Building
Chatbots with
Python
Using Natural Language Processing and
Machine Learning
Sumit Raj
Building Chatbots with
Python
Using Natural Language Processing
and Machine Learning
Sumit Raj
Building Chatbots with Python
Sumit Raj
Bangalore, Karnataka, India
ISBN-13 (pbk): 978-1-4842-4095-3
ISBN-13 (electronic): 978-1-4842-4096-0
https://doi.org/10.1007/978-1-4842-4096-0
Library of Congress Control Number: 2018965181
Copyright © 2019 by Sumit Raj
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Printed on acid-free paper
I want to dedicate this book and all the hard work to achieve
this feat to my elder brother, Nikhil Raj, whom I lost this year.
I can’t seem to imagine how proud he would have been seeing
his brother’s book being published today.
I would like to thank my parents Dinanath Prasad and
Shobha Gupta, my brother and sister, relatives, and all my
dearest friends who always supported & encouraged me and
pardoned my absence at times during the write-up of this book.
Table of Contents
About the Author
xi
About the Technical Reviewer
xiii
Acknowledgments
xv
Introduction
xvii
Chapter 1: The Beloved Chatbots
1
Popularity of Chatbots Usage
2
The Zen of Python and Why It Applies to Chatbots?
3
The Need for Chatbots
5
The Business Perspective
5
The Developer’s Perspective
10
Industries Impacted by Chatbots
12
Brief Timeline of Chatbots
13
1950
13
1966
14
1972
14
1981
14
1985
14
1992
14
1995
14
1996
15
2001
15
2006
15
2010
15
2012
15
v
Table OF COntents
2014
15
2015
16
2016
16
2017
16
What Kind of Problems Can I Solve Using Chatbots?
16
Can the Problem be Solved by Simple Question and Answer or Back-and-Forth
Communication?
17
Does It Have Highly Repetitive Issues That Require Either Analyzing or Fetching of Data?
17
Can Your Bot’s Task be Automated and Fixed?
18
A QnA Bot
18
Starting With Chatbots
20
Decision Trees in Chatbots
20
Using Decision Trees in Chatbots
21
How Does a Decision Tree Help?
21
The Best Chatbots/Bot Frameworks
25
Components of a Chatbot and Terminologies Used
26
Intent
27
Entities
27
Utterances
27
Training the Bot
28
Confidence Score
28
Chapter 2: Natural Language Processing for Chatbots
29
Why Do I Need to Know Natural Language Processing to Build a Chatbot?
29
What Is spaCy?
31
Benchmarks Results of spaCy
31
What Does spaCy Provide?
32
Features of spaCy
32
Installation and Prerequisites
33
What Are SpaCy Models?
35
vi
Table OF COntents
Fundamental Methods of NLP for Building Chatbots
37
POS Tagging
37
Stemming and Lemmatization
42
Named-Entity Recognition
44
Stop Words
47
Dependency Parsing
49
Noun Chunks
54
Finding Similarity
55
Good to Know Things in NLP for Chatbots
58
Tokenization
59
Regular Expressions
60
Summary
61
Chapter 3: Building Chatbots the Easy Way
63
Introduction to Dialogflow
63
Getting Started
65
Building a Food-Ordering Chatbot
65
Deciding the Scope
65
Listing Intents
66
Listing Entities
66
Building a Food Ordering Chatbot
66
Getting Started With Dialogflow
67
Points to Remember When Creating Intents
71
Creating Intents and Adding Utterances
72
Adding Default Response to the Intent
72
Item Description Intent and Belonging Entities
73
Understanding and Replying Back to the User
77
Deploying Dialogflow Chatbot on the Web
82
Integrate Dialogflow Chatbot on Facebook Messenger
86
Setting Up Facebook
86
Creating a Facebook App
87
vii
Table OF COntents
Setting Up the Dialogflow Console
88
Configuring Webhooks
90
Testing the Messenger Bot
91
Fulfillment
96
Enabling Webhook
98
Checking the Response
101
Summary
103
Chapter 4: Building Chatbots the Hard Way
105
What Is Rasa NLU?
106
Why Should I Use Rasa NLU?
106
Diving Straight Into Rasa NLU
107
Training and Building a Chatbot From Scratch
109
Building a Horoscope Bot
109
Conversation Script Between the Horoscope Bot and the User
110
Preparing Data for Chatbot
111
Training the Chatbot Model
116
Predicting From the Model
119
Dialog Management Using Rasa Core
121
Understanding More on Rasa Core and Dialog System
122
Understanding Rasa Concepts
125
Creating Domain File for the Chatbot
127
Writing Custom Actions of the Chatbot
130
Data Preparation for Training the Bot
133
Creating Story Data
134
Interactive Learning
136
Exporting Conversations As Stories
150
Testing the Bot
152
Test Case 1
152
Test Case 2
153
Summary
153
viii
Table OF COntents
Chapter 5: Deploying Your Chatbot
155
First Steps
155
Rasa’s Credential Management
155
Deploying the Chatbot on Facebook
157
Creating an App on Heroku
157
Setting Up Heroku on Your Local System
158
Creating and Setting Up an App at Facebook
158
Creating and Deploying Rasa Actions Server App on Heroku
161
Creating Rasa Chatbot API App
163
Creating a Standalone Script for Facebook Messenger Chatbot
164
Verifying the Deployment of Our Dialog Management App on Heroku
167
Integrating Webhook With Facebook
167
Post-Deployment Verification: Facebook Chatbot
169
Deploying the Chatbot on Slack
171
Creating a Standalone Script for Slack Chatbot
171
Editing your Procfile
175
Final Deployment of Slack Bot to Heroku
175
Subscribe to Slack Events
175
Subscribe to Bot Events
176
Post-Deployment Verification: Slack Bot
177
Deploying the Chatbot on Your Own
178
Writing a Script for Your Own Chatbot Channel
179
Writing the Procfile and Deploying to the Web
181
Verifying Your Chatbot APIs
181
Creating the Chatbot UI
183
Summary
187
Index
189
ix
About the Author
Sumit Raj is a techie at heart, who loves coding and building
applications. He is a Python expert with a keen interest in
Machine Learning and Natural Language Processing. He
believes in the idea of writing code that directly impacts the
revenue of the company.
Sumit has worked in multiple domains, such as personal
finance management, real estate, e-commerce, and revenue
analytics, to build multiple scalable applications. He has
helped various early age startups with their initial design
and architecture of the product, which was later funded by investors and governments.
He comes with a good experience of cutting-edge technologies used in high-volume
internet/enterprise applications for scalability, performance tuning, and optimization
and cost-reduction.
He has been mentoring students/developers on Python programming all across the
globe. He has mentored over 1000 students and professionals using various online and
offline platforms and channels on programming languages, data science, and for career
counseling. Sumit likes to be a part of technical meetups, conferences, and workshops.
He never likes to miss a chance to attend hackathons. His love for building applications
and problem solving has won him multiple awards and accolades. He is regularly invited
to speak at premier educational institutes of India. He is also a speaker at PyLadies
meetup group, ladies who code in Python, which is led by one of the former director of
PSF (Python Software Foundation).
In his free time, he likes to write on his blog and answer questions on computer
programming, chatbots, Python/Django, career advice, and web development on Quora,
having over 1 million views together. Feel free to A2A on his Quora profile.
Currently, Sumit is working as Senior Solutions Architect at GeoSpark R&D in
Bangalore, India, building a developer platform for location tracking. You can get to
know more about him from his website (https://sumitraj.in ). Readers can also ask
their questions and discuss at, https://buildingchatbotswithpython.sumitraj.in/ .
xi
About the Technical Reviewer
Nitin Solanki has extensive experience in Natural Language
Processing, Machine Learning, and Artificial Intelligence
Chatbot development. He has developed AI chatbots in
various domains, such as healthcare, e-commerce, education
and law firms, and more. He has experience working
on NLP libraries, data mining, data cleansing, feature
engineering, data analytics and visualization, and machine
learning algorithms. Nitin loves to make things simple and
automated. In his spare time, his mind starts chattering
about ideas to make money. Therefore, he keeps his mind
busy in exploring technologies and in writing codes.
xiii
Acknowledgments
This book is an outcome of most sincere hard work that I have done in my career. Lots of
sleepless nights have gone into the completion of this book. I will be grateful to my father
and mother for my entire life because they made me who am I am today. I want to thank
my brother Nitish and sister Prity for always being there and sharing the high level of
understanding and emotions without being told.
This acknowledgment can’t be completed without thanking the awesome Apress
Team, including Nikhil and Divya, who have been so patient and supportive of me from
the acquisition through the publication of the book. They are the best people to work
with. Special thanks to Matt for all the guidance I needed for my first book and continued
feedback at every step to improve the book. Huge thanks to Nitin for technically
reviewing the book and suggesting the edits.
xv
Introduction
This book has been written with immense care to keep the teachings from this book
very pragmatic and results-oriented. Building chatbots is not just about completing a
tutorial or following a few steps—it’s a skill in itself. This book will certainly not bore you
with lots of text and process to be read; rather, it takes the learning-by-doing approach.
You must have used at least one chatbot to do something in your life by now. Whether
you are a programmer or not, once you go through this book you will find the building
blocks of chatbots, and all the mysteries will be uncovered. Building chatbots may
seem difficult from the outside, but this book makes it so easy for you. Our brain is not
designed to directly process the complex concepts; rather, we learn step-by-step. When
you are reading this book, from the first chapter through the last chapter, you will find
how clearly things are progressing. Although you can directly go to any chapter, I highly
recommend you start from the first chapter, as it is bound to bolster your thoughts.
This book is like a web series where you won’t be able to resist the next chapter after
completing one. Any chatbot that you interact with after going through this book will
create a picture in your mind on how that chatbot is designed and built internally.
Who This Book Is For
This book will serve as a great resource for learning the concepts related to chatbots and
learning how to build them. Those who will find this book useful include:
• Python web developers looking to expand their knowledge or career
into chatbots development
• Students and aspiring programmers wanting to acquire a new skill
set by hands-on experience to showcase something and stand out in
the crowd
• Natural Language enthusiasts looking to learn how to build a chatbot
from scratch
xvii
IntrOdUctiOn
• Budding entrepreneurs with a great idea but not enough technical
feasibility information on how to go about making a chatbot
• Product/Engineering managers planning for a chatbot-related
project
How Do I Approach This Book?
Remember this book is not written like other books are written. This book is written
keeping in mind that once you are done with this book, you can build a chatbot yourself
or teach someone how to build a chatbot. It’s very important to keep a few points in
mind before approaching this book like any other book:
• This book covers almost everything that you need to build a chatbot,
rather than what exists.
• Tis book is about spending more time in doing things on your
system, with this book by your side. Make sure you execute each code
snippet and try to write the code; do not copy and paste.
• Make sure you follow the steps as they appear in the book; don’t
worry if you don’t understand something. You will get to know about
that later in the chapter.
• Use the source code and Jupyter Notebook provided with this book
for your reference.
What Will You Learn in This Book?
Chapter 1: The Beloved Chatbots In this chapter you will get to know about things
related to chatbots from both a business and developer’s perspective. This chapter sets
the tone for getting our hands dirty with chatbots concepts and converting them into
code. Hopefully, you will have a reason why you should definitely build a chatbot for
yourself or your company by the end of this chapter.
Chapter 2: Natural Language Processing for Chatbots In this chapter you will learn
about what tools and methods to use when NLP is needed for chatbots. This chapter
not only teaches you about the method in NLP but also takes real-life examples and
xviii
IntrOdUctiOn
demonstrates with coding examples. This chapter also discusses why a particular NLP
method may be needed in chatbots. Note that NLP in itself is a skill to have.
Chapter 3: Building Chatbots the Easy Way In this chapter you will learn about building
a chatbot in a nice and easy manner using tools like Dialogflow. If you are a non-
programmer, you will surely like it, as it requires little or no programming skills.
Chapter 4: Building Chatbots the Hard Way In this chapter, you will learn about
building chatbots in a manner that people want to build. The title says the hard way, but
once you have completed the previous chapter, you will be wanting more, as this chapter
will teach how to build chatbots in-house from scratch and how to train chatbots using
machine learning algorithms.
Chapter 5: Deploying Your Chatbot This chapter is purely designed to give your chatbot
app a final push. When you have come through the easy and hard way of building a
chatbot, you will surely not want to keep it to yourself. You will learn how to showcase
your chatbots to the world using Facebook and Slack and, finally, integrate them on your
own website.
xix
CHAPTER 1
The Beloved Chatbots
When you begin to build a chatbot, it’s very important to understand what chatbots do
and what they look like.
You must have heard of Siri, IBM Watson, Google Allo, etc. The basic problem
that these bots try to solve is to become an intermediary and help users become more
productive. They do this by allowing the user to worry less about how information will
be retrieved and about the input format that may be needed to attain specific data. Bots
tend to become more and more intelligent as they handle user data input and gain more
insights from it. Chatbots are successful because they give you exactly what you want.
Does it irritate you or frustrate you when you have to enter the same name, e-mail
ID, address, and pincode every time on different websites? Imagine a single bot that
does your tasks—say, ordering food from different vendors, shopping online from
various e-commerce companies, or booking a flight or train tickets—and you don’t have
to provide the same e-mail ID, shipping address, or payment information every time.
The bot has the capability to know this information already and is intelligent enough
to retrieve what is needed when you ask it in your own language or in what is known in
computer science as Natural Language.
Chatbots development is way easier than it was a few years ago, but chatbots did exist
decades ago as well; however, the popularity of chatbots has increased exponentially in
last few years.
If you are a technical person or have some idea of how a web application or mobile
application works, then you must have heard the term APIs. Any kind of data that
you need today is available to be consumed in the form of APIs provided by different
service providers and institutions. If you are looking for weather information, booking
tickets, ordering food, getting flight information, converting one language to another, or
posting on Facebook or Twitter, all of this can be done using APIs. These APIs are used
by web- or mobile-based applications to do these tasks. Chatbots can also use these
APIs to achieve the same tasks based on our requests.
1
© Sumit Raj 2019
S. Raj, Building Chatbots with Python, https://doi.org/10.1007/978-1-4842-4096-0_1
ChaPTEr 1 ThE BElovEd ChaTboTs
The reason Chatbots get an edge over traditional methods of getting things done
online is you can do multiple things with the help of a chatbot. It’s not just a chatbot, it’s
like your virtual personal assistant. You can think of being able to book a hotel room on
booking.com as well as booking a table in a nearby restaurant of the hotel, but you can
do that using your chatbot. Chatbots fulfill the need of being multipurpose and hence
save a lot of time and money.
In this book we are going to learn how to build natural conversational experiences
using bots and how to teach a bot to understand our natural language and make it do
tasks for us from a single interface.
Bots in general are nothing but a machine that is intelligent enough to understand
your request and then formulate your request in such a way that is understandable by
other software systems to request the data you need.
Popularity of Chatbots Usage
Chatbots have become popular just as anything from the recent past. Let’s try looking at
Figure 1-1, which depicts the rise of chatbots, and also try to understand why there is a
huge demand for building chatbots.
Figure 1-1. Numbers on Y-axis represent search interest relative to the highest
point on the chart across all categories worldwide
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The simple answer that comes to mind is that it’s not a complex software and can be
used by anyone. When we build software we target the audience who will be using it,
but when it’s used by anyone else, it becomes difficult and unusable. When we build
chatbots we keep in mind that it will be used by people of all age groups. This happens
only in case of chatbots, where the software tries to behave like a dumb person (but it’s
intelligent) and lets the user be who he or she is. In all other software, you will find that
you should be aware of some terminologies or gradually be aware of how to optimally
make use of it, but that’s not the case with chatbots. If you know how to talk to a person,
you won’t have any issue using a chatbot.
There is a continuous growing demand for chatbots. However, there has not been
much research that has empirically tried finding out the motivations behind using
chatbots. In a recent study, an online questionnaire asked chatbot users ages 16 to 55
years from the US to describe their need for using chatbots in their daily lives. The survey
revealed the “productivity” to be the primary motivational factor for using chatbots.
The Zen of Python and Why It Applies to Chatbots?
I remember the Zen of Python, which says, “Simple is better than Complex,” and that
applies so many places in software.
Te Zen of Python is a collection of 20 software principles that influences
the design of Python Programming Language.
—Tim Peters
Want to know “What is Zen of Python?” Try the below steps.
If you already have Python installed on your computer. Just go to your Python
interpreter and import this:
Python 2.7.15 (default, May
1 2018, 16:44:08)
[GCC 4.2.1 Compatible Apple LLVM 9.1.0 (clang-902.0.39.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import this
The Zen of Python, by Tim Peters
Beautiful is better than ugly.
Explicit is better than implicit.
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ChaPTEr 1 ThE BElovEd ChaTboTs
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one—and preferably only one—obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea—let's do more of those!
You may not able to make sense of all points above relating to chatbots but surely
you can most of them.
Well, coming back to our topic, I remember finding difficulty starting to use
Facebook User Interface while coming from Orkut background. If you have never used
Orkut, you would not understand it, but just try thinking of a situation in your life where
you started using some new software or application and you had a hard time getting the
hang of it. Maybe switching from Windows to MacOS/Linux or vice versa? When you use
a new application, you need to learn a few things, and it takes time to get used to it and
to know what it does and how it works. It does happen at times that you come to know
some features of the application even after years of using it. If you are on MacOS,
try Shift + Option + Volume Up/Down and see what happens. Let me know if it amazed
you, if you didn’t know it already.
In the case of chatbots, the communication between the user and the server or
backend system is pretty simple. It’s just like talking to some other person using a
messaging app.
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ChaPTEr 1 ThE BElovEd ChaTboTs
You just type what you want, and the bot should be able to either give you what
you want or should guide you how to get that. In other words, it should point you to the
correct information by giving you a link or document. The time has come where bots are
able to even dig up the information from an article and document and provide it to the
users.
Significant progress in AI by companies like Google, Facebook, and IBM and by
machine learning services like Amazon Lex, wit.ai, api.ai, luis.ai, IBM Watson, Amazon
Echo, etc. has led to the extraordinary growth and demand of such robots.
The Need for Chatbots
Now, we will try to look at the need and demand of chatbots in this fast-growing
information creation and retrieval age from two different perspectives: the business
standpoint and the developer’s perspective. So, if you are a product manager, sales
manager, or from marketing or any related domain that drives the business directly,
then you should not skip the business perspective of the chatbots. It will give you a clear
picture that businesses today need to adopt this technology to drive more revenue.
The Business Perspective
We will try to look at the business perspective of the chatbots. Is it good for a business to
have a chatbot or to migrate lots of stuff to be done by chatbots?
The time has already come for businesses to treat chatbots as one of the marketing
tools of this generation.
Accessibility: They are easily accessible. The consumer can open the
website and start asking questions or begin resolving their queries
without having to dial a number and follow the ugly way of “Press 1
for this and Press 2 for that” in the IVR. They can quickly get to the
point with just a basic set of information.
Efficiency: Customers can sit at their desk in their office or on a
couch in their living room while watching a game and get their status
of a credit card application, find their food order status, or raise a
complaint about any issue.
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ChaPTEr 1 ThE BElovEd ChaTboTs
If you make customers efficient and productive, they start loving you.
Bots do exactly that and help boost business.
Availability: Chatbots are available 24 hours per day, 7 days per
week. They would never ask you for leaves or get tired like human
employees. They will do the same tasks or new tasks every time with
the same efficiency and performance. You must get frustrated when
some customer care phone number says, “Please call us between 9:00
AM and 6:00 PM,” just for a piece of information. Your bots would
never say this.
Scalability: One Bot => 1 million employees. You see this? Yes, if your
bot can do what a customer needs, it can easily handle hundreds of
thousands of customer queries at the same time without breaking a
sweat. You don’t need to keep your customers waiting in queue until
the customer representative becomes free.
Cost: Needless to say it saves a hell of a lot of cost for the business.
Who doesn’t like to save money? When bots do that for you, there is
no reason why you shouldn’t like them.
Insights: Your sales representative might not be able to remember
the behavior of the user and give you exclusive insight about
the consumer behavioral pattern, but your bots can using latest
techniques of machine learning and data science.
Chatbots Bring Revenue
Chatbots have proven to be successful in bringing more revenue to the business.
Businesses starting with chatbot support or creating a new chatbot to support customer
queries are doing well in the market compared to their competitors.
As per one of the blogposts on stanfy.com, in the first 2 months after introducing
its Facebook chatbot, 1-800-Flowers.com reported that more than 70 percent of its
Messenger orders were from new customers. These new customers were also generally
younger than the company’s typical shopper, as they were already familiar with the
Facebook Messenger app. This significantly increased their annual revenue.
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ChaPTEr 1 ThE BElovEd ChaTboTs
One of the greatest added values of chatbots is using them for generating
prospects. You can reach your potential clients directly where their atten-
tion is (messengers) and present them your newest products, services or
goods. When a customer would like to purchase a product/service, he/she
can make the purchase within the chatbot, including the payment process.
Bots, like 1-800flowers.com, eBay, and Fynd have already proved that.
—Julien Blancher, Co-Founder @ Recast.AI
In an article by Stefan Kojouharov, founder of ChatbotsLife, he mentions how different
companies are making more money than they would have without chatbots. He says,
The e-commerce space has begun using chatbots in a number of ways that are
quickly adding dollars to their bottom line. Let’s look at the early success stories:
1-800-Flowers: reported that more than 70 percent of its Messenger
orders derived from new customers!
Sephora: increased their makeover appointments by 11 percent via
their Facebook Messenger chatbot.
Nitro Café: increased sales by 20 percent with their Messenger
chatbot, which was designed for easy ordering, direct payments, and
instant two-way communication.
Sun’s Soccer: chatbots drove nearly 50 percent of its users back to
their site throughout specific soccer coverage; 43 percent of chatbot
subscribers clicked through during their best period.
Asos: increased orders by 300 percent using Messenger chatbots
and got a 250 percent return on Spend while reaching 3.5 times more
people.
Figure 1-2 tries to give you an idea of why there is a direct correlation
between chatbots and revenue. Lets have a look at Figure 1-2 to get
some idea about that.
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ChaPTEr 1 ThE BElovEd ChaTboTs
Figure 1-2. Chatbot brings revenue
A Glimpse of Chatbot Usage
We will try to look at how useful chatbot has been for consumers due to its usability and
the efficiency it provides. Everybody in this burning IT age wants to be fast in everything,
and using chatbots makes your jobs easier and faster every day. It is personalized in a
way as to not repeat obvious things; this makes us re-think about traditional usage of
software. Figure 1-3 provides an illustration that should give you a fair idea about chatbot
usage.
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ChaPTEr 1 ThE BElovEd ChaTboTs
Figure 1-3. A glimpse of Chatbot usage by consumers
Customers Prefer Chatbots
Chatbots are not just software in the modern era. Chatbots are like our personal
assistants who understand us and can be microconfigured. They remember our likes and
dislikes and never tend to disappoint us by forgetting what we taught them already, and
this is the reason why everyone loves chatbot. Next time you meet a person or meet your
customer, don’t forget to ask if they prefer conventional software or the new cutting-edge
chatbots. Lets have a look at Figure 1-4 to understand the reasons why customers prefer
chatbots compared to other software systems for human computer interactions.
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Figure 1-4. Customers prefer chatbots
In the next section of this chapter we are going to discuss why chatbots are the next
big thing for budding developers. Whether you are a newer or a mid-level developer or
an experienced SME you must understand what is available to developers when building
chatbots.
The Developer’s Perspective
Have you ever felt the pain when you have to update the OS of your computer or phone
or any other app that you might be using in order to use new features? What if there
is not much need to update the app every time to use new features? Or say, instead of
having multiple apps, one could have one single app that did most of the things currently
done by multiple apps?
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Bots for developers are fun to build. It’s like teaching your kid to walk, talk, behave,
and do things. You love making it more intelligent and self-sufficient. From a developer’s
perspective, chatbots are a very important subject to know about.
Feature Releases and Bug Fixes
Lots of features can be added to the chatbot painlessly without having your users update
your chatbot app. It might be a pain in the neck if you released a version of the app with
some bug, and you have to fix it and release again in the AppStore for approval, and,
most importantly, the users will have to update the app after all. If they don’t update,
then the customer will keep complaining about the issue, which results in productivity
loss for everyone. In chatbots, everything is API-based, so you just fix the issue in the
backend, deploy the changes in PRODUCTION, and woaah—issue fixed for your users
without any worry. You save lots of time from user-reported bugs as well.
Imagine you built a bot to find restaurants and later you wanted to add the capability
of searching for hotels, flights, etc. Users can easily just request such information, and
your backend chatbot system will take care of everything.
Suppose you are building a Facebook Messenger chatbot; you can control almost
everything, including what interface the user sees in his app, directly from your backend.
In Facebook Messenger bots, you can choose whether the user gets to click on a button
to say Yes/No or just enters simple text.
Market Demand
Fifty-four percent of the developers worldwide worked on chatbots for the first time in
2016. There is a huge demand for building a simple chatbot that works for companies,
and they are looking for developers who can build it for them. Once you have completed
Chapter 3 of this book I bet you can start selling your services to companies easily. You
can also do your own startup in an area of your expertise by introducing a chatbot for
that domain. Being able to build a chatbot end-to-end is a new skill to have, and that’s
the reason average market pay is also very good for chatbot developers.
The growing demand for chatbots can be seen in the number of chatbots being
developed on developer platforms like Facebook. Facebook has 100,000 monthly
active bots on the Messenger platform, and counting. You will be amazed to know that
Messenger had 600 million users in April 2015, growing to 900 million in June 2016,
1 billion in July 2016, and 1.2 billion in April 2017.
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Learning Curve
Whether you are from a frontend/backend background or know very little programming,
there is immense possibility to learn new things when you are building or learning to
build a chatbot. In this process you will learn about many things. For example, you get to
learn more about Human Computer Interaction (HCI), which talks about the design and
use of computer technology, focused on the interfaces between people and computers.
You will be learning how to build or use APIs or web services, using third-party APIs like
Google APIs, Twitter APIs, Uber APIs, etc. You will have immense opportunity to learn
about Natural Language Processing, machine learning, consumer behavior, and many
other technical and non-technical things.
Industries Impacted by Chatbots
Let’s have a quick look at the industries that will benefit most from chatbots. A research
study by Mindbowser in association with Chatbots Journal collected data from 300+
individuals who participated from a wide array of industries including online retail,
aviation, logistics, supply chain, e-commerce, hospitality, education, technology,
manufacturing, and marketing & advertising. If we look at the chart in Figure 1-5,
it is pretty much evident that e-commerce, insurance, healthcare, and retail are the
industries benefiting most from chatbots. These industries rely heavily upon the
responsiveness of the customer care team in an efficient manner that saves time. Given
the fact that chatbot is good at that, it is evident that it will hail in these industries pretty
quickly.
