised of the basic intent:

1. Training Phrases
2. Action
3. Parameters
4. Responses

Training Phrases – Training phrases mean example phrases for what the end-users can say. If one of these phrases resembles an end-user expression, then the Dialogflow matches the intent. There is no need to define each possible example as the built-in machine learning of Dialogflow expands with other related phrases on your list.

Action – For the agent, we can define an action. At the time, when we match an intent, then Dialogflow gives the actions to the system, and the action can be used to trigger various actions that are already defined in the system.

Parameters – At run time, if we want to collect an intent then as a parameter the Dialogflow gives the value from the expressions of the end-user. Every parameter contains a type known as an entity type, and the entity type exactly dictates how data is retrieved.

Responses –  We can define speech, visual, or text replies to return to the end-user. These are able to give the answer to the end-user and also ask for more information from the end user and can also terminate the conversation.

The below figure shows the basic flow for intent matching and responding to the end user.

ENTITIES

The intents provided by the user have a type known as Entity Type.

When a user wants to extract the entities from the end user expression. Dialogflow offers to do the same in two ways –

1. System Entities
2. Custom Entities

System Entities – The Dialogflow offers us various system entities to match common data. For example, there are various types of system entities for matching email addresses, colors, times, dates, etc. 

Custom Entities – For matching custom data, we can also make our custom entities. For example, we can define a medical test type entity that matches the kinds of medical tests which are available to book with the agent of a medical chatbot.

Conclusions

Dialogflow is a google framework that can be easily used to build conversational AI. It offers us various benefits like easy integration and deployment, easy creation of chatbots, knowledgebase, etc.

Reference

1. https://marutitech.com/build-a-chatbot-using-dialogflow/
2. https://www.javatpoint.com/dialogflow
3. https://cloud.google.com/dialogflow/docs

Original article source at: https://blog.knoldus.com/

#ai #dialogflow 

A Simple Syntax for Defining NLU Model For A Conversational interface

Expando is a translation language for easily defining user utterance examples when building conversational interfaces for Natural Language Understanding services like Dialogflow, LUIS, or the Alexa Skills Kit. This is roughly analagous to the concept of building grammars for speech recognition systems.

What's all this, then?

The following line of Expando:

(is it possible to|can I|how do I) return (something|an item)

...would be expanded by producing the Cartesian product of the phrases in parentheses that are separated by pipes:

is it possible to return something
is it possible to return an item
can I return something
can I return an item
how do I return something
how do I return an item

This encoding makes it much easier to manage multiple user utterance examples when building conversational interfaces.

Using Expando, you can:

• House your intents and entities in version control, simplifying collaboration.
• Use the CLI to automatically update your intents and entities (only supports Dialogflow, at the moment).
• Make use of the expansion syntax to dramatically simplify the encoding of utterance examples.

Installation

This reference implementation of the Expando language is built with Ruby, and packaged as a gem. You can install it with:

$ gem install expando

Getting started

The Expando CLI features an init command for initializing new Expando projects:

$ mkdir support-bot
$ cd support-bot
$ expando init
✓ intents directory created
✓ entities directory created
✓ .expando.rc.yaml file created
✓ circle.yaml file created

This will create intents and entities directories, for housing the utterance examples themselves, as well as some configuration files.

Configure Dialogflow integration

If you'll be using Expando to update the intents and entities of an existing Dialogflow agent, you'll need to copy the client access token and developer access token for the agent to .expando.rc.yaml:

# Dialogflow credentials - add the credentials for your agent below
:client_access_token: REPLACE_WITH_TOKEN
:developer_access_token: REPLACE_WITH_TOKEN

Set up continuous integration

The circle.yaml file can be used to configure CircleCI to enable automatically updating an Dialogflow agent when commits are pushed to your Expando project's repo.

Create intent and entity files

Let's assume we have an agent on Dialogflow named support-bot. If we want to use the Expando syntax for one of this agent's intents named openHours, we'd create a file in the intents directory named openHours.txt.

It's also possible to create expandable entity examples in the same manner. A file named entities/products.txt would match to a products entity on Dialogflow.