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Figure 1-5. Top industries that will benefit most from chatbots
At this point of time, the chatbots are still getting traction in newer sectors in
different forms. The next 5 to 10 years will be very much crucial for chatbots to spread
the word in different industries that have no experience working with chatbots.
Brief Timeline of Chatbots
Let’s look at the brief history of the timeline of how chatbots were formulated. It’s
very important to know where chatbot technology came from and how it was shaped.
Chatbots have certainly gained popularity recently but the efforts are being made using
decades of work with this technology. The history of chatbots will certainly amaze you
regarding how far we have come since we started.
1950
The Turing test was developed by Alan Turing. It was a test of a machine’s ability to
exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
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1966
Eliza, the first chatbot, was created by Joseph Weizenbaum, designed to be a therapist.
It used to simulate a conversation by using a “pattern matching” and substitution
methodology that gave users an illusion of understanding on the part of the bot.
1972
Parry, a computer program by psychiatrist and Stanford scientist Kenneth Colby,
modeled the behavior of a paranoid schizophrenic.
1981
The Jabberwocky chatbot was created by British programmer Rollo Carpenter. It started
in 1981 and launched on internet in 1997.
The aim of this chatbot was to “simulate natural human chat in an interesting,
entertaining and humorous manner.”
1985
The wireless robot toy, Tomy Chatbot, repeats any message recorded on its tape.
1992
Dr. Sbaitso, a chatbot created by Creative Labs for MS-DOS, “conversed” with the user
as if it were a psychologist in a digitized voice. Repeated swearing and malformed input
from the users caused Dr. Sbaitso to “break down” in a “PARITY ERROR” before it could
reset itself.
1995
A.L.I.C.E (Artificial Linguistic Internet Computer Entity) was developed by Nobel Prize
winner Richard Wallace.
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1996
Hex, developed by Jason Hutchens, was based on Eliza and won the Loebner Prize in 1996.
2001
Smarterchild, an intelligent bot developed by ActiveBuddy, was widely distributed across
global instance messaging and SMS networks. The original implementation quickly
grew to provide instant access to news, weather, stock information, movie times, yellow
pages listings, and detailed sports data, as well as a variety of tools (personal assistant,
calculators, translator, etc.).
2006
The idea of Watson was coined from a dinner table; it was being designed to compete
on the TV show “Jeopardy.” In its first pass it could only get about 15 percent of answers
correct, but later Watson was able to beat human contestants on a regular basis.
2010
Siri, an intelligent personal assistant, was launched as an iPhone app and then integrated
as a part of the iOS. Siri is a spin-out from the SRI International Artificial Intelligence
Center. Its speech recognition engine was provided by Nuance Communications, and
Siri uses advanced machine learning technologies to function.
2012
Google launched the Google Now chatbot. It was originally codenamed “Majel” after
Majel Barrett, the wife of Gene Roddenberry and the voice of computer systems in the
Star Trek franchise; it was also codenamed as “assistant.”
2014
Amazon released Alexa. The word “Alexa” has a hard consonant with the X, and therefore
it can be recognized with higher precision. This was the primary reason Amazon chose
this name.
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2015
Cortana, a virtual assistant created by Microsoft. Cortana can set reminders, recognize
natural voice, and answer questions using information from the Bing search engine. It
was named after a fictional artificial intelligence character in the Halo video game series.
2016
In April 2016, Facebook announced a bot platform for Messenger, including APIs to
build chatbots to interact with users. Later enhancements done included bots being able
to participate in groups, preview screens, and QR scan capability through Messenger’s
camera functionality to take users directly to the bot.
In May 2016, Google unveiled its Amazon Echo competitor voice-enabled bot called
Google Home at the company’s developer conference. It enables users to speak voice
commands to interact with various services.
2017
Woebot is an automated conversational agent that helps you monitor mood, learn about
yourself, and makes you feel better. Woebot uses a combination of NLP techniques,
psychological expertise (Cognitive-behavioral therapy [CBT]), excellent writing, and a
sense of humor to treat depression.
What Kind of Problems Can I Solve Using Chatbots?
This question becomes challenging when you don’t know the scope of your bot or don’t
want to limit it to answer queries.
It’s very important to remember that there is a limit to what chatbots can do. It
always feels that we are talking to a human-like thing that is very intelligent, but the
specific bot is designed and trained to behave in a certain way and solve a specific
problem only. It cannot do everything, at least as of now. The future is definitely bright.
So, we come to the question of finding out if your problem statement is really good to
go and you can build a bot around it.
If the answer to all of these three questions is yes, then you are good to go.
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Can the Problem be Solved by Simple Question
and Answer or Back-and-Forth Communication?
It’s really important to not try to be a hero when solving any problem that is very new
to you. You should always aim to keep the problem scope limited. Build the basic
functionality and then add on top of it. Don’t try to make it complex in the first cut itself.
It doesn’t work in software.
Imagine Mark Zuckerberg thinking out loud and spending time building all the
features of Facebook at the start. Tagging a friend, having a like button, liking a user
comment, better messaging, live video, reactions on comments, etc.—these features
didn’t exist even when Facebook was funded with over 1 million registered users on the
platform. Would he have really succeeded if he would have gone on to first build these
features and then launch the platform?
So, we should always try to create features only needed at the moment without
having to over-engineer things.
Now, coming back to the first question, “Can the problem be solved by simple
question and answer or back-and-forth communication?”
You just have to keep your scope limited and your answer will be yes. We are not at
all limiting ourselves to solving complex problems but definitely limiting ourselves to
solving a complex problem all in one go.
“You have to make every single detail perfect. And you have to limit the
number of details.
—Jack Dorsey
Does It Have Highly Repetitive Issues That Require Either
Analyzing or Fetching of Data?
This question is important because either from a business perspective or a developer’s
perspective, what chatbot does and is made to do is to make people using it efficient and
productive. And how do you do that? By removing the need of a user to do repetitive
things themselves.
Chatbots are definitely more capable of just automating some highly repetitive
stuff, but you will always find that most of the chatbots primarily try to solve the same
issue—be it by learning under supervision (read: “By Supervised Learning”) or
self-teaching (read: “By Un-supervised Learning”).
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Can Your Bot’s Task be Automated and Fixed?
Unless you are thinking of building a chatbot just for your learning purpose, you should
make sure the problem you are trying to solve can be automated. Machines have started
to learn and do things themselves, but still it’s a very nascent stage. What you think can’t
be automated now may be automated in a few years.
A QnA Bot
One of the good examples of a problem statement for building a chatbot could be a QnA
Bot. Imagine a bot that is trained to understand various user questions whose answers
are already available on an FAQ page of a website.
If you go back and try to find the answer of the aforementioned three questions, the
answer will be yes.
See Figure 1-6 and you will find what an FAQ bot is doing.
Figure 1-6. FAQ chatbot example
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These are nothing but very repetitive questions that customers of a particular store
may call and ask or try to find answers to by going to a website and navigating through
the pages.
Think when you have a chatbot like this and it answers your question like a human in
seconds and even does more than you could imagine. This is just a little of what chatbots
are capable of.
Now, let’s try to analyze the aforementioned three questions and their answers in
case of QnA Bot.
Can the problem be solved by simple question and answer or
back-and-forth communication?
Yes, FAQs are nothing but simple frequently asked questions and
their relative answers. There may be a context-based FAQ, but unless
you are solving a multidomain problem using chatbots, you won’t be
having this problem. There could be a situation where two or more
questions may seem similar, but you can always design the bot to ask a
question back to the user when it’s doubtful.
Does it have highly repetitive issues that require either analyzing or
fetching of data?
Yes, FAQs require us to fetch the data from the database and show it
all at once in the website or possibly dynamically. But the user has
to go through all questions one by one to find the question he/she is
looking for and then see its answer. Lots of combing through the UI
before the consumer actually gets his/her answer... or maybe not. Why
not let our bot do that for us?
Can your bot’s task be automated and fixed?
Yes, an FAQ bot would need to get the question, analyze the question,
fetch information from the database, and give it back to the user. There
is nothing here that can’t be done using coding. And also, the process it
pretty much fixed won’t change in real-time.
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Starting With Chatbots
There are three steps one should follow before building chatbots. We’ll discuss each one
of them briefly here.
1. Think about all the scenarios or tasks you want your chatbot to
be able to do, and gather all related questions in different forms
that can be asked to do those tasks. Every task that you want your
chatbot to do will define an intent.
2. Each question that you list or intent can be represented in
multiple ways. It depends on how the user expresses it.
For example: Alexa, Switch off the light. Alexa, Would you please
switch off the light? Can you please switch off the light? A user may
use any of these sentences to instruct the bot to switch off the light. All
of these have the same intent/task to switch off the light, but they are
being asked in different utterances/variances.
3. Write all your logic to keep the user tied to the flow that you have
chosen after you recognize the user’s intent.
For example, suppose you are building a bot to book a doctor’s
appointment. Then you ask your user to give a phone number, name,
and specialist, and then you show the slots and then book it.
In this case you can expect the user to know these details and not try to
accommodate all the things in the bot itself, like a specialist for an ear problem is called
an ENT. However, doing this is not a big deal. So, again it comes back to deciding the
scope of your bot, depending on the time and resource you have to build the application.
Decision Trees in Chatbots
If you know about decision trees , then that’s very good because you will be needing that
knowledge frequently when designing the flow of your chatbots. But if you don’t know
about the decision trees, then just Googling would help you learn this simple concept
widely used in Computer Science.
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Using Decision Trees in Chatbots
In the context of chatbots, a decision tree simply assists us in finding the exact answer to
a user’s question.
A decision tree is a decision support tool that uses a tree-like graph or model
of decisions and their possible consequences, including chance event out-
comes, resource costs, and utility. It is one way to display an algorithm that
only contains conditional control statements.
—Wikipedia
The most difficult part when building a chatbot is to keep track of if...else code
blocks. The greater the number of decisions to make, the more frequently if...else comes
up in the code. But at the same time these blocks are required to encode the complex
conversational flows. If the problem is complex and requires a lot of if…else in real-life,
then that will require code to adjust in the same way.
How Does a Decision Tree Help?
Decision trees are simple to write and understand, but they are a powerful
representation of the solution made for the problem in question. They inherit a unique
capability to help us understand a lot of things.
• Help in creating a full picture of the problem at hand. Looking at the
decision tree, we can easily understand what’s missing or what needs
to be modified.
• Helps debug faster. Decision trees are like a short bible or, say,
a visual representation of a software requirement specification
document, which can be referred by developers, product managers,
or leadership to explain the expected behavior or make any changes
if needed.
• AI is still not at that stage that it can be trained with lots of data and
perform with 100 percent accuracy. It still requires a lot of hand-­
holding by writing business logic and rules. Decision trees help
wherever it becomes a little tough to ask a machine to learn and do it.
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Let’s take a simple example and try to understand how it helps in building chatbots.
Look at the example diagram for a chatbot that starts with a question of whether the user
is looking for a t-shirt or jeans, and based on the input the diagram flow goes further to
give options related to the product by asking more questions. You don’t need to create a
full-fledged decision tree, but you should definitely have a flow of questions defined at
every step before starting to build chatbots.
Suppose you were building a similar chatbot that helps people buy apparel online.
The first thing you would do is to make a similar decision tree or a flowchart to help your
chatbot ask appropriate questions at the right time. This is really needed to set the scope
of each step and what needs to be done at that stage. You will need the state diagrams
or a simple flowchart later when you actually code your first chatbot. Remember to not
be too stringent while creating a diagram like Figure 1-7; keep it as simple as possible
and then add the extended functionalities later. The benefit of such a process is the
development time will be cut down, and later on the functionality will be loosely coupled
and would start making sense as components. Like in the example, after creating the
basic functionality, you can add color choices, price range, ratings, and discount options
as well.
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Figure 1-7. A simple representation of an apparel chatbot for buying clothes
online
There are definitely more things you can add to the earlier use-case depending upon
your requirements. But you have to make sure that you don’t make it too complex for
yourself as well as for the user.
A decision tree not only helps you to keep the user tied to the flow but also is a very
effective way to identify the next intent that might be coming in the form of a question
from the customer.
So, your bot will ask a series of questions following the decision tree that you have
built. Each node narrows down on the customer’s goal through chatbot intents.
Suppose you were creating a chatbot for a financial institution—say, a bank—that can
do a money transfer based on your request after authentication. In this case, your bot
may first want to verify the account details and ask the user to confirm the amount, and
then the bot may ask to validate target account name, account number, account type, etc.
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You cannot or would not want to invoke an OTP (one-time password) API unless you
have validated if the user’s account balance is more than the requested amount.
It happens with all of us and with customers as well. They get frustrated when their
questions are not answered correctly. Using decision trees for your chatbot will definitely
make the experience better for your users than it would be if your didn’t use them.
Lots of times you will find issues solving some intents programmatically. So, the
bottom line is, “If you can’t solve something programmatically then solve it by design.”
Look at Figure 1-8 where the bot is trying to take a health quiz and wants to know if
antibiotics can work for everything.
Figure 1-8. Example of solving a use-case by design
Since the answer is expected to be a Boolean (True/False), you give just two buttons
for the user to click instead of letting them type and wait to fix their mistake.
This is solving by design rather than writing lots of code that will be handling
unexpected user inputs. You will have so many scenarios while building the chatbots
where by just giving buttons, you will be able to quickly know the intent of the user.
It’s important to understand such scenarios and provide buttons both for your own
convenience as well as for users who don’t need to type in obvious cases of optional
answers.
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The Best Chatbots/Bot Frameworks
https://woebot.io/
• Can track your mood
• Helps you feel better
• Gives you insights by seeing your mood pattern
• Teaches you how to be positive and high-energy
https://qnamaker.ai/
• Build, train, and publish a simple question-and-answer bot based
on FAQ, URLs, and structured documents in minutes.
• Test and refine responses using a familiar chat interface.
https://dialogflow.com/
• Formerly known as api.ai and widely popular among chatbot
enthusiasts.
• Give users new ways to interact with your product by building
engaging voice-and text-based conversational interfaces powered
by AI.
• Connect with users on the Google Assistant, Amazon Alexa,
Facebook Messenger, and other popular platforms and devices.
• Analyzes and understands the user’s intent to help you respond
in the most useful way.
https://core.rasa.ai
• A framework for building conversational software
• You can implement the actions your bot can take in Python code.
• Rather than a bunch of if…else statements, the logic of your
bot is based on a probabilistic model trained on example
conversations.
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https://wit.ai
• Wit.ai makes it easy for developers to build applications and
devices that you can talk or text to.
• Acquired by Facebook within 21 months of its launch, wit.ai team
contributes toward Facebook’s own NLP engine inside Facebook.
• You can use wit.ai for building chatbots, home automation, etc.
• Wit.ai is similar to the way Dialogflow works but is not as feature-
rich as Dialogflow. People initially used wit.ai, as it was free and
Dialogflow was not, but later on Dialogflow became free as well.
https://www.luis.ai/
• A machine learning-based service to build natural language into
apps, bots, and IoT devices.
• Quickly create enterprise-ready, custom models that
continuously improve.
http://botkit.ai
• Visual conversation builder
• Built-in stats and metrics
• Can be easily integrated with Facebook, Microsoft, IBM Watson,
Slack, Telegram, etc.
Components of a Chatbot and Terminologies Used
Components of a chatbot system are very few. In this section we’ll be briefly discussing
the components of a chatbot that you will come across in the later chapters.
Having a basic theoretical understanding of any system before diving deep is always
helpful. You should have a fair idea after this section about technical terminologies used
while building chatbots using Python. These terminologies will be used frequently in
coming chapters when we actually start building our chatbots.
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Intent
When a user interacts with a chatbot, what is his intention to use the chatbot/what is he
asking for?
For example, when a user says, “Book a movie ticket,” to a chatbot, we as humans
can understand that the user wants to book a movie ticket. This is intent for a bot. It
could be named “book_movie” intent.
Another example could be when a user says, “I want to order food,” or “Can you help
me order food?” These could be named “order_food” intent. Likewise, you can define as
many intents as you want.
Entities
Intents have metadata about the intent called “Entities.” In the example, “Book a movie
ticket,” booking a ticket could be an intent and the entity is “movie,” which could have
been something else as well, like flight, concert, etc.
You can have general entities labeled for use throughout the intents. Entities could
represent as a quantity, count, or volume. Intents can have multiple entities as well.
For example: Order me a shoe of size 8.
There could be two entities here:
Category: Shoe
Size: 8
Utterances
Utterances are nothing but different forms of the same question/intent your user
may show.
• Remember we discussed the switching off the light intent? That
was an example of how a user can use different utterances for the
same intent.
• It is suggested to have an optimum 10 utterances per intent and a
minimum of 5, but this is not restricted.
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Training the Bot
Training essentially means to build a model that will learn from the existing set of
defined intents/entities and utterances on how to categorize the new utterances and
provide a confidence score along with it.
When we train the system using utterances, this is called supervised learning. We will
soon be learning more about doing this practically.
Confidence Score
Every time you try to find what intent an utterance may belong to, your model will come
up with a confidence score. This score tells you how confident your machine learning
model is about recognizing the intent of the user.
That’s all we wanted to cover in the first chapter of “Introduction to Chatbots.” You
must have a fair idea about chatbots from a business perspective and from a technical
perspective. We walked through the lane of history belonging to chatbots. It’s quite
fascinating how far chatbots have evolved.
We learned about how chatbots have evolved over a period of time and why chatbots
are a must for a business to grow in this cutthroat competition. We learned about the
different chatbot frameworks and also got to know about the terminology used for
chatbots by example. We’ll be using them in the coming chapters. You should be at a
stage now where you know what kind of chatbot you want to build and how it would
behave when built.
Do all your write-ups and decision trees, if needed, and after we have learned the
basics of Natural Language Understanding in the next chapter, we can quickly start
building our chatbot.
Don’t worry even if you don’t have anything in mind. We’ll try to build a cool chatbot
step by step with all the concepts learned in the upcoming chapters.
See you in the next chapter.
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CHAPTER 2
Natural Language
Processing for Chatbots
This chapter is to get you started with Natural Language Processing (NLP) using Python
needed to build chatbots. You will learn the basic methods and techniques of NLP using
an awesome open-source library called spaCy. If you are a beginner or intermediate to
the Python ecosystem, then do not worry, as you’ll get to do every step that is needed to
learn NLP for chatbots. This chapter not only teaches you about the methods in NLP but
also takes real-life examples and demonstrates them with coding examples. We’ll also
discuss why a particular NLP method may be needed for chatbots. Note that NLP in itself
is a skill.
We’ll be closely taking a look at POS tagging, stemming, entity detection, stopwords,
dependency parsing, and noun chunks and finding similarity between words. All of these
methods will be very helpful for you when you are building chatbots of your use-case.
There are a lot more NLP methods than the ones covered in this chapter. Based on
your need for the chatbot you are building, you can try to learn them. The SpaCy library
that we are going to learn to use by the end of this chapter will give you enough idea on
how to scale your knowledge base of NLP and its understanding. So, let’s get started and
first try to understand NLP for chatbots in the next section.
Why Do I Need to Know Natural Language
Processing to Build a Chatbot?
To understand the answer to this question, let’s first understand Natural Language
Processing (NLP).
Natural Language Processing (NLP) is a field of Artificial Intelligence that enables
computers to analyze and understand the human language.
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S. Raj, Building Chatbots with Python, https://doi.org/10.1007/978-1-4842-4096-0_2
ChaPter 2 Natural Language Processing for Chatbots
Now, to perform NLP or, say, Natural Language Understanding (NLU), we have lots
of methods that we are going to discuss next. You heard a new term Natural Language
Understanding (NLU)—what is that now?
In simple terms, NLU is a subset of a bigger picture of NLP, just like machine
learning, deep learning, NLP, and data mining are a subset of a bigger picture of Artificial
Intelligence(AI), which is an umbrella term for any computer program that does
something smart.
A good rule of thumb is to use the term NLU to express a machine’s ability
to understand the natural language in a form provided by humans.
Now, coming to the question of whether you really need to know NLP to build a
chatbot—the answer is both Yes and No. Confused? You heard it right, it’s not that you
can’t build a chatbot at all if you don’t know the method and techniques of NLP, but your
scope will be somewhat limited. You won’t be able to scale the application and keep the
code clean at the same time. NLP gives your chatbot the wings to fly when it can’t just
walk and run.
Chatbots for a common person are nothing but a way to communicate with some
intelligent machine at the other end. This machine can be either voice-based or text-­
based, where the user will provide input in their own language, which in computer
science is generally called natural language.
We know that there is no black box that does the magic, and everything works
just fine. One should know that there is nothing artificial in AI; it’s actually machine
learning and deep learning algorithms written by great people, running under the hood.
Machines haven’t reached a stage where they can think as similar as humans to have
their own intelligence. AI systems today—what they do and the way they behave—are
the outcome of how we have trained them.
So, to understand the natural language of the user in whatsoever language it may be,
or whatever input form it may be (text, voice, image, etc.), we have to write algorithms
and use techniques of NLP. NLP is considered the brain of chatbots that processes the
raw data, does the munging, cleans it, and then prepares to take appropriate actions.
NLP in itself is a huge topic and requires time and perseverance to learn completely,
but there are few methods that are necessary for a chatbot developer to know, which we
are going to learn in this chapter.
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What Is spaCy?
spaCy is an open-source software library for advanced NLP, written in Python and
Cython, built by Matthew Honnibal. It provides intuitive APIs to access its methods
trained by deep learning models.
spaCy offers the fastest syntactic parser in the world. Taken directly from spaCy’s
documentation, they have some amazing benchmarking results, which are shown below.
Benchmarks Results of spaCy
Two peer-reviewed papers in 2015 confirmed that spaCy offers the fastest syntactic parser
in the world and that its accuracy is within 1% of the best available. The few systems that
are more accurate are 20 times slower or more. Lets try to look at Figure 2-1 which shows
spaCy benchmarking results based on its speed and accuracy compared to other libraries.
Figure 2-1. spaCy benchmarking results
spaCy also offers statistical neural network models for a wide range of languages
like English, German, Spanish, Portuguese, French, Italian, Dutch, and multi-language
NER. It also provides tokenization for various other languages. This table shows speed
benchmarked by Choi et al., so it wouldn’t be fair to compare spaCy v2.x benchmarked on
different hardware. This is the reason you do not see the speed column value for spaCy v2.x.
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What Does spaCy Provide?
There are three primary things that spaCy claims to provide and with which it is
extremely helpful. Let’s try to look at those and understand why one should know and
use spaCy as a go-to module for doing NLP.
World’s Fastest Library
spaCy does extremely well at extracting large-scale information. It is written from scratch
with utmost care for memory with help of the Cython library.
Get Things Done
spaCy is designed with “get things done” in mind. It helps us in accomplishing real-­
world NLP scenarios. The clean documentation saves a lot of time for developers and
computational linguistics enthusiasts and makes them more productive. It’s easy to
install, just like any other Python package.
Deep Learning
spaCy is one of the best libraries available in the open-source community to process
text for deep-learning algorithms. It collaborates seamlessly with TensorFlow, PyTorch,
scikit-learn, Gensim, and the rest of Python’s related technologies. Deep learning
developers can easily construct linguistically sophisticated statistical models for a range
of NLP/NLU problems.
Features of spaCy
No other NLP library provides an extremely wide range of APIs to do almost everything,
which is what spaCy does. The best things about this library is it is continuously
evolving and getting better and better. Let’s have a sneak peek at the features of spaCy as
mentioned on their official website [https://spacy.io/ ].
• Non-destructive tokenization
• Named entity recognition
• Support for 28+ languages
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13 statistical models for 8 languages
• Pre-trained word vectors
• Easy deep-learning integration
• Part-of-speech tagging
• Labeled dependency parsing
• Syntax-driven sentence segmentation
• Built-in visualizers for syntax and NER
• Convenient string-to-hash mapping
• Export to numpy data arrays
• Efficient binary serialization
• Easy model packaging and deployment
• State-of-the-art speed
• Robust, rigorously evaluated accuracy
Now, let’s dive into this awesome module for NLP in Python: spaCy.
Installation and Prerequisites
Before we actually dive into spaCy and code snippets, make sure you have Python
installed on your OS. If not, refer to [1].
You can use whichever version of Python you are comfortable with. Most of the
systems today come pre-installed with default Python version 2.7.x. We’ll be using
Python 3 in this chapter. So, if you want to use Python 3, please install Python 3 on your
operating system by downloading it from https://www.python.org/downloads/ . If you
have Python 2 installed already, you can use that as well; it may or may not need minor
code changes.
We will install spaCy via pip [2].
We are going to use a virtual environment [3] and install spaCy into a user directory.
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If you are on macOS/OSX/Linux, follow these steps:
Step 1: python3 -m pip install -U virtualenv
Step 2: virtualenv venv -p /usr/local/bin/python3 #Make sure you use your
own OS path for python 3 executable.
Step 3: source venv/bin/activate
Step 4: pip3 install -U spacy # We'll be using spaCy version 2.0.11.
The final step may take time, so wait patiently.
If you are on Windows ,just change the Step 3 to
venv\Scripts\activate
Now, we are going to install Jupyter Notebook inside our virtual environment, which
we activated in the Step 3. Using Jupyter Notebook is much easier and more productive
than standard Python interpreter. We will be executing all the snippets in Jupyter
Notebook in the coming chapters.
To install Jupyter Notebook, run the following pip command:
pip3 install jupyter
This command will install Jupyter Notebook in your system.
At this point you should have spaCy and Jupyter Notebook installed in your
virtualenv. Let’s verify if everything was successfully installed.
1. Go to your command line interface and type the following and
you should see a server being started and opening a url in your
default browser.
$ jupyter notebook
The default url is http://localhost:8888/tree. It should look
something like Figure 2-2.
2. Click on New as shown in Figure 2-2, and choose Python 3.
It will open a new tab in your current browser and create a new
notebook for you, where you can play with the Python code.
You can execute any Python code, import libraries, plot charts,
and markdown cells.
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ChaPter 2 Natural Language Processing for Chatbots
Figure 2-2. Jupyter Notebook first look
3. Type import spaCy and run the cell by clicking on “Run” button or
by pressing Shift + Enter. It should look something like Figure 2-3.
Figure 2-3. Verifying spaCy installation
If Step 3 doesn’t throw any error messages, then you have successfully installed
the spaCy module on your system. You should see your installed spaCy version in your
notebook. If you want to install the same version of spaCy then you can specify the
version while installing spaCy via pip.
pip3 install -U spacy==2.0.11
What Are SpaCy Models?
SpaCy models are just like any other machine learning or deep learning models. A model
is a yield of an algorithm or, say, an object that is created after training data using a
machine learning algorithm. spaCy has lots of such models that can be placed directly in
our program by downloading it just like any other Python package.
Now, we are going to install spaCy models as Python packages.
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To do that, we’ll run the following command in the notebook by taking use of
notebook’s magic command. By prefixing! [Exclamation Operator] before the shell
command, we can run shell commands as well from Jupyter Notebooks. Let’s see
how it looks.
!python3 -m spacy download en
You might get a permission issue while using Jupyter Notebook to download spaCy
models for Python 3. Go to your terminal and run the following command:
sudo python3 -m download en
See Figure 2-4 for reference.
Figure 2-4. Downloading spaCy models
As you can see in Figure 2-4, spaCy tries to download some core files and installs
them as Python packages.
Note
! [Exclamation Operator] works only in Jupyter Notebook. To install spaCy
models directly from the terminal, you need to remove! [Exclamation Operator];
otherwise it will result into an error.
[1] https://www.python.org/downloads/
[2] https://packaging.python.org/tutorials/installing-
packages/#installing-from-pypi
[3] http://docs.python-guide.org/en/latest/dev/virtualenvs/
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Fundamental Methods of NLP for Building Chatbots
It’s really important to be good at basics to be an expert at something and do it effectively
and efficiently. To build chatbots, we need to know the fundamental methods of
NLP. These methods help us break the input into chunks and make sense of it. In the
next section, we are going to learn some of the most used NLP methods that will not
just help you be good at NLP but also good at building cool chatbots. The more we can
process the input text better and more efficiently, the better we can respond to the user.
POS Tagging
Part-of-speech (POS) tagging is a process where you read some text and assign parts of
speech to each word or token, such as noun, verb, adjective, etc.
POS tagging becomes extremely important when you want to identify some entity in
a given sentence. The first step is to do POS tagging and see what our text contains.
Let’s get our hands dirty with some of the examples of real POS tagging.
Example 1:
nlp = spacy.load('en') #Loads the spacy en model into a python object
doc = nlp(u'I am learning how to build chatbots') #Creates a doc object
for token in doc:
print(token.text, token.pos_) #prints the text and POS
Output:
('I', 'PRON')
('am', 'VERB')
('learning', 'VERB')
('how', 'ADV')
('to', 'PART')
('build', 'VERB')
('chatbots', 'NOUN')
Example 2:
doc = nlp(u'I am going to London next week for a meeting.')
for token in doc:
print(token.text, token.pos_)
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Output:
('I', 'PRON')
('am', 'VERB')
('going', 'VERB')
('to', 'ADP')
('London', 'PROPN')
('next', 'ADJ')
('week', 'NOUN')
('for', 'ADP')
('a', 'DET')
('meeting', 'NOUN')
('.', 'PUNCT')
As we can see, when we print the tokens from the returned Doc object from the
method nlp, which is a container for accessing the annotations, we get the POS tagged
with each of the words in the sentence.
These tags are the properties belonging to the word that determine the word is
used in a grammatically correct sentence. We can use these tags as the word features in
information filtering, etc.
Let’s try to take another example where we try to explore different attributes of the
token coming from Doc object.
Example 3:
doc = nlp(u'Google release "Move Mirror" AI experiment that matches your
pose from 80,000 images')
for token in doc:
print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
token.shape_, token.is_alpha, token.is_stop)
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Output:
Text
Lemma
POS
Tag
Dep
Shape
Alpha
Stop
Google
google
PROPN
NNP
compound
Xxxxx
True
False
Release
release
NOUN
NN
nmod
xxxx
True
False
"
"
PUNCT
``
punct
"
False
False
Move
move
PROPN
NNP
nmod
Xxxx
True
False
Mirror
mirror
PROPN
NNP
nmod
Xxxxx
True
False
"
"
PUNCT
punct
"
False
False
AI
ai
PROPN
NNP
compound
XX
True
False
Experiment
experiment
NOUN
NN
ROOT
xxxx
True
False
That
that
ADJ
WDT
nsubj
xxxx
True
True
Matches
match
VERB
VBZ
relcl
xxxx
True
False
Your
-PRON-
ADJ
PRP$
poss
xxxx
True
True
Pose
pose
NOUN
NN
dobj
xxxx
True
False
From
from
ADP
IN
prep
xxxx
True
True
80,000
80,000
NUM
CD
nummod
dd,ddd
False
False
Images
image
NOUN
NNS
pobj
xxxx
True
False
Example 4:
doc = nlp(u'I am learning how to build chatbots')
for token in doc:
print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
token.shape_, token.is_alpha, token.is_stop)
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Output:
TEXT
LEMMA
POS
TAG
DEP
SHAPE
ALPHA
STOP
I
-PRON-
PRON
PRP
nsubj
X
True
False
am
be
VERB
VBP
aux
xx
True
True
learning
learn
VERB
VBG
ROOT
xxxx
True
False
how
how
ADV
WRB
advmod
xxx
True
True
to
to
PART
TO
aux
xx
True
True
build
build
VERB
VB
xcomp
xxxx
True
False
chatbots
chatbot
NOUN
NNS
dobj
xxxx
True
False
Refer to the below table to find out the meaning of each attribute we printed in
the code.
TEXT
Actual text or word being processed
LEMMA Root form of the word being processed
POS
Part-of-speech of the word
TAG
They express the part-of-speech (e.g., VERB) and some amount of morphological
information (e.g., that the verb is past tense).
DEP
Syntactic dependency (i.e., the relation between tokens)
SHAPE
Shape of the word (e.g., the capitalization, punctuation, digits format)
ALPHA
Is the token an alpha character?
Stop
Is the word a stop word or part of a stop list?
You can refer the below table to understand what each POS attribute values of the
token object mean. This list gives a detailed idea of part-of-speech tags assigned by
spaCy’s models.
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Natural Language Processing for Chatbots
POS
DESCRIPTION
EXAMPLES
ADJ
adjective
big, old, green, incomprehensible, first
ADP
adposition
in, to, during
ADV
adverb
very, tomorrow, down, where, there
AUX
auxiliary
is, has (done), will (do), should (do)
CONJ
conjunction
and, or, but
CCONJ
coordinating conjunction
and, or, but
DET
determiner
a, an, the
INTJ
interjection
psst, ouch, bravo, hello
NOUN
noun
girl, cat, tree, air, beauty
NUM
numeral
1, 2017, one, seventy-seven, IV, MMXIV
PART
particle
‘s, not,
PRON
pronoun
I, you, he, she, myself, themselves,
somebody
PROPN
proper noun
Mary, John, London, NATO, HBO
PUNCT
punctuation
., (, ), ?
SCONJ
subordinating conjunction
if, while, that
SYM
symbol
$, %, §, ©, +, −, ×, ÷, =, :), ½
VERB
verb
run, runs, running, eat, ate, eating
X
other
sfpksdpsxmsa
SPACE
space
So why is POS tagging needed for chatbots?
Answer: to reduce the complexity of understanding a text that can’t be trained or
is trained with less confidence. By use of POS tagging, we can identify parts of the text
input and do string matching only for those parts. For example, if you were to find if a
location exists in a sentence, then POS tagging would tag the location word as NOUN, so
you can take all the NOUNs from the tagged list and see if it’s one of the locations from
your preset list or not.
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Stemming and Lemmatization
Stemming is the process of reducing inflected words to their word stem, base form.
A stemming algorithm reduces the words “saying” to the root word “say,” whereas
“presumably” becomes presum. As you can see, this may or may not always be 100%
correct.
Lemmatization is closely related to stemming, but lemmatization is the algorithmic
process of determining the lemma of a word based on its intended meaning.
For example, in English, the verb “to walk” may appear as “walk,” “walked,” “walks,”
or “walking.” The base form, “walk,” that one might look up in a dictionary, is called
the lemma for the word. spaCy doesn’t have any in-built stemmer, as lemmatization is
considered more correct and productive.
Difference between Stemming and Lemmatization
Stemming does the job in a crude, heuristic way that chops off the
ends of words, assuming that the remaining word is what we are
actually looking for, but it often includes the removal of derivational
affixes.
Lemmatization tries to do the job more elegantly with the use of a
vocabulary and morphological analysis of words. It tries its best to
remove inflectional endings only and return the dictionary form of a
word, known as the lemma.
Though few libraries provide methods for stemming as well as lemmatization, it’s
always a best practice to use lemmatization to get the root word correctly.
Let’s try to explore lemmatization by taking some of the examples:
Example 1:
from spacy.lemmatizer import Lemmatizer
from spacy.lang.en import LEMMA_INDEX, LEMMA_EXC, LEMMA_RULES
lemmatizer = Lemmatizer(LEMMA_INDEX, LEMMA_EXC, LEMMA_RULES)
lemmatizer('chuckles', 'NOUN') # 2nd param is token's part-of-speech tag
Output:
[u'chuckle']
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Example 2:
lemmatizer('blazing', 'VERB')
Output:
[u'blaze']
Example 3:
lemmatizer('fastest', 'ADJ')
Output:
[u'fast']
If you want to see the comparison between a stemmer and lemmatizer, then you
need to install one of the most popular libraries for Python: Natural Language Toolkit
(NLTK). spaCy has gained popularity very recently, but it was NLTK that made every NLP
enthusiast take a plunge into the ocean of NLP and its techniques.
Check the following example where we try to use two stemming techniques provided
by NLTK. First, we try to get the stem of the word “fastest” using PorterStemmer and
then using SnowBallStemmer. Both of them give the same result—that is, “fastest”— but
when we do the lemmatization using spaCy’s method, it gives us “fast” as the stem of
“fastest,” which is more meaningful and correct.
from nltk.stem.porter import *
from nltk.stem.snowball import SnowballStemmer
porter_stemmer = PorterStemmer()
snowball_stemmer = SnowballStemmer("english")
print(porter_stemmer.stem("fastest"))
print(snowball_stemmer.stem("fastest"))
fastest
fastest
Note Make sure to install nltk package using pip3 before trying to run this code.
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ChaPter 2 Natural Language Processing for Chatbots
Since you are pretty much aware what a stemming or lemmatization does in NLP,
you should be able to understand that whenever you come across a situation where you
need the root form of the word, you need to do lemmatization there. For example, it is
often used in building search engines. You must have wondered how Google gives you
the articles in search results that you meant to get even when the search text was not
properly formulated.
This is where one makes use of lemmatization. Imagine you search with the text,
When will the next season of Game of Thrones be releasing?
Now, suppose the search engine does simple document word frequency matching to
give you search results. In this case, the aforementioned query probably won’t match an
article with a caption “Game of Thrones next season release date.”
If we do the lemmatization of the original question before going to match the input
with the documents, then we may get better results.
We’ll try to test this theory as well in upcoming sections.
Named-Entity Recognition
Named-entity recognition (NER), also known by other names like entity identification
or entity extraction, is a process of finding and classifying named entities existing in the
given text into pre-defined categories.
The NER task is hugely dependent on the knowledge base used to train the NE
extraction algorithm, so it may or may not work depending upon the provided dataset it
was trained on.
spaCy comes with a very fast entity recognition model that is capable of identifying
entity phrases from a given document. Entities can be of different types, such as person,
location, organization, dates, numerals, etc. These entities can be accessed through
.ents property of the doc object.
Let’s try to find named-entities by taking some examples with the help of spaCy’s
powerful NER tagging capability.
Example 1:
my_string = u"Google has its headquarters in Mountain View, California
having revenue amounted to 109.65 billion US dollars"
doc = nlp(my_string)
for ent in doc.ents:
print(ent.text, ent.label_)
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ChaPter 2 Natural Language Processing for Chatbots
Output:
('Google', 'ORG')
('Mountain View', 'GPE')
('California', 'GPE')
('109.65 billion US dollars', 'MONEY')
We can see how beautifully and automagically the spaCy model could easily identify
that the word Google as an Organization, California is a Geopolitical entity, and in the
given sentence we are talking about 109.65 billion US dollars, which is actually about
money.
Let’s try to explore some more examples.
Example 2:
my_string = u"Mark Zuckerberg born May 14, 1984 in New York is an American
technology entrepreneur and philanthropist best known for co-founding and
leading Facebook as its chairman and CEO."
doc = nlp(my_string)
for ent in doc.ents:
print(ent.text, ent.label_)
Output:
('Mark Zuckerberg', 'PERSON')
('May 14, 1984', 'DATE')
('New York', 'GPE')
('American', 'NORP')
('Facebook', 'ORG')
Example 3:
my_string = u"I usually wake up at 9:00 AM. 90% of my daytime goes in
learning new things."
doc = nlp(my_string)
for ent in doc.ents:
print(ent.text, ent.label_)
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ChaPter 2 Natural Language Processing for Chatbots
Output:
('9:00 AM', 'TIME')
('90%', 'PERCENT')
As you can see, the entity extractor can easily extract the time information from the
given string. Also as you can see entity extractor not just tries to identify the number but
also exact PERCENTAGE value.
As per the documentation by spaCy, models trained on the OntoNotes 5 1
corpus
support the following entity types.
TYPE
DESCRIPTION
PERSON
People, including fictional
NORP
Nationalities or religious or political groups
FAC
Buildings, airports, highways, bridges, etc.
ORG
Companies, agencies, institutions, etc.
GPE
Countries, cities, states
LOC
Non-GPE locations, mountain ranges, bodies of water
PRODUCT
Objects, vehicles, foods, etc. (not services)
EVENT
Named hurricanes, battles, wars, sports events, etc.
WORK_OF_ART
Titles of books, songs, etc.
LAW
Named documents made into laws
LANGUAGE
Any named language
DATE
Absolute or relative dates or periods
TIME
Times smaller than a day
PERCENT
Percentage, including “%”
MONEY
Monetary values, including unit
QUANTITY
Measurements, as of weight or distance
ORDINAL
“first,” “second,” etc.
CARDINAL
Numerals that do not fall under another type
1https://catalog.ldc.upenn.edu/ldc2013t19
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ChaPter 2 Natural Language Processing for Chatbots
Whenever we intend to build a conversational agent or chatbot in simple terms, we
always have a domain in mind. For example, we want the chatbot to book a doctor’s
appointment, order food, pay a bill, fill out a banking application, e-Commerce, etc. The
chatbot could be solving a combination of these problems as well. By finding out the
entity in the question, one can get a fair idea of the context in which the question was
asked.
Let’s try to understand this by taking an example of two sentences with similar words
and different meanings.
my_string1 = u"Imagine Dragons are the best band."
my_string2 = u"Imagine dragons come and take over the city."
doc1 = nlp(my_string1)
doc2 = nlp(my_string2)
for ent in doc1.ents:
print(ent.text, ent.label_)
The above for loop over doc1 object gives an output:
('Imagine Dragons', 'ORG')
Awesome, isn’t it? It’ll become more intriguing when you will come to realize that
the entity recognizer doesn’t recognize any entity in 2nd string. Run the following code
and doc2 doesn’t produce any output.
for ent in doc2.ents:
print(ent.text, ent.label_)
Now, imagine you were to extract the context of the above two strings in a live
environment. What would you do? With help of Entity Extractor, one can easily figure out
the context of the statement and intelligently take the conversation further.
Stop Words
Stop words are high-frequency words like a, an, the, to and also that we sometimes
want to filter out of a document before further processing. Stop words usually have little
lexical content and do not hold much of a meaning.
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ChaPter 2 Natural Language Processing for Chatbots
Below is a list of 25 semantically non-selective stop words that are common in
Reuters-RCV1.
a an
and
are
as
at
be
by
for
from
has
he
in
is
it
its
of
on
that
the
to
was
were
will
with
Let’s get into some code and try to understand how things work.
To see all the words defined as stop words in spaCy you can run the following
lines of code:
from spacy.lang.en.stop_words import STOP_WORDS
print(STOP_WORDS)
You should see something like:
set(['all', 'six', 'just', 'less', 'being', 'indeed', 'over', 'move',
'anyway', 'fifty', 'four', 'not', 'own', 'through', 'using', 'go', 'only',
'its', 'before', 'one', 'whose', 'how',
'whereby', 'third', 'i', 'whole', 'noone', 'sometimes', 'well', 'together',
'yours', 'their', 'rather', 'without', 'so', 'five', 'the', 'otherwise',
'make', 'once'])
There are about 305 stop words defined in spaCy’s stop words list. You can always
define your own stop words if needed and override the existing list.
To see if a word is a stop word or not, you can use the nlp object of spaCy. We can use
the nlp object’s is_stop attribute.
Example 1:
nlp.vocab[u'is'].is_stop
Output:
True
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ChaPter 2 Natural Language Processing for Chatbots
Example 2:
nlp.vocab[u'hello'].is_stop
Output:
False
Example 3:
nlp.vocab[u'with'].is_stop
Output:
True
Stop words are a very important part of text clean up. It helps removal of meaningless
data before we try to do actual processing to make sense of the text.
Suppose you are in a situation where you are building a bot to make people happy
by assessing their mood. Now, one needs to analyze the sentiment in the text input by
the user so that correct response can be formulated. Here, before begging to do basic
sentiment analysis, we should remove the noise in the data that exists in the form of stop
words.
Dependency Parsing
Dependency parsing is one of the more beautiful and powerful features of spaCy that is
fast and accurate. The parser can also be used for sentence boundary detection and lets
you iterate over base noun phrases, or “chunks.”
This feature of spaCy gives you a parsed tree that explains the parent-child
relationship between the words or phrases and is independent of the order in which
words occur.
Let’s take an example where you have to parse the following sentence:
Book me a flight from Bangalore to Goa
Example 1:
doc = nlp(u'Book me a flight from Bangalore to Goa')
blr, goa = doc[5], doc[7]
list(blr.ancestors)
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ChaPter 2 Natural Language Processing for Chatbots
Output:
[from, flight, Book]
The above output can tell us that user is looking to book the flight from Bangalore.
Let’s try to list the ancestors of goa.ancestors object:
list(goa.ancestors)
Output:
[to, flight, Book]
This output can tell us that the user is looking to book the flight to Goa.
What Are Ancestors in Dependency Parsing?
Ancestors are the rightmost token of this token’s syntactic descendants. Like in the above
example for the object blr the ancestors were from, flight, and Book.
Remember you can always list the ancestors of a doc object item using ancestors
attribute.
list(doc[4].ancestors) #doc[4]==flight
The above code will output:
[flight, Book]
To check if a doc object item is an ancestor of another doc object item
programmatically, we can do the following:
doc[3].is_ancestor(doc[5])
The above returns True because doc[3] (i.e., flight) is an ancestor of doc[5] (i.e.,
Bangalore). You can try more examples like this to get a better understanding of
dependency parsing and the ancestors concept.
If we try to think of a real-world scenario that we might actually face while trying to
build a chatbot, we may come across some sentence like
I want to book a cab to the hotel and a table at a restaurant.
In this sentence, it’s important to know what tasks are requested and where they are
targeted (i.e., whether the user wants to book a cab to the hotel or the restaurant).
Let’s try to do that using the following code:
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Example 1:
doc = nlp(u'Book a table at the restaurant and the taxi to the hotel')
tasks = doc[2], doc[8] #(table, taxi)
tasks_target = doc[5], doc[11] #(restaurant, hotel)
for task in tasks_target:
for tok in task.ancestors:
if tok in tasks:
print("Booking of {} belongs to {}".format(tok, task))
break
Output:
Booking of table belongs to restaurant
Booking of taxi belongs to hotel
What Are Children in Dependency Parsing?
Children are immediate syntactic dependents of the token. We can see the children of a
word by using children attribute just like we used ancestors.
list(doc[3].children)
will output
[a, from, to]
Interactive Visualization for Dependency Parsing
It’s very difficult to understand the complete dependency parsing concept for the first
time. spaCy gives an extremely easy and interactive way to understand its dependency
parsing. spaCy v2.0+ has a visualization module where we can pass a Doc or a list of Doc
objects to displaCy and call serve method of displaCy to run the web server.
Figure 2-5 shows how the interactive visualization will look for dependency parsing.
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Figure 2-5. Interactive visualization for dependency parsing
You can also generate the dependency parsing visualization in Figure 2-5. To create a
visualization like this, run the following code and then go to http://localhost:5000 in
your browser.
Let’s try to do the visualization of our example of tasks and the target of the tasks.
from spacy import displacy
doc = nlp(u'Book a table at the restaurant and the taxi to the hotel')
displacy.serve(doc, style='dep')
Running this code will have output like that in Figure 2-6. If you get something
similar, then go to another tab of your browser and enter http://localhost:5000.
We get a visualization of dependency parsing for this string in the code (shown in
Figure 2-7).
Figure 2-6. Starting dependency parsing server on localhost
Figure 2-7. Example of dependency parsing
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Let’s take one more example of dependency parsing where we assume a user is
asking the following sentence:
What are some places to visit in Berlin and stay in Lubeck?
We will first create the doc object as shown here:
doc = nlp(u"What are some places to visit in Berlin and stay in Lubeck")
Now, we get the places being talked about and the actions the user wants:
places = [doc[7], doc[11]] #[Berlin, Lubeck]
actions = [doc[5], doc[9]] #[visit, stay]
Since you already know POS tagging and entity extraction, you can easily get the
places and actions automatically.
Now that we have the places, let’s iterate through each of its ancestors and check if
any ancestors are found in actions. The first parent of place found in the list of actions
should be the action for the place in question.
for place in places:
for tok in place.ancestors:
if tok in actions:
print("User is referring {} to {}").format(place, tok)
break
Output:
User is referring: Berlin to visit
User is referring: Lubeck to stay
As we see in these examples, dependency parsing makes it quite easy to understand
what the user is referring to. We saw that in the case of two different tasks as well, we can
pretty figure out the expectation and, based on that, formulate the next response.
What Is the Use of Dependency Parsing in Chatbots?
Dependency parsing is one the most important parts when building chatbots from
scratch. It becomes far more important when you want to figure out the meaning of a text
input from your user to your chatbot. There can be cases when you haven’t trained your
chatbots, but still you don’t want to lose your customer or reply like a dumb machine.
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In these cases, dependency parsing really helps to find the relation and explain a bit
more about what the user may be asking for.
If we were to list things for which dependency parsing helps, some might be:
• It helps in finding relationships between words of grammatically
correct sentences.
• It can be used for sentence boundary detection.
• It is quite useful to find out if the user is talking about more than one
context simultaneously.
You must be wondering, what if your bot user says any grammatically incorrect
sentence or uses any SMS textspeak while giving input about something? As discussed in
Chapter 1, you have to be cautious about these situations and handle them accordingly
using NLP techniques.
You need to write your own custom NLP to understand the context of the user
or your chatbot and, based on that, identify the possible grammatical mistakes a user
can make.
All in all, you must be ready for such scenarios where a user will input garbage values
or grammatically incorrect sentences. You can’t handle all such scenarios at once, but
you can keep improving your chatbot by adding custom NLP code or by limiting user
input by design.
Noun Chunks
Noun chunks or NP-chunking are basically “base noun phrases.” We can say they are flat
phrases that have a noun as their head. You can think of noun chunks as a noun with the
words describing the noun.
Let’s try to take an example and understand it better.
Example 1:
doc = nlp(u"Boston Dynamics is gearing up to produce thousands of robot
dogs")
list(doc.noun_chunks)
Output:
[Boston Dynamics, thousands, robot dogs]
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Though having noun chunks from a given sentence helps a lot, spaCy provides other
attributes that can be helpful too. Let’s try to explore some of those.
Example 2:
doc = nlp(u"Deep learning cracks the code of messenger RNAs and protein-­
coding potential")
for chunk in doc.noun_chunks:
print(chunk.text, chunk.root.text, chunk.root.dep_,
chunk.root.head.text)
Output:
TEXT
ROOT.TEXT
ROOT.DEP_
ROOT.HEAD.TEXT
deep learning
learning
nsubj
cracks
the code
code
dobj
cracks
messenger RNAs
RNAs
pobj
of
protein-coding potential
potential
conj
RNAs
As we can see from this table we get the noun chunks and their attributes. The
following table will help you understand each column.
Column
Meaning
Text
Text of the original noun chunk
Root text
Text of the original word that connects the noun chunk with remaining parse
Root dep
Dependency relation that connects the root to its head
Root head text
Text of the root token’s head
Finding Similarity
Finding similarity between two words is a use-case you will find most of the time
working with NLP. Sometimes it becomes fairly important to find if two words are
similar. While building chatbots you will often come to situations where you don’t have
to just find similar-looking words but also how closely related two words are logically.
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spaCy uses high-quality word vectors to find similarity between two words using
GloVe algorithm (Global Vectors for Word Representation).
GloVe is an unsupervised learning algorithm for obtaining vector representations for
words. GloVe algorithm uses aggregated global word-word co-occurrence statistics from
a corpus to train the model.
Let’s try to look at the actual values inside the vectors of spaCy using vector attribute
of the token.
doc = nlp(u'How are you doing today?')
for token in doc:
print(token.text, token.vector[:5])
Output:
(u'How', array([-0.29742685,
0.73939574,
-0.04001453,
0.44034013,
2.8967502 ],
dtype=float32))
(u'are', array([-0.23435134, -1.6145049 ,
1.0197453 ,
0.9928169 ,
0.28227055],
dtype=float32))(u'you', array([ 0.10252178,
-3.564711
,
2.4822793 ,
4.2824993 ,
3.590245
],
dtype=float32))
(u'doing', array([-0.6240922 , -2.0210216 , -0.91014993,
2.7051923 ,
4.189252
],
dtype=float32))(u'today', array([ 3.5409122 ,
-0.62185854,
2.6274266 ,
2.0504875 ,
0.20191991],
dtype=float32))
(u'?', array([ 2.8914998 , -0.25079122,
3.3764176 ,
1.6942682 ,
1.9849057 ],
dtype=float32))
Seeing this output, it doesn’t make much sense and meaning. From an application’s
perspective, what matters the most is how similar the vectors of different words are—that
is, the word’s meaning itself.
In order to find similarity between two words in spaCy, we can do the following.
Example 1:
hello_doc = nlp(u"hello")
hi_doc = nlp(u"hi")
hella_doc = nlp(u"hella")
print(hello_doc.similarity(hi_doc))
print(hello_doc.similarity(hella_doc))
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Output:
0.7879069442766685
0.4193425861242359
If you see the word hello, it is more related and similar to the word hi, even though
there is only a difference of a character between the words hello and hella.
Let’s take one more example of a sentence and learn how spaCy does the
similarity comparison. Remember our Game of Trones example in previ-
ous sections? We are going to try that and see the similarity.
Code:
GoT_str1 = nlp(u"When will next season of Game of Thrones be releasing?")
GoT_str2 = nlp(u"Game of Thrones next season release date?")
GoT_str1.similarity(GoT_str2)
Output:
0.785019122782813
As we can see in this example, the similarity between both of the sentences is about
79%, which is good enough to say that both of the sentences are quite similar, which is
true. This can help us save a lot of time for writing custom code when building chatbots.
So, we come to a fact that spaCy gives us a meaningful similarity between two words
using the word vectors rather than just looking at their spelling or alphabets.