Syntax

Phrase combination

Using the above example, we could include the following line of Expando in the file intents/openHours.txt:

(when|what times) are you open

This would be expanded by creating a version of this utterance with each of the phrases enclosed by paretheses and separated by pipes:

when are you open
what times are you open

If multiple sets of phrases are included on the same line, a Cartesian product of each of the phrases will be created. The following line of Expando:

(when|what times) are (you|y'all|you guys) open

...would result in this full list of utterances:

when are you open
when are y'all open
when are you guys open
what times are you open
what times are y'all open
what times are you guys open

Optional phrases

By making the final phrase in a set blank, you can make it optional. The following Expando:

what are your (open| ) hours

...would result in:

what are your open hours
what are your hours

It's also possible to make an entire set of phrases optional:

what are your (open|business| ) hours

...results in:

what are your open hours
what are your business hours
what are your hours

Essentially, you're making the last phrase in the set an empty string.

Referencing Dialogflow developer entities

If you had the following Dialogflow developer entity location in a file location.txt:

home, house
office, business, work

...you could reference that entity using the Dialogflow template mode syntax in an intent getTemp.txt:

(what is|tell me) the temperature at @location:locationName

Expando will mimic Dialogflow's automatic annotation when you run expando update intents and automatically convert the utterances to template mode syntax by inserting randomly selected canonical entity values for each referenced entity:

what is the temperature at home
tell me the temperature at work

Expando will also automatically annotate the above utterances:

what is the temperature at home
                           ‾‾‾‾
                           @location:locationName => entity:    location
                                                     parameter: locationName

If the message "what is the temperature at home" was received by the Dialogflow agent, it would recognize the following:

• intentName: getTemp
• locationName: home

Referencing Dialogflow system entities

You can reference Dialogflow system entities within Expando just as you would any other entity:

I need a ride at @sys.time:pickupTime

Expando will perform the same type of automated expansion that it does for developer entities, automatically inserting example values for the entity:

I need a ride at 2pm
                 ‾‾‾
                 @sys.time:pickupTime => entity:    @sys.time
                                         parameter: pickupTime

Adding Dialogflow text responses

Expando supports adding Dialogflow text responses to your intents. In the responses directory of your project, create a file with the same name as an existing intent, with one response per line (up to a maximum of 10):

responses/canIReturn.txt:

Definitely! We'll gladly help with your return.
Sure thing! I can help you with that.

Upon running expando update intent canIReturn to update the intent, these text responses will be added to the Dialogflow agent for the intent.

All relevant Expando syntax is supported in these files (i.e. everything except entity referencing.

Comments

Starting a line with a # indicates that it is a comment, and should be ignored. The following Expando:

# TODO: need to add more synonyms for good
I'm feeling (good|great|grand)

...results in:

I'm feeling good
I'm feeling great
I'm feeling grand

Metadata

You can store arbitrary metadata on your intents and entities in the form of YAML front-matter:

# ---
# description: Asking about open hours.
# link: http://realtimeboard/app/board/...
# ---

what are your (open|business| ) hours

You can then list this metadata with the command expando list intents:

Updating Dialogflow

In order to update intents or entities on Dialogflow, use the following commands:

$ expando update intents
$ expando update entities

It's also possible to target specific entities or intents for updating:

$ expando update intents openHours

You can also access full help for the Expando CLI:

$ expando --help

..and also help for specific Expando CLI commands:

$ expando update --help

Documentation

Documentation for the source code of the expando gem itself can be viewed here.

Credits

Initial work on Expando was graciously funded by the good folks at vThreat. Expando is brought to you by Voxable, a conversational interface agency in Austin, Texas.

Author: Voxable
Source Code: https://github.com/voxable/expando 
License: MIT license

#ruby #chatbot #dialogflow 

How to Build a Chat Bot with Flask, Pusher Channels and Dialogflow

This is a demo application showing how to build a Chat bot using Flask, Dialogflow and Pusher. You can read the tutorial on how it was built here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

This tutorial uses the following:

• Python 3.6 (You should have python 3.6 or higher version installed)
• Pusher Channels (Create an account here or login here)
• JavaScript (jQuery)
• Dialogflow (Create an account here or login here)
• Ngrok (Download it here if you don't have it already)

Setting up the project

First of all, clone the repository to your local machine:

 $ git clone https://github.com/dongido001/flask_chatbot.git

Next, create your environment keys:

cp .env.example .env

There are couple of keys you need to set in the .env file. The next sections will show you how you can get these keys.