We will take a very simple example and try to find the similarity between words.
example_doc = nlp(u"car truck google")
for t1 in example_doc:
for t2 in example_doc:
similarity_perc = int(t1.similarity(t2) * 100)
print "Word {} is {}% similar to word {}".format(t1.text,
similarity_perc, t2.text)
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Output:
Word car is 100% similar to word car
Word car is 71% similar to word truck
Word car is 24% similar to word google
Word truck is 71% similar to word car
Word truck is 100% similar to word truck
Word truck is 36% similar to word google
Word google is 24% similar to word car
Word google is 36% similar to word truck
Word google is 100% similar to word google
Finding similarity between words or sentences becomes quite important when we
intend to build any application that is hugely dependent on the implementations of
NLP. If you have ever used StackOverFlow, whenever we try to ask a new question, it tries
to list similar questions already asked on the platform. This is one of the best examples
where finding similarity between two sets of sentences might help. spaCy’s confidence
to find the similarity between two words based on an already trained model purely
depends on the kind of general assumption taken.
When building chatbots, finding similarity can be very handy for the following
situations:
• When building chatbots for recommendation
• Removing duplicates
• Building a spell-checker
These things that we learned are really important while building chatbots so that
we know how to parse user inputs so that they make sense while writing business logic
inside code.
Good to Know Things in NLP for Chatbots
In this section we are going to learn about a couple of interesting topics that come in
handy often when you plan to write your own custom NLP methods to handle certain
scenarios. Make sure you go through them, as when you expect it the least, it’s needed
the most. We will briefly talk about tokenization and usage of regular expressions in a
chatbot scenario.
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Tokenization
Tokenization is one of the simple yet basic concepts of NLP where we split a text into
meaningful segments. spaCy first tokenizes the text (i.e., segments it into words and
then punctuation and other things). A question might come to your mind: Why can’t
I just use the built-in split method of Python language and do the tokenization? Python’s
split method is just a raw method to split the sentence into tokens given a separator.
It doesn’t take any meaning into account, whereas tokenization tries to preserve the
meaning as well.
Let’s try some code and see how tokenization works.
Example 1:
doc = nlp(u'Brexit is the impending withdrawal of the U.K. from the
European Union.')
for token in doc:
print(token.text)
Output:
Brexit
is
the
impending
withdrawal
of
the
U.K.
from
the
EuropeanUnion
If you see in the above output, U.K. comes as a single word after the tokenization
process, which makes sense, as U.K. is a country name and splitting it would be wrong.
Even after this if you not happy with spaCy’s tokenization, then you can use its
add_special_case case method to add your own rule before relying completely on
spaCy’s tokenization method.
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Regular Expressions
You must already know about regular expressions and their usage. This book assumes
you must be familiar with regular expressions in general. In this section, we are just
going to run through some of the examples and see how regular expressions can be
beneficial and useful while building chatbots.
Text analysis and processing is a big subject in itself. Sometimes words play
together in a way that makes it extremely difficult for machines to understand and get
trained upon.
Regular expression can come handy for some fallback for a machine learning model.
It has the power of pattern-matching, which can ensure that the data we are processing
is correct or incorrect. Most of the early chatbots discussed in Chapter 1 under the
History of Chatbots section were hugely dependent on pattern-matching.
Let’s take below two examples that are pretty simple to understand. We’ll try to use
regular expression to extract information from both of the sentences.
Book me a metro from Airport Station to Hong Kong Station.
Book me a cab from Hong Kong Airport to AsiaWorld-Expo.
Here is the code:
sentence1 = "Book me a metro from Airport Station to Hong Kong Station."
sentence2 = "Book me a cab to Hong Kong Airport from AsiaWorld-Expo."
import re
from_to = re.compile('.* from (.*) to (.*)')
to_from = re.compile('.* to (.*) from (.*)')
from_to_match = from_to.match(sentence2)
to_from_match = to_from.match(sentence2)
if from_to_match and from_to_match.groups():
_from = from_to_match.groups()[0]
_to = from_to_match.groups()[1]
print("from_to pattern matched correctly. Printing values\n")
print("From: {}, To: {}".format(_from, _to))
elif to_from_match and to_from_match.groups():
_to = to_from_match.groups()[0]
_from = to_from_match.groups()[1]
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print("to_from pattern matched correctly. Printing values\n")
print("From: {}, To: {}".format(_from, _to))
Output:
to_from pattern matched correctly. Printing values
From: AsiaWorld-Expo., To: Hong Kong Airport
Try changing the sentence2 to sentence1 and see if the code works well to identify
the pattern. Given the power of machine learning these days, regular expression and
pattern-matching has taken a back step, but make sure you brush up a bit about it as
it may be needed at any time to parse specific details from words, sentences, or text
documents.
Summary
At this point you must have fair idea of why we need to know NLP before starting to build
chatbots. In this chapter, we learned about the spaCy module in Python, its features, and
how to install it. We dived into various methods of NLP, which is extensively used while
building chatbots. We learned about POS tagging, the difference between stemming and
lemmatization, entity recognition, noun-chunking, and finding similarity between sets
of words.
We executed code for all of these concepts and learned all of this by doing and
not just reading, which is what this book emphasizes. We brushed up on basics of
tokenization and regular expressions as well. We are good to go on and build our chatbot
in the next chapter using a freely available tool called Dialogflow. We will learn how to
train our chatbot to understand and extract information given by the user in the next
chapter.
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CHAPTER 3
Building Chatbots
the Easy Way
Building chatbots the easy way has been written keeping in mind that sometimes you
don’t want to build everything from scratch and just want to get things done. This
chapter doesn’t require you to do lots of coding but still gives you a fair idea of how to
build chatbots in an easy way and make it public.
The reason why this chapter becomes more important for learning to build chatbots
is because the software world moves too fast to be adapted. Sometimes we need to build
applications very quickly, and we try to look for tools available in open-source libraries
that can be used to quickly build applications, without the need of re-inventing the
wheels. Sometimes we are not good enough at coding to build everything from scratch.
Even though we want to build applications from scratch, we can’t because the learning
curve will be so steep for a newbie.
This chapter will help you to build chatbots very quickly and make them public for
the world to use.
We are going to try a tool formerly known as Api.ai. Now it is known as Dialogflow.
Introduction to Dialogflow
Dialogflow gives users new methods to interact with their product by building engaging
voice- and text-based conversational interfaces, such as voice apps and chatbots.
Dialogflow is powered by AI. It helps you connect with users on your website, mobile
app, the Google Assistant, Amazon Alexa, Facebook Messenger, and other popular
platforms and devices.
The following diagram from Dialogflow shows how they handle a user request.
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© Sumit Raj 2019
S. Raj, Building Chatbots with Python, https://doi.org/10.1007/978-1-4842-4096-0_3
ChaPtEr 3 BuiLding Chatbots thE Easy Way
Figure 3-1. Working diagram of Dialogflow architecture
Here is what happens:
1. User talks to the input device.
2. User query goes into the Dialogflow engine.
3. Dialogflow tries to recognize the intent.
4. Based on the intent, a fulfillment is done and data is returned
from database.
5. Response is returned to the intent.
6. Response is converted into actionable data.
7. User’s request for information is given back to the output device.
There is a concept of agents in Dialogflow that are best described as Natural
Language Understanding (NLU) modules. These can be included in your app, product,
or service and transform natural user requests into actionable data. This transformation
occurs when a user input matches one of the intents inside your agent.
Agents can also be designed to manage a conversation flow in a specific way. This
can be done with the help of contexts, intent priorities, slot filling, responsibilities, and
fulfillment via webhook.
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Getting Started
Knowing what we have learned so far was and is important because free tools and
packages available in open source don’t always help in building a full-fledged chatbot
application.
Many times, you may come across a situation when you want to build everything
yourself so that you have more control over your application. We will learn those in the
next chapter and use the previously learned NLP techniques as well.
This chapter is all about creating a chatbot as a proof of concept and making it ready
for the world to use with minimal programming or no programming experience.
Building a Food-Ordering Chatbot
We are going to create a chatbot with help of Dialogflow for a specific restaurant. Let’s
name it OnlineEatsBot. In short we can call it OnlineEats product. You can choose any
other use-case for which you want to build the chatbot. For this chapter we are going to
build a food-ordering chatbot.
Deciding the Scope
Let’s decide the scope of this chatbot—that is, what it can do and to what extent.
• It should be able to greet the user dynamically.
• It should be able to understand the menu items and their quantity
requested.
• Chatbot should be able to place an order on the user’s behalf.
• Give the user the status of the order when asked.
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Listing Intents
Here we list the intents we want our chatbot to train with so that it can understand those
intents when asked by the user.
Intents
• Default welcome intent: when the user messages the chatbot
• Place order intent: when the user asks the bot to order food
• Item description intent: when the user tells what item and quantity
they want
• Order status: when the user wants to know his order status
• Order_ID: the bot needs to understand the user’s order ID for
tracking.
• User thanks: when the user thanks the bot
Listing Entities
We’ll try to list all possible entities for all intents here. Later in the chapter we’ll see them
being defined for whichever intent they are applicable.
Entities
• food_items: what food does the user want to order?
• quantities: what is the quantity of the food item the user is willing to order?
• order_id: order_id of the place order for user
Building a Food Ordering Chatbot
Let’s also level-set the expectation of the chatbot in terms of the minimum things we
should be able to see it doing. For that, let’s create a script of a conversation between our
chatbot and the user. This helps us to stick to the plan to getting a basic working chatbot
ready, assuming the user is doing the conversation in a nice and correct manner.
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Chatbot and User Conversation Script:
User: Hello
OnlineEatsBot: Hello and Welcome! How may I help you?
User: I am looking to order Chinese food.
OnlineEatsBot: Sure, what would you like to order today?
User: One chicken dumpling and two spring rolls.
OnlineEatsBot: Done. Your final amount is XYZ, and your order is
placed for 1 Chicken dumpling and 2 spring rolls.
User: I haven’t received my order yet. Where is my food?
OnlineEatsBot: Can you please help me with your order ID?
User: 123456
OnlineEatsBot: Order Status for Order ID: 123456. Te delivery
guy is in your locality, your food will arrive in about 5 minutes.
User: Tanks.
OnlineEatsBot: Thanks a lot for your cooperation.
Now that we have written a basic script to follow to build our chatbot, we’ll now dive
into Dialogflow.
Getting Started With Dialogflow
Let’s follow these steps to create an account in Dialogflow and then create an agent.
(An agent is nothing but another name for a chatbot.)
1. Create an account at https://dialogflow.com , and log in to the
account.
2. Create an agent.
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ChaPtEr 3 BuiLding Chatbots thE Easy Way
Figure 3-2. Creating a new agent in Dialogflow
Enter the details, like name of the agent, time zone, default language,
and Google Project that you want to choose or create a new Google
project.
3. Create intents.
If you see Figure 3-3, you will see that we are given two intents already.
Default fallback intent: Fallback intents are triggered if a user’s input
is not matched by any of the regular intents or enabled built-in small
talk. When you create a new agent, a default fallback intent is created
automatically. You can modify or delete it if you wish.
Default welcome intent: We can extend this welcome intent for our
own chatbots. You should add some of your own user expressions
and default responses.
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Figure 3-3. Creating intents in Dialogflow
Before we create our own intents, let’s first add some utterances in default welcome
intent and make it ready using the following steps:
1. Click on the default welcome intent.
2. Add your own user expressions in Training Phrases.
3. Click on SAVE.
When we click on save, the machine learning models behind the scenes run and
train the data that we gave (i.e., the user expressions). Training the data means letting
the machine understand what kind of intent it is based on the data that we provide and
being able to predict when we give any new data to it. For example, if we look at
Figure 3-4, where we have defined five user expressions that the machine already knows
belong to “welcome intent,” what if the user says “Hello there,” which is not defined
anywhere? The machine will still categorize “Hello there” as default welcome intent, as
the features used in training and the machine for welcome intent are similar in the new
user expression.
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Figure 3-4. Defining a default welcome or greeting intent in Dialogflow
Let’s try to see if the welcome intent works for us. With Dialogflow we can do that in
the dashboard itself. See Figure 3-5.
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Figure 3-5. Testing the welcome intent in Dialogflow
Points to Remember When Creating Intents
Let’s look at some of the points important to know while creating intents on Dialogflow.
• Dialogflow’s intent also has the facility to have a default response of
every intent. A default response is a response given back to the user
every time that intent is recognized. In our example, when a user says
“Hello there,” we get “Hello!” as the response from the bot.
• If you want to you can add more responses or delete existing ones,
having more than one response makes the bot look realistic so that it
doesn’t reply with the same response every time and feels human to
the user talking to the bot.
• Intents in Dialogflow also have the capability of being marked as the
end of the conversation. In other words, you can let the bot assume
that the user will not be participating in the conversation anymore,
and the bot can do the necessary action, based on this information,
to end the conversation.
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Creating Intents and Adding Utterances
Now that we have created the welcome intent, let’s create the order intent. I named it
place_order_intent. The following are my user expressions that I entered:
I want food
I want to order food asap
Can you please take my order for food?
Take my order please
I want to place an order for Chinese food
I want to place an order
Would you please help me to order food?
Can you please order food for me?
I want to order food
I am looking to order Tai food
I am looking to order Chinese food
Now, we have built the intent to identify the aforementioned user expressions or
related user expressions. Now, it’s time to add a response back to the user using default
response to the intent.
Adding Default Response to the Intent
We’ll be adding three possible responses that will be given back to the user once
place_order_intent is encountered.
Sure, What would you like to order today?
Definitely, What would you like to have today?
Certainly, I’ll try to help you with that. What are you feeling like eating
today?
Now, the next step is to wait for the user to input the items he wants and parse
the items.
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Now we’ll create a new intent that tells us what the user actually intends to order
(i.e., the food items).
We create a new intent named items_description
First, we add our standard user expression.
One chicken dumpling and two spring rolls.
When we add the user expression then we can select specific words that we want to
specify as entities of the intent. This could be quantity, date or time, location, etc., which
are predefined, but we can create our own entities by clicking on the Create New button
on the bottom right after we get the pop-up box.
Highlight the word in the utterance for which you want to make that selected word
an entity. After that, it opens the pop-up box to create our own entity.
In this example, we should be able to parse the data in a nice readable format so that
we can use that using any programming language. JSON format is the best format we can
have in today’s cross-platform applications. Dialogflow returns the data in JSON format
by default, which can be parsed to look something like the following code. It’s always
suggested to keep your data as minimal as possible; don’t overwhelm the API response
by giving too much data. Remember all of these at scale cost money.
{
"food_items": {
"chicken dumpling": 1,
"Spring rolls": 2
}
}
Item Description Intent and Belonging Entities
We can select One and Two and define them as @sys.number, which is nothing but the
data type. We’ll create a new entity called food_items_entity to identify food items.
If you look at Figure 3-6, you’ll find that we have ENTITY named as food_items_
entity, but when we select the words, then we name the parameters as food_items_
entity1 and food_items_entity2; this is similar for the food quantity, which is a
number where we name the first and second parameters as quantity1 and quantity2,
respectively.
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Figure 3-6. Item description intent
What we define here will help us understand the JSON response, which we’ll be
getting after intent is triggered. We should have all these values there to move forward
with the chatbot flow.
So, select the entire word or combination of words and click on Create New. A new
screen will come to create entities; just enter the name for this new entity and save.
Now, come back to our intent for items_description and you should see something
like Figure 3-6.
Keep adding more user expression in the training phrases, and keep defining the
entities within it.
We have added four utterances so far, and this is how they look. We’re going to add as
many as possible so that our agent’s accuracy for intent classification is better.
Dialogflow also has a feature to share the agent’s training data. Training data used in
this book can be accessed via Apress website, https://github.com/Apress/building-
chatbots-with-python . As you can see in the Figure 3-7 where we try to add some more
examples in the item description intent in our dialogflow agent.
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Figure 3-7. Adding more utterances in item description intent
Now, at this point once we have saved our intent, and our agent has finished training
the models. If we enter the following sentence on the right side, we should be able to see
the following JSON response:
One chicken dumpling and two spring rolls
Response from the intent:
{
"id": "e8cf4a44-6ec9-49ae-9da8-a5542a80d742",
"timestamp": "2018-04-01T21:22:42.846Z",
"lang": "en",
"result": {
"source": "agent",
"resolvedQuery": "One chicken dumpling and two spring rolls",
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"action": "",
"actionIncomplete": false,
"parameters": {
"quantity1": 1,
"food_items_entity1": "chicken dumpling",
"quantity2": 2,
"food_items_entity2": "spring rolls"
},
"contexts": [],
"metadata": {
"intentId": "0b478407-1b37-4f9a-8779-1866714dd44f",
"webhookUsed": "false",
"webhookForSlotFillingUsed": "false",
"intentName": "items_description"
},
"fulfillment": {
"speech": "",
"messages": [
{
"type": 0,
"speech": ""
}
]
},
"score": 1
},
"status": {
"code": 200,
"errorType": "success",
"webhookTimedOut": false
},
"sessionId": "e1ee1860-06a7-4ca1-acae-f92c6e4a023e"
}
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If you look at the parameters section of the JSON response we see
{
"quantity1": 1,
"food_items_entity1": "chicken dumpling",
"quantity2": 2,
"food_items_entity2": "spring rolls"
}
We can easily write some Python code to convert the JSON to our intended format
we discussed.
CAN YOU DO THIS?
Just test your Python skills and try to write a code that reads a JSON like the preceding and
returns the quantity and food item belonging to it in another JSON format we discussed earlier.
Understanding and Replying Back to the User
Now, next in the conversation is to make the bot reply back to the user that the order is
understood along with any new information. New information could be the generated
order_id, the order amount, or the expected delivery time. These things will be
populated at your server end, and you can formulate it with the bot’s response to give it
back to the user.
Now, let’s try to add the order amount in our case; to do that, we can use Dialogflow’s
Default Response feature and add this inside the intent. Let’s hardcode the amount
for now, as this amount will vary depending on the food items, their quantity, or the
restaurant. Later in the chapter, we’ll discuss how to make it dynamic by invoking an API.
The interesting thing here is that we can access the params we got from the intent
(i.e., the food items and their quantities).
Responses can contain references to parameter values. We’ll understand this in a
moment.
If a parameter is present in the parameter table, we can use the following format to
refer to its value in the ‘Text response’field: $parameter_name.
We can use this params in the default response so that the bot confirms the order
back to the user.
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Add “Done. Your final amount is XYZ and your order is placed for $quantity1
$food_
items_entity1 and $quantity2
$food_items_entity2” as the response.
Note In case our intent is not able to parse the food items or their quantity, we
need to give a different default response asking to explain what our bot couldn’t
understand or to at least confirm. We already learned how to add default response
to an intent in the section “Adding Default Response to the Intent.”
Order Status Intent
Now, let’s create the order_status intent, where the user may be trying to ask for the
status of the order after the order is placed.
Figure 3-8 provides some training phrases we added for order status intent, and we
name the intent order_status.
Figure 3-8. Creating order status intent
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Now, let’s try some random order status asking utterances and see if our agent is
intelligent enough to identify the intent.
I tried, “Haven't received my food yet, and voila—my agent got it perfectly right that
it’s an order_status intent.
See the resolvedQuery and its intentName in the JSON in Figure 3-9.
Figure 3-9. JSON response from Dialogflow after query is resolved
User_Order_ID Intent
Now, next is to ask the user for Order ID, so let’s set the default response of this intent to
ask the following question.
Can you please help me with your order ID?
Now, the user will be giving their order ID, and our task is to identify that and give a
response again.
So, for that we need to create another intent to identify when the user is talking about
the order ID.
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Note that the intents we are creating are pretty much independent of each other.
In this case we know that the user is going to give the order ID, and it will mostly be right.
If it’s wrong you can always go back to the user and ask again.
We should also note that in some cases, order_id and phone number both may be
integers. In such cases, we need to do some validation, like number of digits in order_id
or phone number. Also, based on the context of the previous question, you can figure
out if the user is giving an order_id or a phone number. As discussed in Chapter 1, we
can always use decision trees for better flow management of the chatbot. Also we can
programatically keep track that after order_status intent we ask for the order ID, and
the user will be sending some order ID (some number), which is easier to parse in code
rather than creating a new intent altogether.
In this example, we’ll create user_order_id intent, as there is no conflict as such.
Now, we create a new intent called user_order_id
Figure 3-10 shows how our user_order_id intent looks.
Figure 3-10. Defining the user order ID intent in our agent
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I tested a couple of expressions, and it works well to classify them correctly as user_
order_id intent. Always test using the Dialogflow console to see if your intent is behaving
as expected.
Now, let’s set the default response of user_order_id intent to the following response
from the bot:
Order Status for Order ID: $order_id .The delivery guy is in your locality, your food will
arrive in about 5 minutes.
We are again using the parameter parsed from the user_order_id intent to prepare a
reply to the user.
User_Thanks Intent
Next, the user will possibly be thanking, if not something else, so we create a new
intent called user_thanks to identify different ways the user is saying thank you. This is
important because once the user says thank you in some way or another, our bot should
reply the same.
We shouldn’t just expect the user to say thanks after the delivery status default
response and reply blindly but try to identify it using custom intents.
Figure 3-11 shows how our user_thanks intent looks.
Figure 3-11. Defining an intent when the user says thank you
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Now, it’s time to say thank you to the user using the default response feature and
mark this intent as the end of the conversation.
Let’s add some text like, “Thanks a lot for your cooperation,” as our default response.
We can add more such responses so that the bot should look more realistic (see
Figure 3-12).
Figure 3-12. Adding a response against a user’s intent in the agent
Look at Figure 3-12 and see that we have enabled this intent as the end of
conversation.
If we try to integrate our bot with Google Assistant, then enabling this means to close
the microphone in Google Assistant when intent is finished.
Now at this point we have created our bot, built it as per our initial design and script,
and trained it. Now, it’s time to deploy it on web and see how it looks.
Deploying Dialogflow Chatbot on the Web
In this part, we are going to integrate our bot with various platforms like Facebook
Messenger, Twitter, Slack, etc., and see if they work. There are many more platforms
where you can integrate this bot easily.
We will test our bot with Web Demo and Facebook Messenger for now.
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Let’s go to the Integrations page in our Dialogflow account and enable the Web
Demo. You will be getting a pop-up like Figure 3-13. Click on the link in the pop-up.
Figure 3-13. Dialogflow’s web demo link
You will be seeing something similar to Figures 3-14.1 through 3-14.4. I tested my bot
with the conversation we wrote, and my bot works like a charm.
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Figure 3-14.1. OnlineEatsBot Demo Conversation Screen I
Figure 3-14.2. OnlineEatsBot Demo Conversation Screen II
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Figure 3-14.3. OnlineEatsBot Demo Conversation Screen III
Figure 3-14.4. OnlineEatsBot Demo Conversation Screen IV
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Apart from this we can also embed this bot on our own website using the iframe code
found in the pop-up window.
Talk to my OnlineEatsBot here:
https://bot.dialogflow.com/c9b3f731-599e-4396-a35f-0d77497b46ef
Share your own bot with your friends and family as well, and see the way they
interact with the bot in a legitimate manner. If your chatbot is not doing something
expected, then try to fix that.
Integrate Dialogflow Chatbot on Facebook
Messenger
In this section we’ll try to integrate our same chatbot to Facebook Messenger so that our
users on the Facebook platform can also use it without having to come to our website.
Let’s go back to the integrations page in the Dialogflow dashboard and enable the
Facebook Messenger icon, and then click on that, which should bring up a similar
pop-up as before.
Here we need to go to Facebook, register an app, and get the required token.
• Verify Token (any string and is solely for your purposes)
• Page Access Token (Enter the token generated in the Facebook
Developer Console)
The Dialogflow Facebook integration is very helpful to easily create a Facebook
Messenger bot with NLU, based on the Dialogflow technology.
Setting Up Facebook
To make our bot work the same way it worked in Facebook, we would need to do the
following:
1. Create a Facebook account, if you haven’t already .
2. Create Facebook page to which you can add your bot.
When a user visits your Facebook page and sends you a message, they'll be talking to
your bot directly.
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Creating a Facebook App
The following are the steps to create an app:
1. Log into the Facebook Developer Console .
2. Click on My Apps in the upper right-hand corner.
3. Click on Add a New App and enter a name and contact e-mail
address.
4. Click Create App ID as shown in the Figure 3-15 below.
Figure 3-15. Creating a new app at Facebook Developer Platform
5. On the next page, click the Set Up button for the Messenger option.
6. Under the Token Generation section, let’s choose the Facebook
page to which we want our bot to connect (see Figure 3-16).
Figure 3-16. Generating token by selecting your Facebook page for your bot
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This will generate a Page Access Token. Keep this token handy, as we’ll need to
enter it in Dialogflow.
Setting Up the Dialogflow Console
Here are the steps:
1. Click on the Integrations option in your Dialogflow console in
the left menu and switch on Facebook Messenger if you haven’t
already. In the pop-up that opens, enter the following information
as shown in the Figure 3-17 Setting up and integrating Dialogflow
with Facebook Messenger:
Verify Token: this can be any string that you want and for your
own purposes
Page Access Token: enter the token generated in the Facebook
Developer Console
2. Click the Start button.
Figure 3-17. Setting up and integrating Dialogflow with Facebook Messenger
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You should get a message saying, “Bot was started.” That means we are good to go.
You must be wondering what a callback URL, Verify Token, and Page Access Token
are. Let’s try to understand those.
Callback URLs
A callback URL is nothing but a publicly accessible URL where Facebook will POST any
real-time requests coming from your Facebook page.
Suppose you are trying to make payment for your food on OnlineEats and then you
are redirected to a bank’s payment page. Now, OnlineEats must be giving a callback URL
to banks to which they can redirect the user after payment is done.
Here Facebook will not do any redirection but will take everything that our user
messages in the page’s chatbox and POST that to the webhook or callback URL.
Now, once we get the message on our server we do our intent classification and
entities parsing and then formulate what you want to reply back to the user.
Verify Token
A verify token is an arbitrary string sent to your endpoint when the subscription is
verified. The reason why it’s needed is to ensure that our server knows that the request is
being made by Facebook and relates to the subscription we just configured.
Suppose somebody else gets to know your webhook and posts messages posing as
Facebook, then verify_token will come into the picture, and you will verify if the source is
correct or not. Based on this token you can handle POST requests from multiple sources
as well because there will be different tokens defined for different sources but the same
callback url.
Access Tokens
Facebook APIs require Page Access Tokens to manage Facebook pages. They are unique
to each page, admin, and app and have an expiration time.
Note Keep the callback URL and Verify Token handy for configuring the
webhook now.
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Configuring Webhooks
To configure our bot’s webhook, let’s go back to the Facebook Developer Console:
1. Click the Setup button under the Add a product section for
webhooks when you click on the dashboard. If you have not
already subscribed to webhooks, then you will get an option
saying “subscribe to this object.” Click on this to get a new pop-up
and enter the following information:
Callback URL: this is the URL provided on the Facebook
Messenger integration page.
Verify Token: this is the Token you created.
2. Go to Messenger Settings Setup Webhooks. You will get
a pop-up like Figure 3-18. Add your callback url and verify the
token.
Figure 3-18. Setting up webhooks in Facebook for Dialogflow bot
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3. Check the messages and messaging_postbacks options under
Subscription Fields. You can definitely choose whichever is
needed for your use-case.
4. Click the Verify and Save button. Check Figure 3-18 for reference.
You’ll be taken back to the settings page and Webhooks should have a “Complete”
status. Make sure to select a page to which you can subscribe your webhook for page
events.
Testing the Messenger Bot
To be able to make our bot available for testing, we’ll need to make our app public:
1. Click on App Review in the left menu of the Facebook Developer
Console.
2. Click on the switch under Make <Your APP Name> public? If you
get an Invalid Privacy Policy URL prompt, then go to the Basic
Settings link in the dialog box and, if you haven’t already, put any
URL for the Privacy Policy URL, for the time being and then click
on Save Changes. Now, go back to the App Review page and try to
switch the app to public again.
3. You’ll be prompted to choose a category for your app.
4. Choose Education from the list. Feel free to choose whichever
suits your bot best.
5. Click the Confirm button as shown in the Figure 3-19, Making
your facebook app public.
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Figure 3-19. Making your facebook app public
We will also need to create a username for our page. This is the username users will
chat with when using our bot. To set the username, click the Create Page @Username
link under the About section of your page, as shown in Figure 3-20. This is helpful to
share your page or bot with people using just a short name.
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Figure 3-20. Creating your Facebook bot's page username
Let’s test the same flow of our bot on Facebook Messenger that we tested on
Dialogflow’s website. You should be able to see how my Facebook Messenger bot
responded by referring from Figure 3-21.1 up-to Figure 3-21.4.
Figure 3-21.1. Facebook Messenger OnlineEatsBot Demo Screen I
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Figure 3-21.2. Facebook Messenger OnlineEatsBot Demo Screen II
Figure 3-21.3. Facebook Messenger OnlineEatsBot Demo Screen III
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Figure 3-21.4. Facebook Messenger OnlineEatsBot Demo Screen IV
And that, folks, is how we build our bot.
Chapter 4 is going be even more interesting. In Chapter 4, we will try to achieve the
same without having to depend on Dialogflow’s API or dashboard.
It’s always good when you have full control over everything you have, isn’t it?
Note: You can go to your account settings and export an agent or import other agents
directly. You can download the zip file (OnlineEatsBot.zip). You can use this zip to
import this into Dialogflow and play with the chatbot we built in this chapter.
You must be wondering, what if I wanted to make the order placement in real-time
and find the order status using APIs of the vendor/restaurant and reply to the user
accordingly? It could be any API call you want to make—retrieve the data in real-time
and formulate the bot’s response. It’s time to know how to do that before we wrap up this
chapter and get ready for the next one.
Let’s learn about something called “Fulfillment” in Dialogflow.
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Fulfillment
In order to get some real-time information requested by a user, we need to develop some
API or use existing ones. To achieve that using Dialogflow, we would need to set up
fulfillment , which requires deploying a service and calling an API.
We will not go into the nitty-gritty of building APIs and how to deploy it but if you
have ever tried using any Google or Facebook APIs, you should be familiar with at least
how to call them.
I have built a small Flask-based API and deployed it to Heroku. I will be using it for
fulfillment, which just takes any order_id in the url and returns a random order_status.
If you are not familiar with Heroku, then do not worry, you can run the code provided
on your local system and test it. In the upcoming chapter, we’ll be deploying lots of
application using Heroku where you can learn things related to it.
In the code you can read how order_identity, intentName, etc., are parsed.
Find the code here: flask_onlineeats_demo.zip
Request URL: https://radiant-bayou-76095.herokuapp.com/onlineeatsbot/
api/v1.0/order_status/
So, in Dialogflow Fulfillment will POST the JSON response from the intent to this
URL, which you would need to parse to get the relevant entities and their values and do
specific actions.
You can also try to deploy the sample Flask App code on Heroku and have your own
endpoint working in your bot for fulfillment.
Now, Dialogflow will POST the JSON response of the intent for which the webhook call
is enabled on our endpoint. It has the code to parse the order_id entity and take actions
based on that. Currently, the code only returns a randomly chosen status from a list.
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To test if the API is working, go to POSTMAN and test it using the sample data in
Figure 3-22. If you are running the Flask app on local, then use the local url.
Figure 3-22. Testing the fulfillment API deployed on heroku in POSTMAN
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Enabling Webhook
So, let’s go to the fulfillment page in Dialogflow and try to enable webhook
(see Figure 3-23).
Figure 3-23. Setting up webhook for fulfillment in Dialogflow
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Make sure you have enabled webhook call for user_order_id intent
(see Figure 3-24).
Figure 3-24. Enabling webhook call for specific intent
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Dialogflow will POST a JSON body to your webhook URL, which will look like
Figure 3-25.
Figure 3-25. Incoming JSON data from Dialogflow to our webhook endpoint
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Checking the Response
Dialogflow expects a response from your web service in the format shown in Figure 3-26
whenever they POST an intent’s JSON response (shown in Figure 3-25) to your web
service.
Figure 3-26. Response from the webhook URL expected by Dialogflow
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If you are thinking that your API’s response should be exactly in the same format as
Figure 3-26, then relax—that’s not the case. Your intent won’t throw errors because all of
the keys in the JSON body are optional.
Here is how my API response looks and works perfectly:
{
"fulfillmentText": "Order ID: 9999. It's on the way",
"payload": {
"facebook": {
"text": "Order ID: 9999. It's on the way"
}
}
}
When I try to hit the same API again I get a different order status text but with the
same format expected by Dialogflow engine.
{
"fulfillmentText": "Order ID: 9999. Rider has picked up your food,
please wait for another 10-15 minutes",
"payload": {
"facebook": {
"text": "Order ID: 9999. Rider has picked up your food, please
wait for another 10-15 minutes"
}
}
}
fulfillmentText is the key that matters for the agent to reply something back
to the user.
Now, try the bot with the public URL or in the Dialogflow agent itself to see the
responses coming from the API instead of the default static responses we added earlier.
This is how we can integrate an external API or our own API using Dialogflow’s
fulfillment feature into our chatbot to make things dynamic and real-time.
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Summary
In this chapter we learned about Dialogflow and how to use Dialogflow to build a
chatbot. We learned to define intents and their respective entities. We built a simple
food ordering chatbot that understands the order food intent from the user and also
understands what food items the user has asked for as well as the quantity. We also
enhanced our chatbot to let users ask about the status of their order and take their order
ID from them and formulate a response with different order statuses.
We also learned about fulfillment in Dialogflow, where we pulled the status of the
order from our own API and gave the response to the user based on that. We learned to
create a web demo of our chatbot, and we also integrated our bot with Messenger. At this
point, you should have a fair idea of how a chatbot works end-to-end.
In the next chapter we are going to try the harder way of building chatbots. Yes,
you heard it right. We are going to remove the dependency from tools like Dialogflow
and build everything ourselves programmatically. Let’s get ready for the next chapter,
because that is going to be even more fun when you have built everything from scratch
yourself. It’s like training and taming your own chatbots.
See you in the next chapter.
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CHAPTER 4
Building Chatbots
the Hard Way
“Building Chatbots the Hard Way” is not too hard to learn. It’s the hard way of building
chatbots to have full control over your own chatbots. If you want to build everything
yourself, then you take the hard route. The harder route is hard when you go through it
but beautiful and clear when you look back.
It is a rough road that leads to the heights of greatness.
—Lucius Annaeus Seneca
If you know Python well and have a bit of understanding on how to setup
packages, etc., you won’t have any issues learning this chapter. If you are a developer,
it should be easy for you. If you are a manager or non-technical person, you can still
do the steps one-by-one as mentioned in each section and get things done. I strongly
recommend everyone complete this chapter so that you learn core things about
building chatbots.
This chapter not only teaches you to build chatbots from scratch but also shows
you how core machine learning (ML) works with NLP with the help of Rasa NLU. As
mentioned in the first chapter on this book, it is always good to have decision trees when
you are building chatbots. In this chapter, we’ll barely be using any rules, but ML is not
at that stage yet to be 100 percent reliable. So, this decision is based completely on your
use-case and whether you want to apply some business logic along with your ML models
or not. Sometimes, your ML model may work so well that you don’t need any heuristics
at all. But in my experience, when you go on to sell your chatbots or commercialize
them, you have to be a little careful. No functionality is better than having a functionality
that doesn’t make sense.
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© Sumit Raj 2019
S. Raj, Building Chatbots with Python, https://doi.org/10.1007/978-1-4842-4096-0_4
ChaPtEr 4 BUIldIng Chatbots thE Hard Way
We’ll be using an open-source library called Rasa NLU for learning how to build
chatbots from scratch without using any cloud services like Dialogflow, Watson, wit.ai,
etc. Remember Rasa NLU is a very sophisticated library and has lots of features in it. We
are only going to touch upon the concepts and features that are important for us to build
our chatbot.
What Is Rasa NLU?
Rasa NLU is an open-source NLP library for intent classification and entity extraction in
chatbots. It helps you build and write custom NLP for your chatbots.
There are two parts of Rasa that we’ll be covering in this chapter.
Rasa NLU: With Rasa NLU we’ll be learning to prepare our training
data for our chatbot, writing configuration files, choosing a pipeline
and training the model. Lastly, we will be predicting the intent of
a text using our model. We’ll also be learning how to parse entities
using Rasa NLU.
Rasa Core: In the second part we’ll be learning to train the Rasa Core
dialog management model to prepare responses back to the user.
Tis section becomes very important when you have a variety of
intents in your chatbot and their follow-up questions or responses.
Instead of writing lots of conditions in our decision tree and spending
hours debugging it in case of a big enterprise-level application, it’s
better to teach the model to create responses. It’ll be interesting to see
how well our trained model performs in doing that. We can’t just spit
out anything to the user in the form of text; it should make sense.
Why Should I Use Rasa NLU?
Rasa NLU is not just any other library with a bunch of methods to do some things. It has
the capability of building almost any kind of chatbot that you can imagine. Rasa brings
you the magical capability of training the machine to understand the meaning of a text
rather than you writing rules to understand it.
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Let’s look at the following points on why we should use Rasa NLU:
• Rasa NLU is an actively maintained project and has a good
community to support it.
• If we don’t want to share our user’s sensitive data with a third party,
we must use open-source tools like Rasa NLU to build chatbots from
scratch. Tis way all the data remains and gets processed on our own
servers.
• Depending on third-party services for training your data and finding
the intents of user utterances will require you to call APIs that may
not always be reliable. What happens to your chatbot application if
their server is down?
• Using Rasa NLU for building chatbots will give you full command
over your chatbots. You can train, tune, and optimize it the way you
want with the data you want. With Rasa NLU we can experiment
with which ML algorithm works best for our dataset rather than
depending on a fixed algorithm.
Diving Straight Into Rasa NLU
In this section we’ll dive straight into the hands-on part and try installing the Rasa stack
and start working on building our ML models by training our data. We’ll be using some
of the cooler open-source libraries to make our lives easier.
Installing Rasa
To install Rasa, run the following pip command that we tried in previous chapters for
installing spaCy. Note that we’ll be using Rasa version 0.13.2.
pip install rasa-nlu==0.13.2
Rasa NLU has multiple components for classifying intents and recognizing entities.
Different components of Rasa have their own sets of dependencies.
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When we train our model, Rasa NLU checks that all the required dependencies are
installed. If you want to install the full requirements needed to entirely use the Rasa
library, you can execute the following steps:
git clone https://github.com/RasaHQ/rasa_nlu.git #Clone the repo
cd rasa_nlu #Get into the rasa directory
pip install -r alt_requirements/requirements_full.txt #Install full
requirements
The first step may take some time. Be as patient as earth until it completes.
Deciding a Pipeline in Rasa
Pipeline is nothing but a set of algorithms to be used to train your model. Rasa NLU has
two widely used pipelines called spacy_sklearn and tensorflow_embedding. Let’s learn
a bit about both.
spacy_sklearn
spacy_sklearn pipeline makes use of pre-trained word vectors from
either GloVe algorithm or an algorithm developed by the Facebook AI
team called fastText.
spacy_sklearn works amazingly well in situations where, suppose
you have an utterance like, “What is the weather in Boston?” When
we train our model on the same utterance example and then ask it to
predict the intent for, “What is the weather in London?” our model
is now intelligent enough to know that both the words “Boston” and
“London” are similar, and they belong to the same intent.
This pipeline is very useful with small sets of data.
tensorflow_embedding
tensorflow_embedding pipeline doesn’t make use of any pre-trained
word vectors like spacy_sklearn, but it adjusts itself as per our own
provided dataset.
Te good thing about tensorflow_embedding pipeline is that our word
vectors will be as per our domain.
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• To explain how tensorflow_embedding works with an example, in
English language, the word “play” may be closely related to “a sport”
or “an activity of enjoyment or recreation,” and it may seem it very
different from the words “an act.” In a theater domain, “play” and
“an act” are closely related, where “play” means “a form of literature
written by a playwright,” and it is very necessary to tell our model to
learn specific to our domain and not get confused due to some
pre-ŧrained model.
Training and Building a Chatbot From Scratch
If you have gone through Chapter 3 of this book, you must be familiar with the “Food
Ordering Chatbot” we built using Dialogflow. You must be aware of intents, entities, and
responses back to the end user by a chatbot.
Similarly, we are going to take a use-case of a chatbot and build it from scratch in this
chapter. You need not necessarily use the same example. Feel free to find a use-case of
your own, follow the steps in this chapter, and build your own chatbot at the completion
of this chapter.
We are going to build a horoscope bot that understands user queries and tells them
their horoscope for the day. So, let’s get started.
Building a Horoscope Bot
In this example of building a chatbot completely on our own using the open-source
library Rasa NLU, we are going to build a Horoscope Bot. Let’s decide the scope of this
chatbot and see what it does and can do.
• The Horoscope Bot should be able to understand greetings and reply
with a greeting.
• Te bot should be able to understand if the user is asking for a
horoscope.
• Te bot should be able to ask the horoscope sign of the user if the
user doesn’t provide it.
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• If the user doesn’t know his/her horoscope sign, the bot should ask
the user’s date of birth (DOB) and find it for them.
• Te bot should subscribe/unsubscribe the user to get the daily
horoscope.
• Te bot should learn from existing responses to formulate a new
response.
• The bot should be able to handle spelling mistakes by the user.
It is pretty simple what our bot is supposed to do here.
Possible intents
• Greeting Intent: User starting with a greeting
• Get Horoscope Intent: User asking for horoscope
• User’s Horoscope Intent: User telling the horoscope sign
• User’s DOB Intent: User telling his/her DOB
• Subscription Intent: User asking for subscription
Depending on the way you design your bot and write code, you may or may not need
all of the above intents or you may need to define some extra intents as well to cover
all the use-cases we want our bot to do. As we move forward in this chapter, we’ll try to
build the bot that does the basic task of giving a horoscope; later as a part of the exercise,
we can add more use-cases to it.
Well, this is pretty much what we have been doing so far to recognize the possible
intents based on a simple possible conversation between a bot and a user.
Conversation Script Between the Horoscope Bot
and the User
Let’s create a possible conversation script between our chatbot and the user. As
mentioned in the first chapter, writing a conversation script is like preparing a software
requirement document before building the software. It may change later but gives you
an overall view of what’s going to happen in the final chatbot application.
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The following is what our conversation between the user and the bot looks like:
User: Hello
Horoscope Bot: Hello! Hope you are doing well.
User: I want to know my horoscope.
Horoscope Bot: Sure, may I know your horoscope sign?
User: Capricorm
Horoscope Bot: Sorry, did you mean Capricorn?
User: Yes.
Horoscope Bot: Alright, Here is your horoscope for today.
Chances are that you’ll feel upbeat and positive today. Share this
energy with others who feel less than content. Your attitude can
have a profound effect on friends, partners, spouse, and children.
Neighbors and extended family can pick up on your spirit without
you even realizing it. If someone is hurt or angry, be encouraging.
Everything will eventually work out.
Horoscope Bot: Would you like to subscribe for the daily updates?
If yes, just say subscribe.
User: Yes, please subscribe.
Horoscope Bot: Great, you are subscribed now. Have a good day.
This conversation is just to have a fair idea of how our chatbot conversation is going to
look. We may or may not want to change the flow of the conversation based on our needs.
We can have our chatbot model itself trained to prepare a valid response instead of
writing a bunch of if…else statements.
Preparing Data for Chatbot
Rasa NLU has multiple ways of defining the intents and their entities on our custom
data. It supports data in markdown, in JSON as a single file, or as a directory containing
multiple files.
We are going to discuss the most difficult, but highly scalable, method first. Creating
JSON files is difficult by hand but programmatically very easy and scalable.
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Creating Data for Model in JSON Format
The JSON format of the data that Rasa NLU expects has a top-level object called
rasa_nlu_data, with the keys common_examples, entity_synonyms, and
regex_features.
The most important one with which we are going to be working is common_
examples. The following is the skeleton form of how our JSON data is going to look:
{
"rasa_nlu_data": {
"common_examples": [],
"regex_features" : [],
"entity_synonyms": []
}
}
The common_examples key in our JSON data is the central place that’ll be used to
train our model. We will be adding all our training examples in the common_examples
array.
regex_features is a tool to help the intent classifier recognize entities or intents and
improve the accuracy of intent classification.
Let’s start writing our JSON file. Let’s call it data.json.
1. Create a folder called horoscope_bot.
2. Change the current working directory to horoscope_bot.
3. Start Jupyter Notebook #jupyter notebook.
4. Create a new folder called data.
5. Click on the data folder and go to “Text File” under New menu in
Jupyter Notebook.
6. Click on the name of the file created and change the name to
data.json and write your intents for your chatbots.
For Steps 5 and 6, feel free to use your favorite editors like Sublime, Notepad++,
PyCharm, etc., to work with the JSON file.
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This following is what my data.json under data folder looks like:
{
"rasa_nlu_data": {
"common_examples": [
{
"text": "Hello",
"intent": "greeting",
"entities": []
},
{
"text": "I want to know my Horoscope",
"intent": "get_horoscope",
"entities": []
},
{
"text": "Can you please tell me my horoscope?",
"intent": "get_horoscope",
"entities": []
},
{
"text": "Please subscribe me",
"intent": "subscription"
}
],
"regex_features": [],
"entity_synonyms": []
}
}
Well as you can see, it looks very clumsy to prepare this by hand. You must be
remembering the nice and easy method we had in Dialogflow. So, let’s check a cool and
interesting tool for creating training data in the format that Rasa expects. It was created
by Polgár András, and it’s also pretty much good for inspecting and modifying existing
data that we prepared earlier. This tool saves a lot of time if you are working on a small
project where you have to create the data by hand. It’s always a good idea to visualize the
data in any application you are building that is completely data-driven.
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So, just save the earlier data.json file we created until we extend the data collection
using a better method.
Visualizing and Modifying Rasa’s JSON Data Format
In this section we’ll make use of a tool called Rasa NLU trainer to visualize our data.
(i.e., the data that we have created so far). This tool helps us in annotating the data as
well. If you remember when the Dialogflow interface was explained in Chapter 3, it was
so easy to define entities, their names, and the types. We are going to do the same using
an open-source tool.
Rasa NLU trainer is a very nice and handy tool to edit our training data right from our
browser itself. Handling JSON data is tricky and also can lead to errors. With this handy
tool we can easily add more examples to our training data or edit the existing ones. It
saves a lot of time from manually annotating the data. rasa-nlu-trainer is a javascript-­
based tool, so we would need to install node.js to run this tool on our system. It doesn’t
take more than 5 minutes to do that. Let’s get this set up by following these steps:
1. Go to https://www.npmjs.com/get-npm and download node.js.
2. Install the package on your system as guided on the website. Once
installed, go to a fresh terminal/command line interface on your
system and type “npm” to see if it works.
I have installed LTS version 8.11.4. Once installed, run the following command to
install rasa-nlu-trainer:
sudo npm i -g rasa-nlu-trainer
After the successful installation this command, you’ll see logs similar to the
following:
[fsevents] Success: "/usr/local/lib/node_modules/rasa-nlu-trainer/
node_modules/fsevents/lib/binding/Release/node-v57-darwin-x64/fse.node"
already installed
Pass --update-binary to reinstall or --build-from-source to recompile
npm WARN slick-carousel@1.8.1 requires a peer of jquery@>=1.8.0 but none is
installed. You must install peer dependencies yourself.
+ rasa-nlu-trainer@0.2.7
added 492 packages in 10.14s
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Even if your message doesn’t look like this, as long as it doesn’t throw any errors
then don’t worry. We’ll get to know in a moment if our rasa-nlu-trainer is successfully
installed and works well.
Let’s go to our data folder that we created earlier in our terminal and run the
following command:
rasa-nlu-trainer
Typing this command will crank up a local server on port 55703 and open it in the
default browser. It will look something like Figure 4-1.
Figure 4-1. rasa-nlu-trainer in localhost
As you can see, all of our existing data from data.json is picked by this amazing tool
for us to delete or edit and we can also add new examples from here and it will keep on
extending the data.json file.
I would suggest that you add more data to your intents for better training of the
model. You can get this data.json in the source code zip or repo provided by the
publisher, if you are trying to build the same chatbot explained in this chapter.
Just as we selected the entities within utterances to define them in Chapter 3 using
Dialogflow, we can do the same using this tool and give names to our entities for parsing
later. So, click on the toggle button of the example, select the text, and add an entity by
giving it a name.
I have added five to six utterance examples in each of the intents I have defined. The
more examples we add, the better it is for the model to be trained and provide better
accuracy.
If you look at the data.json file now, it will have more examples automatically added
to it. So, go ahead and verify your data.json file to see if you have all your added examples
from rasa-nlu-trainer UI.
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You’ll also notice in the data.json file that the entities you might have defined using
the rasa-nlu-trainer UI are captured in the common_examples list as having start and
end keys, which tells the model at what point the particular entity value starts in the
example and when it ends.
The same dictionary object also depicts the value of the entity and the name of the
entity we defined. For our example, it looks like the following:
{
"text": "19-01",
"intent": "dob_intent",
"entities": [
{
"start": 0,
"end": 2,
"value": "19",
"entity": "DD"
},
{
"start": 3,
"end": 5,
"value": "01",
"entity": "MM"
}
]
}
Training the Chatbot Model
In this section we are going to train a model on the data we prepared. As we used Jupyter
Notebook for our file creation and management, we will be creating a new .ipynb and
start writing our Python code to train our model by choosing one of the pipelines we
discussed earlier in this chapter.
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Creating a Configuration File
Let’s create a JSON file again in the same way we created one earlier using Jupyter and
name it config.json. Let’s keep it outside our data folder (i.e., in the horoscope_bot that
is our project directory).
Add the following configuration to it:
{
"pipeline":"tensorflow_embedding",
"path":"./models/nlu",
"data":"./data/data.json"
}
As you can see, there are some important configuration parameters done in our
config.json file. Let’s try to understand them.
pipeline: Pipeline is going to specify what featurizers or feature
extractors are going to be used to crunch text messages and extract
necessary information. In our case, we are using tensorflow_embedding.
path: path is essentially the directory where we keep our model after
the training. We are going to keep our model in the /models/nlu folder.
data: data is the path we need to specify; it’s basically where our
training data sits.
As we are done with our config.json file, let’s move on to some Python code to train
our ML model.
YAML CONFIGURATION
You can also use .yml file as config files like below. You can get the example config.yml files in
github repo.
• Example 1:
language: "en"
pipeline: "tensorflow_embedding"
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• Example 2:
language: "en"
pipeline:
- name: "nlp_spacy"
- name: "tokenizer_spacy"
- name: "intent_entity_featurizer_regex"
- name: "intent_featurizer_spacy"
- name: "ner_crf"
- name: "ner_synonyms"
- name: "intent_classifier_sklearn"
All incoming messages are processed as per the sequence of components defined.
The defined components are run in a sequential manner, one by one, and hence are
called the processing pipeline. Different components are used for different purposes,
such as entity extraction, intent classification, pre-processing, etc.
The benefit of such a format is that we can specify the predefined pipelines by Rasa
in a clear manner.
Writing Python Code to Train the Model and Predict
Let’s open up a new .ipynb file and start writing our code. Let’s name the ipynb as rasa-­
nlu.ipynb. Make sure you have already successfully installed rasa-nlu==0.13.2 for the
Python version you are using.
The following is what our code looks like to use our data.json and config.json in
Python and train a model using tensorflow_embedding pipeline.
from rasa_nlu.training_data import load_data
from rasa_nlu.model import Trainer
from rasa_nlu import config
from rasa_nlu.model import Interpreter
def train_horoscopebot(data_json, config_file, model_dir):
training_data = load_data(data_json)
trainer = Trainer(config.load(config_file))
trainer.train(training_data)
model_directory = trainer.persist(model_dir, fixed_model_name =
'horoscopebot')
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def predict_intent(text):
interpreter = Interpreter.load('./models/nlu/default/horoscopebot')
print(interpreter.parse(text))
In the first section of the code, we import all the necessary libraries needed
from rasa_nlu package. Then we define two methods called train_horoscopebot and
predict_intent, where the first method trains the model given the data, config_file,
and model_directory (place to store the models) and predict_intent method uses the
Interpreter model from rasa_nlu to load the pre-trained model files and gives the user
the ability to predict any new text examples.
Training the Model
We run the below snippet to call our train_horoscopebot method with the respective
parameters
train_horoscopebot('./data/data.json', 'config.json', './models/nlu')
After running this code in our rasa-nlu.ipynb, we will get an output like this:
Epochs: 100%|
| 300/300 [00:01<00:00, 175.69it/s,
loss=0.075, acc=1.000]
The code for training the chatbot model will create the models folder, which you can
see using Jupyter or using your file explorer or finder app. It will create a bunch of index,
meta, and pickle files at the model directory destination we provided.
Predicting From the Model
Let’s call the predict_intent method by passing a text to see how our trained model
performs.
predict_intent("I am looking for my horoscope for today. I am wondering if
you can tell me that.")
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The method itself prints the output. For the above text, my output looks like the
following:
INFO:tensorflow:Restoring parameters from ./models/nlu/default/
horoscopebot/intent_classifier_tensorflow_embedding.ckpt
{
"intent": {
"name": "get_horoscope",
"confidence": 0.9636583924293518
},
"entities": [],
"intent_ranking": [
{
"name": "get_horoscope",
"confidence": 0.9636583924293518
},
{
"name": "dob_intent",
"confidence": 0.03462183475494385
},
{
"name": "greeting",
"confidence": 0
},
{
"name": "subscription",
"confidence": 0
}
],
"text": "I am looking for my horoscope for today. I am wondering if you
can tell me that."