Pusher

Login to your Pusher account, create a new app and then get your API keys.

Next update the following keys in the .env file with the correct keys:

PUSHER_APP_ID=app_id
PUSHER_KEY=key
PUSHER_SECRET=secret
PUSHER_CLUSTER=cluster

Dialogflow

You also need to create a Dialogflow account. Create it here or login if you have an account already.

Next,

• Create a new agent called named Movie-bot
• Create an Intent named movie
• Create an Entity named movie
• Then enble the intent to use webhook
• Finally, on your Dialogflow console page, click on Fulfillment then enable webhook.

Now, get your Dialogflow API key. Head to Google cloud here

Then,

• Select Dialogflow integrations under Service account.
• Then select JSON under key type.
• Next, make sure at the top menu that our bot is selected as the project name - Movie-Bot
• Finally, click on the Create button to download the your API key

Your API key will be downloaded automatically. It's a JSON file.

Next, copy the downloaded file to the root folder of the project - flask_chatbot

Then, update the keys in the .env file with the correct information:

GOOGLE_APPLICATION_CREDENTIALS=*.json
DIALOGFLOW_PROJECT_ID=project_id

Note

• *.json is the name of the JSON file you just copied to the root folder of the project.
• To get your project_id, open the JSON file with your code editor, you will see a field - "project_id". The value is your project_id

OMDb API key

We are using OMDb to fetch details of a movie. So you need to get an API key.

To get a free API access key, head to OMDb. Enter the required details, then submit the form. An email will be sent to you that contains your API key. You also need to activate your account from the email sent to you.

Once you have your key, update the information in the .env file with the correct detail:

OMDB_API_KEY=API_KEY

Running the App

To get the app running:

• From a command line, cd into the project root folder - flask_chatbot
• Creat a virtual environment:

python3 -m venv env

• Activate the virtual environment:

  source env/bin/activate

On windows? Activate it with the below:

  env/Scripts/activate

• Install the dependencies:

pip install -r requirements.txt

• Finally run the app:

 flask run

Congrats! The app should now be running on http://localhost:5000

Built With

• Flask - A microframework for Python
• Pusher - APIs to enable devs building realtime features
• Dialogflow A Google-owned developer of human–computer interaction technologies based on natural language conversations

Download Details:
 

Author: dongido001
Download Link: Download The Source Code
Official Website: https://github.com/dongido001/flask_chatbot 

#flask #dialogflow #python 
 

Integrate Smart Discord Chatbot with Dialogflow for School

smart-school-chatbot

Smart discord chatbot integrated with Dialogflow to interact with students naturally and manage different classes in a school.

Description

As an effort to manage different classrooms, this smart chatbot is created so students can interact with it on Discord naturally and get things done without TAs or instructors' help.

This chatbot is named Bu, a real name of a dog living in CoderSchool.

Here are what Bu can do:

• Bu can check your attendance by greeting it.
• Bu can give important links (material links, zoom link, score dashboard,...)
• Bu can cheers you up when you are sad :((
• Bu can tell you jokes to make you smile :))
• Bu can clear up its own messages.
• Bu can show who is missing in the class or not yet check their attendance.
• Bu can answer common questions about CoderSchool (opening hours, who is CEO, best instructor, TA,...)
• Bu can do small talks to you :))
• Bu can introduce a bit about itself.
• Bu can choose what kind of food you should have for lunch or dinner if you are indecisive.
• Bu can give points to you (Can only be done by TAs/Instructors).

Getting Started

Requirements & Dependencies

• All the library requirements can be installed through requirements.txt.

pip install -r requirements.txt

• Make sure you have your own discord details in .env file.

DISCORD_TOKEN={BOT DISCORD TOKEN}
DISCORD_GUILD={GUILD NAME}
COHORT_METADATA={METADATA SPREADSHEET ID}
BOT_NAME={BOT NAME}
STUDENT_SCORING={STUDENT SCORING SPREADSHEET ID}

• Your Google Service Account credentials in the credentials.json and dialogflow_bot.json.