}
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Wow! Isn’t it magical? Our model has predicted this text with a confidence of 96
percent. You can see in the provided ipynb file that our model does well in predicting
other intents as well. This is the power of tensorflow and ML overall. Needless to say,
rasa_nlu library makes it so easy to believe. So, it’s time for you go back in retrospection,
and if you remember Chapter 3 of this book, then you must remember whenever we
used to add a new example, the Dialogflow used to re-train the model. It was actually
doing the same as we just did, behind the scenes. We couldn’t have changed the model
or tuned any parameter there, but we can do that all now with full control.
Now that we have successfully built and trained a model using tensorflow and tested
it as well, we’ll move on to the next topic of Dialog Management. I would request you test
all the scenarios your bot might be facing so that you know the points where your model
is not performing well and, accordingly, you can add more data or tune the parameters if
needed.
Also, remember that you only need to re-train the model whenever there is a change
in the training data. If there is no change in the training data, we can load the existing
training model to keep predicting on new examples.
Dialog Management Using Rasa Core
In this section we are going to get our hands dirty by training another model for Rasa
Core dialog management. Remember, at this point we have a model ready to predict the
intent of the text, we can write some Python code to formulate responses, and we can
reply back to the customer. But what if we want to add more intents to our bot? Is that
scalable in the case of a bigger application with lots of features? The answer is no. Here
dialog management from Rasa Core comes to the rescue.
If you have ever tried to use any bot on any platform, you must have seen it failing
in certain conditions. Yes, we all have been there and it still exists as today’s bot fails to
manage the contexts of the conversation miserably or follow the suit of conversation.
With the help of Rasa Core’s ML-based dialog framework we can fix this issue easily.
Rasa Core is well-proven for enterprise-level applications and used by thousands
of developers because it is production-ready, easy to use and extend, and, most
importantly, open source.
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Understanding More on Rasa Core and Dialog System
Before we actually move to the coding part of Rasa Core for our dialog management
model, it’s really important to understand why and where this is coming from.
We’ll try to understand how we have been doing things so far for building the
chatbots and how this is going to be changed forever.
Let’s take an example:
If we were to build a simple chatbot that would help users book flight/bus/movie/
train tickets, the easiest way would be to create a state machine or decision trees, write
a bunch of if…else, and it would be done. This would work but wouldn’t scale. If a
customer is having a good experience initially with something, they want to use it more.
By some heuristics, we can show a chatbot to be intelligent but not for very long. When
the control flow of the code goes from try block to except block, we start scratching our
heads.
Figure 4-2 is a simple representation of how a state machine for building this chatbot
may look.
Figure 4-2. State diagram representation for ticket booking chatbot
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If we look at our state diagram, it may work for a normal conversation where the
user is looking for movie, bus, or train tickets or wants to book a bus ticket after asking
for movie tickets. What if the user asks for both bus and movie tickets together? You
may say that we can add a couple more if…else statements in our already nested code to
handle that. If you are a good developer, it won’t take you much time to write an in-out
from a state machine or extend your decision tree. But think of the situation when these
conditions start growing exponentially and you have to keep adding cases to handle that
and they start interfering with each other as well.
Our brain works in a way where we learn and relearn. If a kid doesn’t know what
a fire is going to do to them, they touch it, but when it hurts them, they do not do it
again. They reinforce a fact that it’s harmful. It works similarly in the case of rewards—
when you do something and get something, you associate a fact that doing something
brings rewards or better rewards and then you intend to do that again. This is called
reinforcement learning in ML, where a machine learns how to behave in a particular
situation by performing actions and understanding the results. Reinforcement learning
is sometimes not the best approach, such as in situations where data is not sufficient to
be learned, data quality is not good to learn the reward scenarios, etc.
Figure 4-3 is a diagram to help you understand how Rasa Core fits well within the
Rasa Stack and its role along with Rasa NLU, which we have been learning so far.
Figure 4-3. Diagram representing the working of Rasa NLU and Rasa Core
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With Rasa Core, we can specify all of the things our bot is supposed to say or do.
These are called actions. One action might be to say a greeting to the user or to query a
database, or it could be to retrieve some information using some web service or API.
Rasa Core provides the facility to train our probabilistic model to predict what action
has to be taken based on the historical conversation of the user or users. Imagine doing
it without an ML model and writing hundreds of use-cases to figure out what response
or action should be sent or executed. In simple terms, Rasa Core solves this problem
for you.
Let’s look at the side-by-side user vs. bot conversation that we built in Chapter 3
while building an OnlineEatsBot.
User
OnlineEatsBot
Hello OnlineEats
Hello! How may I help you?
I am looking to order food
Sure, What would you like to order today?
One chicken burger
Done. Your final amount is $3.99
Thank you
Thanks a lot
This conversation is pretty much simple and can be done without any ML model as
well, even though we did use a trained model to recognize the intent and entities in it.
Now, the interesting question that will come to your mind is, “What if I want my bot
to be able to change the order? What if I want my bot to be able to remove or add more
quantity of the food items?” So, as discussed, add more use-cases, more complexity,
more intents and their utterances, more if…else in code to handle corner-cases, but
when you are building a chatbot for a business you have to scale it to generate more
revenue through it. All software systems do that, and those who don’t fail to survive. The
bottom line is, we can’t keep changing and deploying the code.
So, instead of handling all the cases as they occur and repeatedly doing it, we
can take the ML route to tackle this problem, with the help of Rasa Stack, which
will tell us what the bot should do next based on the user’s context and the state of
the conversation. Since the model learns itself based on the context of the previous
conversation data, it becomes easier for the bot to keep the conversation more natural
and user-friendly rather than randomly choosing from four to five fixed sentences.
Rasa recommends users with little or no data use interactive learning. We’ll get to
know more about interactive learning later in this chapter.
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Before we actually start using Rasa Stack to write the core part of our bot, first we
need to understand a few concepts.
Understanding Rasa Concepts
It’s really important that we understand a few specific concepts related to Rasa before
actually trying to use them in code completely. In this section we’ll be learning a few
important and very useful concepts of Rasa NLU. Make sure you understand these
concepts completely, as we will be using them in building our first in-house chatbot
using Rasa’s domain file format. If we do not understand what these concepts mean,
then it’ll be difficult to take the next step.
Action
As the name suggests, it’s a specific action that can be taken. As per Rasa documentation,
it says, “Next action to be taken in response to a dialog state.”
For example, if a user asks the horoscope for today, our bot could execute the
“GetTodaysHoroscope” action. Let’s see how a “GetTodaysHoroscope” action would
look in form of a code.
from rasa_core.actions import Action
from rasa_core.events import SlotSet
class GetTodaysHoroscope(Action):
def name(self):
return "get_todays_horoscope"
def run(self, dispatcher, tracker, domain):
# type: (Dispatcher, DialogueStateTracker, Domain) -> List[Event]
user_horoscope_sign = tracker.get_slot('horoscope_sign')
"""Write your logic to get today’s horoscope details
for the given Horoscope sign based on some API calls
or retrieval from the database"""
return [SlotSet("horoscope_sign", user_horoscope_sign)]
The name method returns the name of the Action that we’ll refer to in the domain
file as custom method name.
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The run method does the main job for getting the action done—i.e., the core
business logic resides here. As you can see, it takes three parameters: dispatch, tracker,
and domain.
Let’s understand these parameters one by one:
dispatcher: the dispatcher is used to send messages back to our
users. We can use dipatcher.utter_message() to achieve the same.
tracker: the state tracker for the current user. We can access slot
values using tracker.get_slot(slot_name), and to get the most
recent user message we can use tracker.latest_message.text.
domain: the bot’s domain. We’ll discuss in more detail about the
domain later in the chapter.
Note run method returns a list of Events instances.
Slots
Slots are the ones who make the bot responsible for working like a human. Slots are like
a storage space where the information given by the user can be stored or a pre-fetched
information from a database or an API can also be used.
There are different slot types for different use-cases:
For example, in our use-case where we want to build a HoroscopeBot, we may want
to use the slot type as text for the horoscope_sign provided by the user.
Depending on what type of slot you want to keep, Rasa provides some predefined
slot types.
Other than text, Rasa has the following slot types:
Boolean ⇒ Used For True/False
Categorical ⇒ Used for situations where you have to pick one of
some values
Float ⇒ Used for continuous values
List ⇒ Used for list of values
Featurized ⇒ Used for internal value storage that doesn’t affect the
conversation
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Templates
Templates is a word you must have heard in your life at least once, while finding a
template for sending an e-mail, for preparing a document, for building a portfolio
website, or to follow a process.
Templates in Rasa are used for utterances. An utterance template contains a set
of preset text to be sent to the user when some action is triggered. By having the same
name of the action as the utterance or by an action with custom code, we can send our
formatted message in templates to the user.
A simple representation of templates inside the domain file may look like the
following:
templates:
utter_greet:
- "hello {name}!"
# name can be filled by a slot of same name or by
custom code
utter_goodbye:
- "goodbye"
- "take care bye"
# multiple templates allow the bot to randomly pick
from them
utter_default:
- "Sorry, I didn’t get that."
Now, that we have gone through the three concepts of actions, slots, and templates
and we already know what intents and entities are as part of our learning from Chapter 3,
we are now ready to dive deep into Rasa and start writing code for our first chatbot.
Creating Domain File for the Chatbot
The first and foremost task to do while building a chatbot using Rasa stack is to create a
domain file.
As per the documentation of Rasa, “The Domain defines the universe in which your
bot operates. It specifies the intents, entities, slots, and actions your bot should know
about. Optionally, it can also include templates for the things your bot can say.”
Now, you know why we had to be prepared for this situation previously by
understanding Rasa core concepts.
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Let’s create a DefaultDomain file with a YAML definition. Rasa uses .yml files to
define the domain format.
Originally YAML supposedly meant Yet Another Markup Language, depicting
its use as a markup language, but it was changed later to be understood as YAML Ain’t
Markup Language, a recursive acronym, to differentiate its purpose as data-oriented
rather than a document markup language.
Now, let’s go back to our rasa-nlu Jupyter Notebook directory and start creating the
files. Note that we can write all our code in separate files using a command line and
use an editor to edit it. I find Jupyter Notebook more interactive and easily accessible to
browse through the files. Whatever you are comfortable with, go with that, but it is good
to know most of the features that Jupyter Notebook provides.
Go to the main horoscope_bot directory and create a file, let’s call it
horoscope_domain.yml.
The following is the content of horoscope_domain.yml for our bot:
slots:
horoscope_sign:
type: text
DD:
type: text
MM:
type: text
subscribe:
type: bool
intents:
- greeting
- get_horoscope
- subscription
- dob_intent
entities:
- horoscope_sign
- DD
- MM
- subscribe
- dob_intent
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templates:
utter_greet:
- 'Hello! How are you doing today?'
utter_ask_horoscope_sign:
- 'What is your horoscope sign?'
utter_ask_dob:
- 'What is your DOB in DD-MM format?'
utter_subscribe:
- 'Do you want to subscribe for daily updates?'
actions:
- utter_greet
- utter_ask_horoscope_sign
- utter_ask_dob
- utter_subscribe
- get_todays_horoscope
- subscribe_user
As you can see, the domain file is comprised of five important parts: intents,
entities, slots, templates, and actions, which we discussed earlier.
Note that for every template, there is an utterAction defined, such as utter_greet,
utter_ask_horoscope_sign, and utter_ask_dob, we must have a template defined in
the template section with the same name.
As you can see in our example, there are mainly five actions defined, where the first
three actions are just for uttering a template text to the user, but the last two actions
require us to either retrieve the data from the database or make an API call to get the
horoscope for the day and return it back to the user.
In the case of subscribe_user action as well, we need to do an operation where we
add the current user to the subscribe list in the database. These user-defined actions
are called custom actions. To have such custom actions, we need to write what the bot is
supposed to do when these actions are triggered.
In the next section we are going to learn how to write custom actions.
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Writing Custom Actions of the Chatbot
As we know, whenever an UtterAction is triggered, our bot will respond back with the
utter text defined in the template for that Action. But what happens when some custom
action is triggered? In this section we’ll write Python code to create custom actions,
which we can use to make API calls or pretty much any kind of thing that you can do with
Python.
Let’s create a new file called actions.py in our project directory (i.e., in our case
inside horoscope_bot folder).
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import requests
from rasa_core_sdk import Action
from rasa_core_sdk.events import SlotSet
class GetTodaysHoroscope(Action):
def name(self):
return "get_todays_horoscope"
def run(self, dispatcher, tracker, domain):
# type: (Dispatcher, DialogueStateTracker, Domain) -> List[Event]
user_horoscope_sign = tracker.get_slot('horoscope_sign')
base_url = http://horoscope-api.herokuapp.com/horoscope/{day}/{sign}
url = base_url.format(**{'day': "today", 'sign': user_horoscope_
sign})
#http://horoscope-api.herokuapp.com/horoscope/today/capricorn
res = requests.get(url)
todays_horoscope = res.json()['horoscope']
response = "Your today's horoscope:\n{}".format(todays_horoscope)
dispatcher.utter_message(response)
return [SlotSet("horoscope_sign", user_horoscope_sign)]
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As we can see, there are two methods in our action called GetTodaysHoroscope. The
first method name just returns the name of the action. The other method is run and,
as discussed earlier, is the method that actually does the task by executing the business
logic that we write.
In our method we are making use of an open-source API with code hosted on github
[https://github.com/tapasweni-pathak/Horoscope-API ]
The API url looks like this:
http://horoscope-api.herokuapp.com/horoscope/today/capricorn
which returns the data in JSON format:
{
"date": "2018-08-29",
"horoscope": "You will be overpowered with nostalgia and may long to get
in touch with old pals. And as Ganesha says, chances are that you may
take a liking to your ex-lover, while simultaneously strengthening your
social standing. All in all, the day will be a productive one.",
"sunsign": "capricorn"
}
As you can see in the run method, we convert the response from the API to Python
JSON object and then access the ‘horoscope’ key from the JSON to get the actual
horoscope. After getting the actual horoscope from the JSON, we formulate a response and
dispatch it back to the user using the dispatcher object and its method, utter_message.
At the end we set the slot using SlotSet method. SlotSet is like saving the variables
that you figured out from user’s responses so that you can use them any time in your
code during the flow of the conversation.
Note Using the above API we can get today’s horoscope by providing the
horoscope sign. You are free to use your own API or database. You just need to
replace the API call with some other source that you want to use.
Just like we added the GetTodaysHoroscope action in the actions.py file, we’ll also
add SubscribeUser action as well. We are not going to use any database to store the user
subscription preferences, but when you are building a chatbot for real users, you may
have to have user_ids that you can link with their subscriptions in the database.
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The following is how the SubscribeUser action looks:
class SubscribeUser(Action):
def name(self):
return "subscribe_user"
def run(self, dispatcher, tracker, domain):
# type: (Dispatcher, DialogueStateTracker, Domain) -> List[Event]
subscribe = tracker.get_slot('subscribe')
if subscribe == “True”:
response = "You're successfully subscribed"
if subscribe == “False”:
response = "You're successfully unsubscribed"
dispatcher.utter_message(response)
return [SlotSet("subscribe", subscribe)]
Like this, we can write as many custom actions as needed.
The next step is the data. Rasa’s dialog management model is trained on actual
conversations that users and the chatbot do. The important point here is that those
conversations have to be converted into a story format.
A story is nothing but an actual conversation between a user and a chatbot where
user inputs are converted into intents and entities while responses being returned
from the chatbot are treated as actions that the chatbot is supposed to trigger when it’s
required.
One of the examples of how a real conversation between a user and a chatbot may
look as a story is given in the table below.
Scenario I
User
HoroscopeBot
Hello there!
utter_greet
I want to know my horoscope for today
utter_ask_horoscope_sign
My sign is Capricorn
actions.GetTodaysHoroscope
Can you subscribe me for updates?
actions.SubscribeUser
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Scenario II
User
HoroscopeBot
Hello there!
utter_greet
I want to know my horoscope for today
utter_ask_horoscope_sign
I don’t know my sign
utter_ask_dob
12-12
actions.GetTodaysHoroscope
We haven’t yet covered the scenario in the code where the user doesn’t know his
horoscope sign but knows his DOB. Here, our code needs some modification to get the
DATE and MONTH entities when horoscope_sign value is not found.
We could use DD-MM values to check the horoscope sign and then call the
GetTodaysHoroscope method explicitly or train the model in that way.
Data Preparation for Training the Bot
It’s always important to have good quality data before doing any kind of ML. For training
our chatbot we also need data; the conversation between a user and a chatbot is the data
on which we need to train our models. Sometimes it becomes difficult to find a dataset
freely on the web that fits our own need.
We should spend the time we need to get the data gathered. We can ask our friends
and family to provide us the sample conversation text of how they would interact with
a kind of bot you are building. Some people create sample example apps for the same
and crowd-source the data. So, better the data, better the model, and better the chatbot
responses.
When it comes to preparing the data, Rasa leaves no stone unturned and comes with
a cool feature called interactive learning. It helps you in generating story data easily and
also trains the dialog management model as we keep adding the story data. You can call
it real-time ML training. So, as we keep on adding our story data, we get to know if our
model is producing correct output or not. Most importantly we get to see if the model is
improving or degrading when we add new stories. In most cases it will get better because
we’ll be kind of doing the reinforcement learning where we tell the ML model to unlearn
and relearn—pretty much what human beings do.
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Creating Story Data
As we know, story data is just a way of conversation between a user and a chatbot on
how it would lead to a logical end. In general, all chatbots are designed to help users with
a set of predefined things; stories just represent how they are done.
We’ll try to prepare some simple dialogs in the format that Rasa expects. These
dialogs will be stateless—that is, they do not depend on previous dialogs. We will be
using our handmade stateless stories for interactive learning.
We’ll take a couple of minutes to just hand-curate a few stories that we know of, so
that we get acclimatized with how story data is created.
Let’s first create a file named stories.md in our data folder.
## story_001
* greeting
- utter_greet
* get_horoscope
- utter_ask_horoscope_sign
* get_horoscope{"horoscope_sign": "Capricorn"}
- slot{"horoscope_sign": "Aries"}
- get_todays_horoscope
- utter_subscribe
## story_002
* greeting
- utter_greet
* get_horoscope{"horoscope_sign": "Capricorn"}
- slot{"horoscope_sign": "Cancer"}
- get_todays_horoscope
- utter_subscribe
* subscription
- slot{"subscribe": "True"}
- subscribe_user
## Horoscope query with horoscope_sign
* greeting
- utter_greet
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* get_horoscope
- utter_ask_horoscope_sign
* get_horoscope{"horoscope_sign": "capricorn"}
- slot{"horoscope_sign": "capricorn"}
- get_todays_horoscope
- slot{"horoscope_sign": "capricorn"}
- utter_subscribe
* subscription{"subscribe": "True"}
- slot{"subscribe": "True"}
- subscribe_user
- slot{"subscribe": true}
## Horoscope with sign provided
* greeting
- utter_greet
* get_horoscope{"horoscope_sign": "leo"}
- slot{"horoscope_sign": "leo"}
- get_todays_horoscope
- slot{"horoscope_sign": "leo"}
- utter_subscribe
* subscription{"subscribe": "True"}
- slot{"subscribe": "True"}
- subscribe_user
- slot{"subscribe": true}
## When user directly asks for subscription
* greeting
- utter_greet
* subscription{"subscribe": "True"}
- slot{"subscribe": "True"}
- subscribe_user
- slot{"subscribe": true}
If you stare at the stories for a couple of minutes, they will confess to you what they
means; it shouldn’t be difficult to make some sense of what is happening. The main
difference between the first two stories is that in the first story the user doesn’t mention
his horoscope sign and the bot is supposed to ask the horoscope sign and then proceed
with the story.
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In the second story, the user himself tells the horoscope sign and then ends the story
with the subscribe dialog. We have added couple of more stories covering more use-­
cases. Feel free to add your own stories in the same file.
So, basically stories are markdown files where we can write as many stories as
needed in the markdown format shown earlier. Doing this seems a bit of a tough task by
hand. So, we’ll try to learn how to use Rasa’s own interactive learning tool to generate
more stories like this.
Let’s get started.
Interactive Learning
We have been talking about interactive learning in bits and pieces so far, but it’s time
to actually write some code and do it. Interactive learning is one of the coolest features
Rasa has where it makes the ML part fun and easy. There are two parts: in the first part,
we train a model by giving our initial dataset using various policies, and in the second
part we test the model, correct it, and re-train it in an interactive manner.
Training the Chatbot Agent Model
Let’s create a new file called train_initialize.py in our main project directory.
Contents of train_initialize.py looks like this:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from rasa_core import utils
from rasa_core.agent import Agent
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.policies.sklearn_policy import SklearnPolicy
if __name__ == '__main__':
utils.configure_colored_logging(loglevel="DEBUG")
training_data_file = './data/stories.md'
model_path = './models/dialogue'
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agent = Agent("horoscope_domain.yml",
policies=[MemoizationPolicy(), KerasPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(
training_data,
augmentation_factor=50,
epochs=500,
batch_size=10,
validation_split=0.2
)
agent.persist(model_path)
This is the code that we write in train_initialize.py file. Now, before we move on
to the next code file, let’s first try to understand important points in it.
1.
First, we import a few methods from __future__ module. Future
statements in Python have special usage, they change how your
Python module is parsed, and they change the way your existing
method behaves.
Curious personality ? Try the below code in your python
interpreter
from __future__ import braces
2.
Import utils method from rasa_core modules to configure
logging.
3.
Import agent class from agent module to create the agent object.
4.
KerasPolicy, MemorizationPolicy will be passed as policies
parameters to agent class.
5.
configure_colored_logging: Utility method defined in utils.py for
colored logging using Python’s coloredlogs package.
6.
Agent: A class defined by Rasa that provides an interface to make
use of most important Rasa Core functionality, such as training,
handling messages, loading a dialog model, getting the next
action, and handling a channel.
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7.
load_data: loads training data from the given path.
8.
train: trains the given policies/policy ensemble using data from
the file provided.
9.
training_data: object returned by load_data method. List of
DialogueStateTracker. Tis is nothing but our training data file.
10.
augmentation_factor: tells Rasa how many dummy stories
should be created given our initial set of stories. 10x factor is a
heuristic for augmentation rounds for training data generator.
11.
epochs: 1 epoch is a complete training cycle on an entire training
dataset. Total number of forward and backward passes of training
data.
12.
batch_size: tells you the amount of training sample to use in each
pass. 100 examples with a batch_size of 10 will take 10 epochs to
go over the entire dataset.
13.
validation_split: percentage of data to validate the unbiased
accuracy of a model.
14.
persist: this method is used to persist the agent object in a given
directory for re-use.
At this point you should be pretty much clear on what each method does and what’s
happening inside the code.
Before we go on to run the script, make sure you have rasa_core library installed
before executing this script.
You can install rasa_core using the following command:
pip install rasa_core==0.11.1
If you are following the chatbot example in this book, then make sure you installed
the aforementioned version only, as Rasa may not be backward-compatible. They are
rapidly coming up with newer and more optimized methods.
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THE LATEST RASA_CORE
You can also install the bleeding edge version of rasa_core from the github repo. You just have
to execute the following set of commands, which will take the latest code from github directly
before installing.
git clone https://github.com/RasaHQ/rasa_core.git
cd rasa_core
pip install -r requirements.txt
pip install -e .
Let’s try to run this code file to train our model as per the given parameters.
$python train_initialize.py
You can also run this script from Jupyter Notebook itself using Jupyter’s magic
command, as shown here:
!python train_initialize.py #Use python3 if you have installed rasa for python3
It should take about 25 to 30 seconds for the model to be trained on such a
small dataset as ours. I added SklearnPolicy in the list of policies along with
MemorizationPolicy and KerasPolicy to train my model. Different policies have their
own benefits. Read more about them to know which may work better for your use-case;
for my dataset SklearnPolicy seems to be performing better than the KerasPolicy at
times.
After the script is done executing you should see some successful messages like these:
2018-08-30 04:24:31 INFO
rasa_core.policies.keras_policy
- Done fitting
keras policy model
2018-08-30 04:24:31 INFO
rasa_core.featurizers
- Creating states and
action examples from collected trackers (by MaxHistoryTrackerFeaturizer)...
Processed trackers: 100%|
| 96/96 [00:00<00:00,
898.31it/s, # actions=75]
2018-08-30 04:24:31 INFO
rasa_core.featurizers
- Created 75 action
examples.
2018-08-30 04:24:31 INFO
rasa_core.policies.sklearn_policy
- Done
fitting sklearn policy model
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2018-08-30 04:24:31 INFO
rasa_core.agent
- Model directory models/nlu
exists and contains old model files. All files will be overwritten.
2018-08-30 04:24:31 INFO
rasa_core.agent
- Persisted model to
'/Users/sumit/apress_all/Chapter IV/horoscope_bot/models/nlu'
You will also find a couple of folders created as per their model name. Make sure
you have them in the model_path you gave in the script. Below are the folders/files I see
inside my model_path folder.
policy_0_MemoizationPolicy
policy_1_KerasPolicy
policy_2_SklearnPolicy
domain.json
domain.yml
Policy_metadata.json
If you have validated that your model has successfully completed the execution and
has persisted the model in your local system, then we are good to go to the next step of
interactive training.
Real-Time Training by Reinforcement
In this section we are going to write some more code to train our dialog model and
retrain when it gives incorrect output.
So, when our bot does something wrong we immediately jump in and let the model
know that its prediction is wrong by telling it what is right. Without having to stop, the
model retrains itself, and once we are done, the interaction between the user and the
bot gets captured to a file and added to our existing training data. It works more like a
feedback system in every step, rather than waiting for a single reward at the end.
The next step is to create a new file called endpoints.yml with the below contents.
We’ll be using this file in our Python code file train_online.py. With this configuration
we can expose the Rasa method as HTTP APIs.
action_endpoint:
url: http://localhost:5055/webhook
#nlg:
# url: http://localhost:5056/nlg
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core_endpoint:
url: http://localhost:5005
Now, let’s create train_online.py for our online/interactive training purposes.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
from rasa_core import utils, train
from rasa_core.training import online
from rasa_core.interpreter import NaturalLanguageInterpreter
logger = logging.getLogger(__name__)
def train_agent(interpreter):
return train.train_dialog_model(domain_file="horoscope_domain.yml",
stories_file="data/stories.md",
output_path="models/dialog",
nlu_model_path=interpreter,
endpoints="endpoints.yml",
max_history=2,
kwargs={"batch_size": 50,
"epochs": 200,
"max_training_samples": 300
})
if __name__ == '__main__':
utils.configure_colored_logging(loglevel="DEBUG")
nlu_model_path = "./models/nlu/default/horoscopebot"
interpreter = NaturalLanguageInterpreter.create(nlu_model_path)
agent = train_agent(interpreter)
online.serve_agent(agent)
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max_history is the number of states to keep track of by the model.