Workflow

This chatbot is running 24/7 to answer questions from students so I choose to host it for free on replit.com and have uptimerobot.com to ping the server every 5 minute to prevent it from shutting down on replit.

Usage

Here are some real examples during the lectures of the chatbot in action where students just talk to it naturally like to a real TA, where its answers are unique and different everytime:

• Ask what it can do

• Ask who it is

• Ask who is the creator of it

• It can automatically check students' attendance to the system by just greeting to it and it will reply in a funny way

• It can award students who are active in the class

As you can see, the bot will automatically log in students' performances like attendance, activities and more. Students can check out their scores and ranking anytime on the Google Data Studio dashboard.

• It can answer some common questions about the course or the company

• It can show relevant links of the course which students ask for

• It can cheer up students if they are are sad or struggled

• It can tell different jokes

• It can pick a meal for you if you are indecisive what to have for lunch or dinner (A lot of students request for this feature!)

Acknowledgments

Some useful documents are:

• discordpy
• realpython
• dialogflow

Download Details:
Author: TomHuynhSG
Source Code: https://github.com/TomHuynhSG/smart-school-chatbot
License: MIT License

#chatbot #python #dialogflow 

How to Boost up a Helpdesk Chatbot with Dialogflow CX, TFX & Vertex AI

In this article, we’ll illustrate how to formalize a Tensorflow model training in order to run it via TFX on Vertex Pipelines¹. Then we will describe a simple chatbot using Dialogflow CX and we will learn how to integrate the trained model with it in order to provide predictions to users at the moment he/she interacts with the chatbot.

#dialogflow #chatbot #artificial-intelligence #tensorflow 

How to Add Dialogflow Chatbot in Vue.js Apps (Step by Step)

In this post, we will be learning how to add chatbot in Vue.js apps. Vue.js is an open-source JavaScript framework for building user interfaces and single-page applications.

1. Create your Dialogflow chatbot
2. Integrate Dialogflow chatbot with Kommunicate
3.  Integrate chatbot — add chatbot in vue.js apps
4. Installing chatbot in the vue.js component

#vue #vuejs #javascript #chatbot #dialogflow 

How to Integrate Phone Calls to a Dialogflow Chatbot Via Websockets

This tutorial will help you to start with an example Dialogflow bot and interact with it from phone calls using provided sample reference codes using Vonage Voice API.

#chatbot #websocket #dialogflow 

Detailed Instructions on Google Dialogflow 2021

Google Dialogflow is part of the vast Google cloud ecosystem of cloud-based services. It started out as a company called API.ai, specializing in natural-language-understanding and conversational AI. In 2016, Google acquired API.ai to form the backbone of its conversational agent platform. Today, Dialogflow stands out as one of the most well-built platforms for AI solutions and the only platform that you can use to build AI conversational agents, without any coding.
 

#dialogflow 

How to Build, Train & Store A Machine Learning Model with DialogFlow

We will first create a basic Machine Learning (ML) model which will be trained on a dataset. The trained model will be saved using the pickle module. Thereafter, a flask application will utilize the trained model and answer queries like what is the per capita income in a year based on past data.

#chatbot #dialogflow #machine-learning 

How to Create a Chatbot Using Dialogflow

Chatbots are artificially intelligent programs by which we can make our websites more interactive for the internet user, get better information from them, and serve them better with the least human interaction. For example, you have an e-commerce site that has many products, and here, the chatbot will help the user to find what they are really looking for by simulating human behavior.
 

#dialogflow #chatbot 

How to Create a Chatbot for Every Voice Assistant with DialogFlow

In this tutorial, we’ll create a SoulCycle chatbot with DialogFlow, an Natural Language Processing engine, that can be integrated with Google Assistant, Facebook Messenger, Slack, and more. Thank you for watching and happy coding!

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Using APIs with Postman https://youtu.be/0LFKxiATLNQ

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#dialogflow #voice assistant #blondiebytes #chatbot

DialogFlow (API.AI) Google Assistant Action Integration Chatbot Tutorial

In this tutorial, we’ll integrate a DialogFlow chatbot with the Google Assistant. Thank you for watching and happy coding!

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Make a Google Action https://youtu.be/03i5LoO_neU
What is a Framework? https://youtu.be/HXqBlAywTjU
What is a JSON Object? https://youtu.be/nlYiOcMNzyQ
What is an API? https://youtu.be/T74OdSCBJfw
What are API Keys? https://youtu.be/1yFggyk--Zo
Using APIs with Postman https://youtu.be/0LFKxiATLNQ

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https://www.linkedin.com/learning/instructors/kathryn-hodge

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30 Days of Code: https://www.youtube.com/watch?v=K5WxmFfIWbo&index=2&list=PLcLMSci1ZoPs6jV0O3LBJwChjRon3lE1F

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#blondiebytes #chatbot #dialogflow #api.ai #google assistant action

How to Code DialogFlow API.AI Fulfillment Tutorial

Learn how to add fulfillment to your dialog flow agent!

How To Create a DialogFlow Agent: https://youtu.be/yFyNouueu2g

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What is a Framework? https://youtu.be/HXqBlAywTjU
What is a JSON Object? https://youtu.be/nlYiOcMNzyQ
What is an API? https://youtu.be/T74OdSCBJfw
What are API Keys? https://youtu.be/1yFggyk--Zo
Using APIs with Postman https://youtu.be/0LFKxiATLNQ

Check out my courses on LinkedIn Learning!
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#blondiebytes #dialogflow #api.ai

Create FAQ Bot Using Dialogflow Knowledge Connector

Create FAQ Bot Using Dialogflow Knowledge Connector

Bots are the hotshots in the customer support market. Additionally, with the rise of self-service, bots have become more important. In this post, we will learn how to create an FAQ bot. It’s simpler than you imagine.

We will be using Google Dialogflow’s Knowledge Connectors. These are nothing but a set of documents that feed questions to your bot and helps your fetch answers for the same.

While building a bot in Dialogflow, you define intents that reflect the questions your users may ask. What Knowledge Base Connector does is to complement the defined intents by parsing documents such as FAQs and articles to find questions and responses.

• Questions are automatically added as training phrases
• Answers are automatically added as responses

Need for the Dialogflow Knowledge Base Connectors

• With the help of the Knowledge Base Connector, we can set up the bot to answer repetitive questions which will save more time
• This would be more interactive and easy for the users
• It removes the manual process of defining a lot of intents and their answers

Since we have already learned why Knowledge Base Connectors are important. Let’s deep dive into using them to create FAQ bot.

#chatbots #nlp #dialogflow #knowledge-base #kommunicate #faq bot

7 Best DialogFlow and Chatbots Courses to Learn in 2021

Hello guys, do you want to learn how to build chatbots? the darling child of Artificial Intelligence? If yes, then you have come to the right place. Earlier, I have shared the best courses to learn AI and in this course, I am going to share the best chatbots courses for beginners.

If you want to build chatbots but didn’t know where to start? I think joining an online course is a good idea, and if you are looking for some online courses, then you will find some good ones here, but before that, let’s talk about chatbots.

If you are not living under the rocks, you might have seen several applications of chatbots like in your online banking portal or any other websites.

Many companies like Apple, Google, Microsoft, and Amazon are investing millions in building their own AI-powered chatbots like Siri, Google Assistant, Cortena, and Amazon’s Alexa. They all are chatbots, primarily the voice-based chatbots.

Chatbots are nothing but Computer programs which use Artificial Intelligence to answer customers query. They have a huge database of problems and solutions, and they are continually learning and getting better and solving customer’s problems.

They are essential for any business as they not only allow them to provide 24x7 customer support but also it is very scalable. For example, if you have one customer executive for 1000 customers, then you will need 1000 executives for a million customers, but if you use chatbots, you can do all this with just one chatbot.

Based upon one online prediction, by 2021, 85% of customer interactions with the enterprise will be through automated means, which means chatbots and related technologies are going to rule the world.

Now that we are clear with the importance and benefits of chatbots let me tell you one more thing. If you are learning Data Science, Machine Learning, and Artificial Intelligence, building a chatbot is a very engaging and practical activity.

It allows you to use different APIs like NLP in the real world, and that’s why I strongly suggest Machine learning enthusiasts develop at least one chatbot for the learning.

#artificial-intelligence #python #machine-learning #chatbots #dialogflow #7 best dialogflow and chatbots courses to learn in 2021