Before we go on to run our final script, train_online.py, we should know and make
ourselves ready for something called rasa-nlu-sdk.
rasa-nlu-sdk
Rasa NLU stack comes up with rasa-nlu-sdk, which is a Python SDK for the development
of custom actions for Rasa Core. As for our chatbot example, we need to define a few
custom actions, like hitting the API to get today’s horoscope or maybe a database writing
operations to add the user to the subscribe list.
The good news is they have a separate library for this, and we can install that using pip.
Let’s install that using the following command:
pip install rasa-core-sdk==0.11.0
Now, we need to go to another tab of our terminal or a fresh command line and execute
the following command in our project directory (the place where our actions.py file is):
python -m rasa_core_sdk.endpoint --actions actions
INFO:__main__:Starting action endpoint server...
INFO:rasa_core_sdk.executor:Registered function for 'get_todays_horoscope'.
INFO:rasa_core_sdk.executor:Registered function for 'subscribe_user'.
INFO:__main__:Action endpoint is up and running. on ('0.0.0.0', 5055)
This command will start an action server that will be listening to any custom action
predicted by the model. As soon as any action is triggered, it will execute it and give the
response as per the method.
The default port for the action server endpoint is 5055 on localhost. If you want to
change it, you can add pass --port argument in the command line.
A quick question comes to mind: why? Why do I need a separate server for that?
Why can’t we use plain Python? Yes, we can use plain Python, but suppose you were to
develop the required actions in any other language or you have some actions already
exposed as APIs. Now, you just need to go to endpoints.yml, which we already created,
and use that to mention from where your action server should be used and where your
core_endpoint server should be. In a production system both can be different servers
having altogether different urls.
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Now, when we run our next script where we mentioned our endpoints.yml file, Rasa
will read the file and get the configuration for our action_server, which is already up and
running as per the configuration we did earlier.
action_endpoint:
url: http://localhost:5055/webhook
Let’s run the train_online.py in a fresh command line terminal.
$python3 train_online.py
After a successful training of the dialog model, we’ll be getting a message like this:
2018-08-30 07:09:37 INFO
rasa_core.policies.keras_policy
- Done
fitting keras policy model
2018-08-30 07:09:37 INFO
rasa_core.agent
- Model directory models/nlu
exists and contains old model files. All files will be overwritten.
2018-08-30 07:09:37 INFO
rasa_core.agent
- Persisted model to '/Users/
sumit/apress_all/Chapter IV/horoscope_bot/models/nlu'
2018-08-30 07:09:37 INFO
rasa_core.training.online
- Rasa Core server
is up and running on http://localhost:5005
Bot loaded. Type a message and press enter (use '/stop' to exit).
127.0.0.1 - - [2018-08-30 07:09:37] "GET /domain HTTP/1.1" 200 996 0.001847
Now, here you can start talking to your bot that has just been born. From here it’s
solely in your hands how you train it. You can correct it whenever it responds something
bad or unexpected.
Let’s do that and see if it improves.
I enter “Hi” as the first message and the bot returns the following:
---------------------------------------------------------------------------
Chat history:
bot did: action_listen
user said: hi
whose intent is: {'confidence': 0.8472929307505297, 'name': 'greeting'}
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we currently have slots: DD: None, MM: None, horoscope_sign: None,
subscribe: None
------
The bot wants to [utter_greet] due to the intent. Is this correct?
1.
Yes
2.
No, intent is right but the action is wrong
3.
The intent is wrong
0.
Export current conversations as stories and quit
---------------------------------------------------------------------------
Now, the bot is telling me what it did based on what the user said and how he
understands it. Now, it also gives us four options. We’ll talk about that in a moment.
What bot_did here is action_listen meaning; it was just waiting for the user to enter
something, which is fine.
Bot says the intent {‘confidence’: 0.8472929307505297, ‘name: ‘greeting}, which is
correct, so we’ll press 1, meaning what the bot is understanding and trying to do it correct.
1
---------------------------------------------------------------------------
Chat history:
bot did: action_listen
user said: hi
whose intent is: {'confidence': 0.8472929307505297, 'name': 'greeting'}
bot did: utter_greet
we currently have slots: DD: None, MM: None, horoscope_sign: None,
subscribe: None
------
The bot wants to [action_listen]. Is this correct?
1.
Yes.
2.
No, the action is wrong.
0.
Export current conversations as stories and quit
---------------------------------------------------------------------------
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And there you are, our bot at least greeted the user properly. If you see the
aforementioned response, you can actually check what our bot did; it keeps track of all
this while we train it.
bot did: utter_greet
Now, the bot says that the next thing is to wait and listen to what the user is going to
say (i.e., waiting for the user input). Is this correct? Yes, that’s correct, so we press 1 again
and the bot gives a prompt to the user.
I enter, “What’s my horoscope for today?”
---------------------------------------------------------------------------
Chat history:
bot did: action_listen
user said: hi
whose intent is: {'confidence': 0.8472929307505297, 'name':
'greeting'}
bot did: utter_greet
bot did: action_listen
user said: what's my horoscope for today?
whose intent is: {'confidence': 0.8902154738608781, 'name':
'get_horoscope'}
we currently have slots: DD: None, MM: None, horoscope_sign: None,
subscribe: None
------
The bot wants to [utter_ask_horoscope_sign] due to the intent. Is this
correct?
1.
Yes
2.
No, intent is right but the action is wrong
3.
The intent is wrong
0.
Export current conversations as stories and quit
---------------------------------------------------------------------------
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Now, the bot very correctly identifies the intent to be “get_horoscope” intent with
89 percent accuracy, which is quite good. The bot also says that it wants to utter_
ask_horoscope_sign, which is correct again because the user has not mentioned any
horoscope sign yet and the slot is still None, as shown.
We press 1 again.
---------------------------------------------------------------------------
Chat history:
bot did: action_listen
user said: hi
whose intent is: {'confidence': 0.8472929307505297, 'name': 'greeting'}
bot did: utter_greet
bot did: action_listen
user said: what's my horoscope for today?
whose intent is: {'confidence': 0.8902154738608781, 'name':
'get_horoscope'}
bot did: utter_ask_horoscope_sign
we currently have slots: DD: None, MM: None, horoscope_sign: None,
subscribe: None
------
The bot wants to [action_listen]. Is this correct?
1.
Yes.
2.
No, the action is wrong.
0.
Export current conversations as stories and quit
---------------------------------------------------------------------------
Now, the bot asks if it’s time to wait for the user input. Yes, the user needs to provide
the horoscope sign.
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We press 1 again, and when prompted, we entered “Capricorn” as the input.
---------------------------------------------------------------------------
Chat history:
bot did: action_listen
user said: hi
whose intent is: {'confidence': 0.8472929307505297, 'name': 'greeting'}
bot did: utter_greet
bot did: action_listen
user said: what's my horoscope for today?
whose intent is: {'confidence': 0.8902154738608781, 'name':
'get_horoscope'}
bot did: utter_ask_horoscope_sign
bot did: action_listen
user said: Capricorn
whose intent is: {'confidence': 0.8456705103362621, 'name':
'get_horoscope'}
with horoscope_sign: capricorn
we currently have slots: DD: None, MM: None, horoscope_sign: capricorn,
subscribe: None
The bot wants to [get_todays_horoscope] due to the intent. Is this correct?
1.
Yes
2.
No, intent is right but the action is wrong
3.
The intent is wrong
0.
Export current conversations as stories and quit
---------------------------------------------------------------------------
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Now, as we can see, the horoscope_sign slot is automagically set above to “Capricorn.”
The next step is to teach the bot to call get_todays_horoscope and to call the
get_todays_horoscope action. By this time our slot is already set for the horoscope_sign
variable that we defined in horoscope_domain.yml, so if this action is called again, the
chatbot should return the actual horoscope for the day. Let’s try.
I entered “Capricorn.”
The bot hits the action server’s endpoint and brings the result back to the user as we
defined in our custom action.
Your today's horoscope:
The journey from defeat to success is driven by optimism. You will be very optimistic
today—about your work, future, and everything that can lead you to success, says
Ganesha. You will also be very careful while dealing with day-to-day work, and will
capitalize on every opportunity that can get you a step closer to your goals. Determination
and good luck is all you need to realize your dreams; you will have both today.
This seems fair, as I am missing my deadline to complete this chapter, and right now
it’s 6:00 AM in the morning and I have not slept yet. Doing every bit so that your money is
worth spending on this book.
Okay, let’s get back to see what our bot is trying to do next.
------
The bot wants to [action_listen]. Is this correct?
1.
Yes.
2.
No, the action is wrong.
0.
Export current conversations as stories and quit
Maybe we don’t want our bot to listen now after it has uttered the horoscope
for today. We want our bot to ask for the user to subscribe to the updates as per our
conversation script and also as per the stories we created.
So, I would press 2, and it says
------
what is the next action for the bot?
0
action_listen
1.00
1
action_restart
0.00
2
action_default_fallback
0.00
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ChaPtEr 4 BUIldIng Chatbots thE Hard Way
3
utter_greet
0.00
4
utter_ask_horoscope_sign
0.00
5
utter_ask_dob
0.00
6
utter_subscribe
0.00
7
get_todays_horoscope
0.00
8
subscribe_user
0.00
Since we want our user to subscribe, we should press 6 so that the bot asks the user if
he/she wants to subscribe for daily updates.
The bot replies, “Do you want to subscribe for daily updates?”
Now the bot says:
---------------------------------------------------------------------------
The bot wants to [get_todays_horoscope]. Is this correct?
1.
Yes.
2.
No, the action is wrong.
0.
Export current conversations as stories and quit
The action here is not correct, so we press 2 and train the bot to listen to the user’s
action here.
---------------------------------------------------------------------------
what is the next action for the bot?
0
action_listen
0.21
1
action_restart
0.01
2
action_default_fallback
0.01
3
utter_greet
0.21
4
utter_ask_horoscope_sign
0.51
5
utter_ask_dob
0.01
6
utter_subscribe
0.02
7
get_todays_horoscope
0.78
8
subscribe_user
0.22
---------------------------------------------------------------------------
We press 0 when the bot is waiting to know the correct next step. The bot will now
wait for the user input in our interactive learning process.
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The user says here, “Please subscribe me,” and our bot successfully with 80 percent
confidence recognizes this to be a subscription intent and also, based on our training
data in data.json, understands pretty easily that the user is asking to subscribe and not
unsubscribe. In other words, it sets the slot of subscribe to True, as you can see here:
---------------------------------------------------------------------------
user said: Please subscribe me
whose intent is: {'confidence': 0.795172441763619, 'name':
'subscription'}
with subscribe: True
we currently have slots: DD: None, MM: None, horoscope_sign: capricorn,
subscribe: True
---------------------------------------------------------------------------
Now, that our bot has understood the intent and also parsed the entities in it, it’s
time to reply back to the user saying, “He is successfully subscribed,” and bingo—we get
a message from bot:
You're successfully subscribed
There you go! You can successfully build your own in-house chatbot all by
yourselves. As a part of the exercise, I would recommend you add all use-cases we
discussed for HoroscopeBot and see if it works. Try to add more uses-cases and more
functionality when everything we have done so far works well.
You can add more utterance messages here for the bot to say, “Goodbye, have a good
day,” after the conversation is completed. I strongly believe you should be in a position to
do that. Make sure you do that and let us know how it went for you.
Now, the final part is to give all of this learning back to the user so that we don’t have
to teach the same stories again and the bot tries to learn from the existing stories and
more to come.
Exporting Conversations As Stories
Now the next step in the interactive learning part is to press 0 and export our current
conversations as stories and quit.
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We press 0 for that and the bot prompts something like this:
File to export to (if file exists, this will append the stories) [stories.md]:
Just enter our stories.md filename, if you have kept any other name, then make sure
you give the correct filename.
And Rasa does the hard job for us and generates a stories.md file for us
automagically again like this:
## Generated Story 3797421409943253925
* greeting
- utter_greet
* I want to know my horoscope
- utter_ask_horoscope_sign
* get_horoscope
## Generated Story 7304374603374920907
* greeting
- utter_greet
* what is my todays horoscope?
- utter_ask_horoscope_sign
* Cancer
## Generated Story -6877464862083507836
* greeting
- utter_greet
* get_horoscope
- utter_ask_horoscope_sign
* get_horoscope{"horoscope_sign": "capricorn"}
- slot{"horoscope_sign": "capricorn"}
- get_todays_horoscope
- slot{"horoscope_sign": "capricorn"}
- utter_subscribe
* subscription{"subscribe": "True"}
- slot{"subscribe": "True"}
- subscribe_user
- slot{"subscribe": true}
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You can either change the source of the stories file while training the model with
the aforementioned file or you can copy and paste the newly generated stories into
our original stories.md file. For now, I am going to copy and paste these stories in our
original stories.md file. Make sure to run your models again before testing or doing
interactive learning again.
Testing the Bot
Testing the bot is a part of our interactive learning process. We keep checking all
the possible scenarios initially thought for the bot and train it accordingly. I tested
Horoscope Bot for some other scenarios and trained it accordingly. The following is one
of them that is a bit different than what we did earlier in this chapter.
Test Case 1
Let’s run the first test:
User: Hello there!
HoroscopeBot: Hello! What can I do for you?
User: What’s the horoscope for Capricorn today?
HoroscopeBot: Your today’s horoscope:
The journey from defeat to success is driven by optimism. You will be very
optimistic today—about your work, future, and everything that can lead you to success,
says Ganesha. You will also be very careful while dealing with day-to-day work,
and will capitalize on every opportunity that can get you a step closer to your goals.
Determination and good luck is all you need to realize your dreams; you will have both
today.
HoroscopeBot: Do you want to subscribe for daily updates?
User: Please subscribe me.
HoroscopeBot: You’re successfully subscribed.
As you can see in this conversation between the user and the chatbot, we tested if
the bot handles the scenario where the user himself provides the horoscope_sign while
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asking for the horoscope for the day itself. The bot does well in this case and ends up
asking for a subscription and, based on the user response, takes appropriate action.
You should do a sanity testing of the bot’s conversation to determine if normal flow is
working or not.
I also tested the story we mentioned in our stories.md file when a user directly asked
for a subscription and it worked as well.
Test Case 2
Now the second test:
User: Hi
HoroscopeBot: Hello! What can I do for you?
User: Subscribe me please for daily updates.
HoroscopeBot: You’re successfully subscribed.
I will be adding a couple of more new stories as well so that the bot works flawlessly.
But as of now we have a fully working bot. In the second version of the code on github
you will also find the bot to be correcting the spelling mistakes for horoscope sign,
retrieving horoscope sign based on user’s DOB, saying a goodbye message, etc. I strongly
recommend you check the code on how it is done and provide your feedback. But
before that, give a thought to how it’ll be done and where the changes will go. We have
knowingly not mentioned the code for the other use-cases in this chapter, so that it
doesn’t become fussy while you are concentrating on learning the tricks of the trade.
You can download the latest Python code and Jupyter Notebooks from our github
repo and try to install the correct packages to run it. You will find more use-cases
handled in the code as discussed in this chapter.
Summary
In this chapter, we learned about Rasa-NLU and why Rasa-NLU is better than any other
open-source tool available on the market. We learned how to configure pipelines by
using tensorflow, sklearn, and keras in our pipeline configurations.
We learned to create everything from scratch on our local system without being
dependent on any services requiring you to use their APIs, like Dialogflow, wit.ai, etc.
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We also learned how to create stories and how to train an NLU model and a dialog
model and use them both using Rasa Core to build a bot by training using the coolest
feature, interactive learning. We also got a fair idea of how to create training data easily
and annotate that easily with help of the open-source tools like rasa-nlu-trainer. I hope
this chapter was more interactive for you than any other chapter. If you are not having a
feeling of achievement, then brace yourself for the next chapter, where we’ll be actually
taking it live to our audiences and showing the world what bots are capable of. We’ll be
learning to integrate the chatbot from this chapter to various platforms like Facebook
and Slack using our own web servers.
Keep training your bot until in the next chapter we take it live.
See you in the next chapter.
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CHAPTER 5
Deploying Your Chatbot
In this chapter we’ll learn how to deploy our chatbots on the web. There are various ways
and channels through which one can deploy or expose their chatbot web application
to the outside world. For an example, we can expose our HoroscopeBot with NLU and
dialog model on Facebook and Slack as they provide you a user interface already. You
may also want to have your own web app that exactly runs on your own server. We will
also explore how to deploy a chatbot on our own servers using our own user interface by
the end of this chapter.
First Steps
The first step is to create a copy of your existing chatbot you built in Chapter 4 and make
a new copy so that you have a backup with you. Since, we will be doing some changes by
adding some new code, let’s keep both the projects separate.
So, I created a new folder called “Chapter V” and pasted my horoscope_bot folder in
there. So, now all my model files, datasets, and code files are copied, which I can directly
use for deployment.
Rasa’s Credential Management
Rasa provides a way to manage all your credentials at one place. You may have one single
model, but you may want it to be deployed on various other platforms like Facebook,
Slack, Telegram, etc. All of these third-party platforms need some credentials to be used
while connecting. These credentials are stored in a YAML file called credentials.yml.
Let’s create a file named credentials.yml file in our project directory horoscope_bot
folder and add our Facebook credentials there. If you don’t know how to get that then
just create the file for now, and in next section of this chapter you can find the steps to
get Facebook credentials.
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S. Raj, Building Chatbots with Python, https://doi.org/10.1007/978-1-4842-4096-0_5
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Contents of credentials.yml will look like this:
facebook:
verify: "horoscope-bot"
secret: "bfe5a34a8903e745e32asd18"
page-access-token: "HPaCAbJJ1JmQ7qDedQKdjEAAbO4iJKr7H9nx4rEBAAuFk4Q3g
PQcNT0wtD"
These credentials are dummy credentials; the length of the token or secret and
characters type may differ for your Facebook app.
If you are working on a big project where you are integrating your chatbot on various
platforms and you want to make the project more maintainable, then it’s best to make
use of credentials.yml. I highly recommend you maintain a credentials.yml if you
are a business and trying to build a bot that works on various platforms like Facebook,
Slack, Twitter, Telegram, or your own website in the same way. Managing keys and
secrets becomes easier in this case.
A good way to manage application-level secret keys is to store the keys as
environment variables and write the code to read the values of secret keys or any other
sensitive information from the operating system’s environment itself. Remember, it’s
never a good idea to keep any kind of keys inside your code.
You can also create a dot(.) env file on your server and read keys from this file, which
is not tracked anywhere in your code repository.
For the sake of simplicity, we are going to use the access keys and secret keys in our
standalone scripts for deployment. We are going to make it simple to understand so that
you are first able to build the bot, then you can try to scale it, and most importantly you
can think about security-level issues.
In case you need to deploy your bot on multiple platforms and want to use
credentials.yml to maintain different credentials, then you can use it by passing an extra
argument. For an example to use the above credentials file named credentials.yml
while running rasa core you can use the below command.
python -m rasa_core.run -d models/dialogue -u models/nlu/current
--port 5002 --credentials credentials.yml
It is good to know for bigger enterprise-level chatbots development, but as discussed,
we’ll be using the credentials directly in our script in our upcoming examples.
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Deploying the Chatbot on Facebook
In this section we are first going to deploy our chatbot using Heroku in cloud. Heroku
is a platform-as-a-service (PaaS) that enables developers to build, run, and operate
applications entirely in the cloud. The benefit of Heroku is that we can easily get our app
running on https without much pain. We don’t need to go and buy SSL certificates while
we are learning and testing our chatbots. The reason why https is required is because some
platforms like Facebook do not allow developers to use non-https urls as callback URLs.
We’ll be following a set of steps one-by-one to successfully deploy our chatbot as
a web service in cloud. Once we have done that successfully, it will be way easier to
integrate it with different platforms like Slack, Telegram, etc. So, let’s start.
Creating an App on Heroku
Let’s get started:
Sign up on Heroku, create an app, and name it something-actions, as this is going
to be our actions server app. Have a look at the screenshot in Figure 5-1, where you can
give a unique name for your actions server, which should be available on Heroku. Once
that name seems available, you can click on the Create app button to create the actions
server app.
Feel free to name it anything you want if your name is not available, but always try to
give meaningful names.
Figure 5-1. Creating the action server app on Heroku with the name
horoscopebot1212-actions
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Setting Up Heroku on Your Local System
Install Heroku CLI on your local operating system. Refer to this link: https://
devcenter.heroku.com/articles/heroku-cli .
If you are on macOS, use the following command:
brew install heroku/brew/heroku
Creating and Setting Up an App at Facebook
To be able to deploy our chatbot on Facebook, first we need to have credentials of the
Facebook app. In order to get the Facebook credentials, we need to set up a Facebook
app and a page, like we did in one of our Chapter 3.
1. Go to https://developers.facebook.com/ and create an
app if you don’t have one already. We created one for our
OnlineEatsBot; now we’ll create one for HoroscopeBot. Enter the
details and click on Create App ID. Check Figure 5-2 to see how to
enter the display name of your bot and your contact email.
Figure 5-2. Creating app on Facebook for developers
2. Once your app is created, go to Basic under Settings, and click on
the Show button under App Secret. Tis is your fb_secret. Refer
Figure 5-3 to see where exactly you will get your fb_secret key.
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Figure 5-3. Getting App Secret from Facebook's app
3. Go to the dashboard for the app and scroll down to “Add a
Product.” Click Add Product and then add Messenger (click on
SetUp). Check Figure 5-4.
Figure 5-4. Adding Messenger as product to Facebook app
4. Under settings for Messenger, when you scroll down to the Token
Generation section you will get a link to create a new page for your
app. If you don’t have a page already, then create it or choose a
page from the “Select a page” dropdown. Te “Page Access Token”
is your fb_access_token here. Refer Figure 5-5.
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You can go to the following link to create a brand new page for
your bot project:
https://www.facebook.com/pages/creation/
Figure 5-5. Generating token for Facebook Messenger App
5. Right after the Token Generation section, under Webhooks, click
on “Setup Webhooks.” Refer Figure 5-6.
Figure 5-6. Setting up Facebook Webhooks
6. Next, choose a verify token, which we’ll need to use later.
Te verify token can be any random string. Tis will be your
fb_verify. Check Figure 5-7 to understand where to add the
verification token in facebook app. Now, leave the callback URL
section blank as it is. Don’t close the browser; just leave it—we’ll
come back here again.
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Figure 5-7. Adding verify token to Facebook webhook setup
7. Keep fb_verify, fb_secret and fb_access_token handy to
connect your bot to Facebook.
Creating and Deploying Rasa Actions Server App
on Heroku
In this step we are going to use our actions Heroku app for our Rasa action’s server. We
need to have two different applications, as we cannot run two web applications in a
single Heroku app. Go to your command line and execute the following set of commands
from your project directory as directed.
1. Create a new folder called actions_app and get into the directory:
mkdir actions_app
cd actions_app
2. Copy your actions.py from main project directory to actions_app
directory.
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3.
Create a requirements.txt file with the following contents.
requirements.txt will tell the Heroku app to install the packages
with their versions.
rasa-core-sdk==0.11.0
requests==2.18.4
4.
Create a file named Procfile with the following contents. Procfile is
the file for which Heroku understands what to do in order to crank
up the applications.
web: python -m rasa_core_sdk.endpoint --actions actions
--port $PORT
a) Run the below set of commands:
$ heroku login
$ git init
$ heroku git:remote -a <your-heroku-app-name>
$ heroku buildpacks:set heroku/python
$ heroku config:set PORT=5055
$ git add .
$ git commit -am "deploy my bot"
$ git push heroku master
After the last command, Heroku will install all our packages needed as per the
requirements.txt file. If your app is successfully deployed, you should be getting logs
similar to the following:
remote:
remote: -----> Discovering process types
remote:
Procfile declares types -> web
remote:
remote: -----> Compressing...
remote:
Done: 48.3M
remote: -----> Launching...
remote:
Released v4
remote:
https://horoscopebot1212-actions.herokuapp.com/ deployed to
Heroku
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remote:
remote: Verifying deploy... done.
To https://git.heroku.com/horoscopebot1212-actions.git
* [new branch]
master -> master
At this point, we will just verify whether our app is responding to public requests. In
order to do that let’s hit the app url appended by “webhook.”
App url in my case is https://horoscopebot1212-actions.herokuapp.com/ so I’ll
go and check if my action’s server is responding.
I go to this url https://horoscopebot1212-actions.herokuapp.com/webhook , and
as expected, it comes back saying method not allowed, like in Figure 5-8, which is totally
fine and means that app is responding correctly as per the user request.
Figure 5-8. Verifying action server endpoint
Creating Rasa Chatbot API App
In this step we will follow some steps and commands similar to what we just did, but this
is a new app we’ll create that will be our main app for dialog management. So, let’s do
it. First come back to the main project directory (i.e., in horoscope_bot) and create a file
name (Procfile) and add the following contents to it:
web: python -m spacy download en && python facebook.py
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Creating a Standalone Script for Facebook Messenger
Chatbot
Create a file name facebook.py in the same project directory. The contents of the Python
file should be as given here:
from rasa_core.channels.facebook import FacebookInput
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
import os
from rasa_core.utils import EndpointConfig
# load your trained agent
interpreter = RasaNLUInterpreter("models/nlu/default/horoscopebot/")
MODEL_PATH = "models/dialog"
action_endpoint = EndpointConfig(url="https://horoscopebot1212-actions.
herokuapp.com/webhook")
agent = Agent.load(MODEL_PATH, interpreter=interpreter)
input_channel = FacebookInput(
fb_verify="YOUR_FB_VERIFY_TOKEN",
# you need tell facebook this token, to confirm your URL
fb_secret="YOUR_FB_SECRET",
# your app secret
fb_access_token="YOUR_FB_ACCESS_TOKEN"
# token for the page you subscribed to
)
# set serve_forever=False if you want to keep the server running
s = agent.handle_channels([input_channel], int(os.environ.get('PORT',
5004)), serve_forever=True)
Make sure to replace the fb_verify, fb_secret, and fb_access_token variable
values in this code with what we kept in Step 3.
Create a new requirements.txt file and add all the packages and their versions
needed for this project. My requirements.txt looks like the following; for your project the
requirements may differ, but these requirements should be fine if you are following the
same bot example in this chapter.
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rasa-core==0.11.1
rasa-core-sdk==0.11.0
rasa-nlu==0.13.2
gunicorn==19.9.0
requests==2.18.4
spacy==2.0.11
sklearn-crfsuite==0.3.6
to install our packages in the server.
Now let’s create a new app again in Heroku like we did earlier. Go to your Heroku
dashboard and create a new app as shown in Figure 5-9.
Figure 5-9. Creating dialog management app in Heroku
Once you have created the app, you can now go to your project root directory and
run the following set of commands from the command line in your project folder:
$ git init
$ heroku git:remote -a <your-heroku-app-name>
$ heroku buildpacks:set heroku/python
$ heroku config:set PORT=5004
$ git add .
$ git commit -am "deploy my bot"
$ git push heroku master
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If you get a runtime error after deployment, it may look like below this
ValueError: You may be trying to read with Python 3 a joblib pickle generated with
Python 2. This feature is not supported by joblib.
This will mainly happen if you are using Python 2.x version. Heroku, by default, uses
Python 3.x version. So, in case you want to use Python 2.x, you need to perform the steps
below to resolve the above error. Change Python 3.6 to Python-2.7.15. to do this.
Create a file runtime.txt under root app directory of your project. Open the runtime.
txt and add the following line python-2.7.15 and then save it. Heroku will use the
aforementioned Python version only to build your project.
Once the successful deployment is done, you will see a url Heroku gives saying the
app deployed to <url>.
remote: Compressing source files... done.
remote: Building source:
remote:
remote: -----> Python app detected
remote: -----> Installing requirements with pip
remote:
remote: -----> Discovering process types
remote:
Procfile declares types -> web
remote:
remote: -----> Compressing...
remote:
Done: 254M
remote: -----> Launching...
remote:
Released v17
remote:
https://horoscopebot1212.herokuapp.com/ deployed to Heroku
remote:
remote: Verifying deploy... done.
To https://git.heroku.com/horoscopebot1212.git
cd3eb1b..c0e081d master -> master
This deployment is going to take a little time, so be patient as much as you can—you
are about to see magic. If you didn’t get any error messages, then you have successfully
deployed your chatbot to Heroku on cloud to make it work with Facebook Messenger.
Let’s verify if it works.
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Verifying the Deployment of Our Dialog Management App
on Heroku
To verify if our dialog management app is successfully deployed on Heroku, we’ll be
doing the following steps.
1. Take the url given by Heroku and append this endpoint to it:
/webhooks/facebook/webhook?hub.verify_token=YOUR_FB_
VERIFY_TOKEN&hub.challenge=successfully_verified. Make
sure to use the correct verify token that you used for webhooks
settings in Facebook. The complete url for me looks like the
below: https://horoscopebot1212.herokuapp.com/webhooks/
facebook/webhook?hub.verify_token=my-secret-verify-
token&hub.challenge=success .
2. Go to the browser and paste the entire url, and it should return
your hub.challenge value back if your hub.verify_token is
correct. Your complete url will look like the following: https://
horoscopebot1212.herokuapp.com/webhooks/facebook/
webhook?hub.verify_token=YOUR_FB_VERIFY_TOKEN&hub.
challenge=successsfully_verified . If you get the message
successsfully_verified in the browser then your app is
successfully deployed and working.
Integrating Webhook With Facebook
Now let’s go back to our Facebook app configuration. We’ll go to the point where
we left off in Step 3 and add our callback URL. Make sure to check the messages in
Subscription Fields. Check Figure 5-10 for reference.
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Figure 5-10. Facebook Messenger webhooks configuration
Click on “Verify and Save.” Facebook will match the verify token using the above url,
meaning the server, or say our app will only respond to requests that have the correct
verify token. Once the verify token matches, the webhook subscription will get activated
for our app.
Next, select a page to which you can subscribe your webhook to the page events,
under the Webhooks section on the page. Click on subscribe (see Figure 5-11).
Figure 5-11. Subscribe webhook to Facebook page events
All done! Time to test our Horoscope Bot on Facebook.
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Post-Deployment Verification: Facebook Chatbot
In normal software development scenarios, people build software, test it, and then
deploy and do PDV (post-deployment verification). We’ll also be doing something
similar, and we’ll do a PDV for our chatbot after our successful deployment on Facebook
Messenger. This is important because, as you learned, the chatbot has a part where it
needs to connect to the actions server to respond to some intent requests of the user.
PDV is like a sanity testing to see that the health of the app is good overall. If you are
building a bot that uses 10 to 15 different vendors’ APIs, then it’s a must to check all
scenarios where your bot hits the action server and uses the API to return data back to
the user.
So, go to your messenger app or Facebook in your computer browser and search for
your bot to start talking.
Figures 5-12.1 through 5-12.3 show what my Horoscope Bot does and tells me.
Figure 5-12.1. Horoscope_Bot Facebook
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Figure 5-12.2. Horoscope_Bot Facebook
Figure 5-12.3. Horoscope_Bot Facebook
Voila! Our first in-house-built chatbot app is deployed on the web and can be
accessed via the Facebook Messenger platform. So, go ahead and share it with your
family, friends, colleagues, and the entire world.
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Deploying the Chatbot on Slack
In this section we’ll be deploying our chatbot to Slack. Slack is a team collaboration
tool widely popular among developers and corporations. If you are not a social media
person, then you might need Slack’s help to talk to your chatbot using an interface. So,
let’s dive into building our first in-house Slack chatbot.
In order to deploy our Horoscope Chatbot to slack, we’ll be writing a standalone
script just like we did in the case of Facebook.
Creating a Standalone Script for Slack Chatbot
Create a new file called slack.py in your project’s directory. Contents of the file
slack.py will look like the following:
from rasa_core.channels.slack import SlackInput
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
import os
from rasa_core.utils import EndpointConfig
# load your trained agent
interpreter = RasaNLUInterpreter("models/nlu/default/horoscopebot/")
MODEL_PATH = "models/dialogue"
action_endpoint = EndpointConfig(url="https://horoscopebot1212-actions.
herokuapp.com/webhook")
agent = Agent.load(MODEL_PATH, interpreter=interpreter, action_
endpoint=action_endpoint)
input_channel = SlackInput(
slack_token="YOUR_SLACK_TOKEN",
# this is the `bot_user_o_auth_access_token`
slack_channel="YOUR_SLACK_CHANNEL"
# the name of your channel to which the bot posts (optional)
)
# set serve_forever=False if you want to keep the server running
s = agent.handle_channels([input_channel], int(os.environ.get('PORT',
5004)), serve_forever=True)
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The primary difference between facebook.py and slack.py is the input_channel
object that we create. Rasa provides various in-built channels like Facebook, Slack,
Mattermost, Telegram, Twilio, RocketChat, and Microsoft Bot Framework, which we can
use directly to deploy the same bot on various channels easily.
As you can see, we need to add a slack_token and slack_channel to be added into
our script. As we had to create a Facebook application on Facebook’s developer platform,
similarly we’ll have to create an app on Slack as well.
Let’s do this step by step:
1. Got to this url https://api.slack.com/slack-apps and click on
the button “Create App.” Refer Figure 5-13.
Figure 5-13. Creating an application in Slack
2. Te next step is to create a Bot User. To create a bot user, click on
Bots under “Add features and functionality.” In the new page you
will get an option to “Add a Bot User.”. Check Figure 5-14 to see
how to add details and add a bot user.
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Figure 5-14. Giving a name to your bot on Slack
3. Fill in the details as per the chatbot you are building. Display
name can be anything that you like; default username has to be
unique; you can let it be as it is. Toggling the last option to always
show my bot as online is to always show the bot to be available
as a user. Tis is what chatbots are meant for—humans can’t be
available 24/7, but chatbots can, so we turn on this feature. Make
sure you click on save changes.
4. Go back to the “Basic Information” tab. Click “Install your app to
your workspace.” The app will ask to confirm the identity. Please
authorize it like you do for any other app. Check Figure 5-15 which
shows how the authorization would look like.
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Figure 5-15. Authorizing your Slack app
You will find the Bots and Permissions tab under “Add features and
functionality” with a green checkmark, which means our bot and app
are well-integrated. This is a sign that we are doing good so far.
5. Go to your OAuth & Permissions section, and copy Bot User
OAuth Access Token.
6. Paste the copied token into our Python script slack.py. Give a
channel name as you like. If you want your bot to post to a channel,
then you can give a channel name. I have given @slackbot. If you
do not set the slack_channel keyword argument, messages will be
delivered back to the user who sent them.
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Editing your Procfile
In this step we won’t be creating any new Procfile, as we are working with the same
codebase. We’ll be changing our existing Procfile to the following to make it work for our
slack bot. So, we just change the name of the script file from facebook.py to slack.py so
that Heroku uses the given file to start up the application.
web: python -m spacy download en && python slack.py
Final Deployment of Slack Bot to Heroku
To finally deploy our new Slack bot to Heroku, we’ll be running a similar set of Heroku
commands from command line to deploy our application.
$ git init
$ heroku git:remote -a <your-heroku-app-name>
$ git add .
$ git commit -am "deploy my bot"
$ git push heroku master
Subscribe to Slack Events
Now, click on the “Event Subscriptions” tab and activate the events subscriptions
functionality by toggling the button on the screen. Enter the Heroku app’s webhook url
for Slack.
If your app was deployed on Heroku properly with the modified Procfile, your
webhook url for Slack will be app_url + /webhooks/slack/webhook, which looks like
the following:
https://horoscopebot1212.herokuapp.com/webhooks/slack/webhook
You will see a verified tick mark after Slack sends a HTTP POST request to the above
URL with a challenge parameter, and our endpoint must respond with the challenge
value. This is similar to what we discussed while building Facebook chatbot’s secret
token. Check Figure 5-16 to understand more.
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Figure 5-16. Activate event subscriptions for your bot
Subscribe to Bot Events
In this step we’ll simply scroll down the events subscriptions page and go to the
“Subscribe to Bot Events” section, and click on “Add Bot User Event.” Take reference of
Figure 5-17 to understand where to navigate.
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Figure 5-17. Subscribe to bot events
Subscribe to bot events is nothing but declaring the events for which the bot has to
reply. We’ll only be demonstrating two scenarios here: first, when somebody mentions
the bot’s name (i.e., app_mention), and second, when somebody directly sends the bot
the message (i.e., message.im).
Now, click on save changes, and you are done. It’s time to test our Slack chatbot like
we did in the previous section for Facebook.
Post-Deployment Verification: Slack Bot
Let’s go to our workspace we used to create the app, and under Apps on the left side you
will find your bot. Try to talk to it and see if it does well. My bot does pretty well to give
me my horoscope for today, which is good to read. If you have not been able to come till
this point then check Figure 5-18 to see how my Slack bot responds.
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Figure 5-18. Testing the Slack chatbot
So, folks we are done with our Slack Bot. In the next section, we’ll be deploying our
bot to our own UI. Building your own UI may require some front-end skills, but do not
worry—we have plans for that.
Deploying the Chatbot on Your Own
Well the caption sounds cool, doesn’t it? So far, we have been deploying our chatbot
on the web using Facebook or Slack, or we could have used Telegram, etc., but now it’s
time to deploy everything on our own—our own servers, our own data, and our own
configured model using our own user interface. If you are an organization or a budding
entrepreneur, you may have your bot idea on Facebook, Twitter, or Slack, but you would
always want it to be working on your own websites as well so that your brand value
increases more and more as the user base increases.
In this section we are going to use all our hard work so far to finally build a chatbot,
which is fully functional and independent from any third-party API calls or tools like
Dialogflow, wit.ai, Watson, etc. You will have all the control in the world to tweak your
chatbot the way you want and, most importantly, scale it the way you want to millions of
people easily.
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So, let’s get started.
The first step is to ensure that two of our apps that we have deployed so far in the
previous sections are up and running. You already know how to do a basic sanity check.
You always need your dialog manager app and actions app to be running to use your
chatbot model on any platform.
Now, in the same project directory where we have been creating facebook.py and
slack.py, we’ll create a new file called myown_chatbot.py. The scripts created previously,
like facebook.py and slack.py, are standalone scripts that we created so that we can
just tell Heroku in a command which script to run to crank up the application. Now, we
are creating our own script that will expose the request/response between a user and the
chatbot via REST APIs.
Deploying your own chatbot has two parts. In the first part, we’ll be writing a script
to create a custom channel and deploy it as REST APIs. In the second part, we need our
own UI, because so far, we have been using Facebook’s and Slack’s chat screens for the
conversations.
Writing a Script for Your Own Chatbot Channel
This script is similar to what we have learned and written so far, but it needs us to
override some of the existing methods of rasa_core so that we can define our own rule
for API authentication. I have done the basic string check for token verification in the
following code. This is not suggested for production-level systems, so make sure to write
that part with care if you are building a chatbot for larger systems.
Create a new file named myown_chatbot.py and add the following contents to it:
import os
from rasa_core.channels.rasa_chat import RasaChatInput
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.utils import EndpointConfig
# load your trained agent
interpreter = RasaNLUInterpreter("models/nlu/default/horoscopebot/")
MODEL_PATH = "models/dialogue"
action_endpoint = EndpointConfig(url="https://horoscopebot1212-actions.
herokuapp.com/webhook")
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agent = Agent.load(MODEL_PATH, interpreter=interpreter, action_endpoint=
action_endpoint)
class MyNewInput(RasaChatInput):
def _check_token(self, token):
if token == 'mysecret':
return {'username': 1234}
else:
print("Failed to check token: {}.".format(token))
return None
input_channel = MyNewInput(url='https://horoscopebot1212.herokuapp.com')
# set serve_forever=False if you want to keep the server running
s = agent.handle_channels([input_channel], int(os.environ.get('PORT',
5004)), serve_forever=True)
A couple of points to be noted here:
_check_token method in rasa_core basically looks like the following,
which makes an API call to get the user object. This primarily does
the job of user-level authentication/authentication. In the earlier
overridden method, we have kept it simple to make it work and
understand its usage.
def _check_token(self, token):
url = "{}/users/me".format(self.base_url)
headers = {"Authorization": token}
logger.debug("Requesting user information from auth server {}."
"".format(url))
result = requests.get(url,
headers=headers,
timeout=DEFAULT_REQUEST_TIMEOUT)
if result.status_code == 200:
return result.json()
else:
logger.info("Failed to check token: {}. "
"Content: {}".format(token, request.data))
return None
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• Using Rasa’s own _check_token method may require you to write one
API or web service that accepts the request and returns the response
in the specified way.
• Make sure to change the action’s server endpoint to your own url.
• Remember the mysecret string in the code will be used to make the
API calls later.
Writing the Procfile and Deploying to the Web
By this time you must be pretty familiar with creating Procfiles for Heroku deployment.
We’ll again use our existing Procfile and do the modifications there to deploy our API-based
chatbot to the web. Feel free to create a new Procfile after creating backups of existing ones.
The following is what my Procfile content looks like:
web: python -m spacy download en && python myown_chatbot.py
Once you are done, just execute the next set of commands that we already learned
while deploying our Facebook Messenger and Slack Bot.
$ git init
$ heroku git:remote -a <your-heroku-app-name>
$ git add .
$ git commit -am "deploy my bot"
$ git push heroku master
After the last command you will get some logs from Heroku related to deployment
version, changes made to the app, etc.
Verifying Your Chatbot APIs
After getting a successful message for deployment, let’s test if our chatbot APIs are
working or not. In order to quickly do the sanity testing, hit the following url:
<your-basic-app-url>+/webhooks/rasa/
Example:
https://horoscopebot1212.herokuapp.com/webhooks/rasa/
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Opening this url in the browser should give you a response like the following. If it
gives you a status of “ok,” then you are good to go—just relax, sit back, and debug.
{"status":"ok"}
Sometimes, just this verification may not be enough, so let’s test it for real by trying to
check if the chatbot is working to identify the intents and giving responses based on that.
I will be using the POSTMAN tool (POSTMAN is a very nice GUI-based tool to do API
testing). You can use any tool you are comfortable with. We are just going to test one of
the intents our chatbot is supposed to understand and respond to. I tested the greetings
intent and it worked like a charm. The bot came back with an expected response as
shown in Figure 5-19.
Figure 5-19. Testing chatbot API in POSTMAN
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Creating the Chatbot UI
As we discussed earlier, as a part of the second step, we need to have our own UI to give
a user-friendly place for conversation between the chatbot and the user. If you are a
front-end developer or you have a front-end developer on the team, you can easily give
the chatbot APIs we built to this point, and the front-end team should be easily able to
integrate this with their chatbot UI. They can make use of regular HTTP calls to consume
these APIs. Websockets are a better way of doing chatbots, but that is not under the
scope of this book to explain.
If you are not familiar with front-end technologies like HTML/CSS/Javascript, then I
do recommend Pro HTML5 with CSS, JavaScript, and Multimedia (Apress, 2017).
For our reader’s—or, I should say, learner’s—convenience we have created a basic
UI required for a chatbot and user conversation. You will find the entire working code on
github or Apress’s website. I am just going to tell you the configuration needed to make it
work for your bot.
Once you download the code for this chapter you will find the folder inside the main
folder called my_own_chatbot. Go to this folder and go to assets -> js -> script.js file.
Change the following line of javascript code to your own endpoint url. If your app
name was different then below url will be different in your case. Use your own url with
the token in the javascript code as shown in the url below.
var baseUrl = "https://horoscopebot1212.herokuapp.com/webhooks/rasa/
webhook?token=YOUR-SECRET";
Save the file and open the index.html file in the browser and you could easily see
a chatbot UI ready. But making API calls from simple HTML being served locally raises
a CORS issue. So, to avoid this situation we are going to change our existing myown_
chatbot.py a bit to serve the HTML from our Heroku app itself.
Change your myown_chatbot.py to the below and then we’ll discuss the changes made.
import os
from rasa_core.channels.rasa_chat import RasaChatInput
from rasa_core.channels.channel import CollectingOutputChannel, UserMessage
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.utils import EndpointConfig
from rasa_core import utils
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from flask import render_template, Blueprint, jsonify, request
# load your trained agent
interpreter = RasaNLUInterpreter("models/nlu/default/horoscopebot/")
MODEL_PATH = "models/dialogue"
action_endpoint = EndpointConfig(url="https://horoscopebot1212-actions.
herokuapp.com/webhook")
agent = Agent.load(MODEL_PATH, interpreter=interpreter, action_endpoint=
action_endpoint)
class MyNewInput(RasaChatInput):
@classmethod
def name(cls):
return "rasa"
def _check_token(self, token):
if token == 'secret':
return {'username': 1234}
else:
print("Failed to check token: {}.".format(token))
return None
def blueprint(self, on_new_message):
templates_folder = os.path.join(os.path.dirname(os.path.abspath(__
file__)), 'myown_chatbot')
custom_webhook = Blueprint('custom_webhook', __name__, template_
folder = templates_folder)
@custom_webhook.route("/", methods=['GET'])
def health():
return jsonify({"status": "ok"})
@custom_webhook.route("/chat", methods=['GET'])
def chat():
return render_template('index.html')
@custom_webhook.route("/webhook", methods=['POST'])
def receive():
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sender_id = self._extract_sender(request)
text = self._extract_message(request)
should_use_stream = utils.bool_arg("stream", default=False)
if should_use_stream:
return Response(
self.stream_response(on_new_message, text,
sender_id),
content_type='text/event-stream')
else:
collector = CollectingOutputChannel()
on_new_message(UserMessage(text, collector, sender_id))
return jsonify(collector.messages)
return custom_webhook
input_channel = MyNewInput(url='https://horoscopebot1212.herokuapp.com')
# set serve_forever=False if you want to keep the server running
s = agent.handle_channels([input_channel], int(os.environ.get('PORT',
5004)), serve_forever=True)
Here are the changes that we made:
• Override the existing name and blueprint method in our class, which
lets us create our own endpoint and also gives us the liberty to define
how it should behave.
• We created a new endpoint/chat and served the index.html file,
which is nothing but the UI for chatbot. So, this will be the home link
for our chatbot.
• We had to import some necessary classes and methods, like utils,
CollectingOutputChannel, and UserMessage as needed to make
things work.
Save the file and deploy the changes again to our Heroku app using the following
commands:
$ git add .
$ git commit -am "deploy my bot"
$ git push heroku master
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Once deployed successfully—Voila! We have our bot ready to be shared with the
entire world, which works using two Heroku apps: one for dialog management and one
for actions.
Open the following url in the browser where we should see our custom chatbot UI:
https://horoscopebot1212.herokuapp.com/webhooks/rasa/chat
Figures 5-20.1 and 5-20.2 show how my own chatbot looks during the conversation.
Figure 5-20.1. Your own custom chatbot on your own website
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Figure 5-20.2. Your own custom chatbot on your own website
Using Heroku’s custom domain name features, one can easily point the same app to
their own website’s name, like www.example.com. Do that when you feel your chatbot is
good enough to be opened to the entire world for profit or non-profit purposes.
So, that’s all folks! That’s how chatbots are built with Python using Natural Language
Processing and machine learning. I hope this chapter along with the previous chapters
was helpful to you and you could learn the practical approach toward learning and
building chatbots.
Summary
In this chapter, we learned how to deploy our own app to our own servers using Heroku.
We learned how to integrate our chatbot with Facebook using Facebook’s developer
platform. We also learned to create a custom chatbot for a Slack app and tested it as well.
Finally, as promised at the end of chapter-III, removing all dependencies from any social
media platform platform, we created our own UI and deployed it on Heroku and tested
it. We saw it working like a charm—it worked while training it. As we have a basic model
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up and working, now you can handle cases where chatbot doesn’t work well. Determine
if it’s an issue related to data or training, actions server, or custom code handling. Once
you find the root case, fix that, deploy again, and check to see if chatbot improves. We
build big software by starting small.
I am looking forward to hearing from you and curious to know what chatbot you
built after going through this book. I will be happy to help you anytime if you are stuck
on any concepts, code execution, or deployment.
Thanks and Cheers.
188
Index
developer’s perspective
A
feature releases and bug
Automated and fixed bot, 18
fixes, 11
learning curve, 12
B
market demand, 11
development, 1
Business perspective, chatbots
entities, 27, 66
accessibility, 5
FAQ, 18-19
availability, 6
food-ordering (see Food-ordering
cost, 6
chatbot)
customers prefer
frameworks, 25-26
chatbots, 9-10
history, 13-16
efficiency, 5
industries, 12-13
glimpse of chatbot usage, 8-9
intents, 27, 66
insights, 6
myown_chatbot.py file, 179-180,
revenue, 6-8
183-185
scalability, 6
NLP (see Natural Language Processing
(NLP))
C
popularity, 2-3
Chatbots, 1
problem statement, 16
APIs, 1, 181-182
Procfile, 181
automated and fixed, 18
repetitive issues, 17
back-and-forth communication, 17
scope, 65
building, 20
third-party API, 178
business perspective (see Business
traditional methods, 2
perspective, chatbots)
training, 28
confidence score, 28
UI, 183
custom, website, 186-187
utterances, 27
decision trees, 20-24
Zen of Python, 3-5
189
© Sumit Raj 2019
S. Raj, Building Chatbots with Python, https://doi.org/10.1007/978-1-4842-4096-0
IndEx
Custom actions
Facebook Messenger, integrations
conversation, 132
Access Tokens, 89
GetTodaysHoroscope action, 131
callback URL, 89
name method, 131
creating app, 87-88
run method, 131
setting up, 86
SlotSet method, 131
testing bot, 91, 93-95
UtterAction, 130
verifying token, 89
Customers prefer chatbots, 9-10
webhooks configuration, 90-91
food-ordering chatbot
(see Food-ordering chatbot)
D, E
fulfillment
Data preparation, bot
checking response, 101-102
conversations as stories,
POST and JSON response, 96
exporting, 150, 152
testing, 97
interactive learning, 133, 136
webhook, enabling, 98-100
chatbot agent model, 136-140
NLU modules, 64
rasa-nlu-sdk, 142-150
real-time training by
F
reinforcement, 140-142
story data, 134, 136
Facebook app
Decision trees
action server app,
apparel chatbot, representation, 22-23
Heroku, 157
description, 21
Add Product, 159
financial institution, 23
App Secret, 158-159
full-fledged, 22
creating, 158
health quiz, 24
deployment, 166
intents, 24
dialogue management, 165
OTP API, 24
Heroku, deployment of dialogue
representation, 21
management, 167
Dependency parsing
Horoscope_Bot, 169-170
ancestors, 50-51
installing Heroku CLI, 158
children, 51
Messenger, token
interactive visualization, 51-53
generation, 159-160
parent-child relationship, 49
Python file, 164
usage, 53-54
requirements.txt file, 164
Dialogflow chatbot, 63
root directory, 165-166
architecture, 64
webhooks configuration, 167-168
deploying on web, 82-85
Webhook setup, 160-161
190
IndEx
FAQ bot, 18-19
dependency parsing (see Dependency
Food-ordering chatbot, 65
parsing)
conversation script, 67
finding similarity, 55-58
default response to intent, 72-73
intelligent machine, 30
Dialogflow, 67-68
NER, 44-47
default welcome intent, 69-70
NLU, 30
intents, 68-69, 71
noun chucks, 54-55
testing, 71
POS tagging, 37-41
intents and utterances, 72
regular expressions, 60-61
item description intent and
SpaCy, 29, 31-36
entities, 73, 75-76
stemming and lemmatization, 42-44
replying back to user
stop words, 47-49
order_status intent, 78-79
tokenization, 59
user_order_ID intent, 79-81
Natural Language Understanding (NLU)
user_thanks intent, 81-82
modules, 30, 64
Noun chunks, 54-55
G
P
A glimpse of chatbot usage, 8-9
GloVe algorithm, 56
Part-of-speech (POS) tagging, 37-41
Popularity, 2-3
Post-deployment verification
H
(PDV), 169-170
Horoscope bot, 109-110
conversation script of user and, 110-111
Q
testing, 152-153
Horoscope_Bot Facebook, 169-170
QnA Bot, 18-19
I, J, K, L, M
R
Industries, 12-13
Rasa action’s server, Heroku app
commands, 161-162
verification, 163
N, O
webhook, 163
Named-entity recognition (NER), 44-47
Rasa chatbot API app, 163
Natural Language Processing (NLP), 29
Rasa Core, 106
black box, 30
dialog management
definition, 29
action, 125-126
191
IndEx
Rasa Core (cont.)
default username, 173
domain file, creating, 127-129
deployment, Heroku, 175
with Rasa NLU, 123-124
event subscriptions, 175-176
Rasa stack, 125
OAuth & Permissions, 174
slots, 126
Procfile, 175
state machine, 122-123
slack.py file, 171
templates, 127
slack_token and slack_channel, 172
rasa_core, version, 139
subscribe to bot events, 176-177
Rasa NLU, 106
testing, 177-178
installing, 107
SpaCy, 29
pipelines, 108-109
benchmarking results, 31
using, 107
deep learning, 32
Rasa’s credential management, 155-156
definition, 31
Regular expressions, 60-61
documentation, 32
downloading, 36
fastest library, 32
S
features, 32-33
Scratch, chatbot from
installation
configuration file, 117-118
Jupyter Notebook, 34-35
conversation script, 110-111
Python 3, 33
data for
verification, 35
JSON format, 112-113
Python packages, 35-36
Rasa NLU trainer, 114-116
Stop words, 47-49
horoscope bot, 109-110
predict_intent method, 119, 121
Python code, 118-119
T, U, V, W, X, Y
train_horoscopebot method, 119
Tokenization, 59
Slack
authorizing, 173-174
Z
bot user, 172-173
creating app, 172
Zen of Python, 3-5
